CN114742380A - Double-layer resource allocation optimization method for smart park - Google Patents

Double-layer resource allocation optimization method for smart park Download PDF

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CN114742380A
CN114742380A CN202210308326.5A CN202210308326A CN114742380A CN 114742380 A CN114742380 A CN 114742380A CN 202210308326 A CN202210308326 A CN 202210308326A CN 114742380 A CN114742380 A CN 114742380A
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徐跃明
王磊
欧阳世波
安裕强
徐珂
周安祥
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Hongyun Honghe Tobacco Group Co Ltd
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Abstract

The invention discloses a double-layer resource allocation optimization method for a smart park, belonging to the field of smart parks, and the optimization method comprises the following steps: acquiring park warehouse data, logistics vehicle data and resource data, and constructing a double-layer resource allocation optimization model; and step two, based on a differential evolution algorithm, improving the multivariate cosmic algorithm, and completing the solution of the double-layer resource allocation optimization model by utilizing the improved mixed differential multivariate cosmic algorithm. The invention provides an effective reference for solving the problem of construction of the intelligent park at the resource level.

Description

Double-layer resource allocation optimization method for smart park
Technical Field
The invention relates to the field of intelligent parks, in particular to a double-layer resource allocation optimization method for an intelligent park.
Background
With the continuous development and application of the internet of things IoT technology, 5G, cloud computing and other emerging information technologies, the intelligent construction of the industrial park gradually becomes a new development trend. The intelligent park construction comprises the cooperative development of strategy, process and resource, and how to construct an efficient park resource allocation method, realize the integration of park resources and improve the overall utilization rate is the focus of realizing and developing the intelligent park.
The double-layer resource allocation optimization for the intelligent park mainly comprises two aspects of sorting path optimization for park warehouse personnel and path optimization for park logistics vehicles, the efficiency of the personnel sorting operation needs to be improved as much as possible while the logistics vehicle scheduling is ensured, and the maximum park resource utilization rate can be ensured. In addition, the wisdom garden construction is extremely strict to the investigation of practicality, so this optimization model needs to relate to different structure vehicle physics restraint, multidimensional true restraint such as many garden warehouses, many commercial orders, many storehouses platform, operation time window, and the solution of above-mentioned content to garden resource allocation optimization is undoubtedly key and difficult point.
Different from the traditional campus resource management, the intelligent campus-oriented double-layer resource allocation optimization belongs to an NP-Hard problem on a theoretical level, the problem has few optimal solutions and can only find a better solution, while the intelligent optimization algorithm is simpler and has a better optimization result when solving the problem, but the single algorithm has the defects of poor convergence precision speed, poor optimization result and the like. Based on a double-layer resource allocation optimization model, an improved hybrid intelligent algorithm is constructed to improve the convergence of the algorithm, prevent the algorithm from falling into a local optimal solution and avoid the algorithm from being premature. In order to improve the optimization performance of the algorithm, a population initialization strategy based on a chaos theory is constructed to improve the diversity, randomness and ergodicity of an initialization population; an improved strategy based on a differential algorithm is constructed to improve an algorithm iteration selection mode, the occurrence probability of the optimal solution and the global optimization capability of the algorithm are effectively improved, and the optimization effect of the complex optimization problem related to multi-dimensional constraint is guaranteed while the convergence speed and precision are high. The application of the algorithm can not only solve the optimization problem of double-layer resource allocation, but also save the resource allocation calculation time and cost of the park so as to realize resource level cooperative utilization and efficiency improvement.
Disclosure of Invention
The method fully considers the operation demand characteristics of multiple warehouses, multiple service desks and multiple vehicles in the park and the real operation constraints comprising an operation time window, warehouse shelf layout, heterogeneous vehicle load constraints and the like, constructs a double-layer resource allocation optimization model comprising a cargo picking path optimization model of an operator in the park on the upper layer and a logistics vehicle path optimization model of the park on the lower layer, and designs an improved hybrid intelligent algorithm to solve the problems of high solving difficulty, low solving precision and unsatisfactory optimization result in the resource allocation optimization problem.
In order to achieve the purpose, the invention provides a double-layer resource allocation optimization method facing an intelligent park, which comprises the following steps: the optimization method comprises the following steps: acquiring park warehouse data, logistics vehicle data and resource data, and constructing a double-layer resource allocation optimization model; and step two, based on a differential evolution algorithm, improving the multivariate cosmic algorithm, and completing the solution of the double-layer resource allocation optimization model by utilizing the improved mixed differential multivariate cosmic algorithm.
