A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems

Abstract

This paper addresses waste collection problems in which urban household and solid waste are brought from waste collection points to waste disposal plants. The collection of waste from the collection points herein is modeled as a multi-depot vehicle routing problem (MDVRP), aiming at minimizing the total transportation distance. In this study, we propose a heuristic solution method to address this problem. In this method, we firstly assign waste collection points to waste disposal plants according to the nearest distance, then each plant solves the single-vehicle routing problem (VRP) respectively, assigning customers to vehicles and planning the order in which customers are visited by vehicles. In the latter step, we propose the sector combination optimization (SCO) algorithm to generate multiple initial solutions, and then these initial solutions are improved using the merge-head and drop-tail (MHDT) strategy. After a certain number of iterations, the optimal solution in the last generation is reported. Computational experiments on benchmark instances showed that the initial solutions obtained by the sector combination optimization algorithm were more abundant and better than other iterative algorithms using only one solution for initialization, and the solutions with distance gap were obtained using the merge-head and drop-tail strategy in a lower CPU time compared to the Tabu search algorithm.

An Arable Field for Benchmarking of Metaheuristic Algorithms for Capacitated Coverage Path Planning Problems

“…, N. A track can be referred to either by its number, i, or by its endpoints, [2i − 1, 2i]. For a field with 8 tracks, a solution could be noted as a sequence of all 16 track endpoints, e.g., [0,8,7,11,12,6,5,1,2,4,3,9,10,16,15,13,14,0], where 0 is a common starting and ending point of the route. In this solution, the first track to visit is number 4 in the direction from endpoint 8 to 7, then track 6 in the direction from endpoint 11 to 12, and so forth.…”

“…Although, computer scientists have paid considerable attention to the VRP, solving the problem by spending a lot of time by an exact algorithm [11]. As an alternative to searching the entire search space for the global optimum solution (also called the absolute solution), metaheuristic search algorithms have been developed with the ambition to find near-optimal solutions in a realistic time [12]. Over the years, a branch-and-bound algorithm and a branch-and-cut algorithm developed different solution approaches [13][14][15], heuristic algorithms (such as the Clarke-Wright savings algorithm [16]), and metaheuristic algorithms (such as simulated annealing [17,18], genetic algorithms [19], tabu search [20], and ant algorithms [21].…”

IoT-Based Framework for Smart Waste Monitoring and Control System: A Case Study for Smart Cities

“…The authors Padini et al [7] presented a hardware and software approach to waste management that allows the users to be part of the management process. The proposed system employs the use of IoT technology that constantly monitors the level of waste in garbage bins in real time.…”

Extended Maximal Covering Location and Vehicle Routing Problems in Designing Smartphone Waste Collection Channels: A Case Study of Yogyakarta Province, Indonesia

“…Several studies have used tabu searches to solve VRP [56], classical VRP, periodic VRP, multidepot VRP, site-dependent VRP [57], heterogeneous fleet VRP [58], VRP with discrete split deliveries and pickups [59], multicompartment VRP [60], heterogeneous multitype fleet VRP with time windows and an incompatible loading constraint [61], multidepot open VRP [62], VRP with cross docks and split deliveries [63], VRP with private fleet and common carrier [57], time-dependent VRP with time windows on a road network [64], consistent VRP [65], and heterogeneous VRP on a multigraph [66]. Shi et al [67] also used the heuristic solution method for the problem of multidepot vehicle routing-based waste collection and compared the results with the tabu search. Khan et al [68] presented a sustainable closed-loop supply chain framework that uses a metaheuristic approach, tabu search, and simulated annealing.…”