Multi-population symbiotic organisms search algorithm for solving the construction resource leveling problem

During any type of construction project, a necessary factor that determines success is proper scheduling. Different implementation methods seem to have been undertaken for scheduling, like program evaluation review technique (PERT), critical path method (CPM), as well as Gantt charts. These methods assume that there are unlimited resources available, which may fluctuate the demand of resources and may result to increased cost. A method used to efficiently allocate resources such that resource demand fluctuation is reduced is called resource leveling. Some studies have employed some metaheuristic methods as an alternative to resource leveling, such as genetic algorithm (GA) and particle swarm optimization (PSO). However, each method has their own shortcomings; one of these shortcomings is overreliance on parameter tuning. If there is no optimization of the parameter setting, additional computational time is needed to find the best solution to the problem. Recently, there has been an increase in the use of the symbiotic organism’s search (SOS) when addressing many optimization issues in different research fields. SOS does not need a particular parameter tuning given that it uses only common parameters like iteration numbers and population size to operate unlike the majority of other population-based metaheuristic algorithms. Thus, this study investigates the performance of SOS in solving the construction resource leveling problem. A construction project with 44 activities is adopted as the main case study. In this research, the SOS has been modified to accommodate the multi-population feature in order to increase its searching ability. To study the comprehensive effect of resource fluctuation, instead of only using one resource fluctuation criterion, this study integrates two different objective functions with different resource utilization diagrams. It can be seen throughout the simulation process that MP-SOS is able to reduce the resource fluctuation significantly in both objective functions.