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

Minimizing both project time and cost is an important matter in today's competitive environment. Therefore trade-off between project time and cost is necessary. In projects, each activity can be started at different time points, depending on its precedence relationship and resource availability. Also cost and duration of the activities could be changed depending on the allocated resources. In addition, another strategy that affects the project total time and cost is resource leveling, which is applied to reduce excessive fluctuations in the resource usage. In this paper multi-mode resource-constrained project scheduling problem (MRCPSP), discrete time–cost trade-off problem (DTCTP) and also resource allocation and resource leveling problem (RLP) are considered simultaneously. This paper presents the multi-mode resource-constrained discrete time–cost-resource optimization (MRC-DTCRO) model in order to select starting the time and the execution mode of each activity satisfying all the project constraints. To solve these problems, non-domination based genetic algorithm (NSGA-II) is employed to search for the non-dominated solutions considering total project time, cost, and resources moment deviation as three objectives. The results of MRC-DTCRO model presented in this paper show that adding the resource leveling capability to the previously developed multi-mode resource-constrained discrete time–cost trade-off problem (MRC-DTCTP) models provides more practical solutions in terms of resource allocation and leveling, which makes this research applicable to both construction industry and researchers.

► We model MRCPSP, DTCTP, RL and RA problems simultaneously.
► Revised serial SGS is presented to generate schedules from given chromosomes.
► We examine resource leveling effects on project time and cost.
► Decreasing fluctuation in resource usage histogram will increase project cost.
► Scheduling models with resource leveling capability are more practical.