Quantitative Techniques for Decision Making in Construction

Quantitative Techniques for Decision Making in Construction

S.L. Tang
Irtishad U. Ahmad
Syed M. Ahmed
Ming Lu
Copyright Date: 2004
Pages: 226
https://www.jstor.org/stable/j.ctt2jc6xz
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  • Book Info
    Quantitative Techniques for Decision Making in Construction
    Book Description:

    This is a text book as well as a reference book for decision making in construction. The book is written to serve undergraduates of construction-related programmes and postgraduate students undertaking construction management bridging courses. It contains mainly quantitative techniques used to assist decision making, including analytic hierarchy process (AHP), decision theories, conditional probabilities and the value of information, inventory modeling, dynamic programming, Monte-Carlo simulation, CYCLONE simulation modeling, information systems and process of decision making in construction. Plenty of real life examples are used to illustrate the theories, arguments and calculations. It is written in simple and easy to understand language. Readers will benefit from the book even by self studying because all the topics are described and developed in a logical and organized manner, from basic to complex. Moreover, the most up-to-date information on the development of AHP, simulation modelling, information systems and process of decision making are covered.

    eISBN: 978-988-220-260-3
    Subjects: Technology

Table of Contents

  1. Front Matter
    (pp. i-iv)
  2. Table of Contents
    (pp. v-vi)
  3. PREFACE
    (pp. vii-viii)
  4. 1 ANALYTIC HIERARCHY PROCESS I
    (pp. 1-18)

    The “Analytic Hierarchy Process” (or AHP in short), a mathematical tool for management decision making, was introduced by Thomas L. Saaty (1977 and 1980). The mathematical technique is capable of handling a large number of decision factors and provides a systematic procedure of ranking many decision variables. It is a decision analysis technique which can be very useful in construction management. This chapter will firstly give a brief description of the theory of AHP. Cases will then be used to illustrate how this analysis technique can be applied in the field of construction.

    We will use the selection of tenders...

  5. 2 ANALYTIC HIERARCHY PROCESS II
    (pp. 19-34)

    In Chapter 1, we have discussed the approach that Saaty proposed, which is called right eigenvector approach. The right eigenvector approach means that the priority vector X is calculated based on the equation AX = λmax X.

    However, we can also calculate the priority vector using the left eigenvector approach. The left eigenvector approach means that the priority vector is calculated based on

    $ (\textrm{AX})^{\textrm{T}}=(\lambda _{\textrm{max}}\textrm{ X})^{\textrm{T}} $ $ \textrm{ie. }\textrm{X}^{\textrm{T}}\textrm{A}^{\textrm{T}}=\lambda _{\textrm{max}}\textrm{ X}^{\textrm{T}} $

    Johnson, Beine and Wang (1979), two years after Saaty proposed his AHP theory, discovered that the priority vectors calculated from a same reciprocal matrix using the right eigenvector approach and the left eigenvector approach may...

  6. 3 DECISION THEORY USING EMV CRITERION
    (pp. 35-48)

    In day-to-day work, construction managers may face problems which involve probability, that is, for any one particular action that an engineer takes, there may be several probable outcomes.

    Let us consider a simple example. A contractor has to decide whether to hire concrete pumping equipment in order to complete a foundation work tomorrow. If he does not hire the equipment, work will be delayed and he will suffer a loss of $10,000. If he hires the equipment, he anticipates two possible outcomes, depending on the weather:

    a. If the weather is fine, the concrete pumping equipment will be fully utilized....

  7. 4 DECISION THEORY USING EUV CRITERION
    (pp. 49-64)

    In Section 3.3 of Chapter 3, it was explained that the EMV criterion in decision analysis is based on the law of averages. If the decision maker can afford to take a long-term view and accept temporary losses and gains, such a decision may pay off in the long run.

    The EMV criterion provides an objective measure of money value. However, it does not take into account people’s subjective views towards different amounts of money and the degree of risk people are willing to take. It assumes that different people have equal satisfaction when they gain a certain amount of...

  8. 5 THE VALUE OF INFORMATION IN DECISION MODELS
    (pp. 65-72)

    Sometimes, a decision problem may involve conditional probabilities. For example, if a construction problem is related to underground soil conditions, engineers can only give predictions. No matter how experienced the engineer is, he cannot predict 100% the true state of the soil conditions underground. Some engineering test, such as seismic test, can, at best, help provide more reliable predictions of the probability of occurrence of various possible states of the variables.

