CN111061987B - Group tower scheduling optimization method considering active avoidance - Google Patents
Group tower scheduling optimization method considering active avoidance Download PDFInfo
- Publication number
- CN111061987B CN111061987B CN201911268829.9A CN201911268829A CN111061987B CN 111061987 B CN111061987 B CN 111061987B CN 201911268829 A CN201911268829 A CN 201911268829A CN 111061987 B CN111061987 B CN 111061987B
- Authority
- CN
- China
- Prior art keywords
- point
- tower
- tower crane
- demand
- task
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000005457 optimization Methods 0.000 title claims abstract description 23
- 238000010276 construction Methods 0.000 claims abstract description 27
- 239000000463 material Substances 0.000 claims description 179
- 238000003860 storage Methods 0.000 claims description 51
- 238000012163 sequencing technique Methods 0.000 claims description 5
- 239000000725 suspension Substances 0.000 claims description 5
- 230000003139 buffering effect Effects 0.000 claims 1
- 231100000817 safety factor Toxicity 0.000 claims 1
- 238000007726 management method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000000342 Monte Carlo simulation Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 238000007493 shaping process Methods 0.000 description 2
- 206010063659 Aversion Diseases 0.000 description 1
- 235000004522 Pentaglottis sempervirens Nutrition 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009439 industrial construction Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Data Mining & Analysis (AREA)
- Quality & Reliability (AREA)
- Mathematical Physics (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Algebra (AREA)
- Pure & Applied Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Control And Safety Of Cranes (AREA)
Abstract
The invention discloses a group tower scheduling optimization method considering active avoidance, which aims at the problem of group tower scheduling in construction sites, considers reasonable utilization of overlapping areas of the working ranges of the group towers, avoids potential safety hazards caused by the simultaneous occurrence of tower cranes in the overlapping areas, and provides a group tower scheduling optimization method in construction sites based on a mixed integer linear programming theory, so that reference is provided for a safe and reasonable group tower construction scheduling scheme. The model constructed by the invention considers the reasonable utilization of the overlapping area of the group towers, avoids potential safety hazards caused by the simultaneous occurrence of the tower cranes in the overlapping area, and proposes a group tower optimization scheduling method considering active avoidance based on a mixed integer linear programming theory so as to improve the transportation efficiency of the group towers. Compared with the prior art, the invention has the characteristic of greatly improving the transportation efficiency on the premise of ensuring the operation safety of the tower crane.
Description
Technical Field
The invention relates to a group tower scheduling optimization method considering active avoidance, and belongs to the field of civil engineering construction and management.
Background
Along with the continuous development of the construction informatization technology, the construction industry needs to change a rough labor-intensive production mode, convert the labor-intensive production mode into a mechanized and automatic industrial construction mode, and finally improve the production efficiency and profits. The tower crane is used as the most important dispatching tool for the construction site, and the running efficiency of the tower crane greatly influences the timely transportation and construction progress of materials. Shapira et al believe that monitoring and analyzing tower crane operation helps to improve project construction efficiency, thereby improving construction progress and engineering benefits. The current improvement of the operation efficiency of the tower crane can be divided into three modes: optimizing the tower crane position and the material storage position; assisting the driver operation by using an informatization means; and a scheduling scheme is provided, so that the running time is shortened.
The spatial relationship between the tower crane and the material storage position determines the material transportation path, thereby influencing the material transportation efficiency. The good transportation path can ensure quick material supply, and avoid the influence on the construction efficiency caused by untimely material transportation. Zhang et al utilized the monte carlo simulation (Monte Carlo simulation) method, optimized for single and group column positions, respectively. Tam et al then optimized the material supply point location using a genetic algorithm (Genetic Algorithm), reducing shipping time. Finally Huang et al optimized the single tower location and the material supply point location using a mixed integer programming algorithm (Mixed integer Programming), solving to obtain a globally optimal solution to the problem.
