A flower harvesting green cold chain path optimization system and method based on improved spider wasp optimizer
By constructing a green cold chain path optimization model with the lowest comprehensive cost and a genetic neighborhood integrated spider wasp algorithm, the problems of high flower loss rate, large carbon emissions and low customer satisfaction in cold chain logistics were solved, the cost and carbon emissions were optimized, and customer satisfaction was improved.
Patent Information
- Application Number
- CN202411896188.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-20
- Publication Date
- 2025-10-03
- Estimated Expiration
- 2044-12-20
AI Technical Summary
The existing cold chain logistics of flowers has a high loss rate, large carbon emissions, high transportation costs, and low customer satisfaction. Existing algorithms are difficult to effectively solve the multi-constraint and multi-objective green cold chain path optimization problem.
A green cold chain path optimization model for flower harvesting with minimal comprehensive cost was constructed. The genetic neighborhood integrated spider wasp algorithm was combined to generate the initial solution through random generation and nearest neighbor algorithm. A genetic algorithm and a discretized spider wasp optimizer were used for global search, and reversal mutation and mating behavior were introduced to optimize the path.
Effectively reduce the total cost and carbon dioxide emissions during flower transportation, improve customer satisfaction, ensure flower quality and expand market influence.
Smart Images
Figure CN119783931B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a flower harvesting green cold chain path optimization system and method based on an improved spider wasp optimizer, belonging to the field of operations research and optimization. Background Art
[0002] The flower industry is a distinctive sector of Yunnan's economy, generating significant employment and economic revenue. Flowers are perishable and difficult to preserve, typically requiring temperatures between 2°C and 8°C to maintain freshness. Due to their unique characteristics, the loss rate of flowers in standard logistics can reach as high as 30%, and logistics costs account for 60% of overall costs. Therefore, fresh flowers must be transported to cold warehouses as quickly as possible using refrigerated vehicles. Cold chain logistics consumes more energy and emits more carbon than standard logistics. Consequently, accelerating the transition to a low-carbon economy is becoming a major trend for cold chain logistics companies. This places high demands on cold chain logistics routing planning, requiring not only strict control of total transportation costs but also effective reduction of carbon emissions. An increasing number of researchers are incorporating environmental factors, such as carbon emissions, into their research on cold chain vehicle routing optimization. The green cold chain vehicle routing optimization problem has emerged. This problem, building on the traditional cold chain vehicle routing optimization problem, further considers the impact of energy consumption and carbon emissions, prioritizing the selection of routing solutions that balance economic benefits for the enterprise and environmental friendliness. The green cold chain routing optimization problem is essentially a variant of the vehicle routing problem (VRP). The VRP, first proposed by George Dantzig and John Ramser in 1959, is a combinatorial optimization problem widely studied in the field of operations research. Designing optimization models that are close to real-world applications and developing highly accurate and fast algorithms have long been a key research focus for scholars.
[0003] The cold chain harvesting process has problems such as high energy consumption, large carbon emissions, and low customer satisfaction. Therefore, many scholars have conducted in-depth research on the cold chain path optimization problem, but the consideration of cost structure is still insufficient. Studies have shown that the VRP problem is an NP-hard problem, and the scale of the solution space is very large. The optimization system based on real scenarios considers more and more factors, from the previous single objective and a small number of constraints to multiple objectives and multiple constraints, and the complexity of the system has greatly increased. The green cold chain path optimization problem involves more variables and constraints. For the solution algorithm, the exact algorithm cannot avoid the exponential explosion problem. The classic heuristic algorithm is often trapped in the local optimal value. Therefore, it is necessary to find a system that meets practical applications and an efficient solution method. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to provide a green cold chain path optimization system and method for flower harvesting based on an improved spider wasp optimizer, which can effectively solve the problems existing in the prior art.
[0005] The technical solution employed in this invention is to combine the characteristics of flower cold chain route optimization and, based on a hard time window constraint, comprehensively consider fixed costs, refrigeration costs, transportation costs, carbon emissions, flower loss costs, and time penalty costs to construct a green cold chain route optimization model for flower harvesting that minimizes overall costs. A genetic neighborhood integrated spider wasp algorithm is proposed to solve this model. This algorithm first uses random generation and a nearest neighbor algorithm to generate diverse and high-quality initial solutions. In the initial search phase, a crossover operator from a genetic algorithm is used to perform a global search to expand the solution space. Six neighborhood search operators from a discretized spider wasp optimizer are then used to further search for optimal solutions. To prevent the algorithm from becoming trapped in a local optimum, a reverse mutation operation and the mating behavior of the discretized spider wasp optimizer are introduced to generate new solutions. This reduces the total cost and carbon dioxide emissions of flower cold chain transportation and significantly improves customer satisfaction.
