CN110782087A - Offshore emergency material optimal scheduling method and system - Google Patents
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Abstract
The invention relates to an optimized scheduling method and system for marine emergency materials, and the optimized scheduling method is provided based on a mixed heuristic algorithm and takes multiple demand points into consideration for marine major accident emergency materials, and according to actual conditions, marine emergency materials are transported for emergency optimized ships of each accident point, and suitable materials are assembled for each ship during each sailing so as to realize the optimal transportation efficiency; meanwhile, the limited inventory of the emergency base is considered, and a corresponding onshore supply material transportation scheme needs to be optimized according to the scheduling condition of the offshore emergency material, so that the transportation time of onshore supply material and the total cost of the system are minimized.
Description
Technical Field
The invention relates to the technical field of material scheduling, in particular to an offshore emergency material optimal scheduling method and system.
Background
With the rapid development of marine economy, the occurrence frequency of major marine accidents is on the rise, and the marine traffic safety problem is paid much attention to all the social circles. In terms of safety, the marine transportation system is weaker than the land transportation system, and when the sea condition is worsened due to sudden change of external environmental factors such as wind, wave, fog and tide, serious accidents on the sea are easily caused, and even the frequent accidents occur in a short time.
The scientific and efficient emergency material distribution and transportation scheduling scheme can greatly reduce property loss, casualties and damage to the marine environment caused by accidents, and further provides safety guarantee for the healthy development of marine economy. Therefore, the method has quite important practical significance for developing and researching the emergency material scheduling problem under the random occurrence situation of multiple major marine accidents.
With regard to the problem of emergency material scheduling, most scholars at home and abroad are studying on various onshore emergencies or disasters as backgrounds. In the prior art, in order to improve the fire extinguishing efficiency of forest fires, an emergency material scheduling method under the situation of single rescue point and multiple fire points is provided by taking the minimum number of mobilized emergency vehicles and the shortest fire extinguishing time as optimization targets; after uncertain data and the attitude of a decision maker for avoiding risks are considered, a rolling plane method related to disaster relief supplies real-time distribution is provided on the premise of meeting the requirements and aiming at minimizing the total time for transporting the disaster relief supplies; the method provides a solution for the onshore emergency material emergency dispatching problem of the multiple disaster-suffering points and the multiple rescue points by establishing a multi-target model which meets the requirements that the fidelity is maximum and the transportation cost is minimum when the emergency time does not exceed t; an emergency material modular scheduling method based on an improved NSGA-II algorithm is provided, and the multi-objective optimization problem that cost minimization and material unsatisfied degree minimization need to be met simultaneously in the rescue material scheduling process is solved; the disaster rescue emergency resource scheduling method based on the multi-agent genetic algorithm is provided, the problems of high emergency resource scheduling cost and long rescue time in the prior art are mainly solved, and the defects that a large-scale problem is easily solved by a traditional method in the prior art, the local optimal solution is easy to fall into and the speed is too slow are overcome by performing self-learning operation on an agent grid.
At present, the emergency material scheduling research on marine accidents is small in quantity compared to the research on land-related problems. In the prior art, a method for scheduling water and land cooperative emergency materials meeting the transportation capacity, time limit and emergency material requirements is provided according to water accidents and rescue characteristics thereof, and a greedy algorithm is adopted for solving; aiming at the marine chemical leakage accident, a corresponding emergency material scheduling method is provided after limiting factors such as material reserve capacity, ship capacity, emergency fund budget and the like of a marine emergency base are considered.
In the prior art, most researches are directed to the problem of onshore emergency material scheduling, and relatively few researches are conducted on the problem of offshore emergency material scheduling. The existing related research only analyzes the situation that only a single accident point exists at sea, and the problem of emergency material scheduling under the environment of multiple accident points is not discussed. When a plurality of major marine accidents happen randomly, the conventional marine emergency material scheduling method is difficult to effectively provide an efficient emergency material scheduling scheme in time.
Disclosure of Invention
In order to overcome the technical problems in the prior art, the invention provides an offshore emergency material optimal scheduling method and system.
The technical scheme for solving the technical problems is as follows:
according to one aspect of the invention, a marine emergency material optimal scheduling method is provided, which comprises the following steps:
establishing a marine accident occurrence time axis according to the time sequence of marine major accidents;
establishing an offshore emergency material optimization scheduling model taking the sum of emergency response time satisfaction as a target function under the constraint of emergency fund budget for each offshore major accident according to the time axis;
initializing parameters of a binary particle swarm algorithm; the method comprises the following steps: inertia weight, sum of learning factors, population scale and maximum iteration number;
carrying out a carrier selection decision according to element information contained in any particle in the population, determining a carrier participating in the current marine accident emergency, and calculating the fitness value which can be realized by the particle according to a greedy algorithm;
recording the fitness value as an individual extreme value of the current particle, and recording the corresponding position as an individual extreme value position; screening out global extrema in the whole population and recording the corresponding position as a global extremum position;
and updating the speed and the position of each particle, and scheduling the transport ship for the material of the marine major accident according to the speed and the position of the particles.
Updating the self speed and position of each particle, and calculating a corresponding fitness value according to element information contained in the particle at the moment;
if the fitness value of the particle i is better than the current individual extreme value Pbest
iThen the value is taken as a new individual extreme value Pbest
iAt the same time, the corresponding position is updated to the individual extreme value position
Otherwise individual extreme value Pbest
iAnd
keeping the original shape;
if the optimal fitness value in the population is better than the current global extreme value Gbest, the value is taken as a new global extreme value Gbest, and the corresponding position is updated to be the new global extreme value Gbest
Else global extreme value GBest and
and maintained unchanged.
