Tarek Hegazy

Comparison among five evolutionary-based optimization algorithms

Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping. A brief description of each algorithm is presented along with a pseudocode to facilitate the implementation and use of such algorithms by researchers and practitioners. Benchmark comparisons among the algorithms are presented for both continuous and discrete optimization problems, in terms of processing time, convergence speed, and quality of the results. Based on this comparative analysis, the performance of EAs is discussed along with some guidelines for determining the best operators for each algorithm. The study presents sophisticated ideas in a simplified form that should be beneficial to both practitioners and researchers involved in solving optimization problems.

A modified shuffled frog-leaping optimization algorithm: applications to project management

Evolutionary algorithms, such as shuffled frog-leaping, are stochastic search methods that mimic natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to complex and large-scale optimization problems which cannot be solved by gradient-based mathematical programming techniques. The shuffled frog-leaping algorithm draws its formulation from two other search techniques: the local search of the ‘particle swarm optimization’ technique; and the competitiveness mixing of information of the ‘shuffled complex evolution’ technique. A brief description of the original algorithm is presented along with a pseudocode and flowchart to facilitate its implementation. This paper then introduces a new search-acceleration parameter into the formulation of the original shuffled frog-leaping algorithm to create a modified form of the algorithm. A number of simulations are carried out for two continuous optimization problems (known as benchmark test problems) and two discrete optimization problems (large scale problems in the project management domain) to illustrate the positive impact of this new parameter on the performance of the shuffled frog-leaping algorithm. A range of ‘best’ usable values for the search-acceleration parameter is identified and discussed.

Comparison of Two Evolutionary Algorithms for Optimization of Bridge Deck Repairs

Most bridge management systems have been developed to support either network- or project-level decisions. Network-level decisions include the selection of bridges for repair while repair strategies are considered project-level decisions. This article introduces an integrated model for bridge deck repairs with detailed life cycle costs of both network-level and project-level decisions. Two evolutionary-based optimization techniques that are capable of handling large-size problems, namely genetic algorithms and shuffled frog leaping, are then applied on the model to optimize maintenance and repair decisions. Ten trial runs with different numbers of bridges were used to compare the results of both techniques. The results indicate that both techniques can be equally suitable, and that the key issue is determining the set of parameters that optimize performance. The best optimization strategy for this type of problem appears to be a year-by-year strategy coupled with the use of a preprocessing function to allocate repair funds first to critical bridges.

Bridge Deck Management System with Integrated Life-Cycle Cost Optimization

Bridge management systems can be classified as one of two types: network level or project level. The former type is concerned with the prioritization of bridges for inclusion in an upcoming maintenance, repair, and rehabilitation program, and the latter focuses on the repairs that suit the components of a selected bridge. Even though these types are interrelated, most bridge management research treats them as separate aspects. A comprehensive framework is presented for a bridge deck management system that aims at integrating project-and network-level decisions into a unified model to optimize costs at both levels. The novelty of the proposed approach stems from three main aspects: incorporating project-level repair options along with their performance improvements and cost implications; incorporating many flexible and practical features such as variable yearly budget limits, variable yearly discount rates, and optional methods for handling project-level repairs (e.g., single or multiple visits); and using a powerful genetic algorithm-based optimization to consider both project- and network-level variables into bridge life-cycle cost optimization. The proposed model and its implementation are described, and an example application is presented. Although this research focuses on bridge decks, details on future improvements to incorporate all bridge components are outlined.

Schedule-dependent evolution of site layout planning

The appropriate layout of temporary facilities on a construction site has a large impact on construction safety and productivity. For the duration of a project the site layout may need to be efficiently re-organized at various intervals to satisfy the schedule requirements and to maintain site efficiency. This paper presents a practical model for schedule-dependent site layout planning in construction. The proposed model uses a combination of artificial intelligence tools (knowledge-based systems, fuzzy logic, and genetic algorithms) to generate, optimize, and re-organize the site layout plan at frequent intervals during the project. The model incorporates flexible representation of irregular site shapes and several options for placing facilities. Based on the proposed model, an automated system is developed, fully integrated with widely used scheduling software. At each schedule interval, the system recalculates the space requirements and, for the convenience of congested sites, can utilize parts of the constructed space to accommodate temporary facilities. Details of the schedule-dependent model are described, and its application in an actual case study project is presented to demonstrate its capabilities.

