Logistics vehicle optimization allocation method under emergency material transportation condition

Abstract

The invention relates to a logistics vehicle optimal allocation method under an emergency material transportation condition, and belongs to the technical field of transport capacity optimal scheduling. Comprising the following steps: step 1, acquiring data and defining required variables; and step 2, establishing an optimal dispatching model for truck transportation of emergency materials, realizing multi-target integer optimization, converting into an integer optimization problem, and solving the integer optimization problem to obtain an optimal solution of the logistics vehicle optimal dispatching method. According to the method, the problem of multi-objective optimization of shortest transportation task completion time and maximum transportation reliability degree in an emergency material transportation task concurrence scene is effectively solved. According to the method, transportation of non-dangerous goods and non-fragile goods is considered, and transportation of dangerous goods and fragile goods is also considered; the innovation point of the vehicle optimization allocation method is that the shortest task completion time and the maximum transportation reliability degree are considered at the same time.

Description

Technical Field

[0001] The invention relates to a method for optimizing the deployment of logistics vehicles under emergency material transportation conditions, and belongs to the technical field of transportation capacity optimization and scheduling. Background Art

[0002] In order to meet people's needs for supplies in major disasters or disaster events, it is necessary to organize and plan to fully optimize the dispatch of multiple logistics fleets (each vehicle has a limited cargo box capacity and different constraints on the types of supplies that can be transported), and optimize the dispatch of emergency supplies from multiple sources to multiple destinations in the shortest time. While being able to meet the needs of emergency supplies to fight disasters to the greatest extent, it is also necessary to increase the reliability of trucks transporting supplies to the places where supplies are needed (some routes are damaged in disasters, and there are certain risks in driving, which need to be avoided). Only in this way can the losses caused by disasters to people, society, and the economy be minimized. Summary of the invention

[0003] Based on the above problems in the prior art, the present invention proposes a method for optimizing the deployment of logistics vehicles under emergency material transportation conditions, so as to solve the multi-objective optimization problem of minimizing the completion time of transportation tasks and maximizing the transportation reliability in the scenario of concurrent emergency material transportation tasks.

[0004] To solve the above problems, the present invention adopts the following technical solutions:

[0005] A method for optimizing the deployment of logistics vehicles under emergency material transportation conditions of the present invention comprises the following steps:

[0006] Step 1: Data acquisition and required variable definition;

[0007] Step 2: Establish an optimal dispatching model for trucks transporting emergency supplies, realize multi-objective integer optimization, and thus transform it into an integer optimization problem, and solve it to obtain the optimal solution for the optimal dispatching method of logistics vehicles.

[0008] The non-hazardous and non-fragile category is r;

[0009] The set of non-dangerous and non-fragile items is P = {p u |u=1,2,3,…,r};

[0010] Non-hazardous, non-fragile u The weight is Dangerous and fragile varieties are classified as s;

[0011] The set of dangerous and fragile items is Q = {q v |v=1,2,3,…,s};

[0012] Dangerous and fragile itemsv The weight is The truck base contains a number of vehicles carrying non-hazardous and non-fragile goods;

[0013] The truck base contains the number of vehicles carrying dangerous and fragile goods b;

[0014] The set of trucks loaded with non-dangerous and non-fragile goods L = {l k |k=1,2,3,…,a}; the set of trucks carrying dangerous and fragile goods G={g e |e=1,2,3,…,b};

[0015] Trucks loaded with non-hazardous and non-fragile goods k The load capacity is Trucks loaded with dangerous and fragile goods e The load capacity is Trucks loaded with non-hazardous and non-fragile goods k The maximum driving distance is Trucks loaded with dangerous and fragile goods e Maximum driving distance Number of material bases n;

[0016] Material base collection O = {o i |i=1, 2, 3, ..., n};

[0017] Material Base i Contains non-hazardous, non-fragile itemsp u The number of

[0018] Material Base i Contains dangerous and fragile items v The number of

[0019] The number of locations where supplies are needed is m;

[0020] The set of places where materials are needed D = {d j |j=1, 2, 3, ..., m};

[0021] Need supplies location j Need non-hazardous, non-fragile items u The number of

[0022] Need supplies location j Dangerous goods required v The number of

[0023] Material Base i Contains dangerous and fragile items v The number of

[0024] Trucks loaded with non-hazardous and non-fragile goods k The current set of material bases that need to be visited is

[0025] Trucks loaded with non-hazardous and non-fragile goods k The current set of places where supplies are needed is

[0026] Trucks loaded with dangerous and fragile goods e The current set of material bases that need to be visited is

[0027] Trucks loaded with dangerous and fragile goods e The current set of places where supplies are needed is

[0028] Logistics Base i and the location where supplies are needed i The reliability level is A ij ;

[0029] Logistics Base i and the location where supplies are needed j The longest path is B ij ;

[0030] function

[0031] Function φ(S) is the current maximum index of list S;

[0032] S[α] is the value of list S at index α,

[0033] function

[0034] β1 means the index subscript of an element in the S1 list.

