A heuristic approach for scheduling assessment centers

Human resources managers often use assessment centers to evaluate candidates for a job position. During an assessment center, the candidates perform a series of exercises. The exercises require one or two assessors (e.g., managers or psychologists) that observe and evaluate the candidate. If an exercise is designed as a role-play, an actor is required as well which plays, e.g., an unhappy customer with whom the candidate has to deal with. Besides performing the exercises, the candidates have a lunch break within a prescribed time window. Each candidate should be observed by approximately half the number of the assessors. Moreover, an assessor cannot be assigned to a candidate if they personally know each other. The planning problem consists of determining (1) resource-feasible start times of all exercises and lunch breaks and (2) a feasible assignment of assessors to candidates, such that the assessment center duration is minimized. We propose a list-scheduling heuristic that generates feasible schedules for such assessment centers. We develop novel procedures for devising an appropriate scheduling list and for incorporating the problem-specific constraints. Our computational results indicate that our approach is capable of devising optimal or near-optimal solutions to real-world instances within short CPU time.

Efficient list-generation techniques for scheduling assessment centers

Human resources managers often conduct assessment centers to evaluate candidates for a job position. During an assessment center, the candidates perform a series of tasks. The tasks require one or two assessors (e.g., managers or psychologists) that observe and evaluate the candidates. If an exercise is designed as a role-play, an actor is required who plays, e.g., an unhappy customer with whom the candidate has to deal with. Besides performing the tasks, each candidate has a lunch break within a prescribed time window. Each candidate should be observed by approximately half the number of the assessors; however, an assessor may not observe a candidate if they personally know each other. The planning problem consists of determining (1) resource-feasible start times of all tasks and lunch breaks and (2) a feasible assignment of assessors to candidates, such that the assessment center duration is minimized. We present a list-scheduling heuristic that generates feasible schedules for such assessment centers. We propose several novel techniques to generate the respective task lists. Our computational results indicate that our approach is capable of devising optimal or near-optimal schedules for real-world instances within short CPU time.

LTE uplink scheduling - flow level analysis

Long Term Evolution (LTE) is a cellular technology foreseen to extend the capacity and improve the performance of current 3G cellular networks. A key mechanism in the LTE traffic handling is the packet scheduler, which is in charge of allocating resources to active flows in both the frequency and time dimension. In this paper we present a performance comparison of three distinct scheduling schemes for LTE uplink with main focus on the impact of flow-level dynamics resulting from the random user behaviour. We apply a combined analytical/simulation approach which enables fast evaluation of flow-level performance measures. The results show that by considering flow-level dynamics we are able to observe performance trends that would otherwise stay hidden if only packet-level analysis is performed.

Scheduling the factory pick-up of new cars

Car manufacturers increasingly offer delivery programs for the factory pick-up of new cars. Such a program consists of a broad range of event-marketing activities. In this paper we investigate the problem of scheduling the delivery program activities of one day such that the sum of the customers’ waiting times is minimized. We show how to model this problem as a resource-constrained project scheduling problem with nonregular objective function, and we present a relaxation-based beam-search solution heuristic. The relaxations are solved by exploiting a duality relationship between temporal scheduling and min-cost network flow problems. This approach has been developed in cooperation with a German automaker. The performance of the heuristic has been evaluated based on practical and randomly generated test instances.