Performing chaotic initialization to obtain the binary global extreme value position
Compiling the vector into real numerical value vectors of Q decision variables; map it to [0, 1 ]]Interval, thus obtaining the initial vector of chaos at this moment;
sequentially performing chaotic iteration on elements in the chaotic initial vector to obtain a chaotic vector sequence;
inversely mapping the chaotic vector sequence to real numerical value vectors of Q decision variables;
binary coding the real value vector to convert it into h binary vectors in the original feasible domain, and finding outH fitness values which can be realized; if the maximum fitness value is better than the current global extremum GBest, the value is used as a new global extremum GBest, and meanwhile, the corresponding position of the value is updated to be the new global extremum GBest
And are provided with
Randomly replacing one particle position in the current population.
The global extremum position to be binary
Real-valued vector compiled as Q decision variables
It is mapped to [0, 1 ] according to the following formula]Interval to obtain the initial vector of chaos at this time
wherein ,
for carrying out global extreme value position after kth particle swarm algorithm iteration
The real value of the qth decision variable; a is
q and bqThe minimum value and the maximum value of the real value of the decision variable are obtained.
Chaos initial vector B
0Element (1) of
To be provided with
The form of the chaotic vector is sequentially substituted into the following formula to carry out chaotic iteration to obtain a chaotic vector sequence B
t(t ═ 1, 2, … h); wherein Q is more than or equal to 1 and less than or equal to Q;
λ
t+1＝uλ
t(1-λ
t)，t＝0，1，2，…，h；
wherein u is a control parameter; h is the maximum number of chaotic iterations; when u is 4 and λ
0∈[0，1]In time, the whole system is in a complete chaotic state, and all variable values are in [0, 1 ]]。
Chaotic vector sequence B according to the following formula
t(t ═ 1, 2, … h) inverse mapped back to a real-valued vector of Q decision variables
Wherein, t is 1, 2, … h;
wherein ,the real value of the decision variable after the t-th chaotic iteration is carried out.
For real value vector
Binary coding is carried out to convert the binary code into h binary vectors in the original feasible domain
Wherein, t is 1, 2, … h; t is 1, 2, … h; h fitness values which can be realized by the method are calculated;
if the maximum fitness value is better than the current global extremum GBest, the value is used as a new global extremum GBest, and meanwhile, the corresponding position of the value is updated to be the new global extremum GBest
And are provided with
Randomly replacing the position of one particle in the current population.
Determining a transport ship set S participating in the current marine accident emergency material transportation according to the binary coding information of the particle individuals
*, wherein 
Collecting the transport ships participating in the accident emergency in each base; when no transport ship is selected in the base i, the transport ship is selected
Meanwhile, initializing relevant parameters including the stock state of each base, the carrying state of each transport ship, the stock state of each land supply point and the on-road transportation state information of the land supply materials;
carry out the b
τAt the time of dispatch decision, from each set
Medium screening transport ship capable of reaching accident point first
And according to the weight omega of the importance of the required materials
pThe principle of priority given to large (P epsilon P) is that of transport ship
Distributing emergency materials; if the materials with the greatest importance weight and not meeting the requirements at the accident point j have inventory shortage at the base i, the transport ship is started
Distributing materials with suboptimal level, and so on until the transport ship
Fully loading or meeting the requirements of all other materials at the accident point j;
substituting all dispatching schedules into the emergency response time satisfaction function F
jp(P is equal to P), obtaining the time satisfaction value which can be realized by each arrangement, and selecting the optimal scheduling arrangement as the b th
τArranging a secondary dispatching task; checking the stock of the corresponding base, and sending a material supply transportation instruction to a land supply point if the nominal stock residual quantity is lower than the warning stock quantity so as to carry out land supply material transportation; according to the transportation requirement of the supply materials, solving the scheduling problem of the onshore supply materials by adopting a hierarchical sequence method;
updating and updating the stock state of each base, the carrying state of each transport ship, the stock state of each land supply point and the on-land supply material in-transit transport state information respectively;
judging whether all the materials required by the accident point j are met, if not, b
τ←b
τ+1, then back; if the condition is completely met, entering the next step;
judging whether all scheduling decisions meet the requirement of the emergency material demand time window, and if so, calculating the fitness value of the particle; if not, calculating the fitness value, subtracting a positive constant according to the penalty function, taking the positive constant as the fitness value, finishing the algorithm and outputting the calculation result.
In the b th
τAfter making a sub-offshore material allocation and transportation decision, a land supply point set K to which a base j needing to supply material for supplementing stock belongs
iAccording to transport time t
ikArranged from short to long
Screening out a set of alternative supply points that meet a goal of minimizing transit time
And the set satisfies the condition
wherein ,yikp(b
τ) The stock quantity of the material p for the supply point k to which the base i belongs at the time, S
ipFor base i the maximum inventory of material p,
for this time base i nominal stock, q, with respect to material p
jps(b
τ) Transporting the amount of the material p to the accident point j by the transport ship s at the moment;
candidate supply point set AK to be screened out
iPer unit transportation cost of elements (c)
ikLow to high rearrangement
Resulting in a land-based tender material transportation solution that minimizes transportation costs based on meeting a first objective
The supply points at this time are collected as
Wherein the supply point
The amount of material transported to base i is
φ
*(b
τ) The optimal scheme of the onshore supply material scheduling at the moment is provided.
