Study On Multi-mode Resource-constrained Duration-cost Trade-off Problem

The Resource-Constrained Project Scheduling Problem(RCPSP) is a wide spread practical question relating to optimization. The problem studies how to schedule the activities to achieve some optimal managerial objectives under precedence and resource constraints. The models of such problems are generally established basing on the single goal of scheduling, so they can only be used to solve single objective problems. However, with the continuous development of society and the deepening of the process of enterprise project, one project always have to face a number of targets, which need a multi-objective scheduling problem model to solve such problems. As the complexity of the multi-objective problem itself, there's only a little study on the multi-objective resource constrained project scheduling problem.Based on this, this paper summarizes the superiority of the Dual-Population Ant Colony Algorithm(DPACA) after a detail description about the development of Ant Colony Optimization Algorithm at home and abroad, then propose a Dual-Population Ant Colony Algorithm which base on the duration and cost. This paper using a Dual-Population Ant Colony Algorithm to solve the multi-mode resource constrained project scheduling problem. And the problem has two objectives of total cost and total makespan. Main tasks are:consider to divide the ant colony into two populations, corresponding to a different objective function, by sharing their respective populations of the pheromone to search results feedback to the exchange, completing, the Dual-Population Ant Colony Algorithm model through the designing of the rules of conversion, the definition of pheromone,heuristic definitions and elitist multi-objective strategy. Finally, in order to verify the feasibility of the model in solving the multi-objective problem, this paper takes a comparison between the results which obtains by the Dual-Population Ant Colony Algorithm and Single-Population Ant Colony Algorithm each. Then this paper makes a comparison though a single objective evaluation and comprehensive evaluation between the Dual-Population Ant Colony Algorithm and Single-Population Ant Colony Algorithm:from the perspective of single target, compare the solution using Dual-Population Ant Colony Algorithm and the solution using Single-Population Ant Colony Algorithm with the objective value respectively. From the comprehensive target set, normalize time-cost targets into one target, and calculate the deviation between it and comprehensive target value. Last, based on the comparison and analysis, explain feasibility and superiority of Dual-Population Ant Colony Algorithm for solving duration-cost optimization.While achieving the expected objectives basically, this paper also found some problems during the studying. How to solve these problems and develop the Dual-Population Ant Colony Algorithm into Meta-Heuristics Algorithm in the field of scientific research and engineering practice will be a researching direction worth considering.