CN110033121B - Multi-species co-evolution method for solving optimization problem of warehouse operation with corridor - Google Patents
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Abstract
The invention discloses a multi-species co-evolution method for solving an optimization problem of storage operation with a passageway, and belongs to the field of intelligent logistics and storage equipment. The invention adds the transverse passageway on the basis of the traditional warehouse, establishes a warehouse operation optimization model with the passageway, and solves the model. In view of the defects of easy premature, low convergence speed and the like of the existing solving technology, a multi-species co-evolution optimization method based on the common participation of a genetic algorithm, a particle swarm algorithm and an artificial fish swarm algorithm is provided, namely, the capability of each species for adapting to the environment can be enhanced through a multi-species competition symbiotic predation strategy based on a learning mechanism; by introducing a mutation mechanism, the population diversity of all species is synergistically improved, so that the global optimizing capability and solving efficiency of an algorithm are improved while the evolution capability of a single species is improved. The invention not only improves the operation efficiency of the whole warehouse, but also can promote the logistics warehouse to be transformed and upgraded intelligently and environmentally.
Description
Technical Field
The invention belongs to the field of intelligent logistics, and further relates to a multi-species co-evolution method for solving the problem of optimization of warehouse operations with aisles.
Background
Warehouse logistics are used as carriers for article circulation and blood of electronic commerce, and become an integral part of enterprise development. The warehousing operation efficiency is used as a main index for measuring the logistics warehousing system, so that the warehousing operation efficiency is further improved, the requirement of customers on delivery time is met, and the troublesome problem to be solved by the traditional manufacturing enterprises is solved. Under the background, the multi-channel warehouse operation optimization problem different from the traditional automatic three-dimensional warehouse is researched, and the multi-channel warehouse operation optimization method has a good reference function for improving warehouse operation efficiency, so that a multi-species co-evolution method for solving the warehouse operation optimization problem with channels is sought, and the multi-channel warehouse operation optimization method has good scientific significance and social value.
At present, students focus on more optimization problems of traditional single-block (no-channel) type warehousing operations, but the optimization problems of multi-block type warehousing operations are not related or are related less. Such as: de Santis et al (An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses, european Journal of Operational Research, 2018) propose a hybrid meta-heuristic based on the ant colony algorithm and the florid algorithm with the goal of minimizing order picking distance. Poplar and the like (stacker type intensive warehouse system composite operation three-dimensional space path optimization, computer integrated manufacturing system, 2017) propose an improved ant colony algorithm to solve the problem of intensive warehouse system composite operation three-dimensional space path optimization. The invention carries out serious analysis and research on the storage operation optimization problem with the passageway, and provides a multi-species co-evolution method for solving the problem, thereby not only improving the storage operation efficiency, but also enriching the method for solving the storage operation optimization problem with the passageway, and having good demonstration effect on improving the intelligent level of the related field of logistics storage.
Disclosure of Invention
Therefore, the invention aims to provide a multi-species co-evolution method for solving the problem of optimization of warehouse operations with aisles so as to improve the operation efficiency and the intelligent level of logistics warehouse.
In order to achieve the above object, the present invention is conceived as follows: the invention takes the limited load of the stacker, the inadmissible multiple picking of the same order and the like as constraint conditions, takes the shortest time for finishing the picking of all orders as an optimization target, and abstracts the storage operation optimization problem with the aisle into the combination optimization problem with constraint. According to the inventive concept, the invention adopts the following technical scheme:
the multi-species co-evolution method for solving the optimization problem of the warehouse operation with the aisle is characterized by comprising the following steps of:
(1) Analyzing constraints and targets to be optimized existing in a warehouse site, and abstracting the constraints and targets into a mathematical model with constraints;
(2) Initializing parameters: total maximum evolution algebraG_maxMaximum per speciesAlgebra of evolutionG_pmaxAlgebraic counter of evolutiontPopulation size M, crossover probability p c Probability of variation p m Inertial weight
Learning factor
And
step sizestepsizeVisual field range of artificial fishVisualNumber of attemptsTry_numberFactor of degree of congestion
Initializing population individuals of each species;
(3) A global search is performed by a search engine,t=t+1；
(4) Local search, and single species parallel evolution is carried out by utilizing a genetic algorithm, a particle swarm algorithm and an artificial fish swarm algorithm;
(5) Generating random numbersrFor predators, ifr>p i Then competition predation of multiple species based on learning mechanism is carried out; the predators are species with the greatest average fitness, and the balance is predators.
