The traditional supply chain network planning problem is stated as a multi-period resource allocation model involving 0-1 discrete strategic decision variables. The MIP structure of this problem makes it fairly intractable for practical applications, which involve multiple products, factories, warehouses and distribution centres (DCs). The same problem formulated and studied under uncertainty makes it even more intractable. In this paper we consider two related modelling approaches and solution techniques addressing this issue. The first involves scenario analysis of solutions to ''wait and see'' models and the second involves a two-stage integer stochastic programming (ISP) representation and solution of the same problem. We show how the results from the former can be used in the solution of the latter model. We also give some computational results based on serial and parallel implementations of the algorithms

Mirhassani, S., Lucas, C., Mitra, G., Messina, V., Poojari, C. (2000). Computational solution of capacity planning models under uncertainty. PARALLEL COMPUTING, 26(5), 511-538 [10.1016/S0167-8191(99)00118-0].

Computational solution of capacity planning models under uncertainty

Messina, V;
2000

Abstract

The traditional supply chain network planning problem is stated as a multi-period resource allocation model involving 0-1 discrete strategic decision variables. The MIP structure of this problem makes it fairly intractable for practical applications, which involve multiple products, factories, warehouses and distribution centres (DCs). The same problem formulated and studied under uncertainty makes it even more intractable. In this paper we consider two related modelling approaches and solution techniques addressing this issue. The first involves scenario analysis of solutions to ''wait and see'' models and the second involves a two-stage integer stochastic programming (ISP) representation and solution of the same problem. We show how the results from the former can be used in the solution of the latter model. We also give some computational results based on serial and parallel implementations of the algorithms
Articolo in rivista - Articolo scientifico
Benders decomposition; Scenario analysis; Strategic planning; Two-stage stochastic programming; Parallel algorithm
English
2000
26
5
511
538
none
Mirhassani, S., Lucas, C., Mitra, G., Messina, V., Poojari, C. (2000). Computational solution of capacity planning models under uncertainty. PARALLEL COMPUTING, 26(5), 511-538 [10.1016/S0167-8191(99)00118-0].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/376
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