[0006] A green cold chain path optimization system for flower harvesting based on an improved spider wasp optimizer, comprising:
[0007] Flowers, goods that need to be picked up by refrigerated vehicles;
[0008] The warehouse center, the departure point for refrigerated vehicles and the final destination where the vehicles need to return after harvesting flowers;
[0009] Customers need to wait at a designated location for a refrigerated truck to pick up the flowers within a time window;
[0010] Refrigerated vehicles are responsible for picking up flowers at the customer's designated location and transporting them back to the refrigerated warehouse.
[0011] Preferably, the flowers are easily rotten and withered and difficult to preserve, and the types and methods of harvesting the flowers are similar.
[0012] Preferably, the customer location, the weight of flowers to be harvested at each location, the customer time window, and the customer service time are known before the refrigerated vehicle departs. The customer time window is a hard time window. The vehicle can arrive earlier than the customer's earliest service time, but must wait until the customer's earliest service time to start harvesting flowers. If the vehicle arrives later than the latest time specified by the customer, the customer will refuse to accept service.
[0013] Preferably, each location where flowers are placed can only be visited once by a refrigerated truck, and the total weight of flowers to be harvested along the path of each vehicle is less than the vehicle capacity.
[0014] Preferably, the number of vehicles available at the warehouse center, the vehicle loads, and the vehicle travel speeds are known.
[0015] Preferably, the vehicle generates fuel consumption and carbon emissions when transporting flowers, and the engine is shut down when harvesting flowers, so no fuel consumption and carbon emissions are generated.
[0016] A green cold chain path optimization method for flower harvesting based on an improved spider wasp optimizer, the specific steps are as follows:
[0017] Step 1: Chromosomes are encoded using natural numbers, where 0 represents the warehouse center and natural numbers 1, 2, ..., n represent n customers. A chromosome is a sorted sequence of integers, and the order of arrangement represents the order in which vehicles visit customers. During decoding, the warehouse center is inserted into the sequence consisting of all customers based on the maximum load of refrigerated vehicles and the customer's time window constraints. This is then decomposed into multiple different sub-routes. A chromosome represents a complete vehicle routing solution.
[0018] Step 2: Generate the initial chromosome using both random generation and the nearest neighbor algorithm. The random generation method randomly generates a sequence of integers without repeated numbers within the total number of customers as the newly generated solution. The nearest neighbor algorithm generation method first randomly selects the first customer, then starts from the current customer and searches for the next closest customer who has not been visited and meets the load constraint and time window constraint. Repeat this process until all customers are traversed.
[0019] Step 3: Considering various costs such as fixation, refrigeration, transportation, carbon emissions, flower loss, and time penalty, an optimization objective function is constructed to minimize the total cost, and the inverse of the objective function is used as the fitness function of the solution.
[0020] Step 4: There are three selection strategies: Roulette wheel selection, where the fitness value of an individual determines the probability of being selected. The higher the fitness value, the greater the probability of being selected for crossover and mutation; Elite selection strategy, where the 10% of individuals with the highest fitness values are selected for crossover and mutation; Best individual retention strategy, where the individuals with the highest fitness values are retained for the next generation and do not participate in crossover and mutation;
[0021] Step 5: At the beginning of the search, a partial mapping crossover operation in the genetic algorithm is used to perform a preliminary search to expand the solution space. The specific operation is to first randomly select two crossover points, then retain the gene segments between the two crossover points in parent 1 and parent 2, then swap the gene segments in the crossover region of parent 1 and parent 2 to generate two new offspring individuals. Finally, the newly generated offspring needs to deal with duplicate and missing genes to ensure that each generated offspring is a legal and valid solution.
