The Impact of Adding a Make-to-Order Item to a Make-to-Stock Production System

The Impact of Adding a Make-to-Order Item to a Make-to-Stock Production System

Stochastic Economic Lot Scheduling Problems (ELSPs) involve settings where several items need to be produced in a common facility with limited capacity, under significant uncertainty regarding demands, unit production times, setup times, or combinations thereof. We consider systems where some produc...

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Veröffentlicht in:Management Science. - Institute for Operations Research and the Management Sciences, 1954. - 45(1999), 7, Seite 980-994
1. Verfasser: Federgruen, Awi (VerfasserIn)
Weitere Verfasser: Katalan, Ziv
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 1999
Zugriff auf das übergeordnete Werk:Management Science
Schlagworte:Performance/Productivity Manufacturing Queues Priorities Inventory/Production Multi-Item Applied sciences Business Mathematics Economics
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520 |a Stochastic Economic Lot Scheduling Problems (ELSPs) involve settings where several items need to be produced in a common facility with limited capacity, under significant uncertainty regarding demands, unit production times, setup times, or combinations thereof. We consider systems where some products are made-to-stock while another product line is made-to-order. We present a rich and effective class of strategies for which a variety of cost and performance measures can be evaluated and optimized efficiently by analytical methods. These include inventory level and waiting-time distributions, as well as average setup, holding, and backlogging costs. We also characterize how strategy choices are affected by the system parameters. The availability of efficient analytical evaluation and optimization methods permits us to address the impact of product line diversification or standardization on the performance of the manufacturing system, in particular the logistical implications of adding low-volume specialized models to a given make-to-stock product line. 
540 |a Copyright 1999 Institute for Operations Research and the Management Sciences 
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650 4 |a Queues 
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