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This paper describes a case that examines five key topics in production and operations management [1]—forecasting, inventory control, transportation planning, aggregate planning, and the disaggregation of the aggregate plan—in an integrated, realistic manner for upper‐level undergraduate business majors [3]. The case structure stresses the linkage of these interrelated subjects and supplements the regular classroom presentations dealing with them. The case relies on computer calculations at each stage to provide the information input for calculations at the next stage. It is a static model with a twelve‐month planning horizon. Students complete several exercises and assume several roles as the case unfolds. They act on their own in filling some roles and as members of teams in performing others. They do not compete with each other, as the purpose of the case is to help them develop an appreciation of the factors that persons performing the role must consider.

SUMMARYAlthough IMES is apparently a marriage of independently developed modules, the blend is nearly seamless‐there are only minor differences in "feel" between the three modules. IMES is well organized and easy to use. There are help screens at every stage in each module. Selection is efficient‐queries rarely take more than a few seconds on a 386 machine before a report can be generated. There are a few pitfalls in model selection which are difficult to avoid. Improper classification is one of them. For example, IMES lists MINTEQ as a multimedia model. It would be more appropriate from this reviewer's perspective to classify MINTEQ as a geo‐chemical model (for which this version of IMES has no classification category.)One minor concern is that in two modules (Selection and Validation)IMES queries the operator "Do you really want to exit?" or "Exit?" when one simply wants to go back one level in the screening process. It would be less disconcerting to be consistently presented (as is done in the Uncertainty module)with a pop‐up menu selection like "Do you want to return to the previous screen?"IMES was an ambitious undertaking that resulted in a useful and important contribution to Exposure Assessment Model community.

SummaryDaily behaviour patterns in a hunter–gatherer community of Colombian Indians show that individual activities are regulated by ultradian behaviour cycles of about 2 hr and that these cycles can be synchronised by social interaction. A computer model was developed which simulated an artificial community and generated dynamic portraits of locomotor activity and social aggregation similar to those of the observed community of Colombian Indians. Social phase-locking of ultradian behaviour cycles occurred, contributing to the safety of group members and their economy of effort in gathering and related activities. Social synchronisation of ultradian behaviour cycles may also have occurred in early hominid groups.

AbstractQuantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.

Integrated assessment (IA) can be defined as a structured process of dealing with complex issues, using knowledge from various scientific disciplines and/or stakeholders, such that integrated insights are made available to decision makers (J. Rotmans, Enviromental Modelling and Assessment 3 (1998) 155). There is a growing recognition that the participation of stakeholders is a vital element of IA. However, only little is known about methodological requirements for such participatory IA and possible insights to be gained from these approaches. This paper summarizes some of the experiences gathered in the ULYSSES project, which aims at developing procedures that are able to bridge the gap between environmental science and democratic policy making for the issue of climate change. The discussion is based on a total of 52 IA focus groups with citizens, run in six European and one US city. In these groups, different computer models were used, ranging from complex and dynamic global models to simple accounting tools. The analysis in this paper focuses on the role of the computer models. The findings suggest that the computer models were successful at conveying to participants the temporal and spatial scale of climate change, the complexity of the system and the uncertainties in our understanding of it. However, most participants felt that the computer models were less instrumental for the exploration of policy options. Furthermore, both research teams and participants agreed that despite considerable efforts, most models were not sufficiently user-friendly and transparent for being accessed in an IA focus group. With that background, some methodological conclusions are drawn about the inclusion of the computer models in the deliberation process. Furthermore, some suggestions are made about how given models should be adapted and new ones developed in order to be helpful for participatory IA.