CN117273255B - Cold chain transportation path planning method, device, equipment and medium - Google Patents
Cold chain transportation path planning method, device, equipment and medium Download PDFInfo
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Abstract
The invention discloses a cold chain transportation path planning method, device, equipment and medium, which adopts URI risk index to describe the risk that the freshness of a target is violated, constructs a target-oriented distributed robust optimized cold chain transportation model which meets cost constraint and carbon emission constraint and minimizes the freshness violating the risk, and carries out equivalent transformation on the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear planning model of the target-oriented distributed robust optimized cold chain path optimization problem, and finally solves to obtain a cold chain transportation path planning result. The method ensures that the risk of target freshness being violated is as small as possible in the controllable cost and carbon emission range, meets the requirement of customers on the product freshness as much as possible, can effectively solve the problem of small and medium-scale cold chain transportation, and provides an accurate path scheme for decision makers.
Description
Technical Field
The present invention relates to the field of cold chain transportation, and in particular, to a method, apparatus, device, and medium for planning a cold chain transportation path.
Background
According to the 3T principle of cold chain logistics, the freshness of low temperature food after circulation depends on circulation time, storage temperature and spoilage resistance of the low temperature product itself. In reality, cold chain transportation enterprises face two problems when using the 3T principle to control product quality. On the one hand, the circulation time is often uncertain due to factors such as weather, traffic jam and the like, which brings challenges to optimization of the transportation path. On the other hand, low temperature storage requires consumption of large amounts of fossil fuels, and carbon emissions generated by this process may exceed the carbon emission limits of the enterprise. Therefore, how to reasonably plan a cold chain transportation path with uncertain transportation time to meet the freshness requirement of customers is a urgent problem in consideration of economic and environmental effects.
While some methods of planning cold chain transportation problems with respect to product freshness and carbon emissions exist in the prior art, none of these methods take into account transportation time uncertainty, which may make reaching the freshness of the product in the customer's hands undesirable, thereby reducing customer satisfaction.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a cold chain transportation path planning method, device, equipment and medium, which can provide a cold chain transportation scheme with minimum freshness violation risk on the premise of meeting the cost budget and carbon emission limit, and contributes to reducing food loss and improving customer satisfaction.
An embodiment of the cold chain transportation path planning method according to the first aspect of the present invention includes the following steps:
acquiring a mean value and a support set of cold chain transportation time, and constructing a fuzzy set of the cold chain transportation time according to the mean value and the support set of the cold chain transportation time；
Fuzzy set based on URI risk index and cold chain transportation timeConstructing a target freshness risk function, wherein the target freshness risk function is used for representing the risk of violating the freshness of a product in cold chain transportation;
acquiring cost constraints of cold chain transportation according to the cost budget of the cold chain transportation, and acquiring carbon emission constraints of the cold chain transportation according to the carbon emission limits of the cold chain transportation;
constructing a target-oriented distributed robust optimized cold chain transportation model by taking the minimum of a target freshness risk function as a target and meeting cost constraint and carbon emission constraint;
transforming the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem;
and solving a mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem to obtain a cold link transportation path planning result.
The cold chain transportation path planning method according to the embodiment of the first aspect of the invention has at least the following beneficial effects:
the invention adopts URI risk index to describe the risk of target freshness being violated, optimizes the risk as a target, constructs a target-oriented distributed robust optimized cold chain transportation model which meets cost constraint and carbon emission constraint and minimizes the freshness violating risk, and performs equivalent transformation on the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of a solvable target-oriented distributed robust optimized cold chain path optimization problem, and finally solves the mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem to obtain a cold chain transportation path planning result. The invention allows for the freshness of targets in a controlled cost and carbon emissions rangeThe risk of the degree being violated is as small as possible, and the requirement of customers on the freshness of the product is met as much as possible. In addition, the invention considers the uncertainty of the transportation time in the practical problem and uses the fuzzy set of the cold chain transportation timeModeling is performed, reliability of a transportation scheme is improved, a small-medium-scale cold chain transportation problem can be effectively solved, an accurate path planning result is obtained, a scheme set formed by a plurality of solutions is not needed, and an accurate path scheme can be provided for a decision maker.
According to some embodiments of the invention, the fuzzy sets according to URI risk indexes and cold chain transportation timeThe specific steps for constructing the target freshness risk function are as follows:
fuzzy set in cold chain transit timeConstructing an actual freshness function;
obtaining target freshness of each retailer, and constructing a risk URI risk index with violated target freshness according to the target freshness of each retailer and the actual freshness function to obtain a target freshness risk function.
