Solving the multi-trip vehicle routing problem with time windows in urban waste management using grey wolf optimization algorithm | ||
| مدل سازی در مهندسی | ||
| Article 7, Volume 17, Issue 57, July 2019, Pages 93-110 PDF (2.39 M) | ||
| Document Type: Industry Article | ||
| DOI: 10.22075/jme.2019.16445.1633 | ||
| Authors | ||
| Erfan Babaee Tirkolaee* 1; Iraj Mahdavi2; Mir Mehdi Seyyed Esfahani3 | ||
| 1Department of Industrial Engineering, Mazandaran University of Science & Technology, Babol, Iran | ||
| 2Department of Industrial Engineering, Mazandaran University of Science and Technology | ||
| 3Department of Industrial Engineering, Amirkabir University of Technology | ||
| Receive Date: 09 November 2018, Revise Date: 10 February 2019, Accept Date: 02 March 2019 | ||
| Abstract | ||
| One of the most important issues of concern to human societies in recent years is urban waste management that is one of the main requirements of each city, and without any notice of it, it can be problematic for it and even residents of the surrounding villages. Urban areas generate the highest amount of waste and consequently, they need an efficient system for collecting and disposing of waste where its determination and stabilization is very difficult and costly. In this regard, this paper examines the multi-trip vehicle routing problem with time windows specific to the urban waste collection, with the goal is to minimize the total cost including routing costs, the earliness and lateness penalty cost for violating the service time windows and the usage costs of vehicles. To solve the problem in practical dimensions, grey wolf optimization (GWO) algorithm is developed where its performance is tested compared to CPLEX solver of GAMS and simulated annealing (SA) algorithm. The obtained results demonstrate that the proposed GWO have an acceptable performance to generate high-quality solutions. Finally, to study the behavior of the objective function versus the real-world demand parameter changes, a sensitivity analysis is performed on this parameter and the optimal management policy is analyzed.ed. | ||
| Keywords | ||
| Multi-trip vehicle routing problem; Urban waste collection; Grey wolf optimization algorithm; Simulated annealing algorithm | ||
| References | ||
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