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doc13 - Manufacturing Planning and Control Stephen C Graves...

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1 Manufacturing Planning and Control Stephen C. Graves Massachusetts Institute of Technology November 1999 Manufacturing planning and control entails the acquisition and allocation of limited resources to production activities so as to satisfy customer demand over a specified time horizon. As such, planning and control problems are inherently optimization problems, where the objective is to develop a plan that meets demand at minimum cost or that fills the demand that maximizes profit. The underlying optimization problem will vary due to differences in the manufacturing and market context. This chapter provides a framework for discrete-parts manufacturing planning and control and provides an overview of applicable model formulations.
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2 Manufacturing Planning and Control Stephen C. Graves Massachusetts Institute of Technology November 1999 Manufacturing planning and control address decisions on the acquisition, utilization and allocation of production resources to satisfy customer requirements in the most efficient and effective way. Typical decisions include work force level, production lot sizes, assignment of overtime and sequencing of production runs. Optimization models are widely applicable for providing decision support in this context. In this article we focus on optimization models for production planning for discrete-parts, batch manufacturing environments. We do not cover detailed scheduling or sequencing models (e. g., Graves, 1981), nor do we address production planning for continuous processes (e. g., Shapiro, 1993). We consider only discrete-time models, and do not include continuous-time models such as developed by Hackman and Leachman (1989). Our intent is to provide an overview of applicable optimization models; we present the most generic formulations and briefly describe how these models are solved. There is an enormous range of problem contexts and model formulations, as well as solution methods. We make no effort to be exhaustive in the treatment herein. Rather, we have made choices of what to include based on personal judgment and preferences. We have organized the article into four major sections. In the first section we present a framework for the decisions, issues and tradeoffs involved in implementing an optimization model for discrete-part production planning. The remaining three sections present and discuss three distinct types of models. In the second section we discuss linear programming models for production planning, in which we have linear costs. This category is of great practical interest, as many important problem features can be captured with these models and powerful solution methods for linear programs are readily available. In the third section, we present a production-planning model for a single aggregate product with quadratic costs; this model is of historical significance as it represents one of the earliest applications of optimization to manufacturing planning. In the final section we introduce the multi-item capacitated lot-size problem, which is modeled as a mixed integer linear program.
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