The cockpit of tower crane driver in high altitude position, though possess the bird's eye view can clearly observe the construction site situation, but the driver can be apart from the operation position by several tens meters, is unfavorable for the driver to judge. So that a plurality of scholars use an informatization means to assist drivers in judging and timely avoiding obstacles and unsafe areas of a construction site. Shapira et al developed a real-time video system that extended the visual range of the driver and could save 14-29% of the run time. Li et al give GPS and RFID (Radio Frequency Identification)'s principle, through the position information of real-time acquisition tower crane and personnel, control the operation of blind hanging, both promoted hoist and mount efficiency, ensured personnel's safety again. Hwang et al acquire position information of a tower arm and a lifting hook between tower cranes based on UWB (Ultra-wide Band) technology, and send out early warning and take emergency braking measures to avoid accidents when collision possibly occurs in an interference area.
The dispatching scheme directly gives a tower crane dispatching path, and aims to reduce redundant transportation paths of the tower crane, so that efficient transportation of materials is realized. Conventional scheduling methods may be summarized as FIFO (First-in-First-out), SJF (Shortest Job Frist), NNF (Nearest neighbor First), EDF (Earliest Deadline First). The FIFO is a conventional scheduling method in the construction field, and the tower crane scheduling scheme is determined according to the time for providing the material demand task, so that the material demand task which is provided earlier is completed preferentially. FIFO is a method that does not require management personnel to handle and does not contain any optimization ideas. SJF is task execution time sequence according to material requirement, and tasks with short execution time are completed preferentially. The SJF takes into account the characteristics of the material demand task, but its result is similar to FIFO, and the optimization result is not ideal. NNF is according to the position ordering of material demand task, select nearest material demand point to current task as next task execution position, avoided unordered task execution route. The NNF avoids partial redundant paths in the execution process of the tower crane, and the optimization effect is obvious. EDF is the sequencing according to the urgency of the material demand task, prioritizing the completion of the most urgent material demand task. EDF well solves the problem that delay is caused by untimely material dispatching in the construction process, but the dispatching result still has a large number of redundant paths, and the optimization effect is not obvious.
Then, zavichi et al abstract and approximate the idea of the classical knapsack problem (Traveling Salesman Problem, TSP) with single tower scheduling, fix the material supply route and consider the city nodes in the TSP problem, finding the shortest path through all cities using linear programming. Compared with the random hoisting sequence, the optimized hoisting sequence can reduce the transportation time by more than 20 percent, and the saved time presents an increasing trend along with the number of hoisting tasks. Subsequently, huang et al built an optimization model using a mixed integer programming algorithm (Mix-integer Linear Programming, MILP) and solved to obtain a globally optimal solution for the single tower scheduling scheme. Meanwhile, when the model considers that unified materials are stored in a plurality of positions, the optimal material storage position can be automatically selected; when the tasks are ordered, the priorities of the tasks can be considered, so that the dynamic control and optimization of the task sequence are realized.
For large and complex project engineering, a plurality of tower cranes are required to be arranged in a certain area, so that timely material transportation is ensured. Overlap of working areas inevitably occurs between adjacent tower cranes, so that the tower cranes are easy to cross, and a suspension arm collision accident is caused. The existing anti-collision safety system can only force the tower crane to brake and alarm in the collision, so that accidents are avoided, the transportation efficiency is greatly sacrificed, and the construction period is delayed. Therefore, a group tower scheduling optimization method considering active avoidance is needed, so that the safe operation of the group tower is helped, and the construction efficiency is improved.
Disclosure of Invention
According to the method, the influence of the initial position of the lifting hook and the cross operation of the group tower is considered, and based on a mixed integer linear programming algorithm, the group tower scheduling optimization method based on active avoidance is provided for providing reference for group tower service scheduling management.
The method aims at the problem of dispatching the group towers in the construction site, considers reasonable utilization of overlapping areas of the working ranges of the group towers, avoids potential safety hazards caused by the fact that the towers are simultaneously arranged in the overlapping areas, and provides a dispatching optimization method for the group towers in the construction site based on a mixed integer linear programming theory, so that reference is provided for a safe and reasonable dispatching scheme for the group towers.