According to another aspect of the invention, an offshore emergency material optimal scheduling system is provided, which comprises:
the time axis establishing unit is used for establishing an offshore accident occurrence time axis according to the time sequence of major offshore accidents;
the optimized scheduling model establishing unit is used for establishing an offshore emergency material optimized scheduling model which takes the sum of emergency response time satisfaction as a target function under the constraint of emergency fund budget and maximizes for each offshore major accident according to the time axis;
the initialization unit is used for initializing parameters of the binary particle swarm algorithm; the method comprises the following steps: inertia weight, sum of learning factors, population scale and maximum iteration number;
the greedy algorithm unit is used for carrying out a carrier selection decision according to element information contained in any particle in the population, determining a carrier participating in the current marine accident emergency, and then calculating the fitness value which can be realized by the particle according to the greedy algorithm;
the extreme value calculating unit is used for recording the fitness value as an individual extreme value of the current particle and recording the corresponding position as an individual extreme value position; screening out global extrema in the whole population and recording the corresponding position as a global extremum position;
and the scheduling optimization unit is used for updating the speed and the position of each particle and scheduling the transport ship for the material of the marine major accident according to the speed and the position of the particles.
The invention has the beneficial effects that: an offshore emergency material optimal scheduling method and system are provided. The marine major accident emergency material optimal scheduling method considering multiple demand points, which is proposed based on a mixed heuristic algorithm, considers that marine emergency material transportation is carried out on emergency optimal ships of each accident point, and assembles proper material for each ship during each sailing so as to realize the optimal transportation efficiency according to actual conditions; meanwhile, the limited inventory of the emergency base is considered, and a corresponding onshore supply material transportation scheme needs to be optimized according to the scheduling condition of the offshore emergency material, so that the transportation time of onshore supply material and the total cost of the system are minimized.
On the basis, the transportation ship selective adjustment scheme and the specific emergency material transportation scheme provided by the invention can be applied to a marine emergency system, so that the emergency service level of the system is improved. When a fixed sea area is influenced by external environmental factors and multiple major marine accidents occur randomly in a short time, the marine emergency system can send a command for selecting and dispatching transport ships and assembling materials to each marine base according to the method, and send a command for transporting supply materials to corresponding onshore material supply points when the marine bases are in stock shortage, so that a series of major accidents which are sudden at sea can be quickly and effectively dealt with.
Drawings
Fig. 1 is a schematic flow chart of a marine emergency material optimal scheduling method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a time axis according to an embodiment of the present invention.
Fig. 3 is a flowchart of a hybrid heuristic algorithm according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an offshore emergency material optimal scheduling system according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
For the research of the onshore emergency material scheduling problem, it is generally assumed that the number and the positions of material demand points are known before decision making, and only the material demand of a given demand point is discussed. And when the transportation capacity is limited (the transportation of all goods and materials cannot be completed at one time), the transportation of the goods and materials is often completed in a discontinuous multi-cycle mode or in a batch mode in the form of transportation capacity aggregation. In view of the great difference of emergency characteristics of sudden accidents in the sea-land environment, the theoretical results of the research cannot be directly applied to the emergency practice of the marine accidents. When a plurality of major accidents happen at sea and are to be rescued within a certain time, the main influence factor of uncertainty of material demand is not the uncertainty of specific quantity any more, but the uncertainty of accident points (occurrence time and occurrence places) is caused, and the uncertainty enables the whole scheduling process to have time-varying characteristics. Meanwhile, when the transportation capacity limiting factor is considered, the transportation influence of wind waves on different types of ships needs to be discussed in combination with the marine environment, and the transport ship can be ensured to continuously transport materials to the accident point until the requirements are met, so that a material scheduling scheme can not be formulated from the perspective of discontinuous cycle or batch transportation.
The invention provides an offshore major accident emergency material optimal scheduling method considering multiple demand points, which aims at the problem of optimal scheduling of emergency materials under the situation that multiple major accidents occur randomly in a fixed sea area, and combines a mixed heuristic algorithm to prepare an efficient offshore emergency material scheduling scheme for timely dealing with offshore accidents.
When a plurality of major offshore accidents occur continuously and randomly in a fixed sea area in a short time, the optimized scheduling method of the embodiment can accurately grasp the material transportation condition and the material inventory condition in the whole offshore emergency system, and simultaneously, a corresponding material scheduling scheme is established by taking the satisfaction and maximization of offshore emergency response time and the minimization of onshore supply material transportation time and transportation cost as optimization targets, so that various emergency materials are efficiently transported at each accident point, and the guarantee is provided for the smooth development of offshore rescue work.
As shown in fig. 1, a schematic flow chart of a marine emergency material optimal scheduling method provided by the embodiment of the present invention is specifically as follows:
and step 16, updating the speed and the position of each particle, and scheduling the material scheduling transport ship of the marine major accident according to the speed and the position of the particles.
When major accidents frequently occur at sea within a certain time, the marine emergency system not only needs to rapidly make an effective emergency material scheduling scheme aiming at the current marine dangerous case, but also needs to timely and efficiently allocate a transport ship and emergency materials according to the situation of randomly increasing new accidents on the basis of not influencing the emergency efficiency of the established scheme so as to meet the dynamic emergency requirement with high quality to the maximum extent, thereby ensuring the efficiency level of the whole emergency system. At the moment, the marine emergency material scheduling is a process of transporting various emergency materials from each shore-based emergency base to each accident point under limited transportation conditions according to the specific emergency requirements of each accident point, and the requirements of each accident point on the time and quantity of the emergency materials must be met. In view of the limited emergency material inventory of the emergency base, the demand of emergency materials is increased rapidly when major marine accidents happen, and the existing reserve materials of the emergency base cannot meet the emergency requirements of all accident points, onshore supply materials need to be called to supplement the emergency base inventory, so that the emergency base can continuously provide emergency services for marine accidents.
