Developing a Chance-Constrained Free Disposable Hull Model for Selecting Third-Party Reverse Logistics Providers

Developing a Chance-Constrained Free Disposable Hull Model for Selecting Third-Party Reverse Logistics Providers

Majid Azadi (Department of Industrial Management, Faculty of Management and Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran) and Reza Farzipoor Saen (Department of Industrial Management, Faculty of Management and Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran)
DOI: 10.4018/ijoris.2013100106


Demand of third-party reverse logistics (3PL) provider becomes an increasingly significant topic for corporations looking for enhanced customer service and cost reduction. To select the best 3PL providers in the presence of stochastic data, this paper proposes an innovative approach which is based on free disposable hull (FDH). FDH model is one of the classical models in data envelopment analysis (DEA). In many real world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance-constrained DEA. In this paper, a chance-constrained FDH (CCFDH) model is developed and also its deterministic equivalent which is a nonlinear program is derived. Furthermore, it is shown that the deterministic equivalent of the CCFDH model can be converted into a quadratic program. In addition, sensitivity analysis of the CCFDH model is discussed with respect to changes on parameters. Finally, a numerical example demonstrates the application of the proposed model in the field of 3PL provider selection.
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A third-party reverse logistics (3PL) provider is a company that provides a one stop shop service to its patrons of outsourced logistics services for part, or all of their supply chain management functions. 3PL providers characteristically specialize in integrated operation, warehousing and transportation services that can be scaled and customized to patron’s wants based on market circumstances and the demands and delivery service requirements for their products and supplies.

As Farzipoor Saen (2009) addresses, reusing products and materials is not a new phenomenon. Metal scrap brokers, waste paper recycling and deposit systems for softdrink bottles are all examples that have been around for a long time. In these cases, the recovery of used products is economically more attractive than their disposable. In recent years, the growth of environmental concerns has given ‘reuse’ increasing attention. Waste reduction efforts have promoted the idea of material cycles instead of a ‘one-way’ economy. Reuse opportunities give rise to a new material flow from the user back to the sphere of producers. The management of this material flow opposite the conventional supply chain flow is the concern of the recently emerged field of ‘reverse logistics’.

Outsourcing logistics functions to 3PL providers has been a resource of competitive benefit for most corporations. Most companies quote greater flexibility, operational efficiency, enhanced patron service levels, and a better focus on their core commerce as part of the advantages of appealing the services of 3PL providers. The main profits of logistics alliances are to allow the outsourcing company to concentrate on the core competence, escalate the efficiency, enhance the service, lessen the transportation cost, restructure the supply chains, and create the market place legality. Hence, a proper 3PL provider which meets diverse demands is critical for the augmentation and competence of an enterprise. Numerous manufacturers have understood that their core competences are not in the logistics area and have, hence, gradually sought to purchase logistics services and functions from 3PL providers. The significance of an alliance among enterprises and 3PL depends on the following factors: (1) utilizing the resources and capability of 3PL to acquire the scale benefits of logistics operation by reducing the enterprises’ own logistics cost and transaction charge; (2) making use of 3PL’s professional capability and agility to improve the overall operating efficiency and level of customer service in the supply chain; (3) reducing or avoiding the investment of enterprises’ logistics establishment to give more resources to improving the enterprises’ core competencies; (4) developing a credit base through the supplier alliance to cultivate a symbiotic relationship by increasing the overall competition advantage of each firm (Yan et al., 2003). Therefore, the 3PL assessment and subsequent selection of a firm’s supply chain management is a problem of choosing its alliances and consequently has a significant strategic upshot in terms of the firm achieving superior competitive advantage.

Some mathematical programming approaches have been used for 3PL provider selection in the past. Table 1 categorizes the reviewed papers based on applied techniques. Nevertheless, because of the intricacy of the decision making process involved in 3PL provider, all the aforementioned references in Table 1, except for data envelopment analysis (DEA) models, rely on some form of procedures that assigns weights to various performance measures. The primary problem associated with arbitrary weights is that they are subjective, and it is often a complex task for the decision maker to precisely assign numbers to preferences. It is a daunting task for the decision maker to assess weighting information as the number of performance criteria is increased. In the meantime, they do not consider stochastic data1.

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