Abstract
The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers’ expectations and shipper considerations as goals, and a common goal such as transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the genetic algorithm with the local search algorithm to solve the proposed problem. We evaluated the effectiveness of the proposed algorithm with the Tabu Search algorithm and the original genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.
Highlights
Transportation plays an essential role in moving materials from suppliers to manufacturers, from processing plants to the step in the production process, or transporting finished products to customers
Fundamental decisions are made in the Vehicle Routing Problem (VRP) regarding customer assignment to vehicles and the sequence of customers assigned to each vehicle [2]
Regarding VRP with more features and attributes that reflect the complexity of the real problem, a hybrid genetic algorithm that improves the solution by implementing a local search heuristic in the initial phase of the genetic algorithm was proposed by Rabbouch (2019) [32]
Summary
Transportation plays an essential role in moving materials from suppliers to manufacturers, from processing plants to the step in the production process, or transporting finished products to customers This scheduling and planning process needs to be calculated before the actual operation. Multi-trip Vehicle Routing Problem (MT-VRP): in MT-VRP, each vehicle is explicitly allowed to perform multiple trips during its service time in such a manner that the total demand of customers served in each route does not exceed the vehicle’s capacity within a given deadline [9,11,12]. This paper introduces a method to solve a new VRP that combines multi-objective optimization (MOP) and different forms of VRPs. The proposed solver automatically generates the routings for shippers to deliver packages to urban customers.
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