The mixed shaping linear programming theory is an important branch of the linear programming theory, and is widely applied to the fields of logistics transportation, investment decision, warehouse planning and the like. The algorithm is generally NP-hard (Non-deterministic polynomial), namely an analytical solution can not be obtained through formula deduction, and the optimal solution or the local optimal solution of the problem can be searched only by means of an optimization algorithm, and meanwhile, the complexity of the algorithm increases exponentially along with the increase of variables. The method has the core concept that 0-1 variable is used as a decision variable to select the execution sequence of the tower crane; forming a feasible domain through a linear constraint set corresponding to the influence factors; and finally, obtaining an optimal solution meeting the limit adjustment by a branch-and-bound method. According to the hybrid shaping linear programming theory, the basic structure of the optimization model mainly consists of linear constraint conditions comprising continuous and integer variables and a linear optimization target. The main work of optimizing modeling is to put forward a logic relation of a simulation problem, and convert a nonlinear constraint relation in the logic relation into a linear constraint relation, so as to form a basic framework of a model.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a group tower scheduling optimization method considering active avoidance includes the following implementation steps:
step 1, building a site engineering database according to a site layout scheme and construction facility parameters;
determining a material supply point S according to a site arrangement scheme i Material demand point D j Tower position TC k Position coordinates of (a), group tower cantilever length pi k Radial velocityTangential speed->Vertical speed->And storing the field engineering data by the material type m stored by the storage point;
step 2, estimating the running time of the tower crane;
decomposing the moving track of the tower crane into radial movement and tangential movement along the horizontal direction and vertical movement;
wherein,respectively representing the x-axis coordinates of a material storage point i, a material demand point j and a tower crane k;respectively representing the y-axis coordinates of a material storage point i, a material demand point i and a tower crane k; /> Respectively representing the z-axis coordinates of a material storage point i, a material demand point j and a tower crane k; ρ (S) i ，Cr k ) Representing the linear distance between the material storage point i and the tower crane k; ρ (D) j ，Cr k ) Representing the linear distance l between the material demand point j and the tower crane k i，j Representing a material storage point iThe linear distance of the material demand point j; />Representing the radial running time of the tower crane k from the material storage point i to the material demand point along the boom; />Representing the tangential run time of a vertical boom with a tower crane k moving from a material storage point i to a material demand point;the horizontal running time of the tower crane k from the material storage point i to the material demand point is represented, and alpha represents the coordination degree of tangential movement and radial movement on a horizontal running track; />The vertical running time of the tower crane k from the material storage point i to the material demand point j is represented, and h represents the time aversion caused by the consideration of the safety factor of the tower crane; />Indicating the total operating time of the tower k from the material storage point i to the material demand point i,/>Representing the difficulty coefficient and gamma in the running process k Beta represents the continuity degree of the horizontal running track and the vertical running track. The travel time of the tower crane k transport material from the material storage point i to the material demand point i can thus be determined by means of the above formula for estimation.
Step 3, determining the actual task demand;
using a 0-1 parameter epsilon r，j，m Indicating that the task demand r is the material demand point j and the material type m is needed, if epsilon r，j，m When =1, this task is satisfied, if ε r，j，m =0 indicates that the task is not satisfied, e.g. ε 1，8，2 Let 1 denote that the first task demand is material type 1 for material demand point 8; thus, the whole construction site can be established by estimating the progress of construction periodA period of material demand within the earth, thereby giving a range of mission demands.