[0035] β2 means the index subscript of an element in the S2 list.

[0036] α1 means the value of an element in the S1 list.

[0037] α2 means the value of an element in the S2 list.

[0038] The meaning of function θ is to specify an operation to determine whether a truck can go from a certain material center to a place where materials are needed;

[0039] x ijku For trucks carrying non-hazardous and non-fragile goods k From the truck logistics base oi Go to the place where supplies are needed j Transporting non-hazardous and non-fragile items u The number of sets;

[0040] y ijev For trucks carrying dangerous and fragile goods e From the truck logistics base o i Go to the place where supplies are needed j Transporting dangerous and fragile goods v The number of collections.

[0041] Furthermore, in the step 2:

[0042] For trucks carrying non-hazardous and non-fragile goods k In terms of When To the material base Otherwise, it means that it is moving from the material base Drive to where supplies are needed Driving; For trucks loaded with dangerous and fragile goods e Similarly;

[0043] When the material demand order sounds, the truck will go to the nearest logistics base to load the goods, and this part of the journey will not be taken into account;

[0044] Trucks loaded with non-hazardous and non-fragile goods k The distance traveled is

[0045]

[0046] Trucks loaded with dangerous and fragile goods e The distance traveled is

[0047]

[0048] The meaning of σ refers to the iteration value of the table below the element of each location of the required materials, starting from 0;

[0049] Then the distance walked is

[0050]

[0051] Trucks loaded with non-hazardous and non-fragile goods k The total reliability of the paths taken is

[0052]

[0053] Trucks loaded with dangerous and fragile goods e The total reliability of the paths taken is

[0054]

[0055] Then the distance walked is

[0056]

[0057] This problem is to optimize f3 and f6;

[0058] For trucks carrying non-hazardous and non-fragile goods k To ensure that the cargo it loads is less than or equal to its capacity, it is necessary

[0059]

[0060] For trucks carrying dangerous and fragile goods e To ensure that the cargo it loads is less than or equal to its capacity, it is necessary

[0061]

[0062] For trucks carrying non-hazardous and non-fragile goods k For a ship, to ensure that it can return to the nearest material base for refueling after reaching its destination, it is necessary to:

[0063]

[0064] For trucks carrying dangerous and fragile goods e For a ship, to ensure that it can return to the nearest material base for refueling after reaching its destination, it is necessary to:

[0065]

[0066] All trucks carrying non-hazardous and non-fragile goods are located at the base. i The non-dangerous and non-fragile items shipped out cannot exceed the total amount of the base: so it is necessary to:

[0067]

[0068] All trucks loaded with dangerous and fragile goods are at the base. i The dangerous and fragile items shipped out cannot exceed the total quantity of the base.

[0069]

[0070] All trucks deliver to locations where supplies are needed j Non-dangerous goods Non-dangerous and non-fragile goods u The quantity needs to meet its demand, so

[0071]

[0072] Need supplies location j Dangerous goods required v The number of

[0073] All trucks deliver to locations where supplies are needed j Dangerous goods, fragile goods v The quantity needs to meet its demand, so

[0074]

[0075] The following multi-objective integer optimization can be obtained:

[0076]

[0077] This multi-objective integer optimization problem can be decomposed into the following two integer optimization problems:

[0078] The first one is:

[0079]

[0080] The second one is:

[0081] Solving the first problem, the optimal solution is γ1, and the optimal value is η1;

[0082] Solving the second problem, the optimal solution is γ2, and the optimal value is η2.

[0083] When γ1=γ2, this value is the final optimal solution;

[0084] When γ1≠γ2, let

[0085] We get the following integer optimization problem:

[0086]

[0087] By performing integer optimization on this problem, the optimal solution can be obtained.

