A multi-mode resource-constrained discrete time–cost tradeoff problem solving using an adjusted fuzzy dominance genetic algorithm

The resource-constrained project scheduling problem has received broad attentions and was evolved into various sub-problems such as resource-constrained discrete time-cost tradeoff problem. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently with RCPSP. This research proposed a new model which integrates the resource leveling problem and resource-constrained time-cost tradeoff problem. The evolutionary multi-objective optimization technique, strength Pareto evolutionary approach II (SPEA II) was applied to calculate the Pareto front of time and cost. The resource leveling measured by the metric, resource release/re-hiring, was converted to resource cost. The analysis of the time complexity of the model showed that the runtime of the algorithm was polynomial times of the number of activities. The results of case testing showed that the model was reasonably accurate in comparison with a proposed baseline model.

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Multi-resource constrained discrete time/cost trade-off problem and its improved genetic algorithm

2010 International Conference on Management Science & Engineering 17th Annual Conference Proceedings ◽

10.1109/icmse.2010.5719794 ◽

2010 ◽

Author(s):

Zhang Jing-wen ◽

Shan Hui-fang

Keyword(s):

Genetic Algorithm ◽

Discrete Time ◽

Time Cost ◽

Improved Genetic Algorithm ◽

Resource Constrained ◽

Trade Off

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

Periodica Polytechnica Civil Engineering ◽

10.3311/ppci.19145 ◽

2021 ◽

Author(s):

Ali Kaveh ◽

Farivar Rajabi

Keyword(s):

Discrete Time ◽

Project Planning ◽

Resource Optimization ◽

Time Cost ◽

Resource Constrained ◽

Planning And Scheduling ◽

Multi Objective ◽

Construction Companies ◽

Resource Leveling ◽

Multi Mode

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.

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