    We will see an example of the problems of this sort.

    A contractor is about to drive twenty precast concrete piles to support a structure. It is estimated that...

  9. 6 INVENTORY MODELLING I
    (pp. 73-84)

    A construction firm has to keep a constant inventory of stock. If a site runs out of cement, say, then construction may come to a halt. Conversely, if a site carries an excessive amount of cement, a higher cost will be incurred to store the excessive stock.

    Let us consider in more detail the example of storing cement on a site. The decision problem the site agent is faced with is how many bags of cement should be placed in the storage shed. He has to decide how frequent the purchase orders should be made and how many bags should...

  10. 7 INVENTORY MODELLING II
    (pp. 85-100)

    We have seen in Chapter 6 that under normal circumstances, when inventory is delivered at the instant when an order is placed, the inventory level can be represented graphically as shown in Fig. 7.1.

    However, new stock may arrive later than scheduled, and there may be unexpected excessive demand on stock. Such situations lead to what is known as stockout, which is when the inventory on hand cannot cover needs. This shortage of stock is represented by the shaded portion of the graph under zero inventory in Fig. 7.2.

    Notice that the new inventory level after stockout does not rise...

  11. 8 DYNAMIC PROGRAMMING
    (pp. 101-124)

    Dynamic programming is an optimization technique. The word dynamic is used because in this technique decisions are taken at distinct stages. It is based on the principle of optimality as stated by Richard Bellman (Bellman, 1957) that the overall optimal solution contains the sub-optimal solution that is from the start to a certain stage of a problem.

    Let us take a shortest route problem as an example to illustrate what the above statement means, that is, how sub-optimization at an intermediate stage can lead to obtaining the overall optimal solution. Example 8.1 shows the process of finding the shortest route...

  12. 9 SIMULATION I
    (pp. 125-142)

    Simulation is the process of conducting experiments with a model of the system that is being studied or designed. It is a powerful technique for both analyzing and synthesizing engineering and other natural systems.

    The simulation procedure is basically an iterative procedure and may be described as an input-output study with feedback provided to guide the changes in the input parameters.

    The inputs define the set of events and conditions to which the system can be subjected in the real world, and the outputs predict the system response. By studying the outputs at the end of each simulation run, one...

  13. 10 SIMULATION II
    (pp. 143-150)

    Construction planning is the most crucial, knowledge-intensive, ill-structured, and challenging phase in the project development cycle due to the complicated, interactive, and dynamic nature of construction processes (Halpin and Riggs, 1992). The methodology of discrete-event simulation, as discussed in Chapter 9, which concerns “the modelling of a system as it evolves over time by a representation in which the state variables change only at a countable number of points in time” (Law and Kelton, 1982), provides a promising alternative solution to construction planning by predicting the state of a real construction system following the creation of a simulation model based...

  14. 11 INFORMATION SYSTEMS AND PROCESS OF DECISION MAKING
    (pp. 151-162)

    The business of construction is information-intensive, dependent on accurate, reliable, up-do-date and timely information. The amount of information can be vast encompassing legal requirements, building codes, specifications and standards, current and historic data about techniques, cost and schedule. Nowadays construction projects are increasingly more complex, and an enormous amount of information need to be processed for effective decision making. Success of a project in today’s world is critically dependent on timely and reliable decisions.

    Effective decision-making depends on the availability of appropriate information. To facilitate proper and optimal decision making, availability of the desired information at its required level of...

  15. 12 PLANNING AND SCHEDULING DECISIONS
    (pp. 163-180)

    Completing a project on time, within budget and according to specifications should be the overall objective of a project team. This objective cannot be achieved without proper planning and scheduling.

    Planning and scheduling of the functions, operations and resources of a project are amongst the most challenging tasks faced by a project management team. The goal is to sequence operations properly and to allocate efficiently the resources involved. In this chapter, decision-making aspects of planning and scheduling are discussed. Brief introductions to various planning and scheduling tools are also included. For a comprehensive presentation of these quantitative techniques readers are...

  16. BIBLIOGRAPHY
    (pp. 181-184)
  17. Answers to Selected Exercise Questions
    (pp. 185-218)
  18. ABOUT THE AUTHORS
    (pp. 219-220)