[0088] The beneficial effects of the present invention are:

[0089] The invention considers the different types of material demands at the material demand sites, and divides trucks into trucks for non-dangerous and non-fragile goods and trucks for dangerous and fragile goods. After a disaster occurs, in order to reliably transport materials, the invention considers the reliability of the material base to the material demand site. The invention also considers the quantity of each non-dangerous, non-fragile, dangerous and fragile goods at the demand site, the quantity of each non-dangerous, non-fragile, dangerous and fragile goods in the material base, and the weight of each dangerous, fragile, non-dangerous and non-fragile goods. Thus, a solution for timely and reliable transportation of goods by trucks can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a flow chart of the method for optimizing the deployment of logistics vehicles under the conditions of emergency material transportation of the present invention. DETAILED DESCRIPTION

[0091] In order to make the features of the present invention more intuitive, the following will refer to Figure 1 Embodiments of the present invention will be further described, but the present invention is not limited to the following examples.

[0092] The present embodiment provides a method for optimizing the deployment of logistics vehicles under emergency material transportation conditions, including the following steps:

[0093] Step 1: Data acquisition and required variable definition, specifically:

[0094] The non-hazardous and non-fragile category is r;

[0095] The set of non-dangerous and non-fragile items is P = {p u |u=1,2,3,…,r};

[0096] Non-hazardous, non-fragile u The weight is

[0097] Dangerous and fragile varieties are classified as s;

[0098] The set of dangerous and fragile items is Q = {q v |v=1,2,3,…,s};

[0099] Dangerous and fragile items v The weight is

[0100] The vehicle base contains a number of vehicles carrying non-hazardous and non-fragile goods;

[0101] The truck base contains the number of vehicles carrying dangerous and fragile goods b;

[0102] The set of trucks loaded with non-dangerous and non-fragile goods L = {l k|k=1,2,3,…,a};

[0103] The set of trucks loaded with dangerous and fragile goods G = {g e |e=1,2,3,…,b};

[0104] Trucks loaded with non-hazardous and non-fragile goods k The load capacity is

[0105] Trucks loaded with dangerous and fragile goods e The load capacity is

[0106] Trucks loaded with non-hazardous and non-fragile goods k The maximum driving distance is

[0107] Trucks loaded with dangerous and fragile goods e Maximum driving distance

[0108] Number of material bases n;

[0109] Material base collection O = {o i |i=1, 2, 3, ..., n};

[0110] Material Base i Contains non-hazardous, non-fragile itemsp u The number of

[0111] Material Base i Contains dangerous and fragile items v The number of

[0112] The number of locations where supplies are needed is m;

[0113] The set of places where materials are needed D = {d j |j=1, 2, 3, ..., m};

[0114] Need supplies location j Need non-hazardous, non-fragile items u The number of

[0115] Need supplies location j Dangerous goods required v The number of

[0116] Material Base i Contains dangerous and fragile items v The number of

[0117] Trucks loaded with non-hazardous and non-fragile goods k The current set of material bases that need to be visited is

[0118] Trucks loaded with non-hazardous and non-fragile goods k The current set of places where supplies are needed is

[0119] Trucks loaded with dangerous and fragile goods e The current set of material bases that need to be visited is

[0120] Trucks loaded with dangerous and fragile goods e The current set of places where supplies are needed is

[0121] Logistics Base i and the location where the materials are needed j The reliability level is A ij ;

[0122] Logistics Base i and the location where supplies are needed j The longest path is B ij ;

[0123] function

[0124] Function φ(S) is the current maximum index of set S;

[0125] S[α] is the value of set S at index α,

[0126] function

[0127] β1 means the subscript of an element in the S1 set.

[0128] β2 means the subscript of an element in the S2 set.

[0129] α1 means the value of an element in the S1 set.

[0130] α2 means the value of an element in the S2 set.

[0131] The meaning of the θ function is to specify an operation to see whether a truck can go from a material center to the required location; if this function is 1, it means that it is going to this location, so the round-trip distance needs to be calculated for (the number of materials required - 1) locations, and the last one does not need to be calculated because there is no need to return to deliver materials;

[0132] x ijkuFor trucks carrying non-hazardous and non-fragile goods k From the truck logistics base o i Go to the place where supplies are needed j Transporting non-hazardous and non-fragile items u The number of sets;

[0133] y ijev For trucks carrying dangerous and fragile goods e From the truck logistics base o i Go to the place where supplies are needed j Transporting dangerous and fragile goods v The number of collections.

[0134] Step 2: Establish an optimal dispatching model for trucks transporting emergency supplies to achieve multi-objective integer optimization, thereby converting it into an integer optimization problem and solving it to obtain the optimal solution for the optimal deployment method of logistics vehicles.