Therefore, at the moment, the marine emergency material scheduling is a dynamic planning problem, and not only needs to coordinate material scheduling between the accident point and the emergency base, and between the emergency base and the material supply point, but also needs to meet the dynamic emergency service requirement in the whole emergency system. The invention provides an offshore major accident emergency material optimal scheduling method considering multiple demand points, aiming at solving the problem of optimal scheduling of emergency materials under the complex situation, so that an optimal scheme for emergency material scheduling can be obtained, the satisfaction degree and maximization of emergency response time of offshore emergency material transportation are realized, and the transportation time of onshore supply materials and the total cost of a system are minimized.
In the embodiment of the application, the self speed and the position of each particle are updated, and the corresponding fitness value is calculated according to the element information contained in the particle at the moment;
if the fitness value of the particle i is better than the current individual extreme valuePbest
iThen the value is taken as a new individual extreme value Pbest
iAt the same time, the corresponding position is updated to the individual extreme value position
Otherwise individual extreme value Pbest
iAnd
keeping the original shape;
if the optimal fitness value in the population is better than the current global extreme value Gbest, the value is taken as a new global extreme value Gbest, and the corresponding position is updated to be the new global extreme value Gbest
Else global extreme value GBest and
and maintained unchanged.
Performing chaotic initialization to obtain the binary global extreme value position
Compiling the vector into real numerical value vectors of Q decision variables; map it to [0, 1 ]]Interval, thus obtaining the initial vector of chaos at this moment;
sequentially performing chaotic iteration on elements in the chaotic initial vector to obtain a chaotic vector sequence;
inversely mapping the chaotic vector sequence to real numerical value vectors of Q decision variables;
binary coding is carried out on the real numerical value vector to be converted into h binary vectors in the original feasible domain, and h fitness values which can be realized by the binary vectors are calculated; if the maximum fitness value is better than the current global extremum GBest, the value is used as a new global extremum GBest, and meanwhile, the corresponding position of the value is updated to be the new global extremum GBest
And are provided with
Randomly replacing one particle position in the current population.
The global extremum position to be binary
Real-valued vector compiled as Q decision variables
It is mapped to [0, 1 ] according to the following formula]Interval to obtain the initial vector of chaos at this time
wherein ,
for carrying out global extreme value position after kth particle swarm algorithm iteration
The real value of the qth decision variable; a is
q and bqThe minimum value and the maximum value of the real value of the decision variable are obtained.
Chaos initial vector B
0Element (1) of
To be provided with
The form of the chaotic vector is sequentially substituted into the following formula to carry out chaotic iteration to obtain a chaotic vector sequence B
t(t ═ 1, 2, … h); wherein Q is more than or equal to 1 and less than or equal to Q;
λ
t+1＝uλ
t(1-λ
t)，t＝0，1，2，…，h；
wherein u is a control parameter; h is the maximum number of chaotic iterations; when u is 4 and λ
0∈[0，1]In time, the whole system is in a complete chaotic state, and all variable values are in [0, 1 ]]。
Chaotic vector sequence B according to the following formula
t(t ═ 1, 2, … h) inverse mapped back to a real-valued vector of Q decision variables
Wherein, t is 1, 2, … h;
wherein ,
the real value of the decision variable after the t-th chaotic iteration is carried out.
For real value vector
Binary coding is carried out to convert the binary code into h binary vectors in the original feasible domain
Wherein, t is 1, 2, … h; t is 1, 2, … h; h fitness values which can be realized by the method are calculated;
if the maximum fitness value is better than the current global extremum GBest, the value is used as a new global extremum GBest, and meanwhile, the corresponding position of the value is updated to be the new global extremum GBest
And are provided with
Randomly replacing the position of one particle in the current population.
In an embodiment of the present application, a complete scheme flow is provided, which specifically includes:
step 1: in order to simplify the analysis process of the complex dynamic scheduling problem, a time axis concept is introduced to convert the complex dynamic scheduling problemThe problem of multiple periods of relative inactivity is discussed again. Assuming that the set of accident points of a fixed sea area occurring within a period of time is J (J belongs to J), all the emergency material requirements of the last accident are met from the moment of the first accident, and the time spent by all the behaviors in the whole scheduling process is contained on the same time axis. Wherein the occurrence time of the accident point j is t
jThen, the dynamic marine emergency material scheduling process can be represented in a segmented manner by the time axis in fig. 1, where the time period set is T (τ e.t), and the starting time T of the time period τ is
τ＝t
jAnd (tau is j), the emergency material scheduling system is in a relatively stable state in the period tau, and the condition that the demand is suddenly changed due to the addition of accident points is avoided. In order to complete the material transportation aiming at the accident point j, the prepared material scheduling scheme totally comprises n
τ(tau is j) ship transfer tasks, and the serial number of each transfer task is b
τ(b
τ＝1，2，…，n
τ) And (6) labeling.
As shown in fig. 1, there is a time line diagram showing the locations of onshore supply points, emergency bases, accident points and transport vessels, and illustrating the specific path.
