Uncertain chance-constrained programming model for project scheduling problem
04 Mar 2018-Journal of the Operational Research Society (Palgrave Macmillan UK)-Vol. 69, Iss: 3, pp 384-391
TL;DR: Three uncertain chance-constrained programming models for project scheduling problem, in which the chance constraint must reach a predetermined confidence level, are built and can be transformed to their crisp forms.
Abstract: In this paper, we consider an uncertain project scheduling problem, in which activity durations, with no historical data generally, are estimated by belief degrees and assumed to be uncertain variables. To achieve different management goals, we build three uncertain chance-constrained programming models for project scheduling problem, in which the chance constraint must reach a predetermined confidence level. Moreover, these models can all be transformed to their crisp forms, and an intelligent algorithm is designed to search the optimal schedule. Finally, a numerical example is presented to illustrate the usefulness of the proposed model.
Citations
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01 Jan 2002
TL;DR: It is shown that the proposed methodology can assist project managers in selecting a schedule with the least possibility of being late in an uncertain scheduling environment and is developed to maximize the minimum satisfaction degrees of all temporal constraints.
Abstract: The efficient management of product development projects is important to reduce the required development time and cost. However, each project is unique in nature and the duration of activities involed in a project often cannot be predicted accurately. The uncertainty of activity duration may lead to incorrect scheduling decisions. The objective of this research is to develop a fuzzy scheduling methodology to deal with this problem. Possibility theory is used to model the uncertain and flexible temporal information. The concept of schedule risk is proposed to evaluate the schedule performance. A fuzzy beam search algorithm is developed to determine a schedule with the minimum schedule risk and the start time of each activity is selected to maximize the minimum satisfaction degrees of all temporal constraints. In addition, the properties of schedule risk are also discussed. We show that the proposed methodology can assist project managers in selecting a schedule with the least possibility of being late in an uncertain scheduling environment. An example with an electronic product development project is used to illustrate the developed approach.
78 citations
TL;DR: In this article, the authors created a classification for major sources of uncertainty in projects and categorized the studies in project scheduling literature with respect to the uncertainty source(s) they address, and investigated the approaches and methods to manage uncertainty.
43 citations
TL;DR: This study concludes that no one of the taken PR is dominant over others under this dynamic project scheduling environment.
27 citations
TL;DR: This study proposes an inverse modeling based multi-objective evolutionary algorithm using a Gaussian Process to obtain the Pareto set and finds the proposed algorithm to be more effective compared with two other popular algorithms.
Abstract: The problem of integrated project portfolio selection and scheduling (PPSS) is among the most important and highly pursed subjects in project management. In this study, a mathematical model and algorithm are designed specifically to assist decision makers decide which projects are to be chosen and when these projects are to be undertaken. More specifically, the PPSS problem is first formulated as a nonlinear multi-objective model with simultaneous consideration of benefit and risk factors. Due to the complexity and uncertainty involved in most real life situations, fuzzy numbers are incorporated into the model, which can provide decision makers with more flexibility. Then, an inverse modeling based multi-objective evolutionary algorithm using a Gaussian Process is presented to obtain the Pareto set. Finally, an illustrative example is used to demonstrate the high efficacy of the foregoing approach, which can provide decision makers with valuable insights into the PPSS process. The proposed algorithm is found to be more effective compared with two other popular algorithms.
21 citations
TL;DR: It is shown that the novel hybrid portfolio selection procedure consisting of three phases can lead to more robust portfolios in a shorter time, which can aid decision makers throughout the selection process.