[0022] Step 6: Based on the solution found in Step 5, the discrete spider wasp optimizer further performs a global search of the solution space by using a trade-off between the six neighborhood search operators in the search phase, the following and fleeing phase, and the nesting behavior phase. In the search phase, the neighborhood search is guided by the time distance matrix and the common information of the excellent genes in the elite population. In the following and fleeing phase, the neighborhood search is based on genes within the same route. In the nesting behavior phase, the neighborhood search is based on genes from different routes.
[0023] Step 7: Two mutation operations introduce new solutions, reversal mutation, randomly select two positions on the chromosome, determine the reversal operation interval, and reverse the genes in the selected interval; the mating behavior of the discrete spider wasp optimizer, based on the elite population, constructs a temporary set consisting of three consecutive customers with the highest frequency of occurrence, randomly selects two parents 1 and parent 2, and retains the same gene fragments in parent 1 and parent 2 as in the temporary set to offspring 1 and offspring 2 respectively. The vacant positions in offspring 1 are filled by non-repeated genes in parent 2, and the vacant positions in offspring 2 are filled by non-repeated genes in parent 1.
[0024] The beneficial effects of the present invention are: based on the hard time window constraint, a green cold chain path optimization system for flower harvesting with the lowest comprehensive cost is constructed by comprehensively considering fixed costs, refrigeration costs, transportation costs, carbon emission costs, flower loss costs, and time penalty costs. The introduction of the hard time window can force the system to strictly abide by the time plan to serve customers and maximize customer satisfaction. Punctuality is necessary for the cold chain transportation of perishable goods or time-sensitive goods. While ensuring the quality of flowers, because the flowers can be picked up from customers on time, customers can trust the logistics company and expand the market influence of the logistics company. Since there are many factors to consider, the complexity of the problem is greatly increased, and a better performance algorithm is needed to solve it. Therefore, we propose a genetic neighborhood integrated spider wasp algorithm to solve it. The algorithm has a high convergence speed and good stability. It can find a flower harvesting path plan with a smaller total cost and carbon emissions, effectively reducing the total cost and carbon dioxide emissions of flowers during the cold chain transportation process, and greatly improving customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 It is a schematic diagram of the system model of the present invention;
[0026] Figure 2 It is a schematic diagram of chromosome encoding and decoding of the present invention;
[0027] Figure 3 This is a schematic diagram of the operation of partial mapping crossover of the genetic algorithm of the present invention;
[0028] Figure 4Schematic diagram of the operation of the search operator 1 in the follow and escape phases in the discretized spider wasp optimizer of the present invention;
[0029] Figure 5 Schematic diagram of the operation of search operator 2 in the follow and escape phases of the discretized spider wasp optimizer of the present invention;
[0030] Figure 6 1 is a schematic diagram of the operation of the search operator 1 in the nesting behavior phase of the discretized spider wasp optimizer of the present invention;
[0031] Figure 7 2 is a schematic diagram of the operation of the search operator 2 in the nesting behavior phase of the discretized spider wasp optimizer of the present invention;
[0032] Figure 8 is a schematic diagram of the reverse mutation operation of the genetic algorithm of the present invention;
[0033] Figure 9 is a schematic diagram of the mating behavior mutation operation in the discretized spider-wasp optimizer of the present invention;
[0034] Figure 10 It is a schematic flow chart of the solution algorithm of the present invention. DETAILED DESCRIPTION
[0035] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.
[0036] Example 1: Figure 1 As shown, a flower harvesting green cold chain path optimization system based on an improved spider wasp optimizer includes:
[0037] Flowers, goods that need to be picked up by refrigerated vehicles;
[0038] The warehouse center, the departure point for refrigerated vehicles and the final destination where the vehicles need to return after harvesting flowers;
[0039] Customers need to wait at a designated location for a refrigerated truck to pick up the flowers within a time window;
[0040] Refrigerated vehicles are responsible for picking up flowers at the customer's designated location and transporting them back to the refrigerated warehouse.
[0041] Furthermore, the flowers are easy to rot and wither and difficult to preserve. The types of flowers that the refrigerated vehicles need to collect are the same, such as fresh cut flowers (roses), and the way of collecting flowers when the vehicles arrive at the customer's location is the same.
[0042] Furthermore, the customer location, the weight of flowers to be harvested at each location, the customer time window, and the customer service time are all known before the refrigerated vehicle departs. The customer time window is a hard time window. The vehicle can arrive earlier than the customer's earliest service time, but must wait until the customer's earliest service time to start harvesting flowers. If the vehicle arrives later than the latest time specified by the customer, the customer will refuse to accept service.
