Effectiveness of metaheuristics applied to Human Resource Allocation Problem in Short-Term Employment Sector – a case study

This work identifies and defines the real-world Human Resource Allocation Problem in Short-Term Employment Sector (HRAP-STE).HRAP is a subclass of the classic Human Resource Allocation Problem, adopted to the short-term employment sector, where the main everyday objective is assigning employees to the customer facilities (warehouses, factories, logistic centres, etc.).This process has three types of actors: customers, employees, and the company from the short-term employment sector, which provides a platform for cooperation.Usually, customers require significantly more employees than their available number.Since employee assignment is usually a subject of long-term cooperation, all customers should be satisfied (at least partially) even if they do not bring the highest profit.Thus, for a company in the short-term sector, HRAP refers to three objectives: profit from projects, priority of projects, and balance in the project portfolio to satisfy all clients.In this work, we define a specific HRAP-STE problem, consider its crucial elements, and define a benchmark set of real and artificial instances.To investigate the HRAP-STE as a real case study, we apply and compare well-known (meta)heuristics (shown effective in solving real-world problems) dedicated to solving discrete problems.The computational results show the advantages of (meta)heuristics in solving instances of a larger size.