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Ghannadpour, S. (2019). Evolutionary Approach for Energy Minimizing Vehicle Routing Problem with Time Windows and Customers’ Priority. International Journal of Transportation Engineering, 6(3), 237264. doi: 10.22119/ijte.2018.55929Seyed Farid Ghannadpour. "Evolutionary Approach for Energy Minimizing Vehicle Routing Problem with Time Windows and Customers’ Priority". International Journal of Transportation Engineering, 6, 3, 2019, 237264. doi: 10.22119/ijte.2018.55929Ghannadpour, S. (2019). 'Evolutionary Approach for Energy Minimizing Vehicle Routing Problem with Time Windows and Customers’ Priority', International Journal of Transportation Engineering, 6(3), pp. 237264. doi: 10.22119/ijte.2018.55929Ghannadpour, S. Evolutionary Approach for Energy Minimizing Vehicle Routing Problem with Time Windows and Customers’ Priority. International Journal of Transportation Engineering, 2019; 6(3): 237264. doi: 10.22119/ijte.2018.55929
Evolutionary Approach for Energy Minimizing Vehicle Routing Problem with Time Windows and Customers’ Priority
Article 4, Volume 6, Issue 3  Serial Number 23, Winter 2019, Page 237264
PDF (849.52 K)
Document Type: Research Paper
DOI: 10.22119/ijte.2018.55929
Author
Seyed Farid Ghannadpour ^{}
^{}Assistant Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract
A new model and solution for the energy minimizing vehicle routing problem with time windows (EVRPTW) and customers’ priority is presented in this paper. In this paper unlike prior attempts to minimize cost by minimizing overall traveling distance, the model also incorporates energy minimizing which meets the latest requirements of green logistics. This paper includes the vehicles load as an additional indicator of the cost in addition to the distance traveled cost. Moreover, this paper tries to maximize the customers' satisfaction using their preference and considers the customers' priority for servicing. Every customer is assigned to a group (e.g., very important, important, casual and unimportant) and the customers’ preference is represented as a convex fuzzy number with respect to the satisfaction for service time. The detailed mathematical formulation of proposed model is provided and it is interpreted as multiobjective optimization where, the energy consumed and the total number of vehicles are minimized and the total satisfaction rate of customers is maximized. In general, the relationship between these defined objectives is unknown until the problem is solved in a proper multiobjective manner. Thus, a multiobjective evolutionary algorithm is proposed and its performance on several completely random instances is compared with Nondominated Sorting Genetic Algorithm II (NSGA II) and CPLEX Solver. The hypervolume indicator is used to evaluate the two Pareto set approximations found by NSGAII and the proposed approach. The performance proposed evolutionary is further demonstrated through several computational experiments and the results indicate the good quality of the method.
Keywords
vehicle routing problem; energy consumption; Customers' priority; multiobjective; Evolutionary Algorithm
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