A Heuristic Initialized Stochastic Memetic Algorithm for MDPVRP With Interdependent Depot Operations

Abstract

The vehicle routing problem (VRP) is a widely studied combinatorial optimization problem. We introduce a variant of the multidepot and periodic VRP (MDPVRP) and propose a heuristic initialized stochastic memetic algorithm to solve it. The main challenge in designing such an algorithm for a large combinatorial optimization problem is to avoid premature convergence by maintaining a balance between exploration and exploitation of the search space. We employ intelligent initialization and stochastic learning to address this challenge. The intelligent initialization technique constructs a population by a mix of random and heuristic generated solutions. The stochastic learning enhances the solutions' quality selectively using simulated annealing with a set of random and heuristic operators. The hybridization of randomness and greediness in the initialization and learning process helps to maintain the balance between exploration and exploitation. Our proposed algorithm has been tested extensively on the existing benchmark problems and outperformed the baseline algorithms by a large margin. We further compared our results with that of the state-of-the-art algorithms working under MDPVRP formulation and found a significant improvement over their results.

Development of an Algorithm for Optimal Demand Responsive Relocatable Feeder Transit Networks Serving Multiple Trains and Stations

“…Among metaheuristics that have provided better solutions, TS and SA are effective algorithms (especially for small-scale problems) due to their approach of finding an appropriate neighborhood [54][55][56]. The SA algorithm has been widely used as an efficient approach for solving problems similar in structure to the model developed herein [57][58][59].…”

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

“…However, compared with the standard VRPPD and MDVRP, which are performed in only one-time unit, PVRP and MPVRP seek to provide services for various customer demands at multiple consecutive time periods (e.g., weekdays and weekends of a week), and vehicle routes in multiple service periods must be scheduled with a service frequency for each customer [7,[32][33][34]. Multidepot and periodic VRP (MDPVRP) incorporates PVRP and MDVRP to generate new routes originating from multiple depots to clients within a planning horizon of multiple periods [35,36]. The MDPVRPPD in the present study not only coordinates the pickup and delivery service with open-closed mixed trips in a multidepot logistics network but also designs periodic travel routes for customers during multiple periods.…”

“…A three-stage algorithm was developed by Luo et al [7] to find high-quality initial solutions for the MPVRP through a decomposition algorithm, a tree search algorithm, and a tabu search (TS) algorithm. Azad et al [36] introduced a heuristic initialized stochastic memetic algorithm to rebalance exploration and exploitation, and avoid premature convergence in the MDPVRP optimization. Hernandez et al [33] used a unified TS that evaluates each tactical routing plan in the multiperiod logistics network and obtains the minimum cost under the constraints of customer demands and service times.…”

A metaheuristic method for the multireturn-to-depot petrol truck routing problem with time windows

“…To solve the associated routing problem, several solution techniques have been introduced. In the past decade, metaheuristic approaches such as the GA (Bae et al 2007), differential evolution (DE) (Dechampai et al 2017;Mingyong and Erbao 2010;Sethanan and Pitakaso 2016), ant colony optimization (ACO) (Dong and Xiang 2006), TS (Côté and Potvin 2009;Bolduc et al 2010), and simulated annealing (SA) (Osman 1993;Azad et al 2017) methods have been widely used to solve this problem. Some studies have combined these methods in a variable neighbourhood search, VNS, or large neighbourhood search to find a satisfactory solution in a reasonable amount of time.…”