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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2005.02.022 Byline: Xiangtong Qi (a), Jonathan F. Bard (b) Abstract: In this research, we introduce a dual-purpose simulation model that integrates two decision support systems used by the US Postal Service to configure and staff their mail processing and distribution centers (P&DCs). The first system is designed to optimize the daily equipment schedules and the second to optimize the size and composition of the permanent workforce. Large-scale integer programs are solved in both cases. Because some compromise is needed in the time granularity, it is important to have an independent means of validating the results. This is the first purpose of our simulation model. The second involves the generation and validation of labor requirements for a category of workers known as mail handlers. While there is a one-to-one relationship between machine operators and the equipment schedule derived from the mail arrival profiles, no such relationship exists for those responsible for moving the mail between workstations. Neither productivity measures nor formal work rules exist. To resolve this shortcoming, we use simulation again, but this time to estimate mail handler requirements and then to determine whether the weekly schedules derived from the staff optimizer are adequate to meet the facility's service standards. Holistically speaking, the simulation serves as a bridge between the two optimization systems. The procedure is demonstrated with data provided by the Dallas P&DC. Author Affiliation: (a) Department of Industrial Engineering and Engineering Management, The Hong Kong University of Science and Technology, Hong Kong (b) Graduate Program in Operations Research & Industrial Engineering, 1 University Station, C2200, The University of Texas, Austin, TX 78712-0292, USA

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2004.07.003 Byline: Xinhui Zhang (a), Jonathan F. Bard (b) Abstract: Mail processing and distribution centers (P&DCs) are large factories that accept, sort, and sequence mail in preparation for delivery. A central problem in these facilities is how to schedule the equipment over the day to ensure batch production under tight equipment and workforce constraints. The problem falls into the general category of multi-level lot sizing and is notoriously difficult to solve. No exact algorithms exist that are efficient and can consistently provide high-quality solutions. In the paper, we investigate two specialized approaches for solving this problem. The first is a piece-by-piece LP-based heuristic and the second is a Benders decomposition. The heuristic uses the LP fractional solution as a target and attempts to find an integer solution that is as close to it as possible by minimizing the L.sub.1-norm. The procedure consists of solving the LP relaxation of the original model and two reduced mixed-integer programs. The process is similar to what has been called 'piece-by-piece decomposition' in nonlinear programming. While the goal there is to find better initial starting points for the nonlinear code, our goal is to improve solution quality. Computational testing using data provided by the Dallas P&DC confirmed the superiority of the piece-by-piece LP-based heuristic with respect to branch and bound, which proved to be computational expensive, and the Benders algorithm which took several hours to find feasible solutions. In general, the LP-based heuristic was an order of magnitude faster than branch and bound and provided solutions that were able to process more than 99.75% of the daily volume. Author Affiliation: (a) Department of Industrial Engineering, Wright State University, 207 Russ Engineering Center, 3640 Colonel Glen Hwy, Dayton, OH 45435, USA (b) Graduate Program in Operations Research & Industrial Engineering, The University of Texas, 1 University Station, C2200, Austin, TX 78712, USA Article Note: (footnote) [star] This work was supported in part by the National Science Foundation under Grant #DMI-0218701.