Preferably, in the first step, a double-layer resource allocation optimization model is constructed, and the double-layer resource allocation optimization model comprises an upper-layer park-oriented warehouse operator picking path optimization model and a lower-layer park-oriented logistics vehicle path optimization model.
Preferably, the optimization model of the picking path of the operator in the upper park warehouse is as follows:
the number of shelves required by unit order batch, the fairness fitness function of the park warehouse service desks and the picking operation time of park warehouse operators are used as optimization indexes, and the optimization indexes are as follows:
Figure BDA0003562815750000021
Figure BDA0003562815750000022
Figure BDA0003562815750000023
wherein f is1Number of shelves required for unit batch order, f2For park warehouse service counter fairness fitness function, f3Picking up the goods for the park warehouse operator; wherein, Yi bE {0,1}, when Yi bWhen the value is 1, the batch b needs the shelf i, otherwise, the batch b does not need the shelf i;
Figure BDA0003562815750000024
when in use
Figure BDA0003562815750000025
Time indicates that batch b is allocated to service desk R, and vice versa indicates that batch b is not allocated to service desk R;
Figure BDA0003562815750000026
when in use
Figure BDA0003562815750000027
Time-of-day presentation of the order picker p from the service desk DRStarting from, or otherwise indicating not to leave the service desk DRStarting;
Figure BDA0003562815750000028
when in use
Figure BDA0003562815750000029
When the order is picked, the order is picked by the order j after the order i is picked in one batch b, otherwise, the order is not picked by the order j;
Figure BDA00035628157500000210
when the temperature is higher than the set temperature
Figure BDA00035628157500000211
Time indicates that the picking person is negativeThe order o in the batch b is responsible, otherwise, the order o in the batch b is not responsible; hcHeight of a single cargo space; liThe number of layers of a goods position i; dcDepth of a single cargo space; diThe depth of the cargo space i; vpThe walking speed of the goods picking operator; vdThe speed of the auxiliary tool for picking operation; t isbThe packaging time is;
Figure BDA0003562815750000031
distance from any service desk to any cargo space;
Figure BDA0003562815750000032
is the distance between any two cargo spaces;
after normalization processing is carried out on the optimization indexes, linear weighting is carried out, and therefore an objective function of the park warehouse operator picking path optimization model is constructed and is shown as the following formula:
Figure BDA0003562815750000033
wherein λ is1，λ2，λ3Satisfy lambda1+λ2+λ3＝1
According to the management strategy of a general service park, two parts of park warehouse operator scheduling constraint and resource allocation constraint are constructed; the scheduling constraint of the park warehouse operators comprises:
each batch of business orders is charged by a single service desk:
Figure BDA0003562815750000034
the number of the maximum operation persons of the service desk is not limited in each picking operation:
Figure BDA0003562815750000035
each operator is responsible for only one business order:
Figure BDA0003562815750000036
the picking operation personnel return to the service desk when starting after picking:
Figure BDA0003562815750000037
each business order can only be assigned to one batch:
Figure BDA0003562815750000038
shelves can only be picked once in a batch:
Figure BDA0003562815750000039
Figure BDA00035628157500000310
the resource allocation constraints include that the volume of the business order does not exceed the picker limit:
Figure BDA00035628157500000311
the amount of material required for a business order does not exceed the warehouse maximum limit:
Figure BDA0003562815750000041
wherein the content of the first and second substances,
Figure BDA0003562815750000042
as a service desk DRThe maximum number of workers;
Figure BDA0003562815750000043
when the temperature is higher than the set temperature
Figure BDA0003562815750000044
Indicates that business order o belongs to business order batch b; oAFor the type of resource contained in the business order,
Figure BDA0003562815750000045
is the required number of resources A, vAIs the volume of unit resource A; vmaxThe maximum capacity of the picking operation auxiliary tool;
Figure BDA0003562815750000046
is the maximum amount of warehouse asset a.
