Production and inventory management under multiple resource constraints

被引:13
|
作者
Bretthauer, Kurt M. [1 ]
Shetty, Bala
Syam, Siddhartha
Vokurka, Robert J.
机构
[1] Indiana Univ, Kelley Sch Business, Dept Operat & Decis Technol, Bloomington, IN 47405 USA
[2] Texas A&M Univ, Lowry Mays Coll Business, Dept Informat & Operat Management, College Stn, TX 77843 USA
[3] Marquette Univ, Coll Business Adm, Dept Management, Milwaukee, WI 53201 USA
[4] Texas A&M Univ, Coll Business, Dept Econ Finance & Decis Sci, Corpus Christi, TX 78412 USA
关键词
production and inventory management; nonlinear optimization; integer programming;
D O I
10.1016/j.mcm.2005.12.009
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper we present a model and solution methodology for production and inventory management problems that involve multiple resource constraints. The model formulation is quite general, allowing organizations to handle a variety of multi-item decisions such as determining order quantities, production batch sizes, number of production runs, or cycle times. Resource constraints become necessary to handle interaction among the multiple items. Common types of resource constraints include limits on raw materials, machine capacity, workforce capacity, inventory investment, storage space, or the total number of orders placed. For example, in a production environment, there may be limited workforce capacity and limits on machine capacities for manufacturing various product families. In a purchasing environment where a firm has multiple suppliers, there are often constraints for each supplier, such as the total order from each supplier cannot exceed the volume of the truck. We present efficient algorithms for solving both continuous and integer variable versions of the resource constrained production and inventory management model. The algorithms require the solution of a series of two types of subproblems: one is a nonlinear knapsack problem and the other is a nonlinear problem where the only constraints are lower and upper bounds on the variables. Computational testing of the algorithms is reported and indicates that they are effective for solving large-scale problems. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:85 / 95
页数:11
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