Stochastic time-cost optimization model incorporating fuzzy sets theory and nonreplaceable front

In a real construction project, the duration and cost of each activity could change dynamically as a result of many uncertain variables, such as weather, resource availability, productivity, etc. Managers/planners must take these uncertainties into account and provide an optimal balance of time and cost based on their own experience and knowledge. In this paper, fuzzy sets theory is applied to model the managers' behavior in predicting time and cost pertinent to a specific option within an activity. Genetic algorithms are used as a searching mechanism to establish the optimal time-cost profiles under different risk levels. In addition, the nonreplaceable front concept is proposed to assist managers in recognizing promising solutions from numerous candidates on the Pareto front. Economic analysis skills, such as the utility theory and opportunity cost, are integrated into the new model to mimic the decision making process of human experts. A simple case study is used for testing the new model developed. In comparison with the previous models, the new model provides managers with greater flexibility to analyze their decisions in a more realistic manner. The results also indicate that greater robustness may be achieved by taking some risks. This research is relevant to both industry practitioners and researchers. By incorporating the concept of fuzzy sets, managers can represent the range of possible time-cost values as well as their associated degree of belief. The model presented in this paper can, therefore, support decision makers in analyzing their time-cost optimization decision in a more flexible and realistic manner. Many novel ideas have also been incorporated in this paper to benefit the research community. Examples of these include the use of fuzzy sets theory, nonreplaceable front concept, utility theory, opportunity cost, etc. With suitable modifications, these concepts can be applied to model to other similar optimization problems in construction.link_to_OA_fulltex

GA-Based Multiobjective Technique for Multi-Resource Leveling

Resource leveling is a commonly used planning technique to avoid extraordinary demands or excessive fluctuations in labor and plant resources required for a construction project, which could otherwise lead to a drop in productivity or an increase in production cost. In performing resource leveling, many planners or managers would adopt standard heuristic approaches to obtain an acceptable solution. This is because mathematical methods are only considered suitable for small to medium networks due to the combinatorial non-deterministic nature of the problem. The leveling of multiple resources is also dominated by the chosen heuristic methods, e.g. whether by leveling multiple resources in series or through combined resource leveling. Although heuristic approaches are easy to understand, they are problem-dependent. Hence, it is difficult to guarantee that an optimal solution can be achieved. This paper proposes a new Genetic Algorithms (GAs) enabled multiobjective technique for optimizing the multi-resource leveling problem. Adaptive weights are introduced so that each resource is assigned with a certain priority. This could effectively avoid the dominance of only one resource through the optimization process, as the adaptive weights can 'learn' from the last generation and guide the genetic algorithms to balance the search pressure among different resources.link_to_subscribed_fulltex

Applying a genetic algorithm-based multiobjective approach for time-cost optimization

Reducing both project cost and time (duration) is critical in a competitive environment. However, a trade-off between project time and cost is required. This in turn requires contracting organizations to carefully evaluate various approaches to attaining an optimal time-cost equilibrium. Although several analytical models have been developed for time-cost optimization (TCO), they mainly focus on projects where the contract duration is fixed. The optimization objective in those cases is therefore restricted to identifying the minimum total cost only. With the increasing popularity of alternative project delivery systems, clients and contractors are targeting the increased benefits and opportunities of seeking an earlier project completion. The multiobjective model for TCO proposed in this paper is powered by techniques using genetic algorithms (GAs). The proposed model integrates the adaptive weights derived from previous generations, and induces a search pressure toward an ideal point. The concept of the GA-based multiobjective TCO model is illustrated through a simple manual simulation, and the results indicate that the model could assist decision-makers in concurrently arriving at an optimal project duration and total cost.link_to_OA_fulltex

Applying pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimization

Time-cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms (GAs)-driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous "generation." In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the GA-based multiobjective TCO model.link_to_subscribed_fulltex