Preferably, the lower-level park logistics vehicle path optimization model is specifically as follows:
the details are as follows:
Figure BDA0003562815750000047
Figure BDA0003562815750000048
Figure BDA0003562815750000049
Figure BDA00035628157500000410
wherein f is1For the driving time of the logistic vehicles in the park, f2Penalty cost for business order incompletion, f3For park warehouse platform utilization, f4Punishing cost for operation of the logistics vehicles in the park; wherein the content of the first and second substances,
Figure BDA00035628157500000411
when in use
Figure BDA00035628157500000412
When it means that the vehicle k is scheduled to go from the warehouse c to the warehouse d, otherwise it means not going to the warehouse d; dcdDistance between warehouses c, d; dec，DdeRespectively representing the distances from the warehouses c and d to the garden gate; vkIs the travel speed of vehicle k; w is aUload，wloadRespectively representing the penalty coefficients of unloading and loading operation;
Figure BDA00035628157500000413
the weight of resource a needed to unload and load, respectively, vehicle k;
Figure BDA00035628157500000414
the weight of resource a unloaded and loaded for vehicle k at warehouse c;
Figure BDA00035628157500000415
when in use
Figure BDA00035628157500000416
When the vehicle is arranged to go to the platform r, otherwise, the vehicle is not arranged;
Figure BDA00035628157500000417
total number of platforms for warehouse c;
Figure BDA00035628157500000418
respectively representing penalty costs of unloading operation and loading operation, wherein the penalty costs of the unloading operation and the loading operation are specifically as follows:
Figure BDA00035628157500000419
Figure BDA0003562815750000051
wherein wUl，wlRespectively taking the penalty coefficients of unloading and loading;
Figure BDA0003562815750000052
indicating the time at which the vehicle actually begins the unloading and loading operations;
Figure BDA0003562815750000053
indicating the time at which the vehicle actually ended the unloading and loading operations;
Figure BDA0003562815750000054
indicating when the vehicle is scheduled to begin unloading and loading operations;
Figure BDA0003562815750000055
Figure BDA0003562815750000056
indicating a time at which the vehicle is scheduled to end the unloading and loading operations;
based on a linear weighting method, the optimization indexes of the related park logistics vehicle path optimization model are combined, and linear weighting is carried out after normalization processing is carried out on the optimization indexes, so that the objective function of the park logistics vehicle path optimization model is constructed as shown in the following formula:
Figure BDA0003562815750000057
wherein λ is4，λ5，λ6，λ7Satisfy lambda4+λ5+λ6+λ7＝1
According to the management strategy of the general park to be served, the park logistics vehicle scheduling constraint is constructed; the campus logistics vehicle scheduling constraint comprises:
the warehouse satisfies the loading operation demand of vehicle:
Figure BDA0003562815750000058
the logistics vehicle unloading operation in the warehouse does not exceed the warehouse limit:
Figure BDA0003562815750000059
leaving the warehouse after the logistics vehicle finishes the operation:
Figure BDA00035628157500000510
in a single warehouse logistics vehicle can only go to a unique warehouse platform:
Figure BDA00035628157500000511
warehouse dock number limit:
Figure BDA00035628157500000512
wherein the content of the first and second substances,
Figure BDA00035628157500000513
the number of resources a that are warehouses c;
Figure BDA00035628157500000514
is the maximum amount of warehouse resource a.
Preferably, the multivariate universe algorithm is improved based on a differential evolution algorithm, and the improved mixed differential multivariate universe algorithm is used for solving the double-layer resource allocation optimization model;
step 2.1, initializing parameters;
step 2.2, initializing the universe;
2.3, calculating and standardizing the degree of cosmic expansion;
step 2.4, exchanging substances in the universe through the black and white holes;
2.5, receiving the optimal cosmic transmitted substances through the wormholes;
step 2.6, updating the global extreme value, and storing the current universe, namely the current iteration times as
Figure BDA0003562815750000061
Step 2.7, differential evolution operation;
2.7.1, performing variation, randomly selecting two selectable universes, and transmitting information between the universes;
step 2.7.2, crossing, and performing partial replacement on the two previous universes according to the crossing probability;
step 2.7.3, selecting, the search agent judges to reserve the original universe or execute the new universe generated by the cross operation through the fitness function value after the variation and the cross operation;
and 2.8, outputting a result if the maximum iteration number is reached, and otherwise, returning to the step 2.4.
The invention has the beneficial effects that:
the invention provides a double-layer resource allocation optimization method for an intelligent park, which has the following specific beneficial effects: the invention starts from the actual demand characteristic of intelligent park resource allocation, comprehensively considers park resource allocation restriction and operation restriction, constructs a multidimensional optimization index, establishes a double-layer resource allocation optimization model with a park warehouse operator picking path optimization model at the upper layer and a park logistics vehicle path optimization model at the lower layer, further improves the multivariate universe algorithm based on a differential evolution algorithm, completes the improvement of the algorithm by adding variation, intersection and selection operations in the multivariate universe algorithm to relieve the defects of convergence speed, poor precision and the like of the algorithm in solving large-scale optimization problems, and then solves the upper and lower layer models of the double-layer resource allocation optimization model by utilizing the improved mixed differential multivariate universe algorithm to provide effective reference for the construction of the intelligent park at the resource level.