[0135] Specifically:

[0136] For trucks carrying non-hazardous and non-fragile goods k In terms of When To the material base Otherwise, it means that it is moving from the material base Drive to where supplies are needed Driving; For trucks loaded with dangerous and fragile goods e Similarly;

[0137] When the material demand order sounds, the truck will go to the nearest logistics base to load the goods, and this part of the journey will not be taken into account;

[0138] Trucks loaded with non-hazardous and non-fragile goods k The distance traveled is

[0139]

[0140] Trucks loaded with dangerous and fragile goods e The distance traveled is

[0141]

[0142] The meaning of σ refers to the iteration value of the table below the element of each location of the required materials, starting from 0;

[0143] Then the distance walked is

[0144]

[0145] Trucks loaded with non-hazardous and non-fragile goods kThe total reliability of the paths taken is

[0146]

[0147] Trucks loaded with dangerous and fragile goods e The total reliability of the paths taken is

[0148]

[0149] Then the distance walked is

[0150]

[0151] This problem is to optimize f3 and f6;

[0152] For trucks carrying non-hazardous and non-fragile goods k To ensure that the cargo it loads is less than or equal to its capacity, it is necessary

[0153]

[0154] For trucks carrying dangerous and fragile goods e To ensure that the cargo it loads is less than or equal to its capacity, it is necessary

[0155]

[0156] For trucks carrying non-hazardous and non-fragile goods k For a ship, to ensure that it can return to the nearest material base for refueling after reaching its destination, it is necessary to:

[0157]

[0158] For trucks carrying dangerous and fragile goods e For a ship, to ensure that it can return to the nearest material base for refueling after reaching its destination, it is necessary to:

[0159]

[0160] All trucks carrying non-hazardous and non-fragile goods are located at the base. i The non-dangerous and non-fragile items shipped out cannot exceed the total amount of the base: so it is necessary to:

[0161]

[0162] All trucks loaded with dangerous and fragile goods are at the base. i The dangerous and fragile items shipped out cannot exceed the total quantity of the base.

[0163]

[0164] All trucks deliver to locations where supplies are needed j Non-dangerous goods Non-dangerous and non-fragile goods u The quantity needs to meet its demand, so

[0165]

[0166] Need supplies location j Dangerous goods required v The number of

[0167] All trucks deliver to locations where supplies are needed j Dangerous goods, fragile goods v The quantity needs to meet its demand, so

[0168]

[0169] The following multi-objective integer optimization can be obtained:

[0170]

[0171] This multi-objective integer optimization problem can be decomposed into the following two integer optimization problems: The first one is:

[0172]

[0173] The second one is:

[0174] Solving the first problem, the optimal solution is γ1, and the optimal value is η1;

[0175] Solving the second problem, the optimal solution is γ2, and the optimal value is η2.

[0176] When γ1=γ2, then this value is the optimal solution;

[0177] When γ1≠γ2, let

[0178] We get the following integer optimization problem:

[0179]

[0180] By performing integer optimization on this problem, the optimal solution can be obtained.

[0181] The above are only preferred specific implementations of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing the deployment of logistics vehicles under emergency material transportation conditions, characterized in that: The steps include: Step 1: Data acquisition and required variable definition; Step 2: Establish an optimal dispatching model for trucks transporting emergency supplies to achieve multi-objective integer optimization, thereby converting it into an integer optimization problem and solving it to obtain the optimal solution for the optimal deployment method of logistics vehicles.