According to some embodiments of the invention, the specific steps for obtaining the cost constraint of the cold chain transportation according to the cost budget of the cold chain transportation are as follows:
constructing a vehicle transportation cost expression and a product refrigeration cost expression according to fuel consumption in a cold chain transportation process;
obtaining the cost of cold chain transportation according to the vehicle transportation cost expression and the product refrigeration cost expression;
acquiring a cost budget of cold chain transportation;
the cost constraint of the cold chain transportation is obtained from the cost budget of the cold chain transportation and the cost of the cold chain transportation.
According to some embodiments of the invention, the specific steps for obtaining the carbon emission constraint of the cold chain transportation according to the carbon emission allowance of the cold chain transportation are as follows:
obtaining a carbon emission expression in the cold chain transportation process according to the vehicle transportation cost expression and the product refrigeration cost expression;
Obtaining a carbon emission allowance of cold chain transportation;
and acquiring the carbon emission constraint of the cold chain transportation according to the carbon emission allowance and the carbon emission expression of the cold chain transportation.
According to some embodiments of the present invention, the specific steps of transforming the target-oriented distributed robust optimized cold chain transport model by a strong dual algorithm to obtain a mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem are as follows:
converting a freshness constraint formula in the target-oriented distributed robust optimized cold chain transportation model into a freshness constraint equivalent formula through a strong dual algorithm;
converting a cost constraint formula in the target-oriented distributed robust optimized cold chain transportation model into a cost constraint equivalent formula through a strong dual algorithm;
converting a carbon emission constraint formula in the target-oriented distributed robust optimized cold chain transportation model into a carbon emission constraint equivalent formula through a strong dual algorithm;
and converting the cold chain transportation model of the target-oriented distributed robust optimization according to the freshness constraint equivalent formula, the cost constraint equivalent formula and the carbon emission constraint equivalent formula to obtain a mixed integer linear programming model of the cold chain path optimization problem of the target-oriented distributed robust optimization.
According to some embodiments of the present invention, in the solving step of the mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem, the mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem is solved by a Benders decomposition algorithm.
According to some embodiments of the present invention, the specific steps of solving the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem by using the Benders decomposition algorithm are as follows:
decomposing a mixed integer linear programming model of a target-oriented distributed robust optimized cold link path optimization problem into a main problem and a pair problem, wherein the main problem is used for determining arc section selection of a cold link transportation path, and the pair problem is used for determining load when a vehicle runs to a certain arc section;
solving a main problem and a dual problem, obtaining a lower bound of a mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem by solving the main problem, and obtaining an upper bound of the mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem by solving the dual problem, so as to obtain an optimal solution of the mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem.
An embodiment of the cold chain transportation path planning apparatus according to the second aspect of the present invention includes:
the fuzzy set construction unit is used for acquiring the mean value and the support set of the cold chain transportation time and constructing the fuzzy set of the cold chain transportation time according to the mean value and the support set of the cold chain transportation time；
A risk calculation unit for calculating fuzzy sets according to URI risk indexes and cold chain transportation timeConstructing a target freshness risk function, wherein the target freshness risk function is used for representing the risk of violating the freshness of a product in cold chain transportation;
the constraint calculating unit is used for acquiring the cost constraint of the cold chain transportation according to the cost budget of the cold chain transportation and acquiring the carbon emission constraint of the cold chain transportation according to the cold emission allowance;
the model building unit is used for building a target-oriented distributed robust optimized cold chain transportation model with the aim of meeting cost constraint and carbon emission constraint and minimizing a target freshness risk function;
the model conversion unit is used for converting the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem;
And the model solving unit is used for solving a mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem to obtain a cold chain transportation path planning result.
The cold chain transportation path planning device according to the embodiment of the second aspect of the invention has at least the following beneficial effects:
the invention adopts URI risk index to describe the risk of target freshness being violated, optimizes the risk as a target, constructs a target-oriented distributed robust optimized cold chain transportation model which meets cost constraint and carbon emission constraint and minimizes the freshness violating risk, and performs equivalent transformation on the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of a solvable target-oriented distributed robust optimized cold chain path optimization problem, and finally solves the mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem to obtain a cold chain transportation path planning result. The invention makes the risk of target freshness being violated as small as possible in the controllable cost and carbon emission range, and meets the requirement of customers on the product freshness as much as possible. In addition, the invention considers the uncertainty of the transportation time in the practical problem and uses the fuzzy set of the cold chain transportation time Modeling is carried out, the reliability of a transportation scheme is improved, the cold chain transportation problem of a medium and small scale can be effectively solved, and the accurate path planning result is obtained by the method, which is not a scheme set formed by a plurality of solutions, and the method can be used for solving the problem of the medium and small scale cold chain transportationTo provide an accurate path scheme for the decision maker.
