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One of the most important methods in dealing with the optimization of large, complex systems is that of hierarchical decomposition. The idea is to reduce the overall complex problem into manageable approximate problems or subproblems, to solve these problems, and to construct a solution of the original problem from the solutions of these simpler prob lems. Development of such approaches for large complex systems has been identified as a particularly fruitful area by the Committee on the Next Decade in Operations Research (1988) [42] as well as by the Panel on Future Directions in Control Theory (1988) [65]. Most manufacturing firms are complex systems characterized by sev eral decision subsystems, such as finance, personnel, marketing, and op erations. They may have several plants and warehouses and a wide variety of machines and equipment devoted to producing a large number of different products. Moreover, they are subject to deterministic as well as stochastic discrete events, such as purchasing new equipment, hiring and layoff of personnel, and machine setups, failures, and repairs.
| Autorius: | Qing Zhang, Suresh P. Sethi, |
| Serija: | Systems & Control: Foundations & Applications |
| Leidėjas: | Birkhäuser Boston |
| Išleidimo metai: | 1994 |
| Knygos puslapių skaičius: | 444 |
| ISBN-10: | 0817637354 |
| ISBN-13: | 9780817637354 |
| Formatas: | 241 x 160 x 29 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Hierarchical Decision Making in Stochastic Manufacturing Systems“