(6) Synergistic improvement of multi-species population diversity;
(7) If it ist<G_maxReturning to (3); otherwise, outputting the optimal solution.
Still further, the optimization model established in step (1) is established based on the following considerations: the selecting path of the stacker is optimized for the purposes of energy saving and consumption reduction. The goal to be optimized is to minimize the time it takes to complete a given order picking job, the mathematical model of which is expressed as follows:
wherein the method comprises the steps of
For the purpose of the object to be optimized,Ofor a collection of order picking tasks,Rfor a stacker to pick a collection of paths,
the time required for the stacker to continuously pass through two goods places to be picked;
for whether the stacker is continuously passing through the cargo space under a certain path
And goods space
Is a mark of (2);
representing cargo space
Whether or not to belong to a sub-path
。
Still further, step (5) may be further described as:
to co-evolve multiple species, competing predation mechanisms are introduced, specifically: in the course of biological evolution, individuals with poor adaptability to living environments may occur due to their own or natural conditions, and thus the risk of these individuals being prey on by other species is great. In order to realize competition predation among species, predators and predators are found out based on average fitness, the species with the largest average fitness is used as predators, and the rest species are all predators. Predated populations are better adapted to the living environment and require a summary of predated experience training (i.e., whatSo-called learning) to continuously increase its own viability. For this purpose, the probability of an individual being preyp i The definition is as follows:
in order to embody the cooperativity of multi-species evolution, the invention introduces a propagation strategy based on a learning mechanism, namely: if a species is prey on, it will result in a reduced population of that species, in order to enhance the viability of that species, an ecological balance is maintained, in
After the individual is predated, new individuals will be propagated based on learning mechanisms. The specific propagation strategy is defined as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the population space of all species.
Still further, the step (6) may be further described as:
population diversity is a representation of the ability of a species to adapt to the environment, and for optimization algorithms is a measure of the spatial distribution of solutions. If the population distribution is concentrated, the development of a solution space is not facilitated, the algorithm is easily caused to fall into local optimum, and therefore the solution efficiency and the solution quality are reduced. The mutation strategy can improve the diversity of the population to a certain extent, and based on the consideration, the diversity of the population needs to be adjusted and improved.
In order to realize the global development and local development of the solution space, the invention synergistically improves the population diversity of all species based on a mutation mechanism, and the specific strategy is as follows: and (3) arranging all individuals in all species in descending order according to the fitness, calculating the distance between all non-optimal individuals and optimal individuals, and arranging in descending order according to the distance length. In order to overcome the randomness caused by excessive mutation, only 10% of individuals with fitness and distance ranking are mutated.
Compared with the prior art, the multi-species co-evolution algorithm has the advantages that: firstly, the multi-species competition symbiotic mechanism of the algorithm gives consideration to the searching depth of the algorithm, and can guide the algorithm to evolve towards the optimal or suboptimal direction; secondly, the population diversity collaborative improvement mechanism improves the population diversity, gives consideration to the breadth of algorithm search, and further improves the solving precision and the convergence efficiency.
Drawings
FIG. 1 is a flow chart of a multi-species co-evolution method for solving the problem of optimization of warehouse operations with aisles.
Fig. 2 is a diagram of a warehouse layout with aisles according to the present invention.
FIG. 3 is a comparison graph of the solution effects of the algorithms of the present invention for one job instance.
Fig. 4 is a statistical plot of the distribution of the sub-optimal solutions of each algorithm 30 for one job instance of the present invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and the preferred embodiments.