An electronic device according to an embodiment of the third aspect of the present invention includes a memory storing a computer program or instructions, and a processor implementing the cold chain transportation path planning method described above when executing the computer program or instructions.
The electronic equipment according to the embodiment of the third aspect of the invention has at least the following beneficial effects:
the invention adopts URI risk index to describe the risk of target freshness being violated, optimizes the risk as a target, constructs a target-oriented distributed robust optimized cold chain transportation model which meets cost constraint and carbon emission constraint and minimizes the freshness violating risk, and performs equivalent transformation on the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of a solvable target-oriented distributed robust optimized cold chain path optimization problem, and finally solves the mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem to obtain a cold chain transportation path planning result. The invention makes the risk of target freshness being violated as small as possible in the controllable cost and carbon emission range, and meets the requirement of customers on the product freshness as much as possible. In addition, the invention considers the uncertainty of the transportation time in the practical problem and uses the fuzzy set of the cold chain transportation time Modeling is performed, reliability of a transportation scheme is improved, a small-medium-scale cold chain transportation problem can be effectively solved, an accurate path planning result is obtained, a scheme set formed by a plurality of solutions is not needed, and an accurate path scheme can be provided for a decision maker.
According to a fourth aspect of the present invention, the storage medium is a computer-readable storage medium, for computer-readable storage, the storage medium storing one or more programs executable by one or more processors to implement the steps of the cold chain transportation path planning method described above.
The storage medium according to the embodiment of the fourth aspect of the present invention has at least the following advantageous effects:
the invention adopts URI risk index to describe the risk of target freshness being violated, optimizes the risk as a target, constructs a target-oriented distributed robust optimized cold chain transportation model which meets cost constraint and carbon emission constraint and minimizes the freshness violating risk, and performs equivalent transformation on the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of a solvable target-oriented distributed robust optimized cold chain path optimization problem, and finally solves the mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem to obtain a cold chain transportation path planning result. The invention makes the risk of target freshness being violated as small as possible in the controllable cost and carbon emission range, and meets the requirement of customers on the product freshness as much as possible. In addition, the invention considers the uncertainty of the transportation time in the practical problem and uses the fuzzy set of the cold chain transportation time Modeling is performed, reliability of a transportation scheme is improved, a small-medium-scale cold chain transportation problem can be effectively solved, an accurate path planning result is obtained, a scheme set formed by a plurality of solutions is not needed, and an accurate path scheme can be provided for a decision maker.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The invention is further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method for planning a chain transportation path in an embodiment of the present invention;
FIG. 2 is a schematic diagram of cold chain transportation.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, it should be understood that the direction or positional relationship indicated with respect to the description of the orientation, such as up, down, etc., is based on the direction or positional relationship shown in the drawings, is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In the description of the present invention, plural means two or more. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present invention can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.
First, the present invention will be described with respect to the dispensing of a single perishable item. Referring to fig. 2, the distribution network for cold chain transportation is composed of one provider and a plurality of retailers. It is assumed that the needs of the retailer are known. The goal of the suppliers is to optimize the vehicle path to make the product freshness as good as possible to the retailer's requirements, taking into account the carbon emission allowance and the cost budget. It is assumed that only one vehicle is available. The transit times on each arc segment are independent of each other, and the service time at each node is negligible.
The invention adopts the directed graph Representing a distribution network and,wherein->Including distribution centers at the suppliers (i.e., 0 and n + 1) and all retailers. />All possible arcs in the distribution network are covered. />And->All represent arc set +.>Is included in the arc.
For easy understanding, the cold chain transportation path planning method provided in the embodiments of the present application will be described first.
Referring to fig. 1, a cold chain transportation path planning method includes the steps of:
s100, acquiring an average value and a support set of the cold chain transportation time, and constructing an fuzzy set of the cold chain transportation time according to the average value and the support set of the cold chain transportation time；
It should be noted that, referring to fig. 2, the transportation time on each arc in the distribution networkIs uncertain. Even though historical data about transit times is available, calculating an accurate distribution of transit times for each arc remains challenging. The invention uses the way to evaluate the upper and lower limits of the transport time and the average value. Thus, the present invention uses the mean and support sets to characterize the fuzzy set +.>The expression is as follows:
wherein the method comprises the steps ofAnd->Support set and mean value of transport time, respectively, < >>For indefinite transport time, +.>For probability distribution- >Representing when the probability distribution is +.>Average value of time of transport.
