Integrating Linear Physical Programming and Fuzzy Programming for the Management of Third Party Reverse Logistics Providers

Shorter product lifecycles, more liberal return policies and the rise of internet marketing increased the amount of product returns in recent years. Companies must have a well-managed reverse logistics system to ensure the timely and cost-effective collection, processing and disposal of returned products. However, high fixed cost of reverse logistics infrastructure and high level of uncertainty associated with product returns force companies to outsource their reverse logistics operations to third party reverse logistics providers (3PRLPs). The success of outsourcing largely depends on the selection of suitable 3PRLP(s). Although there are many 3PRLP evaluation methodologies, the research on the determination of order quantities from 3PRLPs considering uncertainties associated with budget allocation and capacity is very limited. In addition, the previous studies do not allow decision makers to express their preferences for 3PRLP selection criteria in a physically meaningful way. This study fills these research gaps by proposing a novel 3PRLP evaluation methodology which integrates linear physical programming (LPP) and fuzzy programming (FP). First, an LPP model is constructed based on decision makers' preferences and alternative 3PRLPs are ranked according to their total LPP scores. Then, an FP model takes total LPP scores, budget allocation and capacity constraints as input and determines the number of returned products to be processed by each 3PRLP. A numerical example is also provided to illustrate the feasibility and practicality of the proposed method. The results from this example are analyzed by considering the effects of capacity and budget limitations on order quantities and several managerial insights are proposed.

Automated summary

This article addresses the decision problem of order quantities in reverse logistics outsourcing and proposes an evaluation method that combines linear physical programming and fuzzy programming. Alternative third-party reverse logistics providers are ranked based on decision makers' preferences and suitable order quantities are determined. The proposed method is validated through a numerical example and provides managerial insights.