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Multi-item dynamic production-distribution planning in process industries with divergent finishing stages [An article from: Computers and Operations Research] | ![Multi-item dynamic production-distribution planning in process industries with divergent finishing stages [An article from: Computers and Operations Research]](http://ecx.images-amazon.com/images/I/41SS7GZHX6L._SL160_.jpg)
 enlarge | Authors: N. Rizk, A. Martel, S. D'amours
Publisher: Elsevier
Category: Book
Buy New: $7.95
Format: Html
Media: Digital
Pages: 23
Publication Date: December 1, 2006
Availability: Available for download now
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Product Description
This digital document is a journal article from Computers and Operations Research, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
This paper examines a multi-item dynamic production-distribution planning problem between a manufacturing location and a distribution center. Transportation costs between the manufacturing location and the distribution center offer economies of scale and can be represented by general piecewise linear functions. The production system at the manufacturing location is a serial process with a multiple parallel machines bottleneck stage and divergent finishing stages. A predetermined production sequence must be maintained on the bottleneck machines. A tight mixed-integer programming model of the production process is proposed, as well as three different formulations to represent general piecewise linear functions. These formulations are then used to develop three equivalent mathematical programming models of the manufacturer-distributor flow planning problem. Valid inequalities to strengthen these formulations are proposed and the strategy of adding extra 0-1 variables to improve the branching process is examined. Tests are performed to compare the computational efficiency of these models. Finally, it is shown that by adding valid inequalities and extra 0-1 variables, major computational improvements can be achieved.
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