A decomposition algorithm for robust lot sizing problem with remanufacturing option

In this paper, we propose a decomposition procedure for constructing robust optimal production plans for reverse inventory systems. Our method is motivated by the need of overcoming the excessive computational time requirements, as well as the inaccuracies caused by imprecise representations of problem parameters. The method is based on a min-max formulation that avoids the excessive conservatism of the dualization technique employed by Wei et al. (2011). We perform a computational study using our decomposition framework on several classes of computer generated test instances and we report our experience. Bienstock and Özbay (2008) computed optimal base stock levels for the traditional lot sizing problem when the production cost is linear and we extend this work here by considering return inventories and setup costs for production. We use the approach of Bertsimas and Sim (2004) to model the uncertainties in the input

Value of information analysis for assessing risks and benefits of nanotechnology innovation

Background Decisions on adoption of technological innovation are difficult for manufacturers, especially for small and medium enterprises (SMEs) who have limited resources but often drive product development. Decision analytic methods have been applied to regulatory issues in the nanotechnology sector but such applications to market innovation are not found in the literature. Value of information (VoI) is a decision analytic method for quantifying the benefit of acquiring additional information to support such analyses that can be used to help in a wide range of manufacturing decisions. Results This paper develops a VoI methodology for comparative evaluation of technological alternatives and applies it to a real case study aimed at the selection between a coating system containing nano-TiO2 and alternative conventional paints. The aim of this approach is to aid SMEs and larger industries in deciding whether to further develop the nano-enabled product and in evaluating to which extent investing in more research about risks and/or benefits would be worthwhile. Conclusions Results demonstrated how prioritization in information gaining can improve risk–benefit analyses and impact on both risk management and innovation decision making. By applying the proposed methodology, SMEs and larger industries might easily identify optimal data gathering and/or research strategies to formulate solid development and risk management plans