This paper presents a hybrid cell evaluated genetic algorithm (CEGA) for optimization of the dedicated remanufacturing system with simulation. The paper first summarizes the special characteristics and problems of the dedicated remanufacturing. The paper then proposes a simulation model with a prioritized stochastic batch arrival mechanism, considering factors that affect the total profit. Based on the simulation model, the CEGA algorithm is developed to optimize the production planning and control policies for dedicated remanufacturing. A case study is provided based on the remanufacturing facility located at Austin, USA
Product remanufacturing develops rapidly in recent decades due to intensified environmental legislations and economic concerns. Through remanufacturing, products/components that would otherwise head to land-fill or incineration will instead go through a set of value and material recapturing processes, including distribution, inspection, disassembly, repair, redistribution, and remarketing or recycling. Remanufacturing allows for reusable components and recoverable materials reenter the supply chain for future reuse or new product fabrication (Zhang et al., 2004).
Remanufacturing is generally conducted under two different business strategies: the combined model and the dedicated model. Most remanufacturing operations in European countries employ the combined model. Under this strategy, remanufacturing is done by the original manufacturer combined with its forward production. Remanufacturing in the North America usually adopts a dedicated model, in which remanufacturing is outsourced to dedicated third-party remanufacturers (Patel, 2006). There are a growing number of examples of the dedicated model in the electronics industry. Original equipment manufacturers, such as Dell, Hewlett-Packard, and IBM, retail stores such as CompUSA and Best Buy have outsourced their remanufacturing operations to third-party providers including Noranda Recycling, Image Microsystems, Genco, and among others. The driving force behind this trend lies in the dramatically increased volume, complicated return patterns, and the increasingly complicated material contents of the consumer electronic products. OEMs are no longer capable of processing the huge volume of the returned post consumer products effectively and efficiently with their own facilities. The advantages associated with the dedicated outsourcing model also include: third parties are dedicated and, therefore, are more resourceful and efficient in collection and recovery of returned products; third party providers have more expertise in product recovery processes that result in waste minimization and full potential recovery of returned products; because third party providers are outside companies, there is no interference with the original manufacturer's production line, thus simplifying the operations.
Despite the increased application of the dedicated model in remanufacturing, hardly any theoretical and applied research efforts are directed to the problems associated. Most current research efforts are focused on the combined model (Thierry et al., 1995; Connelly and Koshland, 1997; Ayres et al., 1997; Ferrer and Guide, 2002; White et al., 2003). As for the dedicated model, such research results cannot be applied directly. Intended to fill the gap, this paper presents a study conducted on the dedicated model with batch and prioritized product return. Based on a remanufacturing facility located in Austin, TX, this paper first provides an in-depth analysis of the dedicated model regarding its material flow, production process, and the problems associated. Targeting on these problems, the paper presents a general simulation model created in this research to investigate the impact of production planning and control policy to the performance of the dedicated remanufacturing of electronic products. A factorial design based cell evaluated genetic algorithm (CEGA) is also developed and applied to optimize the remanufacturing system based on the simulation model developed.
The rest of the paper is structured as follows: following this introduction, the characteristics and problems of a dedicated remanufacturing system and related research work are illustrated in Section 2; this is followed by Section 3 which provides a discussion of the general simulation model for dedicated remanufacturing; the hybrid CEGA simulation optimization approach is also included in the section; Section 4 provides a detailed case study of the simulation model and the CEGA simulation optimization approach; Section 5 concludes the paper and provides suggestions for future research in remanufacturing.
Variation and uncertainty of the product return flow make the dedicated remanufacturing unstable and hard to control, leading to poor economic performance with increased production cost. Although dedicated remanufacturing is found more applications in the US, research oriented to this area is still in nascent stage.
This paper summarizes the characteristics and problems of a dedicated remanufacturing model based on the site study conducted at our industrial partner's plant. Due to the stochastic nature of the dedicated model (i.e., random batch compound arrival with priorities) and other difficulties involved in constructing a mathematical model, a simulation model is developed for the dedicated remanufacturing. The CEGA optimization approach based on the simulation model is also developed to obtain the optimal production policy, inventory control policy and resource allocation for remanufacturing. Through simulation analysis, the major factors that affect final cost and profit are identified. Through combining GA and FFD, the CEGA approach has proved to be an effective way for searching the global optimum considering local optima.
It shall be noted that this paper is not aimed to solve all problems involved in dedicated remanufacturing system. For example, inventory control policy for the finished product is not included in this study. For the sake of simplicity, the priorities of different returned products are fixed and predetermined according to the product type. However, product types together with their conditions, quantities, sources and destinations of return, should be considered in determination of the optimal priority, thus a more realistic and complex priority scheduling strategy is necessary for future research. The priority mechanism that assigns dynamic priority to the return product could be of special interest to dedicated remanufacturing.
