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4 results

  • 2 - Mixed-Integer Programming

  • from Part I - Background
  • Christos T. Maravelias, Princeton University, New Jersey
  • Book:
    Chemical Production Scheduling
    Published online:
    01 May 2021
    Print publication:
    06 May 2021, pp 32-64

  • Summary

    This chapter provides an overview of mixed-integer programming (MIP) modeling and solution methods.In Section 2.1, we present some preliminary concepts on optimization and mixed-integer programming. In Section 2.2, we discuss how binary variables can be used to model features commonly found in optimization problems. In Section 2.3, we present some basic MIP problems and models. Finally, in Section 2.4, we overview the basic approaches to solving MIP models and present some concepts regarding formulation tightness and decomposition methods.Finally, we discuss software tools for modeling and solving MIP models in Section 2.5.

Chemical Production Scheduling
  • Mixed-Integer Programming Models and Methods
  • Christos T. Maravelias
  • Published online:
    01 May 2021
    Print publication:
    06 May 2021
  • Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.

  • Optimal Agricultural Credit Association Branch Office Locations

  • Timi M. Scaletta, Jeffrey R. Stokes
  • Journal:
    Journal of Agricultural and Applied Economics / Volume 35 / Issue 1 / April 2003
    Published online by Cambridge University Press:
    28 April 2015, pp. 127-141

  • Since the farm financial crisis of the 1980s, Farm Credit System banks continue to merge and consolidate to enhance competitiveness. Two mixed-integer programming models of AgChoice Agricultural Credit Association (ACA), a recently merged ACA in Pennsylvania, were developed to determine the optimal number, location, and territory of branches. The approach suggests useful information can be determined regarding the reconfiguration process after bank mergers, especially given the fact that the current AgChoice ACA configuration is available for comparison purposes.