Soft computing for multicustomer due-date bargaining

The Due-Date Bargainer is a useful tool to support negotiation on due-dates between a manufacturer and its customers. To improve the computational performance of an earlier version of the Due-Date Bargainer, we present a new soft computing approach. It uses a genetic algorithm to nd the best priority sequence of customer orders for resource allocation and fuzzy logic operations to allocate the resources and determine the order completion times, following the priority sequence of orders. To extend the Due-Date Bargainer to accomodate bargaining with several customers at the same time, we propose a method to distribute the total penalty using marginal penalties for the individual bargainers. A demonstration software package implementing the improved Due Date Bargainer has been developed. It is oriented at apparel manufacturing enterprises. Experiments using realistic resource data and randomly generated orders have achieved satisfactory results. Manuscript received at D. Wang is with the Department of Systems Engineering, College of Information Science and Engineering, Northeastern University, Shenyang, 110006, P. R. of China. He was a visiting professor with North Carolina State University while this research was being conducted. S.-C. Fang and H.L.W. Nuttle are with the Department of Industrial Engineering and Operations Research, North Carolina State University, Raleigh, NC 27695, U.S.A. + This research was supported, in part, by the National Textile Center of the United States of America (Grant Number: I95S-02), the National Natural Science Foundation (No. 69684005) and National High-Tech Program (No. 863-511-9609-003) of the People's Republic of China. Dingwei Wang is currently Professor in the Department of Systems Engineering, the College of Information Science and Engineering of Northeastern University in Shenyang, People's Republic of China. He received his Ph.D. degree in control theory and application from the Northeastern University, China. Dr. Wang has been a post-doctoral follow at the North Carolina State University in USA. He is a member of the New York Academy of Sciences and a member of Automatic Association of China. He serves as on the editorial boards of Chinese Journal of Control and Decision. He has authored two books and has had more than 80 papers published in the international and domestic journals, including IEEE Transactions on SMC, International Journal of Production Research, Fuzzy Sets and Systems, etc. His current research interests include MRP-II, JIT manufacturing systems, production planning and scheduling, fuzzy optimization and genetic algorithms. Shu-Cherng Fang received his Ph.D. degree in Industrial Engineering and Management Science from the Northwestern University, Evanston, IL., in 1979. He holds the Walter Clark Professorship in Industrial Engineering and Operations Research at the North Carolina State University (NCSU), Raleigh, where he also directs the Fuzzy and Neural Group. Before joining NCSU, he was Supervisor at AT&T Bell Laboratories and Department Manager at AT&T Technologies. He has more than 100 publications including the books of Linear Optimization and Extensions: Theory and Algorithms (Prentice Hall, 1993) and Entropy Optimization and Mathematical Programming (Kluwer Academic Publications, 1997). He serves on the editorial boards of Optimization, the International Journal of Operations and Quantitative Management, and the Journal of Chinese Institute of Industrial Engineers.