Drawings
FIG. 1 is a schematic diagram of a warehouse layout of a serviced park;
FIG. 2 is a schematic diagram of a multivariate cosmic material exchange;
FIG. 3 is a flow chart of an improved hybrid differential multivariate cosmic algorithm;
Detailed Description
For further explanation of the present invention, an embodiment of the present invention will be described in detail with reference to the drawings, but the embodiment is not intended to limit the present invention.
The first embodiment is as follows:
the double-layer resource allocation optimization method for the intelligent park disclosed by the embodiment comprises the following specific implementation steps:
step one, constructing a double-layer resource configuration optimization model with an upper layer as a park warehouse operator picking path optimization model and a lower layer as a park logistics vehicle path optimization model based on the data of the number of warehouses, warehouse position information, the number of platforms and service platforms contained in the warehouse, the number of operators and auxiliary operation tools (forklifts, elevators and the like) related to the warehouse, the time of logistics vehicles entering and leaving the park, physical constraints (load upper and lower limit constraints), and carried business order information (required resource types and number), resource types, resource storage capacity, warehouses and goods spaces to which the resources belong;
the optimization model of the picking path of the warehouse operators in the upper park is as follows:
the number of shelves required by unit order batch, the fairness fitness function of the park warehouse service desks and the picking operation time of park warehouse operators are used as optimization indexes, and the optimization indexes are as follows:
Figure BDA0003562815750000071
Figure BDA0003562815750000072
Figure BDA0003562815750000073
wherein f is1Number of shelves required for unit batch order, f2Fairness fitness function, f, for campus warehouse service benches3Picking up the goods for the park warehouse operator; wherein, Yi bE {0,1}, when Yi bWhen the value is 1, the batch b needs the shelf i, otherwise, the batch b does not need the shelf i;
Figure BDA0003562815750000074
when in use
Figure BDA0003562815750000075
The time is that the batch b is allocated to the service desk R, and the time is that the batch b is not allocated to the service desk R;
Figure BDA0003562815750000076
when in use
Figure BDA0003562815750000077
Hour indicates the order picker p from the service desk DRStarting from, or otherwise not from, the service desk DRStarting;
Figure BDA0003562815750000078
when in use
Figure BDA0003562815750000079
When the order is picked, the order is picked at the order position j after the order picking at the order position i is finished in a batch b, otherwise, the order is not picked at the order position j;
Figure BDA00035628157500000710
when the temperature is higher than the set temperature
Figure BDA00035628157500000711
The order picker p is responsible for the order o in the batch b, and the order picker p is not responsible for the order o in the batch b; hcHeight of a single cargo space; liThe number of layers of a goods position i; dcDepth for a single cargo space; diThe depth of the cargo space i; vpThe walking speed of the goods picking operator; vdThe speed of the auxiliary tool for picking operation; t is a unit ofbThe packaging time is;
Figure BDA0003562815750000081
distance from any service desk to any cargo space;
Figure BDA0003562815750000082
is the distance between any two cargo spaces;
according to the layout diagram of the park warehouse shown in fig. 1, the distance from any service desk of the park warehouse to any cargo space and the distance between any two cargo spaces are obtained as follows:
Figure BDA0003562815750000083
Figure BDA0003562815750000084
wherein p isiE {0,1}, when piWhen the goods position i is 1, the goods position i is positioned on the right side of the roadway, otherwise, the goods position i is positioned on the left side of the roadway; r is a radical of hydrogeniThe number of lanes where the cargo space i is located; siThe serial number of the entry of the lane where the cargo space i is located; dRNumbering the service desk; w is a group ofcThe width of a single cargo space; wrThe width of a single roadway; dcDepth for a single cargo space;
based on a linear weighting method, the optimization indexes of the park warehouse operator picking path optimization model are combined, and linear weighting is carried out after normalization processing is carried out on the optimization indexes, so that the objective function of the park warehouse operator picking path optimization model is constructed as shown in the following formula:
Figure BDA0003562815750000085
wherein λ is1，λ2，λ3Satisfy the requirement ofλ1+λ2+λ3＝1
The scheduling constraints of the park warehouse operators comprise:
each batch of business orders is charged by a single service desk:
Figure BDA0003562815750000086
the number of the maximum operation persons of the service desk is not limited in each picking operation:
Figure BDA0003562815750000087
each operator is responsible for only one business order:
Figure BDA0003562815750000088
the picking operation personnel returns to the service desk when starting after finishing picking:
Figure BDA0003562815750000091
each business order can only be assigned to one batch:
Figure BDA0003562815750000092
shelves can only be picked once in a batch:
Figure BDA0003562815750000093
Figure BDA0003562815750000094