S200, fuzzy set according to URI (Underperformance riskiness index) risk index and cold chain transportation timeConstructing a target freshness risk function, wherein the target freshness risk function is used for representing the risk of violating the freshness of the product in cold chain transportation;
the purpose of step S200 is to characterize the risk of violating the freshness of the product in the cold chain transportation problem, which is specifically as follows:
s201, fuzzy set of cold chain transportation timeConstruction of the actual freshness function->；
It should be noted that, in order to characterize the risk of violating the freshness of the product in the cold chain transportation problem, it is necessary to know the freshness of each retailer's targetDegree and actual freshness of the product as it arrives at the retailer. In production practice, the target freshness of each retailer is known, noted as. Product storage temperature->The elapsed time->The fresh degree change expression of the product is as follows:
wherein,is a constant, & gt>Is an activation energy->Is a gas constant.
Assume that the initial freshness of the product is 1. Thus the product is at temperatureDown to the node by transport->The actual freshness expression at that time is:
in the above. The present application uses a linear decision rule to represent +. >，/>Representing arrival at node from distribution center>The time taken.
Wherein,
i.e. toThe time of the spot is equal from the distribution center to +.>Sum of the times of all arcs passed, +.>As an auxiliary variable, +.>，/>For node set, ++>Is an arc set->To meet the requirements ofArc set of->To meet the requirements ofIs a set of arcs.
S202, obtaining target freshness of each retailer, and constructing a risk URI risk index with violated target freshness according to the target freshness of each retailer and the actual freshness function to obtain a target freshness risk function.
It should be noted that the present application uses URI risk metric to measure the risk that the freshness of the target is violated. Given target freshnessAnd fuzzy set is +.>Is>The expression of URI risk index under utility-based shortfall risk metrics is as follows:
in the aboveFor the risk of violating the target freshness of each node, +.>Is a monotonically increasing and concave utility function, < >>For node->Target freshness of (c) a). To enhance the model's solvability, a piecewise linear function is used +.>To approximate the utility function. At this point, the utility-based URI may be rewritten as:
wherein r is the subscript of the piecewise linear function, and represents the number of the piecewise linear function; x is the argument of piecewise linear function; b is a constant term of the piecewise linear function.
In summary, the expression of the target freshness risk function obtained in step S200 is:
。
s300, acquiring cost constraint of cold chain transportation according to the cost budget of the cold chain transportation, and acquiring carbon emission constraint of the cold chain transportation according to the carbon emission limit of the cold chain transportation;
it should be noted that, in order to obtain the cost constraint and the carbon emission constraint of the cold chain transportation, the cost of the cold chain transportation is first calculated in step S300, which includes two parts: the transportation cost of the vehicle and the refrigeration cost of the product. Both vehicle transportation and product refrigeration require fuel consumption, so the present application calculates the cost of cold chain transportation from a fuel consumption perspective, as follows:
transportation cost of vehicle: the fuel consumption during transportation of the vehicle is not only related to the transportation distance but also to the load of the vehicle. The fuel consumption rates of the vehicle at full load and no load are respectively set asAnd->The fuel consumption expression for the vehicle to complete the entire transportation can be derived:
wherein,for node->And node->Distance between->；/>When the vehicle passes through an arc->In the time-course of which the first and second contact surfaces,the method comprises the steps of carrying out a first treatment on the surface of the Otherwise, go (L)>；/>In the arc +.>The load of the vehicle when traveling upward. The cost expression of the transportation process can be obtained from the fuel consumption expression of the vehicle to complete the whole transportation, as follows:
Wherein,in order to consume the cost per unit of fuel,Wis the maximum load of the vehicle.
Refrigeration cost of the product: the present application also builds an expression of refrigeration costs through fuel consumption during transit. Refrigeration fuel consumption and thermal conversion coefficient for on-road transportationSurface area of vehicle->Difference in temperature->，/>For external temperature, add>Is the inner temperature and the depreciation degree of the cargo hold +.>And (5) correlation. Vehicle slave->Point driving to +.>The heat load generated by the dots is:
wherein,for slave node->And node->The expression for the refrigeration costs throughout the transportation is thus:
wherein,the amount of fuel required per unit of heat load.
