Abstract
We address in this paper a multi-compartment vehicle routing problem (MCVRP) that aims to plan the delivery of different products to a set of geographically dispatched customers. The MCVRP is encountered in many industries, our research has been motivated by petrol station replenishment problem. The main objective of the delivery process is to minimize the total driving distance by the used trucks.The problem configuration is described through a prefixed set of trucks with several compartments and a set of customers with demands and prefixed delivery. Given such inputs, the minimization of the total traveled distance is subject to assignment and routing constraints that express the capacity limitations of each truck’s compartment in terms of the pathways’ restrictions. For the NP-hardness of the problem, we propose in this paper two algorithms mainly for large problem instances: an adaptive variable neighborhood search (AVNS) and a Partially Matched Crossover PMX-based Genetic Algorithm to solve this problem with the goal of ensuring a better solution quality. We compare the ability of the proposed AVNS with the exact solution using CPLEX and a set of benchmark problem instances is used to analyze the performance of the both proposed meta-heuristics.
Highlights
The multi-compartment vehicle routing problem (MCVRP) is an extension of the capacitated vehicle routing problem (CVRP), where the MCVRP consists of designing a set of minimal cost routes to serve demands for different types products of a set of customers
We address in this paper a multi-compartment vehicle routing problem (MCVRP) that aims to plan the delivery of different products to a set of geographically dispatched customers
For the NPhardness of the problem, we propose in this paper two algorithms mainly for large problem instances: an adaptive variable neighborhood search (AVNS) and a Partially Matched Crossover PMX-based Genetic Algorithm to solve this problem with the goal of ensuring a better solution quality
Summary
The multi-compartment vehicle routing problem (MCVRP) is an extension of the capacitated vehicle routing problem (CVRP), where the MCVRP consists of designing a set of minimal cost routes to serve demands for different types products of a set of customers. To the best of our knowledge, few other contributions have considered the variant defined above of the MCVRP and have solved the problem without taking into account its periodicity aspect (Avella et al 2004; Coelho and Laporte 2015; Lahyani et al 2015), stochastic demands (Elbek and Wøhlk 2016), time windows (Cornillier et al 2009), free assignment of products to compartments (Oppen and Løkketangen 2008; Cornillier et al 2009; Coelho and Laporte 2015) Both these works fall under the category of inventory-routing problems. Fallahi et al (2008) applied the MCVRP in which the different animal foods is supplied to the farms separately They proposed three algorithms a constructed heuristic, a memetic algorithm combined with a path relinking method used as post optimization, and a tabu search to solve this problem.
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