Abstract
Managing the distribution of goods is a vital operation for many companies. A successful distribution system requires an effective distribution strategy selection and optimum route planning at the right time and minimum cost. Furthermore, customer’s demand and location can vary from order to order. In this situation, a mixed delivery system is a good solution for it and allows the use of different strategies together to decrease delivery costs. Although the “distribution strategy selection” is a critical issue for companies, there are only a few studies that focus on the mixed delivery network problem. There is a need to propose an efficient solution for the mixed delivery problem to guide researchers and practitioners. This paper develops a new “modified” savings-based genetic algorithm which is named “distribution strategy selection and vehicle routing hybrid algorithm (DSSVRHA).” Our new algorithm aims to contribute to the literature a new hybrid solution to solve a mixed delivery network problem that includes three delivery modes: “direct shipment,” “milk run,” and “cross-docking” efficiently. It decides the appropriate distribution strategy and also optimal routes using a heterogeneous fleet of vehicles at minimum cost. The results of the hybrid algorithm are compared with the results of the optimization model. And the performance of the hybrid algorithm is validated with statistical analysis. The computational results reveal that our developed algorithm provides a good solution for reducing the supply chain distribution costs and computational time.
Highlights
Today’s organizations are trying to find better distribution strategies that reduce supply chain costs and enhancing customer satisfaction to survive in the competitive supply chain environment. erefore, delivery with the most costeffective distribution strategy has recently become a critical focus of logistics systems.ere are several types of distribution strategies: direct shipment, cross-docking, milk run, and mixed delivery
Hosseini et al [24] offered an integer programming model and a hybrid heuristic method based on harmony search (HS) and simulated annealing (SA) for the transportation problem which consists of direct shipment, cross-docking, and milk run
Because of the long solution time, the solutions for the data sample involving more than 12 customers are created with the proposed heuristic algorithm. e comparisons of solutions are depicted in Figure 18. e two methods have given similar solutions for small-size samples
Summary
Today’s organizations are trying to find better distribution strategies that reduce supply chain costs and enhancing customer satisfaction to survive in the competitive supply chain environment. erefore, delivery with the most costeffective distribution strategy has recently become a critical focus of logistics systems. Moghadam et al [22] proposed a hybrid algorithm combining the ant colony algorithm and the simulated annealing algorithm to solve the vehicle routing scheduling problem with the cross-docking strategy. Hosseini et al [24] presented a hybrid algorithm that combines the simulated annealing and the harmony search algorithm to find a solution for the vehicle routing problem with cross-docking and milk run strategies. (2) To the best of our knowledge, this is the first time the genetic algorithm has been hybridized with Clarke and Wright’s algorithm for the heterogeneous vehicle routing problem with mixed delivery strategies. E genetic algorithm is an intelligent heuristic technique for solving vehicle routing problems by reducing delivery costs significantly, and the following papers prove this: [25,26,27].
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