TAILIEUCHUNG - The Design Of Manufacturing Systems P2

The sequential engineering approach to product design and development typically treats design and manufacturing as isolated activities. In this approach, the design department designs an artifact and throws it “over the wall” to the manufacturing department without taking into consideration the manufacturing capabilities and limitations of the shop floor. The manufacturing department, in turn, studies the design from a manufacturability viewpoint and throws it back “over the wall” to the design department with a list of manufacturing concerns. Typically, the artifact drawings go back and forth between the two departments until, eventually, the drawings are approved for production. Obviously, this situation prolongs the product realization time. Also,. | 3. Extensions of the MILP model to account for power economies of scale and differing plant types were also presented. 4. Uncertainty in the problem data was approached through fuzzy and stochastic programming formulations of the same problem. Solution strategies developed for these models make possible the solution of large-scale problems. There are several avenues that could be further explored. Some important research directions are identified next. 1. Most of the bounding and cutting plane generation techniques could be used in the context of capacity planning problems from other industrial sectors. 2. A complete complexity classification of the problem would be interesting. 3. The problem being an integer program is inherently difficult. Thus there is considerable motivation for the development of heuristics or approximation schemes. Worst- and average-case performance measures of these heuristics for the process planning problem could be an important contribution. Liu and Sahinidis 7 recently initiated some work in this area such as high variability settings that could also be explored. In conclusion the problem of long-range planning in the chemical industry is a very intriguing one. The complexity of the problem holds considerable challenge for researchers while its application potential is attractive to practitioners. Acknowledgments The authors are grateful for partial financial support from the National Science Foundation under CAREER award DMII 95-02722 to . References 1. Ahmed S. and Sahinidis N. V. Robust process planning under uncertainty Ind. Eng. Chem. Res. 37 1883 1998. 2. Balas E. and Pulleyblank W. The perfectly matchable subgraph polytope of a bipartite graph Networks 13 495 1983. 3. Benders J. F. Partitioning procedures for solving mixed variables programming Num. Math. 4 238 1962. 4. lerapetritou M. G. and Pistikopoulos E. N. Novel optimization approach of stochastic planning models Ind. Eng. Chem. Res. 33 1930 1994. 5. Kall P. and .

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