We introduce a heuristic method for finding good, feasible solutions for multiproduct lot sizing problems with general assembly structures, multiple constrained resources, and nonzero setup costs and setup times. We evaluate the performance of this heuristic by comparing its solutions to optimal solutions of small randomly generated problems and to time-truncated Optimization Subroutine Library (OSL) solutions of medium-sized randomly generated problems. In the first case, the heuristic locates solutions averaging 4 percent worse than optimal in less than 1 percent of time required by OSL. The heuristic solutions to medium-sized problems are approximately 26 percent better than solutions OSL finds after 10,000 CPU seconds, and the heuristic finds these solutions in approximately 10 percent of OSL time.Lot Sizing, General Assembly System, Heuristics, Integer Programming
IntroductionAs cost becomes increasingly important in today's highly competitive international marketplace, more and more companies find themselves moving at least parts of their manufacturing operations to developing countries. This highly competitive marketplace, though, allows few of these companies to compete solely on the basis of price. To be successful, these companies must also achieve high levels of quality and be responsive to changing market needs. One approach that many companies in more developed countries have adopted to achieve these objectives is just-in-time ( JIT) purchasing. With JIT purchasing, a company requires its suppliers to make frequent, reliable deliveries of small lots of very high quality parts and encourages its suppliers to participate in the purchasing plant's continuous improvement efforts [1].The operating environment common in developing countries, though, poses significant obstacles to the use of JIT purchasing. The supplier bases in these countries are typically very weak. Most developing country suppliers cannot provide the levels of quality and delivery reliability required by multinational companies (MNCs) using traditional approaches to purchasing[2,3], let alone meet the more stringent requirements of a JIT purchasing environment. This forces many of these MNCs to turn to distant international suppliers. Studies of plants in the US by Vickery[4] and McClenahen[5], however, indicate international sourcing compromises JIT purchasing efforts due to the longer transportation times and limited face-to-face communication with supplier representatives. In developing countries, these problems are greatly aggravated by weak infrastructures which makes transportation times highly variable and communications problematic [2]. While it has been suggested that the basic simplicity of JIT principles makes them well suited for use in the manufacturing operations of plants in developing countries[6], little research has been conducted to investigate the extension of JIT principles to the purchasing function in these countries.
Group decision making in the presence of multiple conflicting objectives is complex and difficult. This paper describes and evaluates an iterative technique to facilitate multiple objective decision making by multiple decision makers. The proposed method augments an interactive multiobjective optimization procedure with a preference ranking tool and a consensus ranking heuristic. Two multiple objective linear programming (MOLP) solution approaches, the SIMOLP method of Reeves and Franz [39] and the interactive weighted Tchebycheff procedure of Steuer and Choo [49], are recommended optimization strategies to be used independently or in concert. Computational experience suggests that the proposed framework is an effective decision-making tool. The procedure quickly located excellent compromise solutions in a series of test problems with hypothetical decision makers. In addition, human decision makers gave positive evaluations of the procedure and the production plans the procedure provided for a resource allocation case problem.
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