A REVIEW OF DISRUPTION MANAGEMENT IN VEHICLE ROUTING PROBLEM (VRP), TRANSPORT DESIGN AND SCHEDULING

In the case of an unexpected occurrence, disruption management is a method of rescheduling activities and it has been used in a variety of fields, including organized carrier scheduling and project management. The purpose of this review is to examine Ant Colony Optimization (ACO), the problems of Heuristics for Delivery Waste Collection (VRP), ARC Routing, Node Routing, and Container/skip. Other issues and problems examined in the paper were Non-Skip, Algorithms for the VRP, Improvement Algorithms, Simulated Annealing, and ACO for Capacitated Vehicle Routing, Clustering Analysis, and Probabilistic-D Cluster Analysis. It covers the fundamental characteristics of disruption management as well as the related goals and kinds of disruption that may occur in this setting. The various formulations and solution techniques are discussed in facets. A collection of relevant articles has been summarized and categorized according to the kind of disruption problem being addressed, the relevant goals, and the solution method used to resolve the problem. Vehicles must be emptied at a trash disposal facility before they may be used to collect garbage from further clients. The growing amount of solid waste generated as a result of p

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Waste Collection Vehicle Routing Problem: Literature Review

PROMET - Traffic&Transportation ◽

10.7307/ptt.v27i4.1616 ◽

2015 ◽

Vol 27 (4) ◽

pp. 345-358 ◽

Cited By ~ 22

Author(s):

Hui Han ◽

Eva Ponce Cueto

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Research Literature ◽

Arc Routing ◽

Waste Collection ◽

Logistics System ◽

Routing Problems ◽

Routing Problem ◽

Node Routing

Waste generation is an issue which has caused wide public concern in modern societies, not only for the quantitative rise of the amount of waste generated, but also for the increasing complexity of some products and components. Waste collection is a highly relevant activity in the reverse logistics system and how to collect waste in an efficient way is an area that needs to be improved. This paper analyzes the major contribution about Waste Collection Vehicle Routing Problem (WCVRP) in literature. Based on a classification of waste collection (residential, commercial and industrial), firstly the key findings for these three types of waste collection are presented. Therefore, according to the model (Node Routing Problems and Arc Routing problems) used to represent WCVRP, different methods and techniques are analyzed in this paper to solve WCVRP. This paper attempts to serve as a roadmap of research literature produced in the field of WCVRP.

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

Applied Sciences ◽

10.3390/app10072403 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2403

Author(s):

Yanjun Shi ◽

Lingling Lv ◽

Fanyi Hu ◽

Qiaomei Han

Keyword(s):

Vehicle Routing ◽

Waste Disposal ◽

Vehicle Routing Problem ◽

Solution Method ◽

Search Algorithm ◽

Optimal Solution ◽

Waste Collection ◽

Heuristic Solution ◽

Routing Problem ◽

Combination Optimization

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.

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