Step 4, establishing a material supply relation and sequencing the material supply relation;
a complete material supply relation, namely a material demand point j with a task demand r needs a material type m, so that materials are transferred from a material storage point i storing the material type m and transported to the demand point j, and after the material supply relation is completed, the material is moved to a next material supply point i to carry out the following tasks until all the tasks are completed;
wherein the variable alpha is 0-1 i，m =1 represents that the material type m is stored at the material storing point i; 0-1 variable delta r，i，j，m =1 means that the task demand r is provided with the material type m from the material storage point i to the material demand point j;0-1 variable x r，s，k =1 indicates that the task requirement r is executed by the tower crane k, and the execution sequence is s;0-1 variable y s，i，j，k，m =1 denotes the execution sequence s of the tower crane k from the material supply point i to the material demand point j;0-1 variable χ s，j，k =1 indicates that the order of execution s of the tower crane k will reach the material demand point j;0-1 variable z s，j，i，k =1 indicates that the execution sequence s of the tower crane k is to be moved from the material demand point i to the material storage point i; the other symbols are as defined above.
Equations (9), (10) and (11) limit that each material demand task can only select one supply point storing the corresponding material type; equations (12) and (13) limit that each material demand task is assigned to one tower crane and performed only once; equation (14) limits each material demand task to be assigned to a corresponding tower crane and completes sequencing, forming a complete supply function; equation (15) limits the shipment of only one material per material shipment; formulas (16) and (17) limit the tower crane to only complete tasks within the boom; equation (18) limits the material demand location of each material demand task after the ordering is completed; equation (19) limits the relationship of the current material demand location to the material supply point for the next execution sequence; equation (20) limits the number of material feed points at a time and can only be made up of one.
Step 5, considering the influence of the initial position of the lifting hook;
the initial position of the lifting hook can influence the selection of the first execution sequence, and the influence of the initial position of the lifting hook of the group tower on the execution sequence is considered by giving out the initial positions of the lifting hooks of the group tower;
wherein, the variable χ 'is 0-1' s，j，k =1 indicates that the position of the tower crane k in the execution sequence s=1 is the material demand point j, i.e. the initial position of the tower crane k; other symbols are the same. Equations (21) and (22) limit the material supply point from the initial position to the first material demand task.
Step 6, considering the influence caused by the cross operation in the overlapped area;
in order to avoid safety accidents caused by the fact that the suspension arms of adjacent tower cranes are simultaneously positioned in the overlapping area, when one tower crane is positioned in the overlapping area, the other tower crane cannot enter the overlapping area, namely, one suspension arm is positioned in the overlapping area;
wherein ST is k，s，q The starting moment of the tower crane k in the phase q of the execution sequence s is represented; the other symbols are the same as the above;
wherein TI is k，s，q，c The moment when the tower crane k enters the interference area c in the stage q of the execution sequence s is represented, if the tower crane k enters the overlapping area, the tower crane k is a normal value, and if the tower crane k does not enter the overlapping area, the tower crane k is a larger value; TO k，s，q，c Indicating the moment when the tower k leaves the interference zone c at stage q of the execution sequence s; θ'. i，j，k，c，p Indicating whether the path of the tower crane k from the material storage point i to the material demand point j enters the overlapping area through the intersection point p of the overlapping area c; θ'. i，j，k，c，t Indicating whether a path from a material storage point i to a material demand point j of the tower crane k enters an overlapping area through a crossing point t of an overlapping area c; θ i，j，k，c Indicating whether the path of the tower crane k from the material storage point i to the material demand point j passes through the overlapping area c;the tangential movement time from the material storage point i to the intersection point p, the tangential movement time from the material demand point j to the intersection point p and the tangential movement time from the intersection point p to the intersection point t are represented respectively; the other symbols are as defined above.
Wherein,the difference between the moment when the tower crane k enters the interference area c at the stage q of the execution sequence s and the moment when the tower crane 1 leaves the interference area c at the stage h of the execution sequence u is shown; 0-1 variable->The difference between the moment when the tower crane k enters the interference area c in the phase q of the execution sequence s and the moment when the tower crane 1 leaves the interference area c in the phase h of the execution sequence u is larger than 0;
formulas (27-A) and (27-B) limit the timeOr->Above 0, the case is->Or->Equal to 1; and vice versa is 0. Equation (28) limits->And->At most, only one tower crane can be equal to 1, so that the same overlapping area of the group towers is ensured not to appear at the same moment.
