A hybrid meta-heuristic approach for buffer allocation in remanufacturing environment

Remanufacturing system is complicated due to its stochastic nature. Random customer demand, return product rate and system unreliability contribute to this complexity. Remanufacturing systems with unreliable machines usually contain intermediate buffers which are used to decouple the machines, thereby, reducing mutual interference due to machine breakdowns. Intermediate buffers should be optimized to eliminate waste of resources and avoid loss of throughput. The Buffer Allocation Problem (BAP) deals with allocating optimally fixed amount of available buffers to workstations located in manufacturing or remanufacturing systems to achieve specific objectives. Optimal buffer allocation in manufacturing and remanufacturing systems not only minimizes holding cost and stock space, but also makes facilities planning and remanufacturing decisions to be effectively coordinated. BAP in a non-deterministic environment is certainly one of the most difficult optimization problems. Therefore, a mathematical framework is provided to model the dependence of throughput on buffer capacities. Obviously, based on the survey undertaken, not only there exists no algebraic relation between the objective function and buffer size but the current literature does not offer analytical results for buffer capacity design in remanufacturing environment. Decomposition principle, expansion method for evaluating system performance and an efficient hybrid Meta-heuristic search algorithm are implemented to find an optimal buffer allocation for remanufacturing system. The proposed hybrid Simulated Annealing (SA) with Genetic Algorithm (GA) is compared to pure SA and GA. The computational experiments show better quality, more accurate, efficient and reliable solutions obtained by the proposed hybrid algorithm. The improvement obtained is more than 4.18 %. Finally, the proposed method is applied on toner cartridge remanufacturing company as a case study, and the numerical results from hybrid algorithm are presented and compared with results from SA and GA

Design of Extended Warranties in Supply Chains under Additive Demand

This is the author's accepted manuscript. The original publication is available at http://dx.doi.org/10.1111/j.1937-5956.2011.01300.x.We study the design of extended warranties in a supply chain consisting of a manufacturer and an independent retailer. The manufacturer produces a single product and sells it exclusively through the retailer. The extended warranty can be offered either by the manufacturer or by the retailer. The party offering the extended warranty decides on the terms of the policy in its best interest and incurs the repair costs of product failures. We use game theoretic models to answer the following questions. Which scenario leads to a higher supply-chain profit, the retailer offering the extended warranty or the manufacturer? How do the optimum price and extended warranty length vary under different scenarios? We find that, depending on the parameters, either party may provide better extended warranty policies and generate more system profit. We also compare these two decentralized models with a centralized system where a single party manufactures the product, sells it to the consumer and offers the extended warranty. We also consider an extension of our basic model where either the manufacturer or the retailer resells the extended warranty policies of a third party (an independent insurance company, for example), instead of offering its own policy

Reverse supply chain forecasting and decision modeling for improved inventory management

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Global Operations Program at MIT, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 69-71).This thesis details research performed during a six-month engagement with Verizon Wireless (VzW) in the latter half of 2012. The key outcomes are a forecasting model and decision-support framework to improve management of VzW's reverse supply chain inventory. The forecasting model relies on a reliability engineering formulation and incorporates a learning component to allow incremental forecast improvement throughout the device lifecycle. The decision-support model relies on Monte Carlo simulations to quantify the uncertainty and risk associated with different inventory management policies. These tools provide VzW stakeholders with a full-lifecycle perspective so that inventory planners can avoid costly end-of-life underages and overages. Prior to this effort, inventory planners at VzW relied on a three month returns forecast despite the fact that customers can return devices more than three years after launch. The decision-support model replaces existing heuristics to improve inventory management. Model efficacy is demonstrated through case studies. For a variety of representative SKUs, the returns forecast model is found to predict cumulative lifecycle returns within 10% using data available six months from launch. Had inventory been managed according to the policies recommended by the decision support model instead of policies from existing heuristics, VzW could have avoided an end-of-life stockout of more than 20,000 devices for a particular SKU.by Brian J. Petersen.M.B.A.S.M

Impact of product design choices on supply chain performance in the notebook computer industry

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-91).Intel Corporation is the world's leading manufacturer of processors for personal computers. As the company strives to maintain its leadership position in this industry, it identifies significant trends in the industry and attempts to develop product solutions that intercept these trends. One such set of industry vectors is the continued movement toward lower cost and smaller notebook system designs with a coincident shift toward fully outsourced production in China. These trends point to increased future demand for processors utilizing a ball-grid-array (BGA) package in notebook computers, which is the lower cost, smaller size packaging technology available today. This project was initiated to understand why with such a seemingly favorable environment for BGA, it still represents a small minority of Intel's mobile processor volume. The analysis shows that significant changes must be made to Intel's product roadmap, OEM product scalability strategies, or after-sale service models to enable a full transition to BGA processors. SKU levels increase by lOx with a BGA transition resulting in much higher supply chain complexity, management cost, and inventory cost. In addition, simple modeling approaches are developed and utilized for this study that can be leveraged in the future to quantify possible product strategy impacts on the industry supply chain. They can also be used in other industries contemplating supply chain simplification strategies.by Chad Sailer.S.M.M.B.A