Abstract
This paper considers a Capacitated Location-Arc Routing Problem (CLARP) with Deadlines (CLARPD) and a fleet of capacitated heterogeneous vehicles. The proposed mixed integer programming model determines a subset of potential depots to be opened, the served roads within predefined deadlines, and the vehicles assigned to each open depot. In addition, efficient routing plans are determined to minimize total establishment and traveling costs. Since the CLARP is NP-hard, a Genetic Algorithm (GA) is presented to consider proposed operators, and a constructive heuristic to generate initial solutions. In addition, a Simulated Annealing (SA) algorithm is investigated to compare the performance of the GA. Computational experiments are carried out for several test instances. The computational results show that the proposed GA is promising. Finally, sensitivity analysis confirms that the developed model can meet arc routing timing requirements more precisely compared to the classical Capacitated Arc Routing Problem (CARP).
Highlights
Introduction and problem definitionDesign of a distribution network is a fundamental step in building an efficient supply chain
This paper considers a location-arc routing problem in an undirected network
It can be used by planners for locating salt storage depots and specifying efficient routing plans for salt spreader trucks so as to minimize total costs, while some arcs need to be served within their deadline to prevent roads from icing up and to ensure safe roads
Summary
Design of a distribution network is a fundamental step in building an efficient supply chain. This process requires decisions at different levels: the strategic level (e.g., location of depots); the tactical level (e.g., routing plans); and the operational level (e.g., vehicle and personnel scheduling). The problem may be called the Location Routing Problem (LRP) or the Location-Arc Routing Problem (LARP), based on whether demands are located on edges or nodes of the network. One of important extensions of the LRP, according to real-world requirements, is considering the servicing time window for each node, which is called the LRP with Time Window (LRPTW). The vehicle can arrive at customers early. The wait times of the vehicle can be taken into account, with additional costs (Govindan et al 2014) or without any cost (Setak et al 2017)
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