Step 7, calculating the running time of the group tower;
Min(SDT+DST) (31)
wherein SDT represents the sum of all task times for the swarm tower to be transported from material storage point i to material demand point j; DST represents the sum of the times that the swarm tower moves from the material demand point j to the material storage point i; the final objective of the method is the obtained minimum value of SDT+DST.
Model verification is as follows:
taking group tower scheduling of a certain engineering project as an example, the optimization effect of the optimization model is verified. And 2 tower cranes, 6 material supply points and 10 material demand points are arranged on the construction site. The location information in the site is shown in table 1, and the specific material demand tasks are shown in table 2.
Table 1 site layout information
Note that: s1 represents a supply point position 1; d1 represents a supply point position 1; TC1 represents a tower crane 1#
Table 2 material demand tasks
Table 3 optimized scheduling results for tower crane # 1 considering collisions
Table 4 optimized schedule results for tower crane # 2 considering collisions
Table 5 optimized schedule results for tower crane # 1 without regard to collision
Table 6 optimized schedule results for tower crane # 2 without regard to collision
In tables 3 and 4, it is clear that the time points at which the tower crane 1# and the tower crane 2# enter and leave the overlapping area are compared, and that two tower cranes do not appear in the overlapping area at the same time, i.e., there is no risk of collision between the two tower cranes. While the results in tables 5 and 6 are dispatch optimization results that do not take into account collisions, indicating that tower cranes 1# and 2# are the highest efficiency dispatch schemes that can be achieved by the individual individuals. However, 3 collisions (marked) may occur in the tower cranes 1# and 2# and are unsafe scheduling schemes.
The total operating time of the group towers when they collide with each other was 85.677Min, whereas the total operating time of the group towers when they collide with each other was 82.808Min, which is a small difference of only 3.46%. It can be considered that only a small amount of operating time is sacrificed under the constraint of safety, but the entire system of the group tower achieves higher operating efficiency. Comparing tables 3 and 5, the operation sequence of the tower crane # 1 is adjusted in consideration of the limitation of collision, and the occurrence of collision of two tower cranes is avoided.
The group tower optimal scheduling method based on active avoidance, provided by the invention, not only avoids collision of the tower crane, but also improves the operation efficiency of the group tower, and has theoretical rationality.
Reference to the literature
Shapira A，Rosenfeld Y，Mizrahi I.Vision system for tower cranes[J].Journal of Construction Engineering and Management，2008，134(5)：320-332.
Zhang P，Harris F C，Olomolaiye P O.A computer-based model for optimizing the location of a single tower crane[J].Building research and information，1996，24(2)： 113-123.
Zhang P，Harris F C，Olomolaiye P O，et al.Location optimization for a group of tower cranes[J].Journal of construction engineering and management，1999，125(2)： 115-122.
Tam C M，Tong T K L，Chan W K W.Genetic algorithm for optimizing supply locations around tower crane[J].Jourmal of construction engineering and management， 2001，127(4)：315-321.
Huang C，Wong C K，Tam C M.Optimization of tower crane and material supply locations in a high-rise building site by mixed-integer linear programming[J]. Automation in Construction，2011，20(5)：571-580.
Li H，Chan G，Skitmore M.Integrating real time positioning systems to improve blind lifting and loading crane operations[J].Construction Management and Ecomomics， 2013，31(6)：596-605.
Hwang S.Ultra-wide band techmology experiments for real-time preventionof tower crane collisions[J].Automation in Construction，2012，22：545-553.
Zavichi A，Madani K，Xanthopoulos P，Oloufa A A.Enhanced crane operations in construction using service request optimization[J].Automation in Construction，2014， 47：69-77.
Huang C，Wong C K.Optimization of crane setup location and servicing schedule for urgent material requests with mom-homogemeous and mom-fixed material supply[J]. Automation in Construction，2018，89：183-198.