the resource allocation constraints include that the volume of the business order does not exceed the picker limit:
Figure BDA0003562815750000095
the amount of material required for a business order does not exceed the warehouse maximum limit:
Figure BDA0003562815750000096
wherein the content of the first and second substances,
Figure BDA0003562815750000097
for service desk DRThe maximum number of workers;
Figure BDA0003562815750000098
when in use
Figure BDA0003562815750000099
Indicates that business order o belongs to business order batch b; o. oAFor the type of resource contained in the business order,
Figure BDA00035628157500000910
is the required number of resources A, vAIs the volume of unit resource A; vmaxThe maximum capacity of the picking operation auxiliary tool;
Figure BDA00035628157500000911
is the maximum amount of warehouse asset a;
the optimization model of the logistics vehicle path of the lower-layer park comprises the following concrete steps:
the method comprises the following steps of taking the driving time of park logistics vehicles, the unfinished punishment cost of business orders, the utilization rate of a park warehouse platform and the operation punishment cost of park logistics vehicles as optimization indexes, wherein the optimization indexes are as follows:
Figure BDA00035628157500000912
Figure BDA00035628157500000913
Figure BDA00035628157500000914
Figure BDA00035628157500000915
wherein f is1For the logistic vehicle travel time in the park, f2Penalty cost for business order incompletion, f3For park warehouse platform utilization, f4Punishing cost for operation of the logistics vehicles in the park; wherein the content of the first and second substances,
Figure BDA0003562815750000101
when in use
Figure BDA0003562815750000102
When it means that the vehicle k is scheduled to go from the warehouse c to the warehouse d, otherwise it means not going to the warehouse d; dcdDistance between warehouses c, d; dec，DdeRespectively representing the distances from the warehouses c and d to the garden gate; vkIs the travel speed of vehicle k; w is aUload，wloadRespectively representing the penalty coefficients of unloading and loading operation;
Figure BDA0003562815750000103
the weight of resource a needed to unload and load, respectively, vehicle k;
Figure BDA0003562815750000104
the weight of resource a unloaded and loaded for vehicle k at warehouse c;
Figure BDA0003562815750000105
when in use
Figure BDA0003562815750000106
When the vehicle is arranged to go to the platform r, otherwise, the vehicle is not arranged;
Figure BDA0003562815750000107
total number of platforms for warehouse c;
Figure BDA0003562815750000108
respectively representing penalty costs of unloading operation and loading operation, wherein the penalty costs of the unloading operation and the loading operation are specifically as follows:
Figure BDA0003562815750000109
Figure BDA00035628157500001010
wherein, wUl，wlRespectively taking the penalty coefficients of unloading and loading;
Figure BDA00035628157500001011
indicating the time at which the vehicle actually starts the unloading and loading operations;
Figure BDA00035628157500001012
indicating the time at which the vehicle actually ended the unloading and loading operations;
Figure BDA00035628157500001013
indicating when the vehicle is scheduled to begin a load and unload operation;
Figure BDA00035628157500001014
Figure BDA00035628157500001015
indicating when the vehicle is scheduled to end the unloading and loading operations;
based on a linear weighting method, the optimization indexes of the related park logistics vehicle path optimization model are combined, and linear weighting is carried out after normalization processing is carried out on the optimization indexes, so that the objective function of the park logistics vehicle path optimization model is constructed as shown in the following formula:
Figure BDA00035628157500001016
wherein λ is4，λ5，λ6，λ7Satisfy lambda4+λ5+λ6+λ7＝1
The park logistics vehicle scheduling constraint comprises:
the warehouse satisfies the loading operation demand of vehicle:
Figure BDA00035628157500001017
the logistics vehicle unloading operation in the warehouse does not exceed the warehouse limit:
Figure BDA0003562815750000111
leaving the warehouse after the logistics vehicle finishes the operation:
Figure BDA0003562815750000112
in a single warehouse logistics vehicle can only travel to a unique warehouse platform:
Figure BDA0003562815750000113
warehouse dock number limit:
Figure BDA0003562815750000114
wherein the content of the first and second substances,
Figure BDA0003562815750000115
the number of resources a that are warehouses c;
Figure BDA0003562815750000116
is the maximum amount of warehouse resource a;
step two, based on a differential evolution algorithm, improving a multi-element universe algorithm, and completing the solution of a double-layer resource allocation optimization model by utilizing an improved mixed differential multi-element universe algorithm;
step 2.1, parameter initialization
Initializing the universe dimension D, the universe number popmaxNumber of iterations Tmax(ii) a The cross probability CR; a difference coefficient F;
step 2.2, initialize the universe
Number N according to the parkUNumber of platforms NMAnd order grouping scheme X.u, X.m for each order. X.u is [1N ]U]X.m is [1N ]M]Is a random integer of (a). Operator N with X.m months under the park warehouse X.uumThen X and delta are [1N ]um]Is a random integer of (a). Generation of popmaxAfter the individual, screening according to the method of roulette
Figure BDA0003562815750000117
The individual acts as an alternative universe.
