A Memetic Algorithm for the Capacitated Location-Routing Problem

Abstract

Abstract-In this paper, a hybrid genetic algorithm is proposed to solve a Capacitated Location-Routing Problem. The objective is to minimize the total cost of the distribution in a network composed of depots and customers, both depots and vehicles have limited capacities, each depot has a homogenous vehicle fleet and customers' demands are known and must be satisfied. Solving this problem involves making strategic decisions such as the location of depots, as well as tactical and operational decisions which include assigning customers to the opened depots and organization of the vehicle routing. To evaluate the performance of the proposed algorithm, its results are compared to those obtained by a greedy randomized adaptive search procedure, computational results shows that the algorithm gave good quality solutions.

A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

“…This procedure has been successfully applied for addressing the CCVRP [2], introducing efficient move evaluation procedures in operations O(1) for some particular neighborhood structures. MAs have been also employed in solving other routing problems such as split delivery vehicle routing problems [29], capacitated location routing problems [30,31], vehicle routing problems with time windows [32], school bus routing problems [33], and green and healthcare routing problems [34,35].…”

A hybrid particle swarm optimization algorithm for the capacitated location routing problem

“…During the last decade, nature-inspired algorithms for optimization problems continue to increase, such as ant colony optimization, genetic algorithms (GA), artificial bee colony optimization and particle swarm optimization (PSO); these algorithms have been used to solve the CLRP; a memetic algorithm with population management (MA/PM) is developed by Prins et al (2006b), and two other MAs were proposed by Duhamel et al (2008) and Kechmane et al (2016). The CLRP is decomposed into a FLP and a multiple depot vehicle routing problem and solved with a multiple ant colony optimization algorithm by Ting and Chen (2013), the heuristic gave 12 new best solutions.…”

Optimization of a Two-Echelon Location Lot-Sizing Routing Problem with Deterministic Demand

“…Rungreunganaun and Woarawichai [44] applied genetic algorithms to solve an inventory lot-sizing problem with supplier selection under storage space; the objective of their research is to calculate the optimal inventory lot-sizing suppliers and minimize the inventory cost. Prins et al [8] proposed a memetic algorithm combined with population management, which is a technique developed by Sorensen and Sevaux [45], to solve a capacitated location routing problem; Park et al [46] presented a GA for the vendor-managed inventoryrouting problem with lost sales; Marinakis and Marinaki [47] presented a bilevel genetic algorithm and applied it to a real life location routing problem; a hybrid GA for the multiproduct multiperiod inventory-routing problem is presented by Moin et al [20]; Liao et al [48] used a nondominated sorting genetic algorithm (NSGA-II) to optimize a multiobjective location-inventory problem; and Kechmane et al [49] developed a memetic algorithm to solve a capacitated location routing problem.…”