A Bi-Objective Vehicle Routing Problem with Time Window by Considering Customer Satisfaction

A Bi-Objective Vehicle Routing Problem with Time Window by Considering Customer Satisfaction

Masoud Rabbani, Mahyar Taheri, Mohammad Ravanbakhsh
Copyright: © 2016 |Pages: 24
DOI: 10.4018/IJSDS.2016040102
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Abstract

The Vehicle Routing Problem (VRP) by considering Time Windows is an essential and a reality optimization problem consisting in the determination of the set of routes with minimum distance to carry goods, by using some vehicles with capacity constraint; vehicles must visit customers within a time frame. In the recent years, many numbers of algorithm have been considered to solve a single objective formulate of VRPTW problem, such as Meta-heuristic, bender's decomposition, column generation and so on. This paper considers not only the minimum distance and the number of vehicles used to carry goods for customers. The customer satisfaction by considering the priority of the customers is considered which leads to service the customer as soon as possible. In this paper, the MOPSO and NSGAII approaches applied to solve the problem and then the authors compare the results of them; finally, they analysis the sensitivity of the capacity constraint for the vehicles
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2. Literature Review

Vehicle routing problems with time windows have been broadly considering by some researchers as during the past several years. In addition, by considering studies of Solomon (1987), current advantages have been made primarily on several heuristic and Meta-heuristic approaches. Zografos and Androutsopoulos (2004) has directly associated the VRPTW with transportation of goods by considering priority, they propose a bi-objective model, and a novel risk term is considered for each road segment, and the main aim on the objective function is minimizing the total travel time and the total risk. But, the considering priority for each customer does not act as a ‘‘hard’’ requirement. Therefore, this bi-objective model can be efficiently reduced to the cost of problem by considering the priority for each customer.

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