Multiperiod and stochastic formulations for a closed loop supply chain with incentives

Reverse logistics network design problem we focus on is about locating distribution centers, inspection centers and remanufacturing facilities, and determining the acquisition price as well as the amount of returned goods to be collected depending on the unit cost savings and competitor’s acquisitio...

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Bibliographic Details
Published in:Journal of computer & systems sciences international 2014-03, Vol.53 (2), p.201-211
Main Authors: Litvinchev, I., Rios, Y. A., Özdemir, D., Hernández-Landa, L. G.
Format: Article
Language:English
Subjects:
Acquisitions
Analysis
Computer science
Control
Cost control
Design
Engineering
Factories
Inspection
Integer programming
Linear programming
Literature reviews
Logistics
Manufacturing
Marketing
Mathematical models
Mathematical programming
Mechatronics
Medical equipment
Networks
Remanufacturing
Reverse logistics
Robotics
Scheduling
Searching
Stochastic models
Stochasticity
Studies
Supply chains
Systems Analysis and Operations Research
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Summary:Reverse logistics network design problem we focus on is about locating distribution centers, inspection centers and remanufacturing facilities, and determining the acquisition price as well as the amount of returned goods to be collected depending on the unit cost savings and competitor’s acquisition price. We introduce the multiple periods setting and stochastic demand formulated by scenarios. We develop two mathematical programming models to determine the pricing strategy of the recovered products together with the optimal network that must be designed to be the most profitable closed cycle. Our methodology is based on a Golden Section Search with some flexibility that enables us to fix the used product acquisition price and then solve the model as an integer linear programming. Moreover, we establish dependent size fixed costs of opening a distribution, an inspection, and a remanufacturing centers, and show that they have a strong impact on the Golden Section search behavior.
ISSN:1064-2307
1555-6530
DOI:10.1134/S1064230714020129