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  • © 1979

Multiple Objective Decision Making — Methods and Applications

A State-of-the-Art Survey

Part of the book series: Lecture Notes in Economics and Mathematical Systems (LNE, volume 164)

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Table of contents (6 chapters)

  1. Front Matter

    Pages N2-XII
  2. Introduction

    • Ching-Lai Hwang, Abu Syed Md. Masud
    Pages 1-11
  3. Basic Concepts and Terminology

    • Ching-Lai Hwang, Abu Syed Md. Masud
    Pages 12-20
  4. Methods for Multiple Objective Decision Making

    • Ching-Lai Hwang, Abu Syed Md. Masud
    Pages 21-283
  5. Applications

    • Ching-Lai Hwang, Abu Syed Md. Masud
    Pages 284-302
  6. Concluding Remarks

    • Ching-Lai Hwang, Abu Syed Md. Masud
    Pages 303-309
  7. Bibliography

    • Ching-Lai Hwang, Abu Syed Md. Masud
    Pages 310-351
  8. Back Matter

    Pages 355-357

About this book

Decision making is the process of selecting a possible course of action from all the available alternatives. In almost all such problems the multiplicity of criteria for judging the alternatives is pervasive. That is, for many such problems, the decision maker (OM) wants to attain more than one objective or goal in selecting the course of action while satisfying the constraints dictated by environment, processes, and resources. Another characteristic of these problems is that the objectives are apparently non­ commensurable. Mathematically, these problems can be represented as: (1. 1 ) subject to: gi(~) ~ 0, ,', . . . ,. ! where ~ is an n dimensional decision variable vector. The problem consists of n decision variables, m constraints and k objectives. Any or all of the functions may be nonlinear. In literature this problem is often referred to as a vector maximum problem (VMP). Traditionally there are two approaches for solving the VMP. One of them is to optimize one of the objectives while appending the other objectives to a constraint set so that the optimal solution would satisfy these objectives at least up to a predetermined level. The problem is given as: Max f. ~) 1 (1. 2) subject to: where at is any acceptable predetermined level for objective t. The other approach is to optimize a super-objective function created by multiplying each 2 objective function with a suitable weight and then by adding them together.

Authors and Affiliations

  • Dept. of Industrial Engineering, Kansas State University, Manhattan, USA

    Ching-Lai Hwang

  • Dept. of Industrial Engineering and N. M. Solar Energy Institute, New Mexico State University, Las Cruces, USA

    Abu Syed Md. Masud

Bibliographic Information

Buy it now

Buying options

eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access