A novel disjunctive-graph-based meta-heuristic approach for multi-objective resource-constrained project scheduling problem with multi-skilled staff

In the implementation of projects, human resources play a crucial role. The effective assignment of multi-skilled staff among project scheduling can enhance the enterprise competitiveness. Hence, this work addresses the resource-constrained project scheduling problem with multi-skilled staff (MS-RCPSP) to minimize project completion time and total salary cost. A position-based mixed-integer linear programming model, a disjunctive graph model and a novel disjunctive-graph-based objective-guided nearest neighborhood search (DO-NNS) algorithm are proposed. The algorithm includes a resource-oriented encoding, a critical path method-based decoding and a nearest neighborhood search mechanism. By analyzing the disjunctive graph model, this work mines six relational attributes and three properties. Further, this algorithm uses these properties to design three objective-guided neighborhood search operators to enhance its performance. Moreover, the enhanced population update strategy is developed to enhance the quality of Pareto solutions. Finally, the experimental results demonstrate that the improvements are effective and the DO-NNS is superior to five latest multi-objective algorithms in terms of achieving higher-quality Pareto solutions.