Vehicle Routing Problem in Reverse Logistics with Split Demands of Customers and Fuel Consumption Optimization

Eydi, Alireza; Alavi, Hadi

doi:10.1007/s13369-018-3311-2

Cited by 14 publications

(4 citation statements)

References 29 publications

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“…They solved the proposed mathematical problem using the classical ε-constraint method. Eydi, Alavi (2019) investigated VRP in reverse logistics with the aim of fuel consumption optimization. They assumed that the fuel cost of vehicles was dependent on the traveled route and load, and customers could have split delivery.…”

Section: Related Workmentioning

confidence: 99%

A novel two-echelon hierarchical location-allocation-routing optimization for green energy-efficient logistics systems

2021

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The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids.

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Section: Related Workmentioning

confidence: 99%

A novel two-echelon hierarchical location-allocation-routing optimization for green energy-efficient logistics systems

2021

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“…Other than single-objective, researchers that try to develop GVRP concepts also use multi-objective. The example of this multi-objective are minimizing fuel consumption and optimizing customer satisfaction [63], minimizing fuel consumption and carbon emission [64,65,66], minimizing total cost and carbon emission [67,68,69,70,71], minimizing total travel distance and carbon emission [72,74,78,80] or fuel consumption [73,79,81], minimizing total travel distance and energy consumed [75,76,82], minimizing cost and air pollution [87], and improving energy efficiency and customer satisfaction [77].…”

Section: Gvrp With Multi-objectivementioning

confidence: 99%

Heterogeneous Fleet Green Vehicle Routing Problem: A Literature Review

Normasari

Lathifah

2021

Angkasa

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Transportation, as a part of the supply chain process, contributes to carbon emission which leads to climate change and global warming. This environmental issue gives an impact to decisions regarding the supply chain of a company. One way to deal with this issue is by analyzing their vehicle routing problem. In this study, the issue about routing problems in green supply chain by considering the heterogeneous fleet is being discussed. One variant of Green Vehicle Routing Problem (GVRP) reviewed in this paper is about Heterogeneous Alternative Fuel Vehicles for Green Vehicle Routing Problem (HAFVGVRP). The purpose of this study is to review the development of GVRP with heterogeneous alternative fuel vehicles and the gap or state-of-the-art on existing researches. The review was classified according to the objectives, type of fleet, and solution used. Moreover, this study also presents the trend and direction of further research.

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“…Consequently, experts develop heuristic and metaheuristic algorithms to solve this problem. Several metaheuristics algorithms have been studied in GVRP to minimize fuel consumption, such as Simulated Annealing (SA) (Eydi & Alavi, 2018;Felipe et al, 2014;Karagul et al, 2019;Koç & Karaoglan, 2016;Kuo, 2010;Normasari et al, 2019;Yasin & Vincent, 2013), Ant Colony Optimization (ACO) (Jabir et al, 2017;Zhang et al, 2019), and Revised Hybrid Intelligent Algorithm (Wang et al, 2018). Particle Swarm Optimization (PSO) (Norouzi et al, 2017;Poonthalir & Nadarajan, 2018), Tabu Search (TS) (Kuo & Wang, 2011), and Genetic Algorithms (GA) (Franco et al, 2017) were also proposed to solve it.…”

Section: Introductionmentioning

confidence: 99%