We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm: MPAEX. It respects the decentralization of scheduling authorities and continuously improves patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of the health care cycle where a doctor repeatedly orders sets of activities to diagnose and/or treat a patient. We introduce the Theil index to the health care domain to characterize different hospital patient scheduling problems in terms of the degree of relative workload inequality between required resources. In experiments that simulate a broad range of hospital patient scheduling problems, we extensively compare the performance of MPAEX to a set of scheduling benchmarks. The distributed and dynamic MPAEX performs almost as good as the best centralized and static scheduling heuristic, and is robust for variations in the model settings.Keywords Health care · Patient scheduling · Multi-agent systems A preliminary version of this work has appeared as [1].

C e n t r u m v o o r W i s k u n d e e n I n f o r m a t i c aSoftware ENgineering

When we look at successful sales processes occurring in practice, we find they combine two techniques which have been studied separately in the literature. Recommender systems are used to suggest additional products or accessories to include in the bundle under consideration, and multi-issue negotiation focuses on optimizing the precise configuration of the bundle and its price. In this paper, we pursue the joint automation of such interactive sales processes.We present some key insights about, as well as a procedure for locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences, learnt by the shop * We want to thank the anonymous reviewers for helpful comments on our paper. 1 in interactions with previous customers, with current data about the ongoing negotiation process with the current customer. We present a memory-and a model-based method for online learning customer preferences and discuss their pros and cons. The performance of our system is illustrated using extensive computer experiments involving simulated customers with highly non-linear preferences which the system has no trouble learning.

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