Although there are many quantitative models in the literature to design a reverse supply chain, every model assumes that all the recovery facilities that are engaged in the supply chain have enough potential to efficiently re-process the incoming used products. Motivated by the risk of re-processing used products in facilities of insufficient potentiality, this paper proposes a method to identify potential facilities in a set of candidate recovery facilities operating in a region where a reverse supply chain is to be established. In this paper, the problem is solved using a newly developed method called physical programming. The most significant advantage of using physical programming is that it allows a decision maker to express his preferences for values of criteria (for comparing the alternatives), not in the traditional form of weights but in terms of ranges of different degrees of desirability, such as ideal range, desirable range, highly desirable range, undesirable range, and unacceptable range. A numerical example is considered to illustrate the proposed method.


Potential Recovery Facilities, Physical Programming, Reverse Logistics, Uncertainty, Decision-Making

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Reverse supply chain network



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Engineering Commons