Embodiment one:
referring to fig. 1, the multi-species co-evolution method for solving the problem of optimization of warehouse operations with aisles provided by the invention is characterized by comprising the following specific steps:
(1) Analyzing constraints and targets to be optimized existing in a warehouse site, and abstracting the constraints and targets into a mathematical model with constraints;
(2) Initializing parameters: total maximum evolution algebraG_maxMaximum algebra of evolution per speciesG_pmaxAlgebraic counter of evolutiontPopulation size M, crossover probability p c Probability of variation p m Inertial weight
Learning factor
And
step sizestepsizeVisual field range of artificial fishVisualNumber of attemptsTry_numberFactor of degree of congestion
Initializing population individuals of each species;
(3) A global search is performed by a search engine,t=t+1；
(4) Carrying out local search, and carrying out single-species parallel evolution by a genetic algorithm, a particle swarm algorithm and an artificial fish swarm algorithm;
(5) Generating random numbersr. For predators, ifr>p i Then a multi-species competitive predation based on learning mechanisms is performed. The specific implementation process is as follows:
to co-evolve multiple species, competing predation mechanisms are introduced, specifically: in the course of biological evolution, individuals with poor adaptability to living environments may occur due to their own or natural conditions, and thus the risk of these individuals being prey on by other species is great. In order to realize competition predation among species, predators and predators are found out based on average fitness, the species with the largest average fitness is used as predators, and the rest species are all predators. Predated populations are better adapted to living environments and there is a need to summarize predated experience training (so-called learning) to continuously increase their own viability. For this purpose, the probability of an individual being preyp i The definition is as follows:
in order to embody the cooperativity of multi-species evolution, the invention introduces a propagation strategy based on a learning mechanism, namely: if a species has individuals caughtFeeding will result in a reduced population of the species, in order to enhance the viability of the species, maintain ecological balance, in
After the individual is predated, new individuals will be propagated based on learning mechanisms. The specific propagation strategy is defined as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the population space of all species.
(6) The diversity of multiple species populations improves synergistically. The specific implementation process is as follows:
population diversity is a representation of the ability of a species to adapt to the environment, and for optimization algorithms is a measure of the spatial distribution of solutions. If the population distribution is concentrated, the development of a solution space is not facilitated, the algorithm is easily caused to fall into local optimum, and therefore the solution efficiency and the solution quality are reduced. The mutation strategy can improve the diversity of the population to a certain extent, and based on the consideration, the diversity of the population needs to be adjusted and improved.
In order to realize the development of the solution space overall situation and the local development, the invention synergistically improves the population diversity of all species, and the specific strategy is as follows: and (3) arranging all individuals in all species in descending order according to the fitness, calculating the distance between all non-optimal individuals and optimal individuals, and arranging in descending order according to the distance length. In order to overcome the randomness caused by excessive mutation, only 10% of individuals with fitness and distance ranking are mutated.
(7) If it ist<G_maxReturning to (3); otherwise, outputting the optimal solution.
Embodiment two:
this embodiment is substantially the same as the first embodiment, and is specifically as follows:
the optimization model established in the step (1) is established based on the following consideration: the selecting path of the stacker is optimized for the purposes of energy saving and consumption reduction. The goal to be optimized is to minimize the time it takes to complete a given order picking job, the mathematical model of which is expressed as follows:
wherein the method comprises the steps of
For the purpose of the object to be optimized,Ofor a collection of order picking tasks,Rfor a stacker to pick a collection of paths,
the time required for the stacker to continuously pass through two goods places to be picked;
for whether the stacker is continuously passing through the cargo space under a certain path
And goods space
Is a mark of (2);
representing cargo space
Whether or not to belong to a sub-path
。
Embodiment III:
referring to fig. 1, the multi-species co-evolution method for solving the problem of optimization of warehouse operations with aisles provided by the invention comprises the following specific steps:
1. establishing a target and establishing an optimization model
The multi-aisle warehousing operation optimization problem in the warehousing operation of the example has the following characteristics:
the number of relevant bins corresponding to a batch of orders is m, and the positions of the bins in the warehouse are respectively denoted as p 1 , p 1 ,…, p m . This type of warehouse layout is shown in fig. 2.
