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Many companies operate assessment centers to help them select candidates for open job positions. During the assessment process, each candidate performs a set of tasks, and the candidates are evaluated by some so-called assessors. Additional constraints such as preparation and evaluation times, actors' participation in tasks, no-go relationships, and prescribed time windows for lunch breaks contribute to the complexity of planning such assessment processes. We propose a multi-pass list-scheduling heuristic for this novel planning problem; to this end, we develop novel procedures for devising appropriate scheduling lists and for generating a feasible schedule. The computational results for a set of example problems that represent or are derived from real cases indicate that the heuristic generates optimal or nearoptimal schedules within relatively short CPU times. KeywordsReal-world application • Human resource management • Assessment center • List scheduling • Multi-pass heuristic B Adrian Zimmermann

In the literature, various discrete-time and continuous-time mixedinteger linear programming (MIP) formulations for project scheduling problems have been proposed. The performance of these formulations has been analyzed based on generic test instances. The objective of this study is to analyze the performance of discrete-time and continuous-time MIP formulations for a real-life application of project scheduling in human resource management. We consider the problem of scheduling assessment centers. In an assessment center, candidates for job positions perform different tasks while being observed and evaluated by assessors. Because these assessors are highly qualified and expensive personnel, the duration of the assessment center should be minimized. Complex rules for assigning assessors to candidates distinguish this problem from other scheduling problems discussed in the literature. We develop two discrete-time and three continuous-time MIP formulations, and we present problem-specific lower bounds. In a comparative study, we analyze the performance of the five MIP formulations on four real-life instances and a set of 240 instances derived from real-life data. The results indicate that good or optimal solutions are obtained for all instances within short computational time. In particular, one of the real-life instances is solved to optimality. Surprisingly, the continuous-time formulations outperform the discrete-time formulations in terms of solution quality.

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