Step 2: when the accident j is at t
jWhen the time (tau is j), the set of the adjacent emergency bases is known to be I (I belongs to I), the set of the material types required by emergency rescue is known to be P (P belongs to P), and the importance weight of various materials is omega
pThe demand of the emergency material p corresponding to the accident point j is x
jpThe time satisfaction of the incident point j with respect to the material p as a function of the time at which the emergency base responds to the material p at the incident point (i.e., the emergency response time satisfaction function F)
jp) The initial inventory of the emergency base i about the emergency material p is y
ip(b
τ1), the set of transport vessels is S (S e S), and the set of vessels equipped in the emergency base i is S
i(S＝∪
i∈IS
i) The transportation cost of transporting unit materials per sea by using transport ships is vc
sThe fixed cost (including equipment maintenance cost, personnel labor cost, depreciation cost and the like) for starting the transport ship s to provide emergency service for the current accident is fc
sThe carrying capacity of the transport ship s is g
sFromTransport ship S under action of wind and waves (S belongs to S)
i) The round-trip speed is different between the accident point j and the emergency base i, and the round-trip speed is v
s(ij) return velocity v
s(ji), the distance between the accident point j and the emergency base i is d
ijUnder the condition of the emergency fund budget, establishing an offshore emergency material optimization scheduling model taking the maximum sum of the emergency response time satisfaction degrees as an objective function under the constraint of the emergency fund budget.
And step 3: initializing parameters of the binary particle swarm algorithm, including inertia weight omega and learning factor c
1 and c2The population size N, the maximum iteration number K and the like. Randomly generating initial vectors of N particles on the basis of meeting the constraint of emergency fund budget
And initial vector of velocity thereof
Initial amount of position
All elements are 0-1,. The position of the individual extreme of particle i at this time
Is equal to
Itself. The dimension Q of the particle i is equal to the number of decision variables (namely the number of bases which can participate in emergency of major accidents at sea), each decision variable is represented by binary codes, the length of the coded decision variables (equal to the number of transport ships owned by the corresponding base, if the nth transport ship in the base m is selected, the nth code bit of the mth decision variable in the particle takes the value of 1, otherwise, the nth code bit is 0) is respectively l
i1，l
i2，…，l
iQThen, the dimension D of the search space is:
and 4, step 4: according to any particle in the population
And carrying out a carrier selection decision on the contained element information, determining a carrier participating in the current marine accident emergency, and calculating the fitness value (namely the sum of the emergency response time satisfaction) which can be realized by the particle according to a greedy algorithm.
The central idea of the greedy algorithm designed by the invention is that a transport ship is used as a main body, emergency material dispatching and transportation are organized according to the principle that the satisfaction value of emergency response relative time is maximum, and the more quickly the more important emergency materials are transported to the current accident point, the greater the satisfaction value of emergency response time realized by the material transportation is. In the solving process, after each scheduling decision is made, the information of the inventory state of each base, the carrying state of each transport ship, the inventory state of each land supply point, the in-transit transportation state of land supply materials and the like needs to be updated immediately so as to make the next scheduling decision. The concrete solving steps are as follows:
step 4.1: determining a transport ship set S participating in current marine accident emergency material transportation according to binary coding information of particle individuals
*, wherein 
For the set of transport ships participating in the emergency at each base, if no transport ship is selected in base i
Meanwhile, initializing relevant parameters such as the inventory state of each base, the carrying state of each transport ship, the inventory state of each land supply point, the in-transit transportation state of land supply materials and the like.
Step 4.2: carry out the b
τAt the time of dispatch decision, from each set
Medium screening transport ship capable of reaching accident point first
And according to the weight omega of the importance of the required materials
pThe principle of priority given to large (P epsilon P) is that of transport ship
And distributing emergency materials. If the materials with the greatest importance weight and not meeting the requirements at the accident point j have inventory shortage at the base i, the transport ship is started
Distributing materials with suboptimal level, and so on until the transport ship
Fully loaded or all other material requirements at the accident point j are met.
Step 4.3: bringing all the scheduling arrangements formulated in step 4.2 into the emergency response time satisfaction function F
jp(P epsilon P), calculating the time satisfaction value which can be realized by each arrangement, and selecting the optimal scheduling arrangement as the b-th scheduling arrangement
τAnd (5) scheduling the secondary dispatching task. And checking the stock of the corresponding base, and if the nominal stock surplus (equal to the actual stock plus the amount of the materials expected to be replenished from the supply point) is lower than the warning stock, issuing a material replenishing transportation instruction to the land supply point so as to carry out land replenishment material transportation. Because the emergency logistics have the characteristics of time urgency and weak economy, the transportation optimization goal at the moment is to realize the minimization of the transportation cost on the premise of transporting all supply emergency materials to the coastal base in the shortest time, so the minimization of the transportation time is taken as a first optimization goal, and the minimization of the transportation cost is taken as a second optimization goal. According to the transportation requirements of the supply materials, determining to solve the problem of scheduling the terrestrial supply materials by adopting a hierarchical sequence methodAnd (5) solving. The concrete solving steps are as follows:
step 4.3.1: in the b th
τAfter making a sub-offshore material allocation and transportation decision, a land supply point set K to which a base j needing to supply material for supplementing stock belongs
iAccording to transport time t
ikArranged from short to long
Screening out a set of alternative supply points that meet a goal of minimizing transit time
And the set satisfies the condition
wherein ,yikp(b
τ) The stock quantity of the material p for the supply point k to which the base i belongs at the time, S
ipFor base i the maximum inventory of material p,
for this time base i nominal stock, q, with respect to material p
jps(b
τ) For which the transport ship s transports the amount of material p to the accident point j.
Step 4.3.2: set of alternative supply points AK to be filtered out by step 4.3.1
iPer unit transportation cost of elements (c)
ikLow to high rearrangement
On-land tender material transportation scheme to minimize transportation cost based on meeting first objective
The supply points at this time are collected as
Wherein the supply point
The amount of material transported to base i is
φ
*(b
τ) The optimal scheme of the onshore supply material scheduling at the moment is provided.
Finally, the information of the stock state of each base, the carrying state of each transport ship, the stock state of each land supply point, the in-transit transportation state of the land supply material and the like is updated and updated respectively.
