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Surgical rescheduling is necessary for adjusting initial schedules on surgery day after emergency demand is realized. While people-oriented medical service has been emphasized these years, the traditional rescheduling scheme which is only in pursuit of a great cost-related performance is no longer desirable since patients and medical staff are also highly involved in rescheduling and their preferences should not be ignored. In order to provide a satisfactory and people-centered rescheduling plan, this study considers the preferences of three involved participants (i.e. the operating room manager, medical staff, and elective patients) while designing a rescheduling plan. Based on prospect theory, we introduce three functions to evaluate three participants’ dissatisfaction about rescheduling schemes in terms of their respective preferences. Then a multi-objective rescheduling model is established with multiple resource constraints, emergency lead-time target constraints, and the objective of minimizing the dissatisfaction of three participants caused by rescheduling. A hybrid particle swarm optimization (HPSO) algorithm with two improved strategies—an initial population construction strategy and a local search strategy, is then developed to solve the proposed problem. Several numerical experiments are carried out by leveraging data reported in existing case studies in conjunction with simulated data. The results demonstrate the effectiveness of two improved strategies and show that the proposed HPSO algorithm can provide better Pareto solutions for our problem in comparison with the basic non-dominated sorting genetic algorithm.

While timely emergency OR access is strictly required in many specialties, this paper provides a highly responsive preventive-reactive scheduling procedure to address emergency arrivals with waiting time limits before and on surgery day with no assumption on the emergency arrival patterns. To improve responsiveness in situations where existing break-in-moments-based scheduling does not perform well, a MIP model is first developed to generate initial schedules by making ex-ante preparations for future possible arrivals with the objective of minimizing the sacrifice in the throughput of elective surgeries. For applicability, two additional heuristics, which are derived from the MIP model but include other scheduling ideas, are also designed for preventive scheduling. Furthermore, as a supplement to preventive scheduling, a corresponding reactive scheduling method is designed to be applied on surgery day. Facing different emergency realizations, it provides effective and satisfactory adjustments on surgery day by scheduling arrived emergencies timely, properly updating the remaining schedule to prepare for subsequent arrivals with consideration of minimizing the adjusting impact on scheduled elective cases. The effectiveness and performances of the proposed procedures and heuristics are evaluated in comparison with traditional preventive and reactive scheduling strategies through simulations and statistical tests. The numerical results show that the methods developed in this study can significantly improve the responsiveness with an acceptable sacrifice in OR utilization and can provide satisfactory schedules for both types of patients. Some scheduling guidelines based on simulation results and the applicability of the proposed scheduless are also discussed at the end of this study.

This paper addresses a decentralized production decision and scheduling problem in smart electroplating plants considering environmental sustainability. Due to the characteristics of the electroplating process, part processing times are confined to time-window constraints and controlled by independent autonomous agents with the support of multi-agent and distribution manufacturing technologies. It is shown that part processing times in chemical/physical tanks not only determine the productivity but also affect the environmental cost. Hence, two essential issues should be addressed: (a) how to find equilibrium strategies of agents (i.e., part processing times) in such a decentralized manufacturing setting; and (b) how to design an incentive protocol inducing the equilibrium strategies towards the optimal ones concerning the productivity and the environmental cost simultaneously. To deal with the first issue, this paper establishes a non-cooperative sequential game model, which simulates the evolution process of the strategies from an unstable state to an equilibrium one. Then, an iterative algorithm is developed to find Nash equilibrium strategies based on a cyclic bi-value graph. To address the second issue, a novel heuristic rule is proposed for the incentive protocol by exploring the characteristics of time-window constraints and environmental cost functions. The results of numerical experiments validate the performance of the Nash-equilibrium algorithm and the effectiveness of the heuristic rule.