Step 2.3, calculating and standardizing the degree of cosmic expansion
Calculating the expansion degrees of all universes based on an objective function established by the model, wherein the universe with the minimum expansion degree represents the optimal solution of the model because the minimum value of the objective function is solved, screening the expansion degrees of the universe which do not meet the constraint according to the constraint condition to set the expansion degrees to be infinite, and standardizing the expansion degrees of the universe according to a standardized formula, wherein the formula is as follows:
Figure BDA0003562815750000118
step 2.4 exchange of material in the universe through the black and white holes
Each universe individual and corresponding order
Figure BDA0003562815750000121
All randomly generate an e [0,1 ]]Random number of
Figure BDA0003562815750000122
If it is
Figure BDA0003562815750000123
A universe of black holes is generated by means of roulette
Figure BDA0003562815750000124
The universe with the black hole sends material to the universe to update the order:
Figure BDA0003562815750000125
step 2.5, receiving the optimal cosmic transmission substances through the wormholes
If it is
Figure BDA0003562815750000126
Three random numbers between 0 and 1 are generated for the universe
Figure BDA0003562815750000127
Sending material to the optimum universe through wormholes by an update mechanism of the formula:
Figure BDA0003562815750000128
Figure BDA0003562815750000129
step 2.6, updating the global extreme value, and storing the current universe, namely the current iteration times as
Figure BDA00035628157500001210
Step 2.7, differential evolution operation
For the current
Figure BDA00035628157500001211
Performing a differential evolution operation
And 2.7.1, mutation. Randomly selecting two optional universes to transfer information between the universes, wherein the specific operation is as follows:
Figure BDA00035628157500001212
wherein Vi k+1For the universe after performing mutation operation, F is the mutation operator E [0,2]，
Figure BDA00035628157500001213
And
Figure BDA00035628157500001214
is randomly selected different from
Figure BDA00035628157500001215
The universe of cells.
And 2.7.2, crossing. And performing partial replacement on the two previous generations of universes according to the cross probability, wherein the specific operation is as follows:
Figure BDA00035628157500001216
wherein CR is a crossover operator E [0,1 ∈ ]]Rand is the random generation of e [0,1 ∈]Random number of (U)i k+1Is a new universe of cross-production.
And step 2.7.3, selecting. The search agent judges whether to reserve the original universe or execute a new universe generated by the cross operation through the fitness function value after the mutation and the cross operation. The selection formula is as follows:
Figure BDA0003562815750000131
step 2.8: and if the maximum iteration times are reached, outputting the result, otherwise, returning to the step 2.4.
The foregoing detailed description of the invention is merely exemplary in nature and is not intended to limit the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. A double-layer resource allocation optimization method for an intelligent park is characterized by comprising the following steps: the optimization method comprises the following steps: acquiring park warehouse data, logistics vehicle data and resource data, and constructing a double-layer resource allocation optimization model; and step two, based on a differential evolution algorithm, improving a multi-element universe algorithm, and solving a double-layer resource allocation optimization model by using an improved mixed differential multi-element universe algorithm.
2. The intelligent park-oriented double-layer resource allocation optimization method according to claim 1, wherein: and step one, constructing a double-layer resource allocation optimization model, wherein the double-layer resource allocation optimization model comprises an upper-layer warehouse operator picking path optimization model for the garden and a lower-layer logistics vehicle path optimization model for the garden.
