A novel multi-objective scenario-based optimization model for sustainable reverse logistics supply chain network redesign considering facility reconstruction

Gao, Xuehong; Cao, Cejun

doi:10.1016/j.jclepro.2020.122405

Cited by 69 publications

(21 citation statements)

References 63 publications

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“…Most of the optimization models used the e-constraint method to solve the sustainable supply chain issues because of the following advantages [64,81]: (i) it is simple and computationally faster, (ii) it helps produce a set of non-extreme Pareto solutions, (iii) it is not necessary to scale the objective functions to a common scale, and (iv) we can control the number of generated efficient solutions by properly adjusting the number of grid points in each one of the objective function ranges. The second most used solution method was the augmented e-constraint method.…”

Section: Resultsmentioning

confidence: 99%

“…In manufacturing issues, nine papers investigated economic and environmental aspects e.g., [46,47,59], and three papers investigated all three dimensions [58,60,61]. In reverse logistics models, seven papers considered all three dimensions [62][63][64][65][66][67][68] and three papers considered economic and environmental aspects [69][70][71]. Overall, the highest number of papers ( 52) have considered economic and environmental aspects of sustainability, whereas 40 papers have incorporated all three aspects of sustainability.…”

Section: Supply Chain Phases and Decision Levels From A Sustainable P...mentioning

confidence: 99%

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Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review

et al. 2021

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There are several methods available for modeling sustainable supply chain and logistics (SSCL) issues. Multi-objective optimization (MOO) has been a widely used method in SSCL modeling (SSCLM), nonetheless selecting a suitable optimization technique and solution method is still of interest as model performance is highly dependent on decision-making variables of the model development process. This study provides insights from the analysis of 95 scholarly articles to identify research gaps in the MOO for SSCLM and to assist decision-makers in selecting suitable MOO techniques and solution methods. The results of the analysis indicate that economic and environmental aspects of sustainability are the main context of SSCLM, where the social aspect is still limited. More SSCLMs for sourcing, distribution, and transportation phases of the supply chain are required. Additionally, more sophisticated techniques and solution methods, including hybrid metaheuristics approaches, are needed in SSCLM.

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Section: Resultsmentioning

confidence: 99%

Section: Supply Chain Phases and Decision Levels From A Sustainable P...mentioning

confidence: 99%

Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review

et al. 2021

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“…Three methods exist: the weighted sum method, artificial intelligence approaches such as genetic algorithms and neural networks, and multi-objective programming to deal with the multi-objective optimization problems. The weighted sum method, which transforms a multi-objective problem to a single-objective problem, was commonly applied to attain Pareto-efficient solutions [15,37]. Most of the existing studies in low-carbon logistics network design field concentrated on the trade-offs between the total costs and CO 2 emissions.…”

Section: Logistics Network Design Model With Multi-objective Optimizationmentioning

confidence: 99%

An Optimization Model of a Sustainable City Logistics Network Design Based on Goal Programming

Liang

Wang

et al. 2021

Sustainability

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This paper investigates the joint optimization problem on the logistics infrastructure investment and CO2 emission taxes for a sustainable city logistics network design by a goal programming approach where the cost recovery, service level and CO2 emission reduction goals are involved. The above multi-objective logistics infrastructure capacity investment and CO2 emission taxes problem is formulated as a bi-level goal programming model. Given the priority structure of the goals, the total deviations from predetermined goals are minimized in the upper level, while the lower level of the model serves as the service route choice equilibrium problem of logistics users. To solve the proposed model, a genetic algorithm is developed, where the method of successive average (MSA) is embedded. The case study focusing on the urban logistics network of Changsha, China demonstrates the effectiveness of the bi-level goal programming model and the genetic algorithm. The findings reveal that the priority rankings of the goals have a significant impact on the joint decisions of CO2 emission taxes and logistics infrastructure capacity investment. The proposed methodology provides an avenue to balance multiple conflicting objectives and obtain an economical and environmental city logistics network.

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“…The three objective functions were about maximizing profit, maximizing customer satisfaction and minimizing distance between collection centers and customers. Gao & Cao (2020) developed a scenario-based, multi-objective, mixed-integer, nonlinear model to redesign a sustainable SCN under uncertainty. In their proposed network, the facilities have hybrid processing capabilities.…”

Section: Literature On Sustainable Supply Chain Network Design (Sscndmentioning

confidence: 99%

Sustainable tire supply chain network design and operations planning using a multi objective , scenario-based optimization approach

Zenouz

Jamalnia

Farrokh

et al. 2021

Preprint

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Each year, millions of tires reach their end of life. Worn-out tires are either buried or burned, both of which harm the environment through polluting the air and groundwater. Companies need to consider their social responsibility, such as employment and regional development, and the environmental impact of their activities when making strategic and operational decisions. This study addresses the closed-loop supply chain network design (SCND) and operations planning problem with regard to the three dimensions of sustainability using a mathematical programming approach. The options of retreading, recycling, and energy recovery together with the use of green technologies are considered to minimize the environmental impacts. The proposed decision model can help supply chain managers in tire manufacturing industry make better-informed decisions in order to achieve the three-fold objectives of sustainability. The developed mathematical model turns out to be a multi-objective, multi-echelon, and multi-product mixed integer linear programming. The model is solved using the Lp-metric method and CPLEX solver. The scenario approach is used to address the uncertainty in demand of new products and the rate of return of worn-out tires. The model solutions are the optimal location of the facilities considering population density and unemployment rate in addition to economic dimension, the optimal amount of allocation, the flow of materials, and the best green technology selection. Sensitivity analysis is also conducted to validate the model and test the robustness of the obtained solutions. Finally, managerial implications are provided.

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A novel multi-objective scenario-based optimization model for sustainable reverse logistics supply chain network redesign considering facility reconstruction

Cited by 69 publications

References 63 publications

Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review

Multi-Objective Optimization for Sustainable Supply Chain and Logistics: A Review

An Optimization Model of a Sustainable City Logistics Network Design Based on Goal Programming

Sustainable tire supply chain network design and operations planning using a multi objective , scenario-based optimization approach

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