Claims (4)
1. A group tower scheduling optimization method considering active avoidance is characterized in that: the implementation steps of the method are as follows:
step 1, building a site engineering database according to a site layout scheme and construction facility parameters;
determining a material storage point S according to a site arrangement scheme i Material demand point D j Tower position TC k Position coordinates of (a), group tower cantilever length pi k Radial velocityTangential speed->Vertical speed->And storing the field engineering data by the material type m stored by the storage point;
step 2, estimating the running time of the tower crane;
decomposing the moving track of the tower crane into radial movement and tangential movement along the horizontal direction and vertical movement;
step 3, determining the actual task demand;
using a 0-1 parameter epsilon r,j,m Indicating that the task demand r is the material demand point j and the material type m is needed, ifε r,j,m When =1, this task is satisfied, if ε r,j,m =0 indicates that the task is not satisfied; the material requirements of a period of time in the whole construction site can be established by predicting the construction period progress, and a series of task requirements are given;
step 4, establishing a material supply relation and sequencing the material supply relation;
a complete material supply relation represents that a material demand point j with a task demand r needs a material type m, so that materials are transferred from a material storage point i storing the material type m and transported to the demand point j, and after the material supply relation is completed, the material is moved to a next material storage point i to carry out the following tasks until all the tasks are completed;
step 5, considering the influence of the initial position of the lifting hook;
the initial position of the lifting hook can influence the selection of the first execution sequence, and the influence of the initial position of each lifting hook of the group tower on the execution sequence is considered by giving out the initial position of each lifting hook of the group tower;
step 6, considering the influence caused by the cross operation in the overlapped area;
in order to avoid safety accidents caused by the fact that the suspension arms of adjacent tower cranes are simultaneously positioned in the overlapping area, when one tower crane is positioned in the overlapping area, the other tower crane cannot enter the overlapping area, namely, one suspension arm is positioned in the overlapping area;
step 7, calculating the running time of the group tower;
χ′ s,j,k ≥z s,j,i,k (22)
wherein, the variable χ 'is 0-1' s,j,k =1 indicates that the position of the tower crane k in the execution sequence s=1 is the material demand point j, i.e. the initial position of the tower crane k; other symbols have the same meaning; equation (21) and22 A material storage point that is limited to move from an initial position to a first material demand task;
wherein ST is k,s,q The starting moment of the tower crane k in the phase q of the execution sequence s is represented; the other symbols are the same as the above;
wherein TI is k,s,q,c Indicating when the tower k enters the interference zone c at stage q of the execution sequence s, if it enters the overlap zoneThe domain is a normal value; TO k,s,q,c Indicating the moment when the tower k leaves the interference zone c at stage q of the execution sequence s; θ'. i,j,k,c,p Indicating whether the path of the tower crane k from the material storage point i to the material demand point j enters the overlapping area through the intersection point p of the overlapping area c; θ i,j,k,c Indicating whether the path of the tower crane k from the material storage point i to the material demand point j passes through the overlapping area c;the tangential movement time between the material storage point i and the intersection point p, the tangential movement time from the material demand point j to the intersection point p and the tangential movement time between the intersection point p and the intersection point t are represented respectively;
Min(SDT+DST) (31)
wherein SDT represents the sum of all task times for the swarm tower to be transported from material storage point i to material demand point j; DST represents the sum of the times that the swarm tower moves from the material demand point j to the material storage point i; the final objective of the method is the obtained minimum value of SDT+DST.