To sum up, in step S300, the specific steps of obtaining the cost constraint of the cold chain transportation according to the cost budget of the cold chain transportation are as follows:
s311, constructing a vehicle transportation cost expression and a product refrigeration cost expression according to fuel consumption in the cold chain transportation process;
wherein, the vehicle transportation cost expression is:
the expression of the refrigerating cost of the product is as follows:
s312, obtaining the cost of cold chain transportation according to the vehicle transportation cost expression and the product refrigeration cost expression;
it should be appreciated that adding the two together results in the cost of cold chain transportation.
S313, acquiring a cost budget of cold chain transportation;
s314, acquiring the cost constraint of the cold chain transportation according to the cost budget of the cold chain transportation and the cost of the cold chain transportation.
Wherein, the cost constraint expression of cold chain transportation is:
carbon emission constraints for cold chain transport are then calculated, as fuel consumption will produce carbon emissions. There are two sources of carbon emissions for cold chain transportation, one for vehicular transportation and the other for product refrigeration, based on the analysis of the cost constraints of cold chain transportation described above. Therefore, the expression of the carbon emission amount to complete one dispensing is:
wherein,carbon emissions are produced per unit of fuel consumption.
To sum up, the specific steps of obtaining the carbon emission constraint of the cold chain transportation according to the carbon emission allowance of the cold chain transportation in step S300 are as follows:
s321, obtaining a carbon emission expression in the cold chain transportation process according to the vehicle transportation cost expression and the product refrigeration cost expression;
s322, obtaining a carbon emission allowance of cold chain transportation;
s323, acquiring carbon emission constraint of the cold chain transportation according to the carbon emission allowance and the carbon emission expression of the cold chain transportation.
Wherein, the carbon emission constraint expression of cold chain transportation is:
s400, constructing a target-oriented distributed robust optimized cold chain transportation model with the aim of meeting cost constraint and carbon emission constraint and minimizing a target freshness risk function as a target;
It should be noted that, based on the analysis of the target freshness in step S100-step S300 against risk, cost constraint, and carbon emission constraint, a target-oriented distributed robust optimized cold chain transport model (target-oriented robust cold chain routing problem, TR-CCRP), that is, a TR-CCRP model, may be constructed as follows:
wherein the objective function (1) is to minimize the risk that the freshness of the objective is violated. Constraint (2) is the freshness constraint of the URI risk metric. Constraints (3) and (4) represent cost constraints and carbon emission constraints, respectively. Constraints (5) and (6) ensure that the flows are balanced and that each node is accessed only once. Constraint (7) represents the passage of a vehicle through a nodeThe change in front and rear load capacity is equal to +.>Demand for dots. Constraint (8) represents a load limit of the vehicle. Constraints (9) - (13) are used to construct the arrival node +.>The elapsed time. Constraints (14) - (17) are variable range constraints.
S500, converting the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem;
It should be noted that due to the uncertain transportation timeAnd the presence of a minimization operator, the target-oriented distributed robust optimized cold chain transport model constructed in step S400 cannot be solved directly. The target-oriented distributed robust optimized cold chain transport model is thus transformed by a strong dual algorithm into a hybrid integer linear programming model (mixed integer linear programming model of the target-oriented distributionally robust cold chain routing problem), the TR-CCRP-MILP model, of the solvable target-oriented distributed robust optimized cold chain path optimization problem.
The specific steps of step S500 are as follows:
s501, converting a freshness constraint formula in a target-oriented distributed robust optimized cold chain transportation model into a freshness constraint equivalent formula through a strong dual algorithm;
specifically, based on fuzzy sets of uncertain transit times and strong dual algorithms, we can derive the equivalent formula of freshness constraint (2):
s502, converting a cost constraint formula in a target-oriented distributed robust optimized cold chain transportation model into a cost constraint equivalent formula through a strong dual algorithm;
it will be appreciated that the equivalent formula for cost constraint (3) can be obtained using the same reconstruction scheme above:
S503, converting a carbon emission constraint formula in the target-oriented distributed robust optimization cold chain transportation model into a carbon emission constraint equivalent formula through a strong dual algorithm;
it will be appreciated that in the same manner as above, an equivalent formula for the carbon emission constraint (4) can be obtained:
s504, converting the cold chain transportation model of the target-oriented distributed robust optimization according to the freshness constraint equivalent formula, the cost constraint equivalent formula and the carbon emission constraint equivalent formula to obtain a mixed integer linear programming model of the cold chain path optimization problem of the target-oriented distributed robust optimization.
