Fuzzy-multi-mode Resource-constrained Discrete Time-cost-resource Optimization in Project Scheduling Using ENSCBO

Abstract

Construction companies are required to employ effective methods of project planning and scheduling in today's competitive environment. Time and cost are critical factors in project success, and they can vary based on the type and amount of resources used for activities, such as labor, tools, and materials. In addition, resource leveling strategies that are used to limit fluctuations in a project's resource consumption also affect project time and cost. The multi-mode resource-constrained discrete-time–cost-resource optimization (MRC-DTCRO) is an optimization tool that is developed for scheduling of a set of activities involving multiple execution modes with the aim of minimizing time, cost, and resource moment. Moreover, uncertainty in cost should be accounted for in project planning because activities are exposed to risks that can cause delays and budget overruns. This paper presents a fuzzy-multi-mode resource-constrained discrete-time–cost-resource optimization (F-MRC-DTCRO) model for the time-cost-resource moment tradeoff in a fuzzy environment while satisfying all the project constraints. In the proposed model, fuzzy numbers are used to characterize the uncertainty of direct cost of activities. Using this model, different risk acceptance levels of the decision maker can be addressed in the optimization process. A newly developed multi-objective optimization algorithm called ENSCBO is used to search non-dominated solutions to the fuzzy multi-objective model. Finally, the developed model is applied to solve a benchmark test problem. The results indicate that incorporating the fuzzy structure of uncertainty in costs to previously developed MRC-DTCRO models facilitates the decision-making process and provides more realistic solutions.

Resource allocation capabilities of commercial project management software packages for resource leveling and resource constrained project scheduling problems: A comparative study

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Optimum structural design of spatial truss structures via migration-based imperialist competitive algorithm

“…The Big Bang-Big Crunch (BB-BC) [7], Simulated Annealing (SA) [8], Charged System Search (CSS) [9], and Colliding Bodies Optimization (CBO) [10] are cases in point. Numerous studies in literature have been conducted to apply various versions of the above mentioned algorithms for the optimal design of practical systems and models with complex configurations [11][12][13]. In the case of structural optimization, in order to minimize the weight of different types of structures to achieve optimal design under specific criteria, Kaveh et al [14] proposed an Enhanced Forensic-Based Investigation (EFBI) for dome truss optimization problems with frequency constraints.…”