Ant Colony Optimization for Multimode Resource-Constrained Project Scheduling

Abstract

An ant colony optimization (ACO)-based methodology for solving a multimode resource-constrained project scheduling problem (MRCPSP) with the objective of minimizing project duration is presented. With regards to the need to determine sequence and mode selection of activities for the MRCPSP, two levels of pheromones for each ant are proposed to guide the search course in the ACO algorithm. The corresponding heuristics and probabilities for each type of the pheromone are considered, and their calculation algorithms are presented. The flowchart of the proposed ACO algorithm is described, where a serial schedule generation scheme is adopted to transform an ACO solution into a feasible schedule. The effectiveness and efficiency of the proposed ACO methodology are justified through a series of computational analyses. The study is expected to provide a more effective alternative methodology for solving the MRCPSP by utilizing the ACO theory.

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