It should be noted that, according to step S504, an equivalent TR-CCRP model may be obtained, and the model is a mixed integer planning model, i.e. a TR-CCRP-MILP model, with the following specific modifications:
it should be appreciated that the remaining constraint parts described above as examples of the transition part of the TR-CCRP-MILP model are identical to the TR-CCRP model described aboveThe rows are identical. And S600, solving a mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem to obtain a cold link transportation path planning result.
Specifically, in the invention, a mixed integer linear programming model of a target-oriented distributed robust optimization cold link path optimization problem is solved by a Benders decomposition algorithm, and the solving steps are as follows:
S601, decomposing a mixed integer linear programming model of a target-oriented distributed robust optimization cold link path optimization problem into a main problem and a pair problem, wherein the main problem is used for determining arc section selection of a cold link transportation path, and the pair problem is used for determining load when a vehicle runs to a certain section of arc;
s602, solving a main problem and a pair-sub problem, obtaining a lower bound of a mixed integer linear programming model of a target-oriented distributed robust optimization cold link path optimization problem by solving the main problem, and obtaining an upper bound of the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem by solving the pair-sub problem, thereby obtaining an optimal solution of the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem.
The solving step is described in detail below:
the basic logic of the Benders decomposition algorithm is as follows: the TR-CCRP-MILP model is broken down into a Master Problem (MP) and a pair problem (DSP). The main issue is a traveler issue (Traveling Salesman Problem) that decides which arcs to select to complete the delivery task. The decision on the dipole problem is what the load is when the vehicle is traveling on an arc. The main problem and the pair problem provide a Lower Bound (LB) and an Upper Bound (UB) of the original problem, respectively.
It should be noted that the main problem model decomposed by the TR-CCRP-MILP model is:
and (3) feasible cutting:
optimally cutting:
the model of the dipole problem decomposed by the TR-CCRP-MILP model is as follows: />
Specifically, the pseudo code of the Benders decomposition algorithm is shown in table 1 below:
TABLE 1 Benders decomposition Algorithm
It should be appreciated that the optimal solution of the TR-CCRP-MILP model, as derived by the Benders decomposition algorithm, is the optimal result of the cold chain transportation path planning.
In summary, the invention adopts URI risk index to describe the risk of target freshness being violated, optimizes the risk as a target, constructs a target-oriented distributed robust optimized cold chain transportation model which meets cost constraint and carbon emission constraint and minimizes the freshness violating risk, and performs equivalent transformation on the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of a solvable target-oriented distributed robust optimized cold chain path optimization problem, and finally solves the mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem to obtain a cold chain transportation path planning result. The invention makes the risk of target freshness being violated as small as possible in the controllable cost and carbon emission range, and meets the requirement of customers on the product freshness as much as possible. In addition, the invention considers the uncertainty of the transportation time in the practical problem and uses the fuzzy set of the cold chain transportation time Modeling is performed, reliability of a transportation scheme is improved, a small-medium-scale cold chain transportation problem can be effectively solved, an accurate path planning result is obtained, a scheme set formed by a plurality of solutions is not needed, and an accurate path scheme can be provided for a decision maker.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages. Having described the method embodiments provided herein, embodiments of the apparatus provided herein are described below. It is to be understood that the description of the device embodiments corresponds to the description of the method embodiments, and that reference may therefore be made to the method embodiments above for what is not described in detail.
The invention also relates to a cold chain transportation path planning device, which comprises:
the risk calculation unit is used for constructing a target freshness risk function according to the URI risk index, and the target freshness risk function is used for representing the risk of the violating of the freshness of the product in the cold chain transportation problem;
the constraint calculating unit is used for acquiring the cost constraint of the cold chain transportation according to the cost budget of the cold chain transportation and acquiring the carbon emission constraint of the cold chain transportation according to the cold emission limit;
the model building unit is used for building a target-oriented distributed robust optimized cold chain transportation model with the aim of meeting cost constraint and carbon emission constraint and minimizing a target freshness risk function;
the model conversion unit is used for converting the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem;
and the model solving unit is used for solving a mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem to obtain a cold link transportation path planning result.
The cold chain transportation path planning device in the embodiment of the present invention is used for executing the cold chain transportation path planning method in the above embodiment, and the specific processing procedure is the same as that of the cold chain transportation path planning method in the above embodiment, and will not be described in detail here.
The invention also relates to an electronic device comprising a memory and a processor, wherein the memory stores a computer program or instructions, and the processor implements the cold chain transportation path planning method of the above embodiment when executing the computer program or instructions.
