A multi-objective optimization for green supply chain network design

Wang, Fan; Lai, Xiaofan; Shi, Ning

doi:10.1016/j.dss.2010.11.020

Cited by 559 publications

(271 citation statements)

References 33 publications

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“…Various mathematical modeling approaches such as Mixed Integer Linear Programming (MILP) [3]- [5], Mixed Integer Non-Linear Programming (MINLP) [6], Continuous Approximation (CA) [7], System Dynamic Modeling (SDM) [8]- [10] and Fuzzy Goal Programming (FGP) [11] have been used by researchers to design mathematical modeling GrSC problems. Recently, with regards to literatures in most of cases considering environmental constraints, parameters and variables along with economic issues to supply chain leads to complex models.…”

Section: Introductionmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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Abstract-In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.Index Terms-Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.

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Section: Introductionmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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“…Green initiatives associated with distribution in supply chains have in recent years been presented, particularly in green reverse logistics (Fleischmann et al 2001;Zhu et al 2008;Neto et al 2009). Trade-offs between cost factors and environmental impact of a supply chain is reported in Wang et al (2011). A green-vehicle routing problem is reported by Erdoğan and Miller-Hooks (2012), which uses a mixed integer linear programming approach.…”

Section: Distribution Systems and The Food Supply Chainmentioning

confidence: 99%

A case analysis of a sustainable food supply chain distribution system—A multi-objective approach

Validi

Bhattacharya

Byrne

2014

International Journal of Production Economics

240

111

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Abstract:Sustainable supply chain management is a topical area which is continuing to grow and evolve. Within supply chains, downstream distribution from producers to customers plays a significant role in the environmental performance of production supply chains. With consumer consciousness growing in the area of sustainable food supply, food distribution needs to embrace and adapt to improve its environmental performance, while still remaining economically competitive. With a particular focus on the dairy industry, a robust solution approach is presented for the design of a capacitated distribution network for a two-layer supply chain involved in the distribution of milk in Ireland. In particular the green multiobjective optimisation model minimises CO2 emissions from transportation and total costs in the distribution chain. These distribution channels are analysed to ensure the non-dominated solutions are distributed along the Pareto fronts. A multi-attribute decision-making approach, TOPSIS, has been used to rank the realistic feasible transportation routes resulting from the trade-offs between total costs and CO 2 emissions. The refined realistic solution space allows the decision-makers to geographically locate the sustainable transportation routes. In addition to geographical mapping the decision maker is also presented with a number of alternative analysed scenarios which forcibly open closed distribution routes to build resiliency into the solution approach. In terms of model performance, three separate GA based optimisers have been evaluated and reported upon. In the case presented NSGA-II was found to outperform its counterparts of MOGA-II and HYBRID.

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“…Many researchers have recently reported the results of their multi-objective optimization studies. For example, a multi-objective programming model is proposed in [2], [11] to analyze solid waste management. The model for simultaneously optimizing the operations of both integrated logistics and its corresponding used-product reverse logistics in a close-looped supply chain has been presented in [12].…”

Section: Literature Review Of Supply Chains Models and Multi-objementioning

confidence: 99%

A Hybrid Multi-Objective Programming Framework for Modeling and Optimization of Supply Chain Problems

Sitek¹

2015

Annals of Computer Science and Information Systems

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Abstract-This paper presents a hybrid programming framework for solving multi-objective optimization problems in supply chain. The proposed approach consists of the integration and hybridization of two modeling and solving environments, i.e., constraint logic programming and mathematical programming, to obtain a programming framework that offers significant advantages over the classical approach derived from operational research. The strongest points of both components are combined in the hybrid framework, which by introducing transformation allows a significant reduction in size of a problem and the optimal solution is found a lot faster. This is particularly important in the multi-objective optimization where problems have to be solved over and over again to find a set of Pareto-optimal solutions. An over two thousand-fold reduction in size was obtained for the illustrative examples together with a few hundred-fold reduction in the speed of finding the solution in relation to the mathematical programming method. In addition, the proposed framework allows the introduction of logical constraints that are difficult or impossible to model in operational research environments.

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A multi-objective optimization for green supply chain network design

Cited by 559 publications

References 33 publications

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

A case analysis of a sustainable food supply chain distribution system—A multi-objective approach

A Hybrid Multi-Objective Programming Framework for Modeling and Optimization of Supply Chain Problems

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