The Study On Multi-objective Resource Constrained Project Scheduling Problem Based On Bacteria Foraging Optimization Algorithm

With the development of society and economy, project management has become themain form of innovation for business management. Project scheduling problem is one of thecore issues of project management. Network planning technique developed in the1950s, haswidespread application in the field of project scheduling. It is mainly to the project evaluationand review technique and the critical path method, but the two methods did not consider theresources constraint, therefore, resource-constrained project scheduling problem has graduallybecome a research hotspot. The classical RCPSP has been studied by many scholars, andmany kinds of algorithms for solving the problem have been put forward. RCPSP is mainly inview of the single objective optimization, while in the actual project, project schedulingproblems often require consideration of multiple objectives. Currently, there is little researchabout multi-objective resource constrained project scheduling problem. In this research, themulti-objective resource constrained project scheduling problem with two objectives of totalmakespan and resource balance is solved by bacteria foraging optimization algorithm.Considering the shortage of BFO, this paper combine the two algorithms-BFO and PSOto improve the search ability of the algorithm. The quorum-sensing mechanism is introducedby PSO. Before chemotaxis operation, bacteria will update the position according to theupdating mechanism of PSO until the fitness is improved. On this basis, chemotaxis operationand other operations of BFO then are performed on bacterial. The simulation results show thatthe improved algorithm has good searching performance.The evolution strategy of multi-objective optimization problem is analyzed. The bankerlaw which has high construct efficiency is used to construct the pareto solutions, and thegroup profile is adopted to save the solutions. In order to maintain the population diversityand convergence speed of the algorithm, a new representation of fitness function based onthe difference between ideal value and crowded degree is proposed. The effectiveness andefficiency of the algorithm are tested by solving the problem of the PSPLIB. Finally, theimproved bacteria foraging optimization algorithm is used to solve the H Enterpriseautomobile product development project scheduling problem, and the result indicates that thealgorithm has a practical application value.