In implementation, each step of the above method may be implemented by an integrated logic circuit of hardware in a processor or a computer program or instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
It should be noted that the processor in the embodiments of the present application may be an integrated circuit chip with signal processing capability. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or by computer programs or instructions in the form of software. The processor may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, or discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
It will be appreciated that the memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (programmableROM, PROM), an erasable programmable read-only memory (erasablePROM, EPROM), an electrically erasable programmable read-only memory (electricallyEPROM, EEPROM), or a flash memory, among others. The volatile memory may be Random Access Memory (RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic random access memory (dynamicRAM, DRAM), synchronous dynamic random access memory (synchronousDRAM, SDRAM), double data rate synchronous dynamic random access memory (doubledatarateSDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (enhancedSDRAM, ESDRAM), synchronous link dynamic random access memory (synchlinkDRAM, SLDRAM), and direct memory bus random access memory (directrambusRAM, DRRAM). It should be noted that the memory described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
The embodiment of the invention also relates to a storage medium, which is a computer readable storage medium and is used for computer readable storage, the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the cold chain transportation path planning method in the embodiment.
In an implementation process, the computer readable storage medium stores a computer program or instructions, where the computer program or instructions are executed by one or more control processors, for example, by a processor in the electronic device, and when executed by the processor, implement a cold chain transportation path planning method provided by an embodiment of the present application.
The apparatus, the electronic device, and the computer readable storage medium provided in this embodiment are all configured to execute the corresponding methods provided above, so that the beneficial effects achieved by the apparatus, the electronic device, and the computer readable storage medium can refer to the beneficial effects in the corresponding methods provided above, and are not described herein.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relation of association objects, and indicates that there may be three kinds of relations, for example, a and/or B, and may indicate that a alone exists, a and B together, and B alone exists. Wherein A, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of the following" and the like means any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c may represent: a alone, b alone, c alone, a and b together, a and c together, b and c together, or a and b and c together, wherein a, b, c may be single or plural.
In embodiments of the present application, "indication" may include direct indication and indirect indication, as well as explicit indication and implicit indication. The information indicated by a certain information is referred to as information to be indicated, and in a specific implementation process, there may be various ways of indicating the information to be indicated, for example, but not limited to, directly indicating the information to be indicated, such as indicating the information to be indicated itself or an index of the information to be indicated. The information to be indicated can also be indicated indirectly by indicating other information, wherein the other information and the information to be indicated have an association relation. It is also possible to indicate only a part of the information to be indicated, while other parts of the information to be indicated are known or agreed in advance. For example, the indication of the specific information may also be achieved by means of a pre-agreed (e.g., protocol-specified) arrangement sequence of the respective information, thereby reducing the indication overhead to some extent.
In the embodiments of the present application, each term and english abbreviation are given as exemplary examples for convenience of description, and should not constitute any limitation to the present application. This application does not exclude the possibility of defining other terms in existing or future protocols that perform the same or similar functions.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application.
Claims (8)
1. A cold chain transportation path planning method, comprising the steps of:
acquiring a mean value and a support set of cold chain transportation time, and constructing a fuzzy set of the cold chain transportation time according to the mean value and the support set of the cold chain transportation time；
Fuzzy set based on URI risk index and cold chain transportation timeConstructing a target freshness risk function, wherein the target freshness risk function is used for representing the risk of violating the freshness of a product in cold chain transportation;
acquiring cost constraints of cold chain transportation according to the cost budget of the cold chain transportation, and acquiring carbon emission constraints of the cold chain transportation according to the carbon emission limits of the cold chain transportation;
Constructing a target-oriented distributed robust optimized cold chain transportation model by taking the minimum of a target freshness risk function as a target and meeting cost constraint and carbon emission constraint;
transforming the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem;
solving a mixed integer linear programming model of a target-oriented distributed robust optimization cold link path optimization problem to obtain a cold link transportation path planning result;
in the solving step of the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem, solving the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem through a Benders decomposition algorithm;
the specific steps for solving the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem through the Benders decomposition algorithm are as follows:
decomposing a mixed integer linear programming model of a target-oriented distributed robust optimized cold link path optimization problem into a main problem and a pair problem, wherein the main problem is used for determining arc section selection of a cold link transportation path, and the pair problem is used for determining load when a vehicle runs to a certain arc section;
Solving a main problem and a dual problem, obtaining a lower bound of a mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem by solving the main problem, and obtaining an upper bound of the mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem by solving the dual problem, so as to obtain an optimal solution of the mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem.
