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The objective of this research is to study the cost of Net Zero Energy (NZE) communities of different urban scales and densities, while taking into consideration the local climate and the type of buildings in the community. A comprehensive model was developed for this purpose, with which the cost-optimal configuration of renewable energy-related technologies for an NZE community can be identified. To validate the model, data from two case studies that differed in their climate and building types were used. The results of this study contribute to a better understanding of the implications of NZE requirements for urban planning. An increase in the scale of a community was found to reduce energy costs, up to a certain point. Urban density, on the other hand, was found to have a more complex impact on costs, which depends on the local climate of the community and the subsequent energy demand. This underlines the importance of addressing the technological design of energy systems at the initial stage of the urban planning of energy-efficient communities, before the urban density, the unbuilt areas and the building types are set.

It is widely agreed that expanding the Net Zero Energy concept to a community scale brings multiple advantages in terms of design flexibility, cost reduction, clean energy and resource management. However, it is less clear what the scale or the density of the community should be to utilize these advantages in the best possible way. The objective of this work is to study how the Life Cycle Costs of new Net Zero Energy communities are affected by changes in their density and scale. To achieve this objective, a model was developed for identifying the optimal configuration of energy technologies for each specific scale and density. The analysis takes into consideration the local climate and the types of buildings in the community. The results of the implementation of the model show that both urban density and scale have a direct impact on the costs of NZE communities, but in ways that depend on the local climate.

Production shop is a complex task that impacts the ability of an organization to integrate between economic and production performance measurements. The CONWIP (CONstant Work In Process) approach is the simplest pull system to implement. Two-machine transfer line with the exponentially distributed duration of the process, break and repair were formulated and solved as a Markovian-chain. Decomposition was used to iteratively employ this solution in solving K-machine transfer line. These models assume that batch size is fixed. In this study we extend the existing transfer line solution, to deal with 1 unit process batch and t units transfer batch along the line.

Wafer fabrication is characterised with advanced equipment, complex processes and high cost. Good production output, measured by yield given total throughput, should rapidly increase while considering the associated cost. Yield improvement models based on inspection usually consider the effect of excursion monitoring, while this work considers the effect of learning from experience as well. It assumes a production model where each machine’s output is inspected via wafers it processes, triggering repair if required. The yield improvement is modelled as a function of machine’s quality performance, accumulated inspections, inspection capacity and inspection rate. It exhibits a sigmoid shape curve with slow rise in startup, acceleration in ramp and almost a plateau in high volume manufacturing. Higher inspection rate enables more inspections over time, faster learning and higher yield. Yet, higher inspection rate at constant capacity prolongs the response time and can further drive lower yield. Clearly, highe...

This paper describes the development of a set of control and optimization tools that support the implementation of highly innovative energy technologies in the design of Net Zero Energy settlements. The incorporation of novel technologies in building and settlement design involves unique challenges, such as frequent design changes, the risk of cost overruns and the need to synchronize the work of different stakeholders who did not previously collaborate. Three tools were developed and implemented to cope with those challenges. A cost control tool tracks the technology’s initial costs during the design phase, so that any cost overrun can be easily recognized and treated. A change management tool allows the project’s designers to identify, examine and discuss the implications of every proposed change in the design before the change is executed, and prevent deviations from the project’s cost and energy performance requirements. A third tool supports a life cycle cost analysis of each technology and an identification of their optimal configuration. In response to a tight schedule and the uncertainty regarding the users’ exact requirements, an Evolutionary Prototyping methodology was adopted to develop the tools. They were implemented in a project involving the design of Net Zero Energy settlements in four different European countries.

Queuing networks have been used with partial success for analytical modelling of manufacturing systems. In this paper, we consider a tandem system with high traffic variability caused by downtime events in the first queue. We propose improved approximation for departure variability in order to predict the waiting duration at the bottleneck queue located last in the line. We demonstrate that existing methods do not properly approximate such systems and provide some reasons and insights. Thus, a new decomposition method which employs the variability function principles is proposed. We differentiate between two components of the departure variability in multi-class systems: the ‘within-class effect’ – the variability caused by the class’ own inter-arrival and service time distributions – and the ‘between-class effect’ – the variability caused by interactions with other classes. Our analysis shows that the first effect can be approximated by existing multi-class decomposition methods, while the second effect ...

This work studies the relationship between Yield and Flow Time (FT) in a production system monitored by in-line inspection. It originates in the known semiconductors Yield vs. FT trade-off premise, but can be adapted to other industries. We challenge the common premise, and suggest an alternate analytical model to demonstrate this relationship. The model relies on a simplified production system that represents a repetitive segment in a production line. It illustrates that rising inspection rate increases both Yield and FT while exhibiting a trade-off. However with further growing inspection rate the Yield reaches a maximum and then starts to decline, while FT continues to increase. The Yield decline is explained by longer delay of inspection results which trigger the repair of an out-of-control machine. Clearly, lower Yield performance and higher FT are undesired. Our work defines this relationship with the analytical model and validates it with simulation. The model can be embedded in a decision support ...

This article proposes a stochastic attention allocation and reactive scheduling model to prevent illegal border crossings. To intercept infiltrators, a limited pool of security agents is dynamically assigned to heterogeneous frontier segments that transmit erratic signals of crossing attempts by independent trespassers. The frontier segments may differ in terms of rates of crossing attempts, ease of crossing, and reliability of the detection systems. Due to the huge complexity of the agent scheduling decision, a relaxed Markovian model is proposed whose solution is a set of optimal steady-state allocation rates for sending security agents to any frontier segment where a crossing attempt is apparently taking place. This solution is used to derive a