Decision Support for Collaboration of Carriers Based on Clustering, Swarm Intelligence and Shapley Value

Decision Support for Collaboration of Carriers Based on Clustering, Swarm Intelligence and Shapley Value

Fu-Shiung Hsieh
Copyright: © 2020 |Pages: 21
DOI: 10.4018/IJDSST.2020010102
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Abstract

Transportation costs constitute an important part in providing services and goods to customers. How to reduce transportation costs has a significant influence on competitive advantage of carriers. Although a lot of vehicle routing problems (VRP) and their variants have been extensively studied to reduce transportation costs via optimization of vehicle routes, little research focuses on how to achieve lower transportation costs through cooperation of carriers while fulfilling customer requests. This article aims to develop a decision support framework to facilitate cooperation of carriers to reduce transportation costs further based on information sharing, clustering requests, swarm intelligence, and the Shapley value cost allocation scheme. Two decision models for two carriers are compared: one reflecting the scenario without cooperation between the two carriers and the other one reflecting the scenario with cooperation between the two carriers. The simulation results indicate that the swarm intelligence and Shapley value based cooperative decision model outperforms that of the independent decision model.
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1. Introduction

Transportation is an important part of carriers as it imposes considerable cost on providing goods and has a significant influence on their competitive advantage. How to reduce transportation costs is an important issue in improving the profit of carriers. Vehicle routing is a critical factor in reducing transportation costs. Finding optimal vehicle routes offers great potential to efficiently manage fleets, reduce costs and improve service quality. An effective scheme to manage fleets and determine vehicle routes for delivering goods is important for carriers to survive. In the existing literature, a variety of vehicle routing problems (VRP) have been studied extensively. The classical VRP can be defined as the determination of an optimal set of routes for a fleet of vehicles to serve a set of customers (Dantzig & Ramser, 1959). VRP is NP-hard and therefore heuristic algorithms are required for tackling real-life instances (Toth, Vigo, 1998). Earlier surveys on VRP can be found in (Laporte et al. 2000) and (Christofides et al. 1979). Several methods to solve the classical VRP include the constructive heuristics proposed by Clarke and Wright (Clarke and Wright, 1964), generalized savings approach proposed by Mole and Jameson (Mole & Jamson, 1976), and the sweep algorithm proposed by Gillett and Miller (Gillett and Miller, 1974). Two earlier surveys for this very active research area are provided by Gendreau et al. (Gendreau et al. 1998) and by Golden et al. (Golden et al. 1998). Following the development of the classical VRP, several variants of VRP have been studied (Casco et al. 1988; Gendreau et al. 1996; Goetschalckx & Jacobs-Blecha, 1989; Kalantari et al. 1985; Vigo & Toth, 1997; Thangiah et al. 1996; Savelsbergh, 1985; Goksal et al. 2013; Chen & Wu, 2006; Salhi & Nagy 1999). For a recent survey of VRP, please refer to (Adewumi & Adeleke, 2016) and (Ritzinger et al. 2016). Although VRP has been extensively studied, most studies focus on VRP or its variants for one single carrier. How to cut down transportation cost through cooperation of carriers while fulfilling the customers’ requests is an interesting and important issue.

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