Tools of soft computing as applied to the problem of facilities layout planning

The layout of temporary facilities in a construction site deals with the selection of their most efficient layout in order to operate efficiently and cost effectively. The layout design seeks the best arrangement of facilities within the available area. In the design process of the layout, many objectives must be considered to effectively utilize people resources, equipment, space, and energy. This study proposes a soft-computing-based approach to improve the layout process of facilities. The main objective is on obtaining the closeness relationship values between each pair of facilities in a construction site. To achieve this, an integrated approach, using fuzzy set theory and genetic algorithms, is used to investigate the layout of temporary facilities in relation with the planned building(s) in a construction site. An example application is presented to illustrate the proposed approach and the results are then discussed along with recommendations for further work. Depending on the importance of relationships among the various facilities in the construction site, this study is expected to provide engineers with an appropriate tool to compare and evaluate different layouts and select the most appropriate and efficient one.

Critical Path Segments Scheduling Technique

While the critical path method (CPM) has been useful for scheduling construction projects, years of practice and research have highlighted serious drawbacks that hinder its use as a decision support tool. This paper argues that many of CPM drawbacks stem from the rough level of detail at which the analysis is conducted, where activities’ durations are considered as continuous blocks of time. The paper thus proposes a new critical path segments (CPS) mechanism with a finer level of granularity by decomposing the duration of each activity into separate time segments. Three cases are used to prove the benefits of using separate time segments in avoiding complex network relationships, accurately identifying all critical path fluctuation, better allocation of limited resources, avoiding multiple-calendar problems, and accurate analysis of project delays. The paper discusses the proposed CPS mechanism and comments on several issues related to its calculation complexity, its impact on existing procedures, and fu...

Attitude-Based Negotiation Methodology for the Management of Construction Disputes

A systematic negotiation methodology for construction disputes is presented to take into consideration the attitudes of negotiators at two complementary levels of decision making: strategic and tactical. At the strategic level, the proposed methodology employs the graph model for conflict resolution and helps negotiators find the most beneficial subset of solutions to the conflict. At the tactical level, the proposed methodology examines the most beneficial strategic decisions using utility functions to provide agreed-upon tradeoffs with respect to any conflicting issues. A construction case study is used to illustrate the proposed methodology and demonstrate the importance of incorporating decision makers’ attitudes into negotiation to better identify the most feasible decisions. The proposed methodology may assist negotiators with the challenges of conventional negotiation through the incorporation of decision makers’ attitudes into a range of analytical tools that will clarify interests, determine equi...

Multimode Resource-Constrained Scheduling and Leveling for Practical-Size Projects

AbstractThis paper aims at providing a fast near-optimum solution to the multimode resource-constrained project scheduling problem (MRCPSP) in large-scale projects, with and without resource-leveling constraints. The MRCPSP problem is known to be nondeterministic polynomial-time hard (NP-hard) and has been solved using various exact, heuristic, and metaheuristic procedures. In this paper, constraint programming (CP) is used as an advanced mathematical optimization technique that suits scheduling problems. The IBM ILOG modeling software and its CPLEX-CP solver engine have been used to develop a CP optimization model for the MRCPSP problem. Unlike many metaheuristic methods in literature, the CP model is fast and provides a near-optimum solution to the MRCPSP for projects with hundreds of activities within minutes. The paper compares the CP results with two case studies from the literature to prove the practicality and usefulness of the CP approach to both researchers and practitioners. One case study was u...

Computerized DSS for Construction Conflict Resolution under Uncertainty

Due to the inherent nature of construction projects, conflicts are unavoidable among the various parties involved. Such conflicts often delay projects and cause losses for all parties. This paper presents the development of a decision support system (DSS) to help in resolving construction disputes. The DSS integrates the elimination method to shortlist promising resolutions to a conflict, the graph model for conflict resolution to determine the best resolution that satisfies all decision makers’ preferences, and the information gap theory to consider uncertain decision preferences. A prototype system has been developed and a case study of a construction conflict used to demonstrate its features. The presented methodology for construction conflict resolution is useful for both researchers and practitioners to better deal with the dispute-prone nature of the construction industry under uncertainty and lack of information. In this paper, the proposed prototype successfully simulated and predicted the sequenc...