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Journal ArticleDOI

Optimization of Resource Allocation and Leveling Using Genetic Algorithms

Tarek Hegazy
- 01 Jun 1999 - 
- Vol. 125, Iss: 3, pp 167-175
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TLDR
In this paper, improvements are proposed to resource allocation and leveling heuristics, and the GA technique is used to search for near-optimum solution, considering both aspects simultaneously.
Abstract
Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA proced...

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Citations
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Time-Cost-Quality Trade-Off Analysis for Highway Construction

TL;DR: In this article, a multi-objective optimization model that supports decision makers in performing this challenging task is presented. The model is designed to transform the traditional two-dimensional time-cost tradeoff analysis to an advanced three-dimensional quality trade-off analysis.
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Schedule risks in prefabrication housing production in Hong Kong: a social network analysis

TL;DR: In this paper, the authors apply social network analysis (SNA) to recognize and investigate the underlying network of stakeholder-associated risk factors in prefabrication housing construction projects.
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Fuzzy critical chain method for project scheduling under resource constraints and uncertainty

TL;DR: The use of a project buffer makes the method akin to critical chain project management (CCPM), although no feeding buffers are used, and the proposed method is useful for both project planning and execution.
Journal ArticleDOI

Use of Genetic Algorithms in Resource Scheduling of Construction Projects

TL;DR: In this article, an augmented Lagrangian genetic algorithm model for resource scheduling is presented, which considers all precedence relationships, multiple crew strategies, total project cost minimization, and time-cost trade-off.
Journal ArticleDOI

Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm

TL;DR: In this paper, a multi-mode resource-constrained discrete time-cost-resource optimization (MRC-DTCRO) model is presented to select starting time and the execution mode of each activity satisfying all the project constraints.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

Genetic algorithms in search, optimization and machine learning

TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book

An Introduction to Genetic Algorithms

TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Journal ArticleDOI

A Comparison of Heuristic and Optimum Solutions in Resource-Constrained Project Scheduling

TL;DR: A comparison of eight different heuristic scheduling rules, including those found most effective in previous research on this problem, to find the rules most effective relative to an optimum solution.
Journal ArticleDOI

Using genetic algorithms to solve construction time-cost trade-off problems

TL;DR: An algorithm based on the principles of GAs for construction time-cost trade-off optimization and a computer program that can execute the algorithm efficiently are presented.
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