2. The cold chain transportation path planning method according to claim 1, wherein the fuzzy sets according to URI risk index and cold chain transportation timeThe specific steps for constructing the target freshness risk function are as follows:
fuzzy set in cold chain transit timeConstructing an actual freshness function;
obtaining target freshness of each retailer, and constructing a risk URI risk index with violated target freshness according to the target freshness of each retailer and the actual freshness function to obtain a target freshness risk function.
3. The cold chain transportation path planning method according to claim 1, wherein the specific step of obtaining the cost constraint of the cold chain transportation according to the cost budget of the cold chain transportation is:
Constructing a vehicle transportation cost expression and a product refrigeration cost expression according to fuel consumption in a cold chain transportation process;
obtaining the cost of cold chain transportation according to the vehicle transportation cost expression and the product refrigeration cost expression;
acquiring a cost budget of cold chain transportation;
the cost constraint of the cold chain transportation is obtained from the cost budget of the cold chain transportation and the cost of the cold chain transportation.
4. The cold chain transportation path planning method according to claim 3, wherein the specific step of obtaining the carbon emission constraint of the cold chain transportation according to the carbon emission allowance of the cold chain transportation comprises the steps of:
obtaining a carbon emission expression in the cold chain transportation process according to the vehicle transportation cost expression and the product refrigeration cost expression;
obtaining a carbon emission allowance of cold chain transportation;
and acquiring the carbon emission constraint of the cold chain transportation according to the carbon emission allowance and the carbon emission expression of the cold chain transportation.
5. The cold chain transportation path planning method according to claim 1, wherein the specific steps of transforming the target-oriented distributed robust optimized cold chain transportation model by a strong dual algorithm to obtain a mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem are as follows:
Converting a freshness constraint formula in the target-oriented distributed robust optimized cold chain transportation model into a freshness constraint equivalent formula through a strong dual algorithm;
converting a cost constraint formula in the target-oriented distributed robust optimized cold chain transportation model into a cost constraint equivalent formula through a strong dual algorithm;
converting a carbon emission constraint formula in the target-oriented distributed robust optimized cold chain transportation model into a carbon emission constraint equivalent formula through a strong dual algorithm;
and converting the cold chain transportation model of the target-oriented distributed robust optimization according to the freshness constraint equivalent formula, the cost constraint equivalent formula and the carbon emission constraint equivalent formula to obtain a mixed integer linear programming model of the cold chain path optimization problem of the target-oriented distributed robust optimization.
6. A cold chain transportation path planning apparatus, comprising:
the fuzzy set construction unit is used for acquiring the mean value and the support set of the cold chain transportation time and constructing the fuzzy set of the cold chain transportation time according to the mean value and the support set of the cold chain transportation time；
A risk calculation unit for calculating fuzzy sets according to URI risk indexes and cold chain transportation time Constructing a target freshness risk function, wherein the target freshness risk function is used for representing the risk of violating the freshness of a product in cold chain transportation;
the constraint calculating unit is used for acquiring the cost constraint of the cold chain transportation according to the cost budget of the cold chain transportation and acquiring the carbon emission constraint of the cold chain transportation according to the cold emission allowance;
the model building unit is used for building a target-oriented distributed robust optimized cold chain transportation model with the aim of meeting cost constraint and carbon emission constraint and minimizing a target freshness risk function;
the model conversion unit is used for converting the target-oriented distributed robust optimized cold chain transportation model through a strong dual algorithm to obtain a mixed integer linear programming model of the target-oriented distributed robust optimized cold chain path optimization problem;
the model solving unit is used for solving a mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem to obtain a cold link transportation path planning result;
in the solving step of the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem, solving the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem through a Benders decomposition algorithm;
The specific steps for solving the mixed integer linear programming model of the target-oriented distributed robust optimization cold link path optimization problem through the Benders decomposition algorithm are as follows:
decomposing a mixed integer linear programming model of a target-oriented distributed robust optimized cold link path optimization problem into a main problem and a pair problem, wherein the main problem is used for determining arc section selection of a cold link transportation path, and the pair problem is used for determining load when a vehicle runs to a certain arc section;
solving a main problem and a dual problem, obtaining a lower bound of a mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem by solving the main problem, and obtaining an upper bound of the mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem by solving the dual problem, so as to obtain an optimal solution of the mixed integer linear programming model of the target-oriented distributed robust optimized cold link path optimization problem.
7. An electronic device comprising a memory storing a computer program or instructions and a processor that when executed implements the cold chain transportation path planning method of any one of claims 1 to 5.
8. A storage medium, which is a computer-readable storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs executable by one or more processors to implement the steps of the cold chain transportation path planning method of any one of claims 1 to 5.
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