Step 4.4: judging whether all the materials required by the accident point j are met, if not, b
τ←b
τ+1, then return to step 4.2; if it is completely fulfilled, step 4.5 is entered.
Step 4.5: judging whether all scheduling decisions meet the requirement of the emergency material demand time window, and if so, calculating the fitness value of the particle; if not, the fitness value is calculated according to the method, a positive constant is subtracted according to the penalty function, the result is used as the fitness value, the algorithm is finished, and the calculation result is output.
Step 5, recording the fitness value calculated in the step 4 as the current individual extreme value Pbest of the particle
iRecording the corresponding position as the position of the individual extremum
Meanwhile, screening out global extremum Gbest in the whole population and recording the corresponding position as the position of the global extremum
Step 6: updating the self speed and position of each particle, calculating a corresponding fitness value according to the element information contained in the particle at the moment, and if the fitness value of the particle i at the moment is superior to the current Pbest
iThen the value is taken as the new Pbest
iAt the same time, the corresponding position is updated to the individual extreme value position
Otherwise Pbest
iAnd
and maintained unchanged. If the optimal fitness value in the population is better than the current Gbest, the value is used as a new Gbest, and the corresponding position is updated to be the new Gbest
Otherwise GBest and
and maintained unchanged.
And 7: performing chaotic initialization to obtain binary global extreme value position
Real-valued vector compiled as Q decision variables
It is then mapped to [0, 1 ] according to the following formula]Interval to obtain the initial vector of chaos at this time
in the formula ,
for carrying out global extreme value position after kth particle swarm algorithm iteration
The real value of the qth decision variable; a is
q and bqThe minimum value and the maximum value of the real value of the decision variable are obtained.
And 8: chaos initial vector B
0Element (1) of
To be provided with
The form of the chaotic vector is sequentially substituted into the following formula to carry out chaotic iteration to obtain a chaotic vector sequence B
t(t＝1，2，…h)。
λ
t+1＝uλ
t(1-λ
t)，t＝0，1，2，…，h；
In the formula, u is a control parameter; h is the maximum number of chaotic iterations. When u is 4 and λ
0∈[0，1]In time, the whole system is in a complete chaotic state, and all variable values are in [0, 1 ]]。
And step 9: chaotic vector sequence B according to the following formula
t(t ═ 1, 2, … h) inverse mapped back to a real-valued vector of Q decision variables
in the formula ,
the real value of the decision variable after the t-th chaotic iteration is carried out.
Step 10, to the real value vector
Binary coding is carried out to convert the binary code into h binary vectors in the original feasible domain
And h fitness values which can be realized by the method are calculated. If the maximum fitness value is better than the current GBest, the value is used as a new GBest, and the corresponding position of the new GBest is updated to be the corresponding position of the new GBest
And are provided with
Randomly replacing the position of one particle in the current population.
Step 11: judging whether the improvement is iterated for K times or not, if not, then K ← K +1, and returning to the step 6; and if the maximum iteration number K is reached, finishing the algorithm and outputting a calculation result.
As shown in fig. 3, a basic flow of the hybrid heuristic algorithm proposed in the present application is shown, which includes the whole calculation flow from the occurrence of a major accident at sea to the completion of the material scheduling calculation. The dynamic scheduling is simplified by utilizing the segmented time axis, when the frequent occurrence condition of major marine accidents occurs within a certain time, the marine emergency system not only needs to rapidly make an effective emergency material scheduling scheme aiming at the current marine dangerous situation, but also needs to timely and efficiently allocate a transport ship and emergency materials according to the condition of randomly increasing new accidents on the basis of not influencing the emergency efficiency of the established scheme so as to meet the dynamic emergency demand with high quality to the maximum extent, thereby ensuring the emergency efficiency of the whole emergency system. At the moment, the problem of marine emergency material scheduling belongs to the problem of dynamic planning. The method particularly introduces a time axis concept, divides the time axis by taking different accident occurrence moments as nodes, converts a dynamic planning problem into a relatively static multi-period planning problem and discusses the problem, and ensures that an emergency material scheduling system is in a relatively stable state in each period, so that the condition of sudden change of demand caused by newly increased accident points can not occur. The method can effectively simplify the complexity of the dynamic programming problem and improve the operability and accuracy of the method.
And (4) making a selecting and adjusting decision of the transport ship based on an improved binary particle swarm algorithm. When a major accident occurs at sea, a plurality of emergency bases exist in the marine emergency system to provide emergency service for an accident point, and each base is provided with a plurality of transport ships for selective adjustment. How to optimize the transport ship to participate in the emergency activities under the constraint of the emergency fund budget so as to ensure that the emergency efficiency can be optimized is a key problem in the whole emergency process. Considering that the particle swarm algorithm has the characteristics of high convergence speed and strong robustness, the complex genetic operation similar to the genetic algorithm can be avoided, the implementation is easy, and the problem of selecting and adjusting the transport ship belongs to the target optimization problem in a discrete space, so that the binary particle swarm algorithm is adopted to solve the problem of selecting and adjusting the decision making of the transport ship. In the practice process, the convergence speed of the binary particle swarm algorithm is reduced in the later period of the solving process, and the binary particle swarm algorithm is easy to fall into the local optimal solution.