Abstract: A novel hybrid portfolio selection procedure consisting of three phases is proposed for choosing an appropriate project portfolio based on a set of criteria having fuzzy measurements. Different from other existing selection procedures, in which decisions are based mainly on the projects’ characteristics at decision points, the proposed procedure takes into account projects’ historical performances. In particular, projects are first evaluated and assigned comparable scores, based on which a multi-objective model is built. The model selects a portfolio with a view to maximizing its expected value and development trend, while minimizing its risk with other selected projects. Three important elements are considered: (1) criteria weighting over each other, (2) uncertainty in decision making, and (3) projects’ historical performances. The aim is to select more robust project portfolios in the long run, which lead to fewer replacements. Comparative studies are performed to verify the validity of the approach. It is shown that our method can lead to more robust portfolios in a shorter time, which can aid decision makers throughout the selection process.
19 citations
References
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Book•
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14 Aug 2007
TL;DR: Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, industrial engineering, computer science, artificial intelligence, and management science will find this work a stimulating and useful reference.
Abstract: Uncertainty theory is a branch of mathematics based on normality, monotonicity, self-duality, and countable subadditivity axioms. The goal of uncertainty theory is to study the behavior of uncertain phenomena such as fuzziness and randomness. The main topics include probability theory, credibility theory, and chance theory. For this new edition the entire text has been totally rewritten. More importantly, the chapters on chance theory and uncertainty theory are completely new. This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The purpose is to equip the readers with an axiomatic approach to deal with uncertainty. Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, industrial engineering, computer science, artificial intelligence, and management science will find this work a stimulating and useful reference.
1,450 citations
Book•
29 Apr 2003
TL;DR: This book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem.
Abstract: Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.
1,352 citations
Book•
01 Jan 2002
TL;DR: In this article, a self-contained, comprehensive and up-to-date presentation of uncertain programming theory is provided, which includes numerous modeling ideas, hybrid intelligent algorithms, and various applications in transportation problem inventory system, facility location & allocation, capital budgeting, topological optimization, vehicle routing problem, redundancy optimization, and scheduling.
Abstract: From the Publisher:
This book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory. It includes numerous modeling ideas, hybrid intelligent algorithms, and various applications in transportation problem inventory system, facility location & allocation, capital budgeting, topological optimization, vehicle routing problem, redundancy optimization, and scheduling. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.
1,264 citations
Book•
07 Nov 2011
TL;DR: Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, system science, industrial engineering, computer science, artificial intelligence, finance, control, and management science will find this work a stimulating and useful reference.
Abstract: Uncertainty theory is a branch of mathematics based on normality, monotonicity, self-duality, countable subadditivity, and product measure axioms. Uncertainty is any concept that satisfies the axioms of uncertainty theory. Thus uncertainty is neither randomness nor fuzziness. It is also known from some surveys that a lot of phenomena do behave like uncertainty. How do we model uncertainty? How do we use uncertainty theory? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory, including uncertain programming, uncertain risk analysis, uncertain reliability analysis, uncertain process, uncertain calculus, uncertain differential equation, uncertain logic, uncertain entailment, and uncertain inference. Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, system science, industrial engineering, computer science, artificial intelligence, finance, control, and management science will find this work a stimulating and useful reference.
1,004 citations
"Uncertain chance-constrained progra..." refers background in this paper
...Keywords: project scheduling problem; uncertain chance-constrained programming; uncertainty theory...
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01 Jan 2009
TL;DR: In this article, a new uncertain calculus is proposed and applied to uncertain difierential equation, flnance, control, flltering and dynamical systems based on the uncertainty theory.
Abstract: In addition to the four axioms of uncertainty theory, this paper presents the flfth axiom called product measure axiom. This paper also gives an operational law of independent uncertain variables and a concept of entropy of continuous uncertain variables. Based on the uncertainty theory, a new uncertain calculus is proposed and applied to uncertain difierential equation, flnance, control, flltering and dynamical systems. Finally, an uncertain inference will be presented. c
987 citations
"Uncertain chance-constrained progra..." refers background in this paper
...5 (Liu [14]) The uncertain variables ξ1, ξ2, · · · , ξn are said to be independent if...
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...Besides, the product uncertain measure on the product σ-algebre is defined by Liu [14] as follows: Axiom 4....
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