Modelling has been used extensively by all national governments and the World Health Organisation in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community. ARTICLE HISTORY

Even though we have moved beyond the Industrial Age and into the Information Age, manufacturing remains an important part of the global economy.There is a need for the pervasive use of modeling and simulation for decision support in current and future manufacturing systems, and several challenges need to be addressed by the simulation community to realize this vision. First, an order of magnitude reduction in problem-solving cycles is needed. The second grand challenge is the development of real-time, simulation-based problem-solving capability. The third grand challenge is the need for true plug-and-play interoperability of simulations and supporting software. Finally, there is the biggest challenge facing modeling and simulation analysts today: that of convincing management to sponsor modeling and simulation projects instead of, or in addition to, more commonly used manufacturing system design and improvement methods such as lean manufacturing and six sigma.

International audienceIn this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations

Uncertainty in the duration of surgical procedures can cause long patient wait times, poor utilization of resources, and high overtime costs. We compare several heuristics for scheduling an Outpatient Procedure Center. First, a discrete event simulation model is used to evaluate how 12 different sequencing and patient appointment time‐setting heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared with current practice. Second, a bi‐criteria genetic algorithm (GA) is used to determine if better solutions can be obtained for this single day scheduling problem. Third, we investigate the efficacy of the bi‐criteria GA when surgeries are allowed to be moved to other days. We present numerical experiments based on real data from a large health care provider. Our analysis provides insight into the best scheduling heuristics, and the trade‐off between patient and health care provider‐based criteria. Finally, we summarize several important managerial insights based on our findings.

In this chapter, we discuss production planning approaches for semiconductor manufacturing. Planning is on the highest level of the PPC hierarchy. Planning approaches provide important input for the order release schemes discussed in Chap. 6. We start by describing short-term planning approaches. Spreadsheet modeling and simulation are used in this situation.Then, we continue by describing master planning approaches in semiconductor manufacturing. They are used to assign production quantities to different facilities in different periods of time for a horizon of several months. Weekly time periods are considered. Simulation-based performance assessment of master planning approaches is briefly discussed. Next, we discuss capacity planning approaches. In contrast to master planning, these approaches deal with a longer planning horizon and monthly time periods. We discuss only deterministic planning approaches for master and capacity planning. Then, we present enterprise-wide planning approaches. In this situation, we consider a planning horizon of several years and quarters as periods. We also deal with the question of whether or not it is beneficial to open new facilities. Deterministic and stochastic settings are described for enterprise-wide planning problems.One typical assumption in planning approaches is a fixed CT; however, the CT is load-dependent. Therefore, we discuss different possibilities to model load-dependent CT within planning approaches. We consider CT-TP curves, iterative simulation, and finally clearing functions. Short-Term Capacity PlanningIn this section, we start by discussing the motivation of spreadsheet-based and simulation-based short-term capacity planning. We then make the first approach more concrete for wafer fabs. Spreadsheet-based short-term capacity planning approaches are discussed for back-end facilities. Finally, short-term capacity planning based on discrete-event simulation is described.

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