Setting the horizontal moving average speed and the vertical moving average speed of the stacker as respectively
、
And the horizontal and vertical movements are independent of each other, the maximum cargo capacity of the stacker isCThe length, width and height of each bin of the warehouse are respectively recorded asL、WAndH. The roadway width isW 1 The aisle width isW 2 . The number of blocks isL 1 The number of goods shelves per tunnel per block isL 2 The layer number isL 3 The number of lanes isL 4 . Assume thatP={p 1 ,p 2 ,…,p n Is of ordernGoods locations to be picked, the goods locations thereofp i (
) The coordinates can be expressed as%x i ,y i ,z i ,b i (ii) or (iii), wherein,x i 、y i 、z i andb i the buffer area is marked as the roadway number where the goods place is located, the column number and layer number of the goods shelf where the goods place is located and the block number where the goods place is locatedp 0 (0,0,0,1). At the same time, the order requirement of each goods at the goods-to-be-picked position is recorded as
。
Definition 1: if the stacker continues to pass the cargo space during the picking taskp i Andp j thene ij =1, otherwisee ij =0。
In definition 1, the stacker is defined by a cargo spacep i To the point ofp j The time taken can be expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
definition 2: due to weight limitations on the stacker, an order may require a stacker to makeRAnd (5) secondary picking operation. If the goods are to be pickedp i In the first placer（
) Completed in the secondary picking operationg ir =1, otherwiseg ir =0。
The optimization objective of the warehouse operation problem with the aisle is that the time for completing order picking is shortest, and a mathematical model comprises an objective function and a constraint, and is defined as follows:
2. clear constraint conditions, establish constraint relationship
Wherein, the formula (2) is an objective function; the formulae (3) to (10) are various constraints, specifically: equation (3) indicates that the to-be-picked item is allowed to appear only once in the picking path; the formula (4) limits that the goods loaded by the stacker for picking operation cannot exceed the maximum load of the stacker; equation (5) and equation (6) indicate that each of the to-be-picked locations cannot form a self-loop during the picking process. The starting position and the ending position of the stacker picking path defined by the formula (7) and the formula (8) are both in the warehouse-in buffer zone; equation (9) and equation (10) are binary range constraints for decision variables.
3. The optimization method of the embodiment is selected to solve the warehouse operation optimization problem with the aisle, and the method is to perform evolutionary computation in the feasible domain of the decision variables through a multi-species collaborative optimization method so as to solve the optimal solution or suboptimal solution.
The optimization method comprises the following specific steps:
step1 initialization parameters: total maximum evolution algebraG_maxMaximum algebra of evolution per speciesG_pmaxAlgebraic counter of evolutiontPopulation size M, crossover probability p c Probability of variation p m Inertial weight
Learning factor
And
step sizestepsizeVisual field range of artificial fishVisualNumber of attemptsTry_numberFactor of degree of congestion
Initializing population individuals of each species;
step2 a global search is performed and,t=t+1；
step3, carrying out local search, and carrying out single-species parallel evolution by a genetic algorithm, a particle swarm algorithm and an artificial fish swarm algorithm;
step4 generation of random numbersr. For predators, ifr>p i Then competition predation of multiple species based on learning mechanism is carried out;
step5, synergistically improving the diversity of multiple species populations;
step6 ift<G_maxReturning to Step2; otherwise, outputting the optimal solution.
Embodiment four:
the embodiment designs the problem of optimizing the warehouse operation of the passageway of the supermarket on the large-scale network, and obtains the optimal solution or the suboptimal solution meeting the constraint condition by using the method.
1. Overview of problems
According to the technical scheme, a certain large online supermarket is taken as an application background for illustration. 60 customer orders were randomly generated for testing and compared to standard genetic algorithms (Genetic Algorithm, GA), standard particle swarm algorithm (Particle Swarm Optimization, PSO), standard artificial fish swarm algorithm (Artificial Fish Swarm Algorithm, AFS). Experiments were performed under the Win10 system platform, matlab7.0 development environment. The evolution algebra is 600, wherein, for MSCA, the evolution algebra of each sub-algorithm is 50, and the sub-population ruleThe modulus is 60, and the crossover probability and variation probability of GA are 0.8 and 0.06 respectively. For PSO, inertial weights
Is 1.3, learning factor
And
all are 2. For AFS, step sizestepsizeIs 0.5 in visual field rangeVisual15 times of attemptsTry_numberIs 30, the crowding degree factor
0.618. The parameters of the automated stereoscopic warehouse system are as follows:L=30cm，W=50cm，H=40cm，W 1 =80cm，W 2 =80cm，L 1 =8，L 2 =100，L 3 =15，L 4 =7，v x =1m/s，v y =0.5m/s，C=500kg。
2. optimizing result contrast analysis
According to the problem profile in1, fig. 3 shows the trend of the optimal solution obtained by each algorithm according to the evolution algebra, for the test example described above. In order to have universality, orders of different scales are tested, and algorithms involved in comparison are run 30 times each, fig. 4 shows a box diagram of the results of 30 runs when the order scale is 60, and table 1 shows the optimal solution opt and average avg of 30 times.