And making a specific material transportation scheme based on a greedy algorithm. After the transport ships which actually participate in emergency work are selected from all the transport ships which can be selected and adjusted, the specific transport material distribution and the arrangement of material scheduling through the selected transport ships are mainly formulated through a greedy algorithm. The central idea of the algorithm is that a transport ship is used as a main body, and emergency material allocation and transportation are organized according to the principle that the satisfaction value of emergency response relative time is maximum. In the solving process, the maritime emergency material transportation process and the emergency material supply transportation process are mutually related, and the information such as the stock state of each base, the carrying state of each transport ship, the stock state of each land supply point, the in-transit transportation state of land supply materials and the like needs to be updated immediately after each scheduling decision is made so as to make the next scheduling decision.
As shown in fig. 3, an embodiment of the present invention provides an optimal scheduling system for marine emergency materials, which includes:
a time axis establishing unit 21 for establishing an offshore accident occurrence time axis according to the time sequence of the occurrence of major offshore accidents;
the optimized scheduling model establishing unit 22 is used for establishing an offshore emergency material optimized scheduling model which takes the sum of the emergency response time satisfaction degrees as a target function under the constraint of emergency fund budget for each offshore major accident according to the time axis;
an initializing unit 23, configured to initialize binary particle swarm algorithm parameters; the method comprises the following steps: inertia weight, sum of learning factors, population scale and maximum iteration number;
the greedy algorithm unit 24 is used for making a carrier selection decision according to element information contained in any particle in the population, determining a carrier participating in emergency of the current marine accident, and then calculating the fitness value which can be realized by the particle according to the greedy algorithm;
an extreme value calculating unit 25, configured to record the fitness value as an individual extreme value of the current particle, and record a position corresponding to the fitness value as an individual extreme value position; screening out global extrema in the whole population and recording the corresponding position as a global extremum position;
and the scheduling optimization unit 26 is used for updating the speed and the position of each particle, and scheduling the transport ship for the material of the marine major accident according to the speed and the position of the particle.
According to the marine major accident emergency material optimal scheduling method considering multiple demand points, which is provided based on a mixed heuristic algorithm, the marine emergency material transportation is considered for the emergency optimal ship of each accident point according to the actual situation, and the proper material is assembled for each ship during each sailing so as to realize the optimal transportation efficiency; meanwhile, the limited inventory of the emergency base is considered, and a corresponding onshore supply material transportation scheme needs to be optimized according to the scheduling condition of the offshore emergency material, so that the transportation time of onshore supply material and the total cost of the system are minimized.
On the basis, the transportation ship selective adjustment scheme and the specific emergency material transportation scheme provided by the invention can be applied to a marine emergency system, so that the emergency service level of the system is improved. When a fixed sea area is influenced by external environmental factors and multiple major marine accidents occur randomly in a short time, the marine emergency system can send a command for selecting and dispatching transport ships and assembling materials to each marine base according to the method, and send a command for transporting supply materials to corresponding onshore material supply points when the marine bases are in stock shortage, so that a series of major accidents which are sudden at sea can be quickly and effectively dealt with.
In the description of the present invention, it should be noted that the terms "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An optimal scheduling method for offshore emergency materials is characterized by comprising the following steps:
establishing a marine accident occurrence time axis according to the time sequence of marine major accidents;
establishing an offshore emergency material optimization scheduling model taking the sum of emergency response time satisfaction as a target function under the constraint of emergency fund budget for each offshore major accident according to the time axis;
initializing parameters of a binary particle swarm algorithm; the method comprises the following steps: inertia weight, sum of learning factors, population scale and maximum iteration number;
carrying out a carrier selection decision according to element information contained in any particle in the population, determining a carrier participating in the current marine accident emergency, and calculating the fitness value which can be realized by the particle according to a greedy algorithm;
recording the fitness value as an individual extreme value of the current particle, and recording the corresponding position as an individual extreme value position; screening out global extrema in the whole population and recording the corresponding position as a global extremum position;
and updating the speed and the position of each particle, and scheduling the transport ship for the material of the marine major accident according to the speed and the position of the particles.
2. The method of claim 1, wherein the method further comprises:
updating the self speed and position of each particle, and calculating a corresponding fitness value according to element information contained in the particle at the moment;
if the fitness value of the particle i is better than the current individual extreme value Pbest
iThen the value is taken as a new individual extreme value Pbest
iAt the same time, the corresponding position is updated to the individual extreme value position
Otherwise individual extreme value Pbest
iAnd
keeping the original shape;
if the optimal fitness value in the population is better than the current global extreme value Gbest, the value is taken as a new global extreme value Gbest, and the corresponding position is updated to be the new global extreme value Gbest
Else global extreme value GBest and
and maintained unchanged.
3. The method of claim 2, wherein the method further comprises:
performing chaotic initialization to obtain the binary global extreme value position
Compiling the vector into real numerical value vectors of Q decision variables; map it to [0, 1 ]]Interval, thus obtaining the initial vector of chaos at this moment;
sequentially performing chaotic iteration on elements in the chaotic initial vector to obtain a chaotic vector sequence;
inversely mapping the chaotic vector sequence to real numerical value vectors of Q decision variables;
binary coding is carried out on the real numerical value vector to be converted into h binary vectors in the original feasible domain, and h fitness values which can be realized by the binary vectors are calculated; if the maximum fitness value is better than the current global extremum GBest, the value is used as a new global extremum GBest, and meanwhile, the corresponding position of the value is updated to be the new global extremum GBest
And are provided with
Randomly replacing one particle position in the current population.
4. The method of claim 3, wherein the method further comprises:
the global extremum position to be binary
Real-valued vector compiled as Q decision variables
It is mapped to [0, 1 ] according to the following formula]Interval to obtain the initial vector of chaos at this time
wherein ,for carrying out global extreme value position after kth particle swarm algorithm iteration
The real value of the qth decision variable; a is
q and bqThe minimum value and the maximum value of the real value of the decision variable are obtained.