2. The method for optimizing group tower scheduling in consideration of active avoidance according to claim 1, wherein the method comprises the following steps:
(0≤arccos(θ)≤π)
wherein,respectively representing the x-axis coordinates of a material storage point i, a material demand point j and a tower crane k; />Respectively representing the y-axis coordinates of a material storage point i, a material demand point j and a tower crane k; /> Respectively representing the z-axis coordinates of a material storage point i, a material demand point j and a tower crane k; ρ (S) i ,Cr k ) Representing the linear distance between the material storage point i and the tower crane k; ρ (D) j ,Cr k ) Representing the linear distance l between the material demand point j and the tower crane k i,j Representing the linear distance between the material storage point i and the material demand point j; />Representing the radial running time of the tower crane k from the material storage point i to the material demand point j along the boom; />Representing the vertical tangential travel time of the tower crane k along the boom from the material storage point i to the material demand point j; />The horizontal running time of the tower crane k from the material storage point i to the material demand point j is represented, and alpha represents the coordination degree of tangential movement and radial movement on a horizontal running track; />The vertical running time of the tower crane k from the material storage point i to the material demand point j is represented, and h represents time buffering caused by the consideration of safety factors of the tower crane; />Indicating the total operating time of the tower k from the material storage point i to the material demand point j,/>Representing the difficulty coefficient and gamma in the running process k Beta represents the continuity degree of the horizontal running track and the vertical running track; the travel time of the tower crane k for transporting material from the material storage point i to the material demand point j is determined by the above formula.
3. The method for optimizing group tower scheduling in consideration of active avoidance according to claim 1, wherein the method comprises the following steps:
wherein the variable alpha is 0-1 i,m =1 represents that the material type m is stored at the material storing point i; 0-1 variable delta r,i,j,m =1 means that the task demand r is provided with the material type m from the material storage point i to the material demand point j;0-1 variable x r,s,k =1 indicates that the task requirement r is executed by the tower crane k, and the execution sequence is s;0-1 variable y s,i,j,k,m =1 denotes the order of execution s of the tower crane k from the material storage point i to the material demand point j;0-1 variable χ s,j,k =1 indicates that the order of execution s of the tower crane k will reach the material demand point j;0-1 variable z s,j,i,k =1 indicates that the execution sequence s of the tower crane k is to be moved from the material demand point j to the material storage point i; the other symbols are the same as the above;
equations (9), (10) and (11) limit that each material demand task can only select one storage point storing the corresponding material type; equations (12) and (13) limit that each material demand task is assigned to only one tower and performed once; equation (14) limits each material demand task to be assigned to a corresponding tower crane and completes sequencing, forming a complete supply function; equation (15) limits the shipment of only one material per material shipment; formulas (16) and (17) limit the tower crane to only complete tasks within the boom; equation (18) limits the material demand location of each material demand task after the ordering is completed; equation (19) limits the relationship of the current material demand location to the material storage point of the next execution sequence; equation (20) limits the number of material storage points at a time and can only be made up of one.
4. The method for optimizing group tower scheduling in consideration of active avoidance according to claim 1, wherein the method comprises the following steps:
wherein,the difference between the moment when the tower crane k enters the interference area c at the stage q of the execution sequence s and the moment when the tower crane l leaves the interference area c at the stage h of the execution sequence u is shown; 0-1 variable->The difference between the moment when the tower crane k enters the interference area c at the stage q of the execution sequence s and the moment when the tower crane l leaves the interference area c at the stage h of the execution sequence u is larger than 0;
formulas (27-A) and (27-B) limit the timeOr->Above 0, the case is->Or->Equal to 1; otherwise, 0; equation (28) limits->And->At most, only one tower crane is equal to 1, so that the same overlapping area of the group towers cannot appear at the same moment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911268829.9A CN111061987B (en) | 2019-12-11 | 2019-12-11 | Group tower scheduling optimization method considering active avoidance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911268829.9A CN111061987B (en) | 2019-12-11 | 2019-12-11 | Group tower scheduling optimization method considering active avoidance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111061987A CN111061987A (en) | 2020-04-24 |
