Introduction to Computational Optimization Models for Production Planning in a Supply Chain
Springer Science & Business Media, Mar 20, 2006 - Business & Economics - 260 pages
provide models that could be used by do-it-yourselfers and also can be used toprovideunderstandingofthebackgroundissuessothatonecandoabetter job of working with the (proprietary) algorithms of the software vendors. In this book we strive to provide models that capture many of the - tails faced by ?rms operating in a modern supply chain, but we stop short of proposing models for economic analysis of the entire multi-player chain. In other words, we produce models that are useful for planning within a supply chain rather than models for planning the supply chain. The usefulness of the models is enhanced greatly by the fact that they have been implemented - ing computer modeling languages. Implementations are shown in Chapter 7, which allows solutions to be found using a computer. A reasonable question is: why write the book now? It is a combination of opportunities that have recently become available. The availability of mod- inglanguagesandcomputersthatprovidestheopportunitytomakepractical use of the models that we develop. Meanwhile, software companies are p- viding software for optimized production planning in a supply chain. The opportunity to make use of such software gives rise to a need to understand some of the issues in computational models for optimized planning. This is best done by considering simple models and examples.
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Starting with an mrp Model
Extending to an MRP II Model
A Better Model 45
Extensions to the Model
Implementation Examples 81
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abstract aggregate algorithm binary variables bottleneck branch and bound capacity constraints capacity utilization components consider create data element decision variables declared developed example external demand feasible solution forall fraction of resource genetic algorithm given holding cost implementation index sets integer variables inventory of SKU large number linear load dependent lead logistics lot sizing LP relaxation LS(i LT(i marginal costs master SKU materials requirements Materials Requirements Planning minimize minimum lot modeling languages Mosel mrp model MRPII model needed node non-linear notation Number of SKUs Numbertime objective function objective function value optimal solution optimization models overtime param parameter period planning model problem production indicator production planning production quantities programming queuing random refer result scenario scheduling SCPc model sequence simulated annealing solve solver SOS1 stochastic supply chain management supply chain planning tabu search tardiness tion vector xi,t yk,t zero