Table 1 comparison of solution conditions for different Scale orders
As can be seen intuitively from fig. 3, the MSCA algorithm proposed in the present application is significantly superior to the other three algorithms in convergence speed and solving quality. Meanwhile, fig. 4 shows that the optimal solution distribution obtained by the MSCA is more concentrated for solving the same problem for multiple times, which shows that the algorithm has good robustness. To further evaluate the performance of the MSCA algorithm, table 1 compares MSCA with GA, PSO and AFS from the two angles of warehouse presence/absence of a lane according to the solution of orders of different scales. As can be seen from table 1, the influence of the initial solution can cause a certain fluctuation of the solution quality, and at the same time, the overall distribution characteristic of the bin to which the order belongs can also cause the bin with the aisle to not show a remarkable advantage, for example, if the bins to which the order belongs are mostly distributed in the same roadway and belong to the same block. But MSCA shows its own advantages for solving all scale order picking problems as a whole, especially as order scale increases. MSCA exhibits these characteristics, mainly thanks to the proposed multi-species competitive predation and multi-species population diversity synergistic improvement strategy based on learning mechanisms. This allows for complementation of the advantages of each species and improves the diversity of the population, thereby enhancing the global development and local exploration capabilities of MSCA. More importantly, table 1 reveals that aisle-containing warehouses are better in order picking efficiency than aisle-free warehouses.
These examples are only for illustrating the present invention and are not intended to limit the scope of the present invention. Further, various modifications and adaptations of the invention may occur to those skilled in the art, and such equivalents are intended to fall within the scope of the present application as defined in the appended claims.
Claims (1)
1. A multi-species co-evolution method for solving the problem of optimization of warehouse operations with aisles is characterized by comprising the following specific steps:
(1) Establishing a mathematical model by taking the shortest time for completing order picking as an optimization target; the mathematical model established in the step (1) is represented as follows:
where minf (e) is the target to be optimized, O is the set of order picking tasks, R is the set of stacker picking paths, t ij The time required for the stacker to continuously pass through two goods places to be picked; e, e ij For whether the stacker is continuously passing through the cargo space p in a certain path i And cargo space p j Is a mark of (2); g ir Representing the cargo space p i Whether it belongs to sub-path r;
(2) Initializing parameters: total maximum algebra G_max, maximum algebra G_pmax per species, algebra counter t, population size M, crossover probability p c Probability of variation p m Inertial weight w, learning factor c 1 And c 2 Initializing population individuals of each species, wherein the population individuals comprise step size, visual field of the artificial fish, try_number of attempts and crowding factor delta;
(3) Global search, t=t+1;
(4) Carrying out local search, and carrying out single-species parallel evolution by a genetic algorithm, a particle swarm algorithm and an artificial fish swarm algorithm;
(5) Generating a random number r, for predators, if r>p i Then competition predation of multiple species based on learning mechanism is carried out; in the step (5), multi-species competition predation based on a learning mechanism is carried out; to co-evolve multiple species, competing predation mechanisms are introduced, specifically: in the process of biological evolution, individuals with poor adaptability to living environments can appear due to the factors such as self or natural conditions, so that the risks of predation of the individuals by other species are high; in order to realize competition predation among species, firstly, a predator and a predator are found out based on average fitness, the species with the largest average fitness is taken as the predator, and the rest species are all predators; the prey population is better suitable for living environment, and the prey experience teaching training is summarized to continuously improve the living capacity of the prey population; for this purpose, the probability p of the individual being prey i The definition is as follows:
to embody the synergic nature of multi-species evolution, a propagation strategy based on a learning mechanism is introduced, namely: if a species is prey on, it will result in a reduced population of that species, in order to enhance the viability of that species, an ecological balance is maintained, in
After the individual is predated, new individuals will be propagated based on the learning mechanism; the specific propagation strategy is defined as follows:
wherein Ω is the population space of all species
(6) Synergistic improvement of multi-species population diversity;
(7) If t < G_max, returning to the step (3); otherwise, outputting an optimal solution;
in order to realize the global development and local development of the solution space, the population diversity of all species is synergistically improved, and the specific strategy is as follows: all individuals in all species are arranged in descending order according to the fitness, then the distances between all non-optimal individuals and optimal individuals are calculated, and the individuals are arranged in descending order according to the distance length; in order to overcome the randomness caused by excessive mutation, only 10% of individuals with fitness and distance ranking are mutated.
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