Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem

Yue, Yixiang; Zhang, Tong; Yue, Qun-xing

doi:10.1155/2015/375163

Cited by 6 publications

(6 citation statements)

References 12 publications

(11 reference statements)

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“…However, China's distribution is still in a relatively extensive stage, the utilization rate of vehicles is not high, and the efficiency of distribution is also low, which is not conducive to the long-term development of the logistics industry. Therefore, research on VRP problem has a very important practical value [14].…”

Section: Discussionmentioning

confidence: 99%

Analysis of logistics distribution path optimization planning based on traffic network data

Li¹,

2021

Computer Optics

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With the development of economy, the distribution problem of logistics becomes more and more complex. Based on the traffic network data, this study analyzed the vehicle routing problem (VRP), designed a dynamic vehicle routing problem with time window (DVRPTW) model, and solved it with genetic algorithm (GA). In order to improve the performance of the algorithm, the genetic operation was improved, and the output solution was further optimized by hill climbing algorithm. The analysis of example showed that the improved GA algorithm had better performance in path optimization planning, the total cost of planning results was 31.44 % less than that of GA algorithm, and the total cost of planning results increased by 11.48 % considering the traffic network data. The experimental results show that the improved GA algorithm has good performance and can significantly reduce the cost of distribution and that research on VRP based on the traffic network data is more in line with the actual situation of logistics distribution, which is conducive to the further application of the improved GA algorithm in VRP.

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Section: Discussionmentioning

confidence: 99%

Analysis of logistics distribution path optimization planning based on traffic network data

Li¹,

2021

Computer Optics

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“…First 3 c |31|32|33|34|35|36|37|38| 1 c |11|12|13|14|15|16|17|18| Second 3 c |37|32|38|35|36|34|33|31| 1 c |15|14|18|12|16|11|17|13| Third 3 c |14|15|38|35|36|34|33|31| 1 c |37|32|18|12|16|11|17|13| Mutation Operation. In this paper mutation strategy is to determine the probability of the mutation.…”

Section: Algorithm Design Calculation Rulesmentioning

confidence: 99%

“…If it is greater than the random number then set the first gene of the new chromosome as a random number. 3 c |14|15|38|35|36|34|33|31|---…”

Section: Algorithm Design Calculation Rulesmentioning

confidence: 99%

“…{( , ) : , , } E i j i j V i j    represents the arc set [1] .It can be described as the following: Vehicles come from one or more of logistics center in order to achieve the shortest travel distance or the least cost or the least time after the completion of loading and unloading tasks for all the customer points [2] . VRP with time windows (VRPTW) is based on the VRP problem with the customer's access time window constraints [3] . In the soft time window constraints the customer points have the opportunity to be included in the set of feasible solutions only cause penalty cost of violating the time window .The vehicle comes early to the customer point caused the waiting time and late to the customer point caused the delay time [4].…”

Section: Introductionmentioning

confidence: 99%

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The Solution of VRPPDTW Model with Service Cost Based on Genetic Algorithm

Ma¹,

Xiang²,

Liu³

2017

dtmse

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Abstract. This paper combines with the traditional pick-up and delivery vehicle routing problem, in addition to consider the fixed cost and the variable cost and the penalty cost of violating the time window in the process of delivery, it add the service cost of the loading and unloading cargo. By calculating the proportion of the service cost and other parts of the cost to determine the weight of each part of the cost in the total cost. A model of vehicle routing problem with pick-up and delivery and time window (VRPPDTW) which includes the service cost is established. It designs rules to determine the adaptive crossover probability then basing on the genetic algorithm to solve the problem. Using the operator of select crossover and mutate to ensure the diversity of the population and using the elite strategy to ensure the stability of the population. At last, a numerical example is given to verify the model and the calculation method. As a result the genetic algorithm using adaptive rules to determine crossover probability is more suitable than SGA to solve this model.

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“…The main challenge of these metaheuristics is to solve large combinatorial optimization problems in an adequate time period. Different authors have used metaheuristic algorithms to solve VRP: local search [24], simulated annealing [25], greedy randomized adaptive search procedure (GRASP) [26], swarm intelligence [27], tabu search (TS) [28,29], genetic algorithms [30], colony optimization [31], reactive search [32], and maximum coverage [33].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

et al. 2021

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The research evaluates the vehicular routing problem for distributing refrigerated products. The mathematical model corresponds to the vehicle routing problem with hard time windows and a stochastic service time (VRPTW-ST) model applied in Santiago de Chile. For model optimization, we used tabu search, chaotic search and general algebraic modeling. The model’s objective function is to minimize the total distance traveled and the number of vehicles using stochastic waiting restrictions at the customers’ facilities. The experiments were implemented in ten scenarios by modifying the number of customers. Experiments were established with several customers that can be solved using the general algebraic modeling technique in order to validate the tabu search and the chaotic search methods. The study considered two algorithms modified with Monte Carlo (tabu search and chaotic search). Additionally, two modified algorithms, TSv2 and CSv2, were proposed to reduce execution time. These algorithms were modified by delaying the Monte Carlo procedure until the first set of sub-optimal routes were found. The results validate the metaheuristic chaotic search to solve the VRPTW-ST. The chaotic search method obtained a superior performance than the tabu search method when solving a real problem in a large city. Finally, the experiments demonstrated a direct relationship between the percentage of customers with stochastic waiting time and the model resolution time.

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Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem

Cited by 6 publications

References 12 publications

Analysis of logistics distribution path optimization planning based on traffic network data

Analysis of logistics distribution path optimization planning based on traffic network data

The Solution of VRPPDTW Model with Service Cost Based on Genetic Algorithm

Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

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