| CN111061987B true CN111061987B (en) | 2024-01-26 |
Family
ID=70300670
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911268829.9A Active CN111061987B (en) | 2019-12-11 | 2019-12-11 | Group tower scheduling optimization method considering active avoidance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111061987B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112173984B (en) * | 2020-09-22 | 2022-04-05 | 广州珠江建设发展有限公司 | Method, device, equipment and medium for managing operation safety of tower group based on Internet of things |
| CN113434996B (en) * | 2021-01-21 | 2023-04-25 | 北京建筑大学 | Method for scheduling hoisting service of assembled concrete structure tower crane |
| CN113516359B (en) * | 2021-05-11 | 2023-07-07 | 中山市至高机械设备租赁有限公司 | Multi-tower crane transferring and dispatching method and system |
| CN115936268B (en) * | 2023-03-10 | 2023-05-23 | 北京建筑大学 | Group tower multi-objective scheduling optimization method considering conflict task interval time |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109160419A (en) * | 2017-08-11 | 2019-01-08 | 北京建筑大学 | A kind of construction crane machine group anti-collision central processing unit |
| CN110399629A (en) * | 2019-05-29 | 2019-11-01 | 北京工业大学 | A kind of tower crane type selecting and layout optimization modeling method based on mixing shaping planning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10037704B1 (en) * | 2017-02-01 | 2018-07-31 | David Myr | Automatic real-time air traffic control system and method for maximizing landings / takeoffs capacity of the airport and minimizing aircrafts landing times |
-
2019
- 2019-12-11 CN CN201911268829.9A patent/CN111061987B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109160419A (en) * | 2017-08-11 | 2019-01-08 | 北京建筑大学 | A kind of construction crane machine group anti-collision central processing unit |
| CN110399629A (en) * | 2019-05-29 | 2019-11-01 | 北京工业大学 | A kind of tower crane type selecting and layout optimization modeling method based on mixing shaping planning |
Non-Patent Citations (2)
| Title |
|---|
| 朱浩奇 ; .关于建筑施工质量安全管理措施的探讨.中外建筑.2017,(第08期),全文. * |
| 王涌 ; 雷超 ; .浅谈塔式起重机选型定位优化及任务分配设计.价值工程.2019,(第16期),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111061987A (en) | 2020-04-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111061987B (en) | Group tower scheduling optimization method considering active avoidance | |
| US9061843B2 (en) | System and method for integral planning and control of container flow operations in container terminals | |
| CN110264120A (en) | A kind of intelligent storage route planning system and method based on more AGV | |
| Kemme | Design and operation of automated container storage systems | |
| CN111596658A (en) | Multi-AGV collision-free operation path planning method and scheduling system | |
| CN111882215B (en) | Personalized customization flexible job shop scheduling method containing AGV | |
| CN105243512A (en) | Dynamic scheduling method of steelmaking operation plan | |
| CN111563672B (en) | Method for configuring number of multiple AGV | |
| CN110399629A (en) | A kind of tower crane type selecting and layout optimization modeling method based on mixing shaping planning | |
| KR20150058332A (en) | Shipping work planning system, shipping work planning method, and shipping work method | |
| Riga et al. | Mixed integer programming for dynamic tower crane and storage area optimization on construction sites | |
| CN111815158A (en) | Horizontal transportation scheduling system for container terminal | |
| Sun et al. | AGV-based vehicle transportation in automated container terminals: A survey | |
| CN104077638A (en) | Portal jib crane dispatching method | |
| Huang et al. | Optimization of multiple-crane service schedules in overlapping areas through consideration of transportation efficiency and operational safety | |
| JP5686072B2 (en) | Warehouse work plan creation device | |
| CN114380199A (en) | Crown block control method and device | |
| Taghaddos et al. | Simulation-based scheduling of modular construction using multi-agent resource allocation | |
| CN114565296A (en) | Intelligent running scheduling method and system for rail transit construction line | |
| CN106647652A (en) | Tower crane group multitask scheduling method based on genetic algorithm and MMAS algorithm | |
| CN111832965A (en) | Unmanned same-span multi-crown-block cooperative scheduling method, system, medium and terminal | |
| Ye et al. | A mixed-integer linear programming-based scheduling model for refined-oil shipping | |
| Homayouni et al. | Integrated scheduling of SP-AS/RS and handling equipment in automated container terminals | |
| JP7338617B2 (en) | Bottleneck visualization device and bottleneck visualization method | |
| Zhen et al. | A decision framework for automatic guided vehicle routing problem with traffic congestions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |