A stochastic bi-objective location model for strategic reverse logistics

Fonseca, Maria da Conceição; García-Sánchez, Álvaro; Ortega-Mier, Míguel; Saldanha-da-Gama, Francisco

doi:10.1007/s11750-009-0107-2

Cited by 83 publications

(40 citation statements)

References 25 publications

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“…Mutha & Pokharel, 2009) and multi-objective (e.g. Fonseca et al, 2010). Jayaraman et al (2003) developed their previous work to model the single product twolevel hierarchical location problem considering the reverse logistics operations of hazardous products.…”

Section: Reverse Supply Chain Network Designmentioning

confidence: 99%

A new fuzzy mathematical model for green supply chain network design

Amalnick¹,

Saffar

2017

10.5267/j.ijiec

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The environmental changes caused by industrial activities have spurred a significant interest in designing supply chain networks by considering environmental issues such as CO2 emission. The pivotal role of taking uncertainty and risk into account in closed-loop supply chain networks has induced numerous researchers and practitioners to develop appropriate decision making tools to cope with these issues in such networks. To design a supply chain regarding environmental impacts under uncertainty of the input data and to cope with the operational risks, this paper proposes a multi objective possibilistic optimization model. The proposed model minimizes traditional costs such as cost of products shipment, purchasing machines and so on, as well as minimizing the environmental impact, and as a results strikes a balance between the two objective functions. Furthermore, in order to solve the proposed multi objective fuzzy mathematical programming model, an interactive fuzzy solution approach is applied. Numerical experiments are used to prove the applicability and feasibility of the developed possibilistic programming model and the usefulness of the applied hybrid solution approach.

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Section: Reverse Supply Chain Network Designmentioning

confidence: 99%

A new fuzzy mathematical model for green supply chain network design

Amalnick¹,

Saffar

2017

10.5267/j.ijiec

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“…Most articles in stochastic IFRLN literature are single-period (e.g., [53][54][55][56][57][58]). Lee et al [59] introduced a multi-period, multi-product dynamic location and allocation model, under demand uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty

Vahdat

Vahdatzad

2017

Logistics

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Abstract:In this paper, a two-stage stochastic programming modelling is proposed, to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. The problem involved both strategic and tactical decision levels. The first stage dealt with strategic decisions, which are the number, capacity, and location of forward and reverse facilities. In the second stage, tactical decisions, such as base stock level as an inventory policy, were determined. The generic introduced model consisted of suppliers, manufactures, and distribution centers in forward logistic and collection centers, remanufactures, redistribution, and disposal centers in reverse logistic. The strength of the proposed model is its applicability to various industries. The problem was formulated as a mixed-integer linear programming model and was solved by using Benders' Decomposition (BD) approach. In order to accelerate the Benders' decomposition, a number of valid inequalities were added to the master problem. The proposed accelerated BD was evaluated through small-, medium-, and large-sized test problems. Numerical results confirmed that the proposed solution algorithm improved the convergence of BD lower bound and the upper bound, enabling to reach an acceptable optimality gap in a convenient time.

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“…16 for further details). We can refer, among others, to Aghezzaf (2005), Listeş and Dekker (2005), Mo and Harrison (2005), Romauch and Hartl (2005), Pan and Nagi (2010), Fonseca et al (2010), andNickel et al (2012).…”

Section: Stochastic Facility Location Problemsmentioning

confidence: 99%

Facility Location Under Uncertainty

Correia

Saldanha-da-Gama²

2015

Location Science

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In this chapter, we cover some essential knowledge on facility location under uncertainty. We put a major emphasis on modeling aspects related with discrete facility location problems. Different modeling frameworks are discussed. In particular, we distinguish between robust optimization, stochastic programming and chance-constrained models. We also discuss relevant aspects such as solution techniques, multi-stage stochastic programming models, scenario generation, and extensions of basic problems.

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A stochastic bi-objective location model for strategic reverse logistics

Abstract: Reverse logistics, Stochastic programming, Multicriteria optimization, 90B06, 90B80, 90B90, 90C06, 90C15, 90C29,

Cited by 83 publications

References 25 publications

A new fuzzy mathematical model for green supply chain network design

A new fuzzy mathematical model for green supply chain network design

Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty

Facility Location Under Uncertainty

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