A stochastic optimization model for integrated forward/reverse logistics network design

Pishvaee, Mir Saman; Jolai, Fariborz; Razmi, Jafar

doi:10.1016/j.jmsy.2010.05.001

Cited by 224 publications

(110 citation statements)

References 23 publications

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“…The Hybrid Production-Recovery (HPR) and Hybrid DistributionCollection (HDC) facilities are considered in the Integrated Forward-Reverse Logistics (IFRL) network, which has several advantages such as cost saving and pollution reduction [2,4,46]. The HPR facilities play the role of production centers in the forward ow and recovery centers in the reverse ow.…”

Section: Problem De Nition and Formulationmentioning

confidence: 99%

“…Constraints (2) and (3) ensure that each customer zone should be exactly assigned to one HDC facility in the forward and reverse ows, respectively. Constraint (4) shows that at least one reliable HDC center must be opened to enforce the sharing strategy in a disruption situation. Constraint (5) indicates that both reliable and unreliable HDC facilities cannot be opened simultaneously at a potential node j.…”

Section: Problem Formulationmentioning

confidence: 99%

“…It has been recognized that robust and reliable design of such networks helps rms to maintain and enhance their competitive advantages, and assists them to cope with the growing environmental turbulence. A part of literature refers to con guration of integrated forward-reverse logistics networks, includ-ing both forward and reverse ows, because of the existing legal requirements, environmental protection, as well as related economic bene ts [3,4]. The goal of a forward network is to provide a value for the end consumer in terms of product attributes like quality and cost, while the reverse network tries to recover the economic and environmental value from used products in a cost-e ective manner.…”

Section: Introductionmentioning

confidence: 99%

“…Application of stochastic programming for modeling uncertain supply chain network design can be seen in [4,[10][11][12][13][14][15]. However, in many real applications of stochastic programming, there is no enough historical data to estimate the probability distribution of uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Forward-reverse Logistics Network Design under Uncertainty and Reliability Consideration

et al. 2016

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Abstract. This paper proposes a robust optimization model for robust and reliable design of an integrated forward-reverse logistics network with hybrid facilities under uncertainty and random facility disruptions. The proposed model utilizes several e ective reliability strategies to mitigate the impact of random facility disruptions. First, the proposed model allows two types of hybrid facilities, namely, reliable and unreliable, to be located in the concerned logistics network where unreliable ones may be partially or fully disrupted, and thus a percentage of their capacities may be lost. However, they can still serve their customers with the remaining of their available capacities. Furthermore, a sharing strategy is taken into account, in which goods can be shipped from reliable hybrid facilities to unreliable ones to compensate their lost capacity. A robust optimization approach is applied on the developed model to handle the uncertainties in the parameters of the concerned network. Finally, several numerical experiments along with a sensitivity analysis are conducted to illustrate the signi cance and applicability of the proposed model as well as the e ectiveness of the robust optimization approach in this context.

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Section: Problem De Nition and Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated Forward-reverse Logistics Network Design under Uncertainty and Reliability Consideration

et al. 2016

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“…The scenario-based approach is used in this paper to model the problem under uncertainty. The compact form of the stochastic model can be stated as follows [1,18]:…”

Section: The Mathematical Formulationmentioning

confidence: 99%

Development and implementation of a green logistics-oriented framework for batch process industries: two case studies

El-Berishy

Scholz‐Reiter

2016

Logist. Res.

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In recent years, green logistics has received increasing interest due to the rise in greenhouse gases emission from transport operations. It is a promising approach to managing supply chain decisions to reduce environmental damages. Many models have been developed in the literature to study the environmental aspects of routing problems. However, these models have not been implemented in practice, particularly in batch processing industries. In this paper, we developed a two-stage stochastic model which integrates the tactical and operational decision levels to reduce the economic and environmental impacts of transport activities. This model generates optimum vehicle routes and their delivery velocities with the objective of minimizing the total distribution cost and the related emissions. We implemented the proposed model in two case studies drawn from the fast-moving consumer goods industry. This resulted in a cost reduction of up to 13 % of the total related economic and environmental costs compared to the actual situation at the test sites. These results also show the potential of green logistics-based models to improve the current modelling capabilities for batch distribution planning.

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Risk Based Optimization of Electronics Manufacturing Supply Chains

Nezamoddini

Aqlan

Gholami

2019

Optimization in Large Scale Problems

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A stochastic optimization model for integrated forward/reverse logistics network design

Cited by 224 publications

References 23 publications

Integrated Forward-reverse Logistics Network Design under Uncertainty and Reliability Consideration

Integrated Forward-reverse Logistics Network Design under Uncertainty and Reliability Consideration

Development and implementation of a green logistics-oriented framework for batch process industries: two case studies

Risk Based Optimization of Electronics Manufacturing Supply Chains

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