A bi-objective model for scheduling of multiple projects under multi-skilled workforce for distributed load energy usage

Abstract

Satisfying the clients' uncompromising priorities is a challenge for decision makers of organizations that face multiple projects. This paper considers an organization with a multi-skilled workforce working on several predetermined projects under time-of-use energy tariffs where distributed load energy usage is the primary concern of energy suppliers. This paper also considers different time-of-use energy tariffs, which are among the most common strategies to reach a more balanced energy utilization. The problem is stated as a bi-objective mixed-integer programming model containing two conflicting objectives; to minimize the total cost of the multi-skilled workforce and obtain a sustainable schedule with minimum deviation from the projects' priorities. The problem is formulated mathematically, and the GAMS solver is applied for justifying the conflict between the objectives and validating the proposed formulation. In order to tackle real-life instances of the problem, intelligent algorithms based on cuckoo search, particle swarm, and genetic algorithms are developed. In the proposed algorithms, the application of a well-designed encoding and decoding structure efficiently ensures the generated solutions' feasibility. The Taguchi method is used for calibrating the parameters of the proposed meta-heuristics. Performance of the solving methods is evaluated based on some experiments, where ELECTRE method is utilized as a decision-making technique to prioritize the developed algorithms. To this aim, some well-known multi-objective measures are applied for comparative analysis of the results, where the supremacy of FSCS in terms of all metrics is declared.