Forward-backward Improvement for Genetic Algorithm Based Optimization of Resource Constrained Scheduling Problem

Abstract

Due to the development of management idea and the scarcity of some resources, the lean management has become the necessary request to implement effective control of resource constrained project. Resource constrained project scheduling is the significant guarantee to attain the lean management. Genetic algorithm is one of the basic heuristics to solve the resource constrained project scheduling problem (RCPSP), the precedence relations of which is described by an activity-on-arrow (AOA) network. The chromosomes are encoded as the extended priority value list (EPL) and decoded by the parallel schedule generation scheme (PSGS). To enhance the exploitation ability, the iterative forward-backward improvement as the local search procedure is applied upon all generated solutions using PSGS. The chromosomes supplied by the genetic algorithm are then adjusted to reflect the solutions obtained by the improvement procedure. The overall framework of the GA with forward-backward improvement for RCPSP is developed and the algorithm of FBI is schematically designed. Comparative computational experiments demonstrate the effectiveness of the proposed algorithm in solution to a medium-sized benchmark RCPSP with its precedence relation of activities being diagramed as an AOA network.