Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration

Kaoud, Essam; Abdel-Aal, Mohammad A. M.; Sakaguchi, Tatsuhiko; Uchiyama, Naoki

doi:10.3390/su141610281

Cited by 5 publications

(4 citation statements)

References 59 publications

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“…Kaoud et al [ 59 ] consider a robust multi-objective model for a green closed-loop supply chain network considering maximizing the profit and minimizing the carbon emissions. This proposed model considers presorting, heterogeneous fleet, and uncertainty on costs and selling prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust design of a logistics system using FePIA procedure and analysis of trade-offs between CO2 emissions and net present value

Roa

Escobar

Montoya

2023

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Robust design of a logistics system using FePIA procedure and analysis of trade-offs between CO2 emissions and net present value

Roa

Escobar

Montoya

2023

Heliyon

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“…It has been lately applied to various applications, despite it being one of the most effective methods in solving multi-objective optimization problems [32]. For instance, Samanlioglu [12] applied the method in solving industrial hazardous waste location-routing problem; Lui and Tsai [33] in parallel machine scheduling; Zhao and Zhu [22] in multi-depot explosive waste recycling; Khalilpourazari and Khalilpourazary [34] in optimizing a surface grinding process; Ito et al [35] in reducing backup capacity due to random failure for a cloud service provider; Aydin et al [36] in determining car sharing points for sustainable transportation; and Kaoud et al [37] in maximizing the total costs and minimizing the carbon dioxide emissions of a transportation system. The method determines non-dominated efficient points on the Pareto optimal frontier by reformulating the initial optimization problem and solving with several weight vectors [32].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization Model for Multi-Facility Decisions of Infectious Waste Transshipment and Disposal

Kailomsom

Khompatraporn

2023

Sustainability

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Infectious waste disposal is a crucial concern in many areas. Not only is the waste obnoxious, but it can also pose a vital risk to human health. Disposal of infectious waste incurs higher costs than general waste disposal and must abide by stricter regulations. In this paper, the infectious waste disposal is formulated as a multi-objective optimization model. The objectives encompass economic, social, and environmental concerns. To save cost, waste transshipment facilities to function as consolidation points are proposed and integrated in the model. The economic objective includes construction and operational costs of the transshipment and disposal facilities. The social objective considers the communities surrounding the disposal facilities, while carbon dioxide emission is used as the measure in the environmental objective. The model is reformulated based on the lexicographic weighted Tchebycheff method to ensure that the Pareto frontier of the solutions is obtained. Then the model is applied to a health region in Thailand. Daily and every-other-day waste collection intervals are compared to examine additional benefits. Certain sensitivity of the solutions is also analyzed. After comparing several solutions, a compromise among all three objectives is suggested. It is composed of three transshipment and two disposal facilities, each with 1000 kg capacity. Moreover, if the solution is executed with the every-other-day waste collection interval, the overall costs can be saved. A sensitivity analysis of the solution on fuel price found that the solution was not very sensitive against an increase in the fuel price, in that when the fuel price increased by 20% the overall costs only increased by 7%. Lastly, when the daily infectious wastes are doubled, all the objective function values rise, ranging from 56% to 163%. The new solution suggests an increase in the number of the disposal facilities to four, but a decrease of the transshipment ones to only two.

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“…However, it assumes that the uncertain parameters fluctuate in an interval [26][27][28][29][30]. Since its emergence, robust optimization theories have been applied to many fields, such as group decision-making [31][32][33][34][35][36], portfolios [37][38][39][40][41], efficiency evaluation [42,43], supply chain management [44][45][46][47][48][49], etc. In emergency medical location decisions, some scholars have adopted the stochastic programming method for modeling [50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

The Robust Emergency Medical Facilities Location-Allocation Models under Uncertain Environment: A Hybrid Approach

Meng

Wang

et al. 2022

Sustainability

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In emergency medical facilities location, the hierarchical diagnosis and treatment system plays an obvious role in the rational allocation of medical resources and improving the use efficiency of medical resources. However, few studies have investigated the operational mechanism of hierarchical medical systems in uncertain environments. To address this research gap, this paper proposes a hybrid approach for emergency medical facilities’ location-allocation. In the first stage, in order to concentrate on the utilization of medical resources, we choose alternative facility points from the whole facilities through the entropy weight method (EWM). In the second stage, uncertainty sets are used to describe the uncertain number of patients at emergency medical points more accurately. We propose a robust model to configure large base hospitals based on the robust optimization method. Furthermore, the proposed robust models are applied to the emergency management of Huanggang City under COVID-19. The results show that the optimal emergency medical facility location-allocation scheme meets the actual treatment needs. Simultaneously, the disturbance ratio and uncertainty level have a significant impact on the configuration scheme.

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Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration

Cited by 5 publications

References 59 publications

Robust design of a logistics system using FePIA procedure and analysis of trade-offs between CO2 emissions and net present value

Robust design of a logistics system using FePIA procedure and analysis of trade-offs between CO2 emissions and net present value

A Multi-Objective Optimization Model for Multi-Facility Decisions of Infectious Waste Transshipment and Disposal

The Robust Emergency Medical Facilities Location-Allocation Models under Uncertain Environment: A Hybrid Approach

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