[0043] Furthermore, each location where flowers are placed can only be visited once by a refrigerated truck, and the sum of the weight of flowers that need to be harvested along the path of each vehicle must be less than the vehicle capacity.
[0044] Furthermore, the number of available vehicles, vehicle loads, and vehicle travel speeds at the warehouse center are known.
[0045] Furthermore, the vehicle consumes fuel and emits carbon when transporting flowers, but does not consume fuel or emit carbon when the engine is shut down during the harvesting of flowers.
[0046] like Figure 2 As shown, the chromosome is encoded using natural numbers, where 0 represents the warehouse center and the natural numbers 1, 2, ..., 8 represent 8 customers. The chromosome encoding is a sequence of integers. During decoding, 0 is inserted into the integer array consisting of customers based on the maximum load of the vehicle and the customer's time window constraint. The decoded chromosome represents a complete path, and the interval between 0 and 0 represents a route.
[0047] like Figure 3 As shown, two crossover points are randomly selected, the gene segments between the crossover points in parent 1 and parent 2 are retained, then the gene segments in the crossover region between parent 1 and parent 2 are exchanged, and finally the duplicated genes in the chromosomes are deleted and the missing genes are filled, thus generating offspring 1 and offspring 2.
[0048] like Figure 4 As shown, two adjacent edges in the route are randomly selected to be disconnected and reconnected. If the total cost of the newly formed path is reduced, the operation is performed, otherwise the status quo is maintained.
[0049] like Figure 5 As shown in Figure 1, a small segment in the route is randomly selected, which is 2 consecutive nodes, and moved to another location in the route. If the cost of the new path after the move is lower than the cost of the original path, the move is accepted.
[0050] like Figure 6 As shown in the figure, a customer in the current route is randomly deleted and inserted into another different route. If the total cost of the new path is less than the total cost of the original path, the operation is performed, otherwise it is not performed.
[0051] like Figure 7As shown, two different routes are randomly selected and one of their customers is exchanged. If the total cost of the newly formed path is less than the total cost of the original path, the exchange operation is performed, otherwise it is not performed.
[0052] like Figure 8 As shown, two positions of the chromosome are randomly selected, the reversal operation interval is determined, and the genes in the selected interval are reversed.
[0053] like Figure 9 As shown, based on the elite population, a temporary set consisting of three consecutive customers with the highest frequency of occurrence is constructed, and two parents 1 and 2 are randomly selected. The gene fragments in parents 1 and 2 that are the same as those in the temporary set are reserved for offspring 1 and offspring 2 respectively. The vacant positions in offspring 1 are filled by non-repeated genes in parent 2, and the vacant positions in offspring 2 are filled by non-repeated genes in parent 1.
[0054] like Figure 10 As shown, the algorithm first uses random generation and the nearest neighbor algorithm to generate diverse, high-quality initial solutions. A preliminary search is performed using the genetic algorithm's crossover operation in the initial search phase. Six neighborhood search operators are then used to further globally search the solution space, using a trade-off between the search phase, the follow-and-flee phase, and the nesting behavior phase of the discretized spider wasp optimizer. Finally, new solutions are introduced using the genetic algorithm's mutation operation and the discretized spider wasp optimizer's mating operation. Search operator 1 in the discretized spider wasp optimizer's search phase consists of selecting the 10% of chromosomes with the best fitness in the population to form an elite population. The most frequent customers at each chromosome position in the elite population are counted to construct a temporary path. Customers with the same path as the temporary path are retained, while the remaining customers are deleted. Finally, the connection probability between two customers is calculated based on the spatiotemporal distance matrix and the shared information of the elite population. The deleted customers are then reinserted into the path. A higher connection probability indicates a lower incremental cost for the customer when inserted into the position. The search operator 2 in the search phase is: retain the longest common subsequence between the individual and the elite chromosome, delete the remaining customers, and finally calculate the connection probability between the two customers based on the spatiotemporal distance matrix and the common information of the elite population, and insert the deleted customers back into the path.