5. The method of claim 4, wherein the method further comprises:
chaos initial vector B
0Element (1) of
To be provided with
The form of the chaotic vector is sequentially substituted into the following formula to carry out chaotic iteration to obtain a chaotic vector sequence B
t(t ═ 1, 2, … h); wherein Q is more than or equal to 1 and less than or equal to Q;
λ
t+1＝uλ
t(1-λ
t)，t＝0，1，2，…，h；
wherein u is a control parameter; h is the maximum number of chaotic iterations; when u is 4 and λ
0∈[0，1]In time, the whole system is in a complete chaotic state, and all variable values are in [0, 1 ]]。
6. The method of claim 5, wherein the method further comprises:
chaotic vector sequence B according to the following formula
t(t＝1，2，… h) inverse mapping back to a real-valued vector of Q decision variables
Wherein, t is 1, 2, … h;
wherein ,
the real value of the decision variable after the t-th chaotic iteration is carried out.
7. The method of claim 6, wherein the method further comprises:
for real value vector
Binary coding is carried out to convert the binary code into h binary vectors in the original feasible domain
Wherein, t is 1, 2, … h; t is 1, 2, … h; h fitness values which can be realized by the method are calculated;
if the maximum fitness value is better than the current global extremum GBest, the value is used as a new global extremum GBest, and meanwhile, the corresponding position of the value is updated to be the new global extremum GBest
And are provided with
Randomly replacing the position of one particle in the current population.
8. The method of claim 1, wherein the method further comprises:
according to the particlesDetermining a transport ship set S participating in current marine accident emergency material transportation by using individual binary coded information
*, wherein 
Collecting the transport ships participating in the accident emergency in each base; when no transport ship is groaning at the base, then
Meanwhile, initializing relevant parameters including the stock state of each base, the carrying state of each transport ship, the stock state of each land supply point and the on-road transportation state information of the land supply materials;
carry out the b
τAt the time of dispatch decision, from each set
Medium screening transport ship capable of reaching accident point first
And according to the weight omega of the importance of the required materials
pThe principle of priority given to large (P epsilon P) is that of transport ship
Distributing emergency materials; if the materials with the greatest importance weight and not meeting the requirements at the accident point j have inventory shortage at the base i, the transport ship is started
Distributing materials with suboptimal level, and so on until the transport ship
Fully loading or meeting the requirements of all other materials at the accident point j;
substituting all dispatching schedules into the emergency response time satisfaction function F
jp(P is equal to P), obtaining the time satisfaction value which can be realized by each arrangement, and selecting the optimal scheduling arrangement as the b th
τArranging a secondary dispatching task; checking the stock of the corresponding base, and sending a material supply transportation instruction to a land supply point if the nominal stock residual quantity is lower than the warning stock quantity so as to carry out land supply material transportation; according to the transportation requirement of the supply materials, solving the scheduling problem of the onshore supply materials by adopting a hierarchical sequence method;
updating and updating the stock state of each base, the carrying state of each transport ship, the stock state of each land supply point and the on-land supply material in-transit transport state information respectively;
judging whether all the materials required by the accident point j are met, if not, b
τ←b
τ+
1And then back; if the condition is completely met, entering the next step;
judging whether all scheduling decisions meet the requirement of the emergency material demand time window, and if so, calculating the fitness value of the particle; if not, calculating the fitness value, subtracting a positive constant according to the penalty function, taking the positive constant as the fitness value, finishing the algorithm and outputting the calculation result.
9. The method of claim 8, wherein the method further comprises:
in the b th
τAfter making a sub-offshore material allocation and transportation decision, a land supply point set K to which a base j needing to supply material for supplementing stock belongs
iAccording to transport time t
ikArranged from short to long
Screening out a set of alternative supply points that meet a goal of minimizing transit time
And the set satisfies the condition
wherein ,yikp(b
τ) The stock quantity of the material p for the supply point k to which the base i belongs at the time, S
ipFor base i the maximum inventory of material p,
for this time base i nominal stock, q, with respect to material p
jps(b
τ) Transporting the amount of the material p to the accident point j by the transport ship s at the moment;
candidate supply point set AK to be screened out
iPer unit transportation cost of elements (c)
ikLow to high rearrangement
Resulting in a land-based tender material transportation solution that minimizes transportation costs based on meeting a first objective
The supply points at this time are collected as
Wherein the supply point
The amount of material transported to base i is
φ
*(b
τ) The optimal scheme of the onshore supply material scheduling at the moment is provided.
10. An offshore emergency material optimal scheduling system, comprising:
the time axis establishing unit is used for establishing an offshore accident occurrence time axis according to the time sequence of major offshore accidents;
the optimized scheduling model establishing unit is used for establishing an offshore emergency material optimized scheduling model which takes the sum of emergency response time satisfaction as a target function under the constraint of emergency fund budget and maximizes for each offshore major accident according to the time axis;
the initialization unit is used for initializing parameters of the binary particle swarm algorithm; the method comprises the following steps: inertia weight, sum of learning factors, population scale and maximum iteration number;
the greedy algorithm unit is used for carrying out a carrier selection decision according to element information contained in any particle in the population, determining a carrier participating in the current marine accident emergency, and then calculating the fitness value which can be realized by the particle according to the greedy algorithm;
the extreme value calculating unit is used for recording the fitness value as an individual extreme value of the current particle and recording the corresponding position as an individual extreme value position; screening out global extrema in the whole population and recording the corresponding position as a global extremum position;
and the scheduling optimization unit is used for updating the speed and the position of each particle and scheduling the transport ship for the material of the marine major accident according to the speed and the position of the particles.
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