Waste Collection of Touristics Services Sector Residues Vehicle Routing Problem with Time Windows to an Industrial Polygon in a Smart City

2021 ◽  
pp. 117-130
Author(s):  
Diego Hurtado-Olivares ◽  
José Alberto Hernández-Aguilar ◽  
Alberto Ochoa-Zezzatti ◽  
José Crispín Zavala-Díaz ◽  
Guillermo Santamaría-Bonfil
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Smart City ◽  
Time Windows ◽  
Waste Collection ◽  
Routing Problem ◽  
Services Sector

scholarly journals Developing an applied algorithm for multi-trip vehicle routing problem with time windows in urban waste collection: A case study

2019 ◽  
Vol 37 (1_suppl) ◽  
pp. 4-13 ◽  
Author(s):  
Erfan Babaee Tirkolaee ◽  
Parvin Abbasian ◽  
Mehdi Soltani ◽  
Seyed Ali Ghaffarian
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Computational Time ◽  
Waste Collection ◽  
Urban Waste ◽  
Routing Problem ◽  
Great Performance ◽  
Hard Problems

This paper studies a multi-trip vehicle routing problem with time windows specifically related to urban waste collection. Urban waste collection is one of the municipal activities with large costs and has many practical difficulties. In other words, waste collection and disposal is a costly task due to high operating expenses (fuel, maintenance, recycling, manpower, etc.) and small improvements in this field can result in tremendous savings on municipal expenditure. In the raised problem, the goal is to minimize total cost including traversing cost, vehicle employment cost, and exit penalty from permissible time windows. In this problem, the waste is deposited at the points indicating the demand nodes, in which each demand shows the volume of generated waste. Considering multiple trips for vehicles and time windows are the most critical features of the problem, so that the priorities of serving some specific places such as hospitals can be observed. Since vehicle routing problems (VRP) belongs to NP-hard problems, an efficient simulated annealing (SA) is proposed to solve the problem. The computational results show that our proposed algorithm has a great performance in a short computational time in comparison with the CPLEX solver. Finally, in order to demonstrate the applicability of the model, a case study is analyzed in Iran, and the optimal policies are presented.

Waste collection vehicle routing problem with time windows

2006 ◽  
Vol 33 (12) ◽  
pp. 3624-3642 ◽  
Author(s):  
Byung-In Kim ◽  
Seongbae Kim ◽  
Surya Sahoo
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Waste Collection ◽  
Routing Problem

scholarly journals Solving practical waste collection with time windows in an urban area

2020 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Abdulwahab Almutairi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Optimal Solutions ◽  
Waste Collection ◽  
Routing Problem ◽  
Iterated Greedy

In logistics, several algorithms can be implemented in order to solve the problems of the vehicle routing with variants in order to find near-optimal solutions. Waste Collection can be considered as an essential logistic activity within any area or city. This kind of paper is aimed to implement Iterated greedy (IG) and Adaptive Large Neighborhood Search (ALNS) to solve waste collection vehicle routing problem with time windows on a real-case study. The idea is to generate an efficient way to collect waste problems in an area located in Riyadh, Saudi Arabia. Moreover, generating a route plays a significant role in terms of serving all customers’ demands who have own different time windows of receiving goods. Also, the performance of the proposed algorithms according to all instances is examined and minimizing the total costs and meeting all constraints that related to capacity, time windows, and others. To evaluate the execution of the presented algorithms, the computational results showed essential improvements, and also ALNS algorithm generates reasonable solutions in terms of total costs and a reasonable amount of time, when compared to other algorithms.  

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