Journal of Scheduling最新文献

Examination timetabling is a problem well known to the scheduling community. Its simplest version, which is the uncapacitated examination timetabling problem, is easily described and comprehended. Nevertheless, proof of optimality is notoriously difficult even for moderate size problems. In this paper, we describe the effort that our team exercised in finally proving the optimality of the sta83 instance of Carter’s dataset. The problem was decomposed naturally in three parts and for each part a different approach managed to prove optimality of the currently best known solution. This work also presents optimal solutions to subproblems that exist in various public datasets problems and two best known solutions of such problems.

Abstract

No-shows and non-punctual appointments have always been uncertain factors faced by managers of service-oriented enterprises and institutions, which usually lead to a low utilization rate of resources and a rapid decline in service satisfaction. Taking clinics as an example, this paper proposes a novel and efficient real-time sequencing strategy to minimize the cost associated with patient waiting time, provider idle time and overtime considering no-shows and unpunctuality. Four types of patient waiting time are considered for the first time, based on which the developed real-time sequencing strategy is used for scheduling the waiting patients when the provider becomes idle. Then, a biased random-key genetic algorithm is adopted to determine the number of patients on appointment slots and the length of each appointment slot. Extensive computational experiments show that the derived real-time sequencing strategy achieves a significant cost reduction compared with the famous FCFS (first come first served) and the state-of-art LAR (the larger of appointment time and real arrival time) rules.

Distributed key-value stores employ replication for high availability. Yet, they do not always efficiently take advantage of the availability of multiple replicas for each value and read operations often exhibit high tail latencies. Various replica selection strategies have been proposed to address this problem, together with local request scheduling policies. It is difficult, however, to determine what is the absolute performance gain each of these strategies can achieve. We present a formal framework allowing the systematic study of request scheduling strategies in key-value stores. We contribute a definition of the optimization problem related to reducing tail latency in a replicated key-value store as a minimization problem with respect to the maximum weighted flow criterion. By using scheduling theory, we show the difficulty of this problem and therefore the need to develop performance guarantees. We also study the behavior of heuristic methods using simulations that highlight which properties enable limiting tail latency: for instance, the EarliestFinishTime strategy—which uses the earliest next available time of servers—exhibits a tail latency that is less than half that of state-of-the-art strategies, often matching the lower bound. Our study also emphasizes the importance of metrics such as the stretch to properly evaluate replica selection and local execution policies.

This work addresses a home health care scheduling problem faced by home care agencies. In home health care scheduling, there is a desire to retain consistency with respect to the home health aide servicing each patient; this consistency is referred to as continuity of care. To address this preference for continuity of care, we propose a rolling horizon approach to the scheduling problem and introduce the consistent home health care scheduling problem (Con-HHCSP). The goal is to be able to quantify and control the deviation of the new schedule suggested each day from the existing schedule in place, so that some of the existing assignments may be retained in the new schedule that is produced. We present two different methods to solve Con-HHCSP on a daily basis: an integer programming-based method with approximations and a variant of a petal heuristic. We discuss the performance and computational efficiency of these methods.

Abstract

We propose a multi-neighbourhood simulated annealing algorithm for the ITC-2007 version of the capacitated examination timetabling problem. The proposed solver is based on a combination of existing as well as newly proposed neighbourhoods that better exploit the disconnected structure of the underlying conflict graph and that explicitly deal with the assignment of exams to rooms. We use a principled tuning procedure to determine the parameters of the algorithm and assess the contribution of the various neighbourhoods by means of an ablation analysis. The resulting algorithm is able to compete with existing state-of-the-art solvers and finds several new best solutions for a variety of well-known problem instances.