[0055] A green cold chain path optimization method for flower harvesting based on an improved spider wasp optimizer, the specific steps are as follows:
[0056] Step 1: Chromosomes are encoded using natural numbers, where 0 represents the warehouse center and natural numbers 1, 2, ..., n represent n customers. A chromosome is a sorted sequence of integers, and the order of arrangement represents the order in which vehicles visit customers. During decoding, the warehouse center is inserted into the sequence consisting of all customers based on the maximum load of refrigerated vehicles and the customer's time window constraints. This is then decomposed into multiple different sub-routes. A chromosome represents a complete vehicle routing solution.
[0057] Step 2: Generate the initial chromosome using both random generation and the nearest neighbor algorithm. The random generation method randomly generates a sequence of integers without repeated numbers within the total number of customers as the newly generated solution. The nearest neighbor algorithm generation method first randomly selects the first customer, then starts from the current customer and searches for the next closest customer who has not been visited and meets the load constraint and time window constraint. Repeat this process until all customers are traversed.
[0058] Step 3: Considering various costs such as fixation, refrigeration, transportation, carbon emissions, flower loss, and time penalty, an optimization objective function is constructed to minimize the total cost, and the inverse of the objective function is used as the fitness function of the solution.
[0059] Step 4: There are three selection strategies: Roulette wheel selection, where the fitness value of an individual determines the probability of being selected. The higher the fitness value, the greater the probability of being selected for crossover and mutation; Elite selection strategy, where the 10% of individuals with the highest fitness values are selected for crossover and mutation; Best individual retention strategy, where the individuals with the highest fitness values are retained for the next generation and do not participate in crossover and mutation;
[0060] Step 5: At the beginning of the search, a partial mapping crossover operation in the genetic algorithm is used to perform a preliminary search to expand the solution space. The specific operation is to first randomly select two crossover points, then retain the gene segments between the two crossover points in parent 1 and parent 2, then swap the gene segments in the crossover region of parent 1 and parent 2 to generate two new offspring individuals. Finally, the newly generated offspring needs to deal with duplicate and missing genes to ensure that each generated offspring is a legal and valid solution.
[0061] Step 6: Based on the solution found in Step 5, the discrete spider wasp optimizer further performs a global search of the solution space by using a trade-off between the six neighborhood search operators in the search phase, the following and fleeing phase, and the nesting behavior phase. In the search phase, the neighborhood search is guided by the time distance matrix and the common information of the excellent genes in the elite population. In the following and fleeing phase, the neighborhood search is based on genes within the same route. In the nesting behavior phase, the neighborhood search is based on genes from different routes.
[0062] Step 7: Two mutation operations introduce new solutions, reversal mutation, randomly select two positions on the chromosome, determine the reversal operation interval, and reverse the genes in the selected interval; the mating behavior of the discrete spider wasp optimizer, based on the elite population, constructs a temporary set consisting of three consecutive customers with the highest frequency of occurrence, randomly selects two parents 1 and parent 2, and retains the same gene fragments in parent 1 and parent 2 as in the temporary set to offspring 1 and offspring 2 respectively. The vacant positions in offspring 1 are filled by non-repeated genes in parent 2, and the vacant positions in offspring 2 are filled by non-repeated genes in parent 1.
[0063] The specific embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.
