Reoptimization Heuristic for the Capacitated Vehicle Routing Problem

Abstract

The solution to a dynamic context of the Capacitated Vehicle Routing Problem (CVRP) is challenging. Routing and replenishment decisions are necessary by considering the assignment of customers to vehicles when the information is gradually revealed over horizon time. The procedure to solve this type of problems is referred to as route reoptimization, which is the best option for minimizing expected transportation cost without incurring failures of unsatisfied demand on a route. This paper proposes a heuristic algorithm for the reoptimization of CVRP in which the number of customers increases. The algorithm uses proposed performance metrics to reduce route dispersion and minimize length. The initial solution is generated using the savings algorithm and then enhanced using the Record-to-Record travel metaheuristic. By including or reducing new customers in the system, a reoptimization is performed which considers the visited nodes and edges as fixed. The optimization of the algorithm is implemented hierarchically by first minimizing dispersion and then minimizing distance. Next, the local search procedure is executed to improve the solution. A classic optimization is performed on all instances using the original and new customers’ information for later comparison to minimize distance. The efficiency of the proposed algorithm was validated using real-world cases from the literature. The results are promising and show the effectiveness of the proposed method for solving the considered problem by using reoptimization procedures in order to achieve good approximation ratios within short computing times.

Heuristic reoptimization of time‐extended multi‐robot task allocation problems

“…DVRPs with various characteristics have been studied, including the consideration of differing transportation modes, varying logistical contexts like pickup and/or delivery problems, the consideration of application specific constraints, different modifications like the insertion of new customer requests or changes in travel time, and others [27]. A great variety of solution methods for these NP-hard problems [27] has been proposed including tabu search [15,22], genetic algorithms [1], local search approaches [23], particle swarm optimization [19] and insertion heuristics [13]. Nevertheless, for all of the above approaches guarantees on the resulting solution quality are either impossible to give due to the nature of the applied metaheuristics or have not been investigated by the research.…”

Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

“…The main challenge of these metaheuristics is to solve large combinatorial optimization problems in an adequate time period. Different authors have used metaheuristic algorithms to solve VRP: local search [24], simulated annealing [25], greedy randomized adaptive search procedure (GRASP) [26], swarm intelligence [27], tabu search (TS) [28,29], genetic algorithms [30], colony optimization [31], reactive search [32], and maximum coverage [33].…”

Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm

“…Zulvia proposed a hybrid ant-colony optimization and genetic algorithm for solving CVRP with a time window and fuzzy travel time and demand [ 16 ]; Osman Gokalp proposed a novel algorithm based on iterated local search and the random variable neighborhood descent metaheuristic method for the purpose of solving CVRP [ 17 ]; Mahmuda Akhtar presented a modified backtracking search algorithm in CVRP models, with the smart bin concept to find the best optimized waste collection path distances [ 18 ]; Sami Faiz developed a decision support system for solving CVRP that integrated GIS enriched by a tabu search model [ 19 ]; Chengming Qi proposed a two-stage hybrid Ant Colony System (ACS) algorithm for CVRP that minimized the number of vehicles used and travel cost [ 20 ]; A. Gomez presented a new artificial bee colony algorithm for solving CVRP [ 21 ]; M. Ammi and S. Chikhi proposed an island model for solving CVRP, which consists of using a paradigm, called the island model, that rules the cooperation held by different islands [ 22 ]; S.L. Gadegaard proposed a new polynomially sized formulation of the well-known symmetric CVRP [ 23 ]; Yiyong Xiao presented a mathematical optimization model to formally characterized the fuel consumption rate considered in CVRP [ 24 ]; Rodrigo Linfati proposed a heuristic algorithm for the reoptimization of CVRP in which the number of customers increases, which uses the proposed performance to reduce route dispersion and minimize length [ 25 ]; Jiashan Zhang presented a novel two-phase heuristic approach for the CVRP to overcome limitation [ 26 ]; Ali Asghar Rahmani Hosseinabadi introduced a new metaheuristic optimization algorithm to solve CVRP that is based on the law of gravity and group interactions [ 27 ]; Asma M. Altabeeb proposed a new hybrid firefly algorithm to solve CVRP [ 28 ]; Hadi Karimi investigated various stabilization techniques for improving the column generation algorithm and proposed a novel stabilization technique specialized for CVRP [ 29 ]; A.K.M. Foysal Ahmed proposed an efficient algorithm, bilayer local search-based particle swarm optimization, along with a novel decoding method to solve CVRP [ 30 ]; Mauro Dell’Amico proposed a new iterated local search metaheuristic method for CVRP that also includes a vital mechanism from the adaptive large neighborhood search combined with further intensification through local search [ 31 ]; R. Baldacci described a new integer programming formulation for CVRP based on a two-commodity network flow approach [ 32 ]; Fernando Afonso Santos introduced a branch-and-cut-and-price algorithm for two-echelon CVRP [ 33 ]; Jiafu Tang developed a BEAM–MMAX algorithm that combines a MAX–MIN ant system with beam search to solve CVRP [ 34 ]; Jacek Mańdziuk proposed a solution to CVRP with traffic jams, which relies on application of the upper confidence ...…”