3. The intelligent park-oriented double-layer resource allocation optimization method according to claim 2, wherein: the optimization model of the picking path of the upper park warehouse operators is as follows:
the number of shelves required by unit order batch, the fairness fitness function of the park warehouse service desks and the picking operation time of park warehouse operators are used as optimization indexes, and the optimization indexes are as follows:
Figure FDA0003562815740000011
Figure FDA0003562815740000012
Figure FDA0003562815740000013
wherein f is1Number of shelves required for unit batch order, f2For park warehouse service counter fairness fitness function, f3Picking up the goods for the park warehouse operator; wherein the content of the first and second substances,
Figure FDA0003562815740000014
when in use
Figure FDA0003562815740000015
If so, indicating that the batch b needs the shelf i, otherwise, indicating that the shelf i is not needed;
Figure FDA0003562815740000016
when in use
Figure FDA0003562815740000017
Time indicates that batch b is allocated to service desk R, and vice versa indicates that batch b is not allocated to service desk R;
Figure FDA0003562815740000018
when in use
Figure FDA0003562815740000019
Time-of-day presentation of the order picker p from the service desk DRStarting from, or otherwise not from, the service desk DRStarting;
Figure FDA00035628157400000110
when in use
Figure FDA00035628157400000111
When the order is picked, the order is picked by the order j after the order i is picked in one batch b, otherwise, the order is not picked by the order j;
Figure FDA00035628157400000112
when the temperature is higher than the set temperature
Figure FDA00035628157400000113
The order picker p is responsible for the order o in the batch b, and the order picker p is not responsible for the order o in the batch b; hcHeight of a single cargo space; liThe number of layers of a goods position i; dcDepth of a single cargo space; diThe depth of the cargo space i; vpThe walking speed of the goods picking operator; vdThe speed of the auxiliary tool for picking operation; t isbThe packaging time is;
Figure FDA0003562815740000021
distance from any service desk to any cargo space;
Figure FDA0003562815740000022
is the distance between any two cargo spaces;
after normalization processing is carried out on the optimization indexes, linear weighting is carried out, and therefore an objective function of the park warehouse operator picking path optimization model is constructed and is shown as the following formula:
Figure FDA0003562815740000023
wherein λ is1，λ2，λ3Satisfy lambda1+λ2+λ3＝1
According to a management strategy of a general service park, two parts of park warehouse operator scheduling constraint and resource allocation constraint are constructed; the park warehouse operator scheduling constraint comprises:
each batch of business orders is charged by a single service desk:
Figure FDA0003562815740000024
the picking operation does not exceed the maximum operation personnel number limit of the service desk each time:
Figure FDA0003562815740000025
each operator is responsible for only one business order:
Figure FDA0003562815740000026
the picking operation personnel returns to the service desk when starting after finishing picking:
Figure FDA0003562815740000027
each business order can only be assigned to one batch:
Figure FDA0003562815740000028
shelves can only be picked once in a batch:
Figure FDA0003562815740000029
Figure FDA00035628157400000210
the resource allocation constraints include that a business order capacity does not exceed a picker limit:
Figure FDA00035628157400000211
the amount of material required for a business order does not exceed the warehouse maximum limit:
Figure FDA00035628157400000212
wherein the content of the first and second substances,
Figure FDA0003562815740000031
as a service desk DRThe maximum number of workers;
Figure FDA0003562815740000032
when the temperature is higher than the set temperature
Figure FDA0003562815740000033
Indicating that business order o belongs to business order batch b; oAFor the type of resource contained in the business order,
Figure FDA0003562815740000034
is the required number of resources A, vAIs the volume of unit resource A; vmaxThe maximum capacity of the picking operation auxiliary tool;
Figure FDA0003562815740000035
is the maximum amount of warehouse asset a.