Claims
1. A green cold chain path optimization method for flower harvesting based on an improved spider wasp optimizer, characterized by: The specific steps are as follows: Step 1: Chromosomes are encoded using natural numbers, where 0 represents the warehouse center and natural numbers 1, 2, ..., n represent n customers. A chromosome is a sorted sequence of integers, and the order of arrangement represents the order in which vehicles visit customers. During decoding, the warehouse center is inserted into the sequence consisting of all customers based on the maximum load of refrigerated vehicles and the customer's time window constraints. This is then decomposed into multiple different sub-routes. A chromosome represents a complete vehicle routing solution. Step 2: Generate the initial chromosome using both random generation and the nearest neighbor algorithm. The random generation method randomly generates a sequence of integers without repeated numbers within the total number of customers as the newly generated solution. The nearest neighbor algorithm generation method first randomly selects the first customer, then starts from the current customer and searches for the next closest customer who has not been visited and meets the load constraint and time window constraint. Repeat this process until all customers are traversed. Step 3: Considering various costs such as fixation, refrigeration, transportation, carbon emissions, flower loss, and time penalty, an optimization objective function is constructed to minimize the total cost, and the inverse of the objective function is used as the fitness function of the solution. Step 4: There are three selection strategies: Roulette wheel selection, where the fitness value of an individual determines the probability of being selected. The higher the fitness value, the greater the probability of being selected for crossover and mutation; Elite selection strategy, where a set proportion of individuals with the highest fitness values are selected for crossover and mutation; Best individual retention strategy, where the individuals with the highest fitness values are retained to enter the next generation and do not participate in crossover and mutation; Step 5: At the beginning of the search, a partial mapping crossover operation in the genetic algorithm is used to perform a preliminary search to expand the solution space. The specific operation is to first randomly select two crossover points, then retain the gene segments between the two crossover points in parent 1 and parent 2, then swap the gene segments in the crossover region of parent 1 and parent 2 to generate two new offspring individuals. Finally, the newly generated offspring needs to deal with duplicate and missing genes to ensure that each generated offspring is a legal and valid solution. Step 6: Based on the solution found in Step 5, the discrete spider wasp optimizer further performs a global search of the solution space by using a trade-off between six neighborhood search operators in the search phase, the following and fleeing phase, and the nesting behavior phase. In the search phase, the neighborhood search is guided by the shared information of the temporal distance matrix and the excellent genes in the elite population. In the following and fleeing phase, the neighborhood search is based on genes within the same route. In the nesting behavior phase, the neighborhood search is based on genes from different routes. Step 7: Two mutation operations introduce new solutions, reversal mutation, randomly select two positions on the chromosome, determine the reversal operation interval, and reverse the genes in the selected interval; the mating behavior of the discrete spider wasp optimizer, based on the elite population, constructs a temporary set consisting of three consecutive customers with the highest frequency of occurrence, randomly selects two parents 1 and parent 2, and retains the same gene fragments in parent 1 and parent 2 as in the temporary set to offspring 1 and offspring 2 respectively. The vacant positions in offspring 1 are filled by non-repeated genes in parent 2, and the vacant positions in offspring 2 are filled by non-repeated genes in parent 1.
2. The flower harvesting green cold chain path optimization method based on the improved spider wasp optimizer according to claim 1, characterized in that: In Step 4, the ratio is set to 10%.
3. A flower harvesting green cold chain path optimization system based on an improved spider wasp optimizer, characterized by: The green cold chain path optimization method for flower harvesting based on the improved spider wasp optimizer according to claim 1 comprises: Flowers, goods that need to be picked up by refrigerated vehicles; The warehouse center, the departure point for refrigerated vehicles and the final destination where the vehicles need to return after harvesting flowers; Customers need to wait at a designated location for a refrigerated truck to pick up the flowers within a time window; Refrigerated vehicles are responsible for picking up flowers at the customer's designated location and transporting them back to the refrigerated warehouse.
4. The flower harvesting green cold chain path optimization system based on the improved spider wasp optimizer according to claim 3, characterized in that: The flowers are easily rotten and withered and difficult to preserve. The types of flowers harvested by the refrigerated vehicles are the same as the way the flowers are harvested when they arrive at the customer's location.
5. The flower harvesting green cold chain path optimization system based on the improved spider wasp optimizer according to claim 3, characterized in that: The customer location, the weight of flowers to be harvested at each location, the customer time window, and the customer service time are all known before the refrigerated vehicle departs. The customer time window is a hard time window. The vehicle can arrive earlier than the customer's earliest service time, but must wait until the customer's earliest service time to start harvesting flowers. If the vehicle arrives later than the latest time specified by the customer, the customer will refuse to accept service.
6. The flower harvesting green cold chain path optimization system based on the improved spider wasp optimizer according to claim 3, characterized in that: Each flower placement location can only be visited once by a refrigerated truck, and the sum of the weight of flowers that need to be harvested along each vehicle's path must be less than the vehicle's capacity.
7. The flower harvesting green cold chain path optimization system based on the improved spider wasp optimizer according to claim 3, characterized in that: The number of vehicles available at the warehouse center, vehicle loads, and vehicle speeds are known.
8. The flower harvesting green cold chain path optimization system based on the improved spider wasp optimizer according to claim 3, characterized in that: The vehicle consumes fuel and emits carbon when transporting flowers. When harvesting flowers, the engine is turned off and no fuel consumption or carbon emissions are generated.