2. The method for optimizing the deployment of logistics vehicles under emergency material transportation conditions according to claim 1 is characterized in that: In the step 1: The non-hazardous and non-fragile category is r; The set of non-dangerous and non-fragile items is P = {p u |u=1,2,3,…,r}; Non-hazardous, non-fragile u The weight is Dangerous and fragile varieties are classified as s; The set of dangerous and fragile items is Q = {q v |v=1,2,3,…,s}; Dangerous and fragile items v The weight is The truck base contains a number of vehicles carrying non-hazardous and non-fragile goods; The truck base contains the number of vehicles carrying dangerous and fragile goods b; The set of trucks loaded with non-dangerous and non-fragile goods is L = {l k |k=1,2,3,…,a}; The set of trucks loaded with dangerous and fragile goods G = { ge |e=1,2,3,…,b}; Trucks loaded with non-hazardous and non-fragile goods k The load capacity is Trucks loaded with dangerous and fragile goods e The load capacity is Trucks loaded with non-hazardous and non-fragile goods k The maximum driving distance is Trucks loaded with dangerous and fragile goods e Maximum driving distance Number of material bases n; Material base set O = {oi|i = 1, 2, 3, ..., n}; Material Base i Contains non-hazardous, non-fragile itemsp u The number of Material Base i Contains dangerous and fragile items v The number of The number of places where materials are needed is m; The set of places where materials are needed D = {d j |j=1, 2, 3, ..., m}; Need supplies location j Need non-hazardous, non-fragile items u The number of Need supplies location j Dangerous goods required v The number of Material Base i Contains dangerous and fragile items v The number of Trucks loaded with non-hazardous and non-fragile goods k The current set of material bases that need to be visited is Trucks loaded with non-hazardous and non-fragile goods k The current set of places where supplies are needed is Trucks loaded with dangerous and fragile goods e The current set of material bases that need to be visited is Trucks loaded with dangerous and fragile goods e The current set of places where supplies are needed is Logistics Base i and the location where supplies are needed j The reliability level is A ij ; Logistics Base i and the location where supplies are needed j The longest path is B ij ; function Function φ(S) is the current maximum index of list S; S[α] is the value of list S at index α, function β1 means the index subscript of an element in the S1 list. β2 means the index subscript of an element in the S2 list. a1 means the value of an element in the S1 list. a2 means the value of an element in the S2 list. The meaning of function θ is to specify an operation to determine whether a truck can go from a certain material center to a place where materials are needed; x ijku For trucks carrying non-hazardous and non-fragile goods k From the truck logistics base o i Go to the place where supplies are needed j Transporting non-hazardous and non-fragile items u The number of sets; y ijev For trucks carrying dangerous and fragile goods e From the truck logistics base o i Go to the place where supplies are needed j Transporting dangerous and fragile goods v The number of collections.

3. The method for optimizing the deployment of logistics vehicles under emergency material transportation conditions according to claim 1 is characterized in that: In the step 2: For trucks carrying non-hazardous and non-fragile goods k In terms of When To the material base Otherwise, it means that it is moving from the material base Drive to where supplies are needed Driving; For trucks loaded with dangerous and fragile goods ge Similarly; When the material demand order sounds, the truck will go to the nearest logistics base to load the goods, and this part of the journey will not be taken into account; Trucks loaded with non-hazardous and non-fragile goods k The distance traveled is Trucks loaded with dangerous and fragile goods e The distance traveled is σ means the iterative value of the element below the table at each location where the required materials are located, starting from 0; then the sum of the distances walked is Trucks loaded with non-hazardous and non-fragile goods k The total reliability of the paths taken is Trucks loaded with dangerous and fragile goods e The total reliability of the paths taken is Then the distance walked is This problem is to optimize f3 and f6; For trucks carrying non-hazardous and non-fragile goods k To ensure that the cargo it loads is less than or equal to its capacity, it is necessary For trucks carrying dangerous and fragile goods e To ensure that the cargo it loads is less than or equal to its capacity, it is necessary For trucks carrying non-hazardous and non-fragile goods k For a ship, to ensure that it can return to the nearest material base for refueling after reaching its destination, it is necessary to: For trucks loaded with dangerous and fragile goods ge For a ship, to ensure that it can return to the nearest material base for refueling after reaching its destination, it is necessary to: All trucks carrying non-hazardous and non-fragile goods are located at the base. i The non-dangerous and non-fragile items shipped out cannot exceed the total amount of the base: so it is necessary to: All trucks loaded with dangerous and fragile goods are at the base. i The dangerous and fragile items shipped out cannot exceed the total quantity of the base. All trucks deliver to locations where supplies are needed j Non-dangerous goods Non-dangerous and non-fragile goods u The quantity needs to meet its demand, so Need supplies location j Dangerous goods required v The number of All trucks deliver to locations where supplies are needed j Dangerous goods, fragile goods v The quantity needs to meet its demand, so The following multi-objective integer optimization can be obtained:

4. The method for optimizing the deployment of logistics vehicles under emergency material transportation conditions according to claim 3 is characterized in that: In step 2, the multi-objective integer optimization problem can be decomposed into the following two integer optimization problems: The first one is: The second one is: Solving the first problem, we get the optimal solution to be γ1, and the optimal value is η1; Solving the second problem, we get the optimal solution to be γ2, and the optimal value is η2. When γ1=γ2, this value is the final optimal solution; When γ1≠γ2, let We get the following integer optimization problem: By performing integer optimization on this problem, the optimal solution can be obtained.

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