4. The intelligent park-oriented double-layer resource allocation optimization method according to claim 2, wherein: the lower-layer park logistics vehicle path optimization model is as follows:
the details are as follows:
Figure FDA0003562815740000036
Figure FDA0003562815740000037
Figure FDA0003562815740000038
Figure FDA0003562815740000039
wherein f is1For the driving time of the logistic vehicles in the park, f2Penalty cost for business order incompletion, f3For park warehouse platform utilization, f4Punishing cost for operation of logistics vehicles in the park; wherein the content of the first and second substances,
Figure FDA00035628157400000310
when in use
Figure FDA00035628157400000311
When it means that the vehicle k is scheduled to go from the warehouse c to the warehouse d, otherwise it means not going to the warehouse d; dcdDistance between warehouses c, d; dec，DdeRespectively representing the distances from the warehouses c and d to the garden gate; vkIs the travel speed of vehicle k; w is aUload，wloadRespectively representing the penalty coefficients of unloading and loading operation;
Figure FDA00035628157400000312
the weight of resource a needed to unload and load, respectively, vehicle k;
Figure FDA00035628157400000313
the weight of resource a unloaded and loaded for vehicle k at warehouse c;
Figure FDA00035628157400000314
when in use
Figure FDA00035628157400000315
When the vehicle is scheduled to go to the platform r, otherwise, the vehicle is not scheduled;
Figure FDA00035628157400000316
total number of platforms for warehouse c;
Figure FDA00035628157400000317
respectively representing penalty costs of unloading operation and loading operation, wherein the penalty costs of the unloading operation and the loading operation are specifically as follows:
Figure FDA00035628157400000318
Figure FDA00035628157400000319
wherein, wUl，wlRespectively taking the penalty coefficients of unloading and loading;
Figure FDA0003562815740000041
indicating the time at which the vehicle actually begins the unloading and loading operations;
Figure FDA0003562815740000042
indicating the time at which the vehicle actually ended the unloading and loading operations;
Figure FDA0003562815740000043
indicating when the vehicle is scheduled to begin a load and unload operation;
Figure FDA0003562815740000044
Figure FDA0003562815740000045
indicating when the vehicle is scheduled to end the unloading and loading operations;
based on a linear weighting method, the optimization indexes of the related park logistics vehicle path optimization model are combined, and linear weighting is carried out after normalization processing is carried out on the optimization indexes, so that the objective function of the park logistics vehicle path optimization model is constructed as shown in the following formula:
Figure FDA0003562815740000046
wherein λ is4，λ5，λ6，λ7Satisfy lambda4+λ5+λ6+λ7＝1
According to the management strategy of the general park to be served, the park logistics vehicle scheduling constraint is constructed; the campus logistics vehicle scheduling constraint comprises:
the warehouse satisfies the loading operation demand of vehicle:
Figure FDA0003562815740000047
the logistics vehicle unloading operation in the warehouse does not exceed the warehouse limit:
Figure FDA0003562815740000048
leaving the warehouse after the logistics vehicle finishes the operation:
Figure FDA0003562815740000049
in a single warehouse logistics vehicle can only travel to a unique warehouse platform:
Figure FDA00035628157400000410
warehouse dock number limit:
Figure FDA00035628157400000411
wherein the content of the first and second substances,
Figure FDA00035628157400000412
the number of resources A that are warehouse c;
Figure FDA00035628157400000413
is the maximum amount of warehouse resource a.
5. The intelligent park-oriented double-layer resource allocation optimization method according to claim 1, wherein: step two, based on a differential evolution algorithm, improving a multi-element universe algorithm, and completing the solution of a double-layer resource allocation optimization model by utilizing an improved mixed differential multi-element universe algorithm;
step 2.1, initializing parameters;
step 2.2, initializing the universe;
2.3, calculating and standardizing the degree of cosmic expansion;
step 2.4, exchanging substances in the universe through the black and white holes;
2.5, receiving the optimal cosmically transmitted substances through the wormholes;
step 2.6, updating the global extreme value, and storing the current universe, namely the current iteration times as
Figure FDA0003562815740000051
Step 2.7, differential evolution operation;
2.7.1, performing variation, randomly selecting two selectable universes, and transmitting information between the universes;
step 2.7.2, crossing, and performing partial replacement on the two previous universes according to the crossing probability;
step 2.7.3, selecting, the search agent judges to reserve the original universe or execute the new universe generated by the cross operation through the fitness function value after the variation and the cross operation;
and 2.8, outputting a result if the maximum iteration number is reached, and otherwise, returning to the step 2.4.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115456523A (en) * 2022-09-06 2022-12-09 上海聚货通电子商务有限公司 Method and system for planning goods picking channel of e-commerce warehouse
CN116663854A (en) * 2023-07-24 2023-08-29 匠人智慧(江苏)科技有限公司 Resource scheduling management method, system and storage medium based on intelligent park

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115456523A (en) * 2022-09-06 2022-12-09 上海聚货通电子商务有限公司 Method and system for planning goods picking channel of e-commerce warehouse
CN116663854A (en) * 2023-07-24 2023-08-29 匠人智慧(江苏)科技有限公司 Resource scheduling management method, system and storage medium based on intelligent park
CN116663854B (en) * 2023-07-24 2023-10-17 匠人智慧(江苏)科技有限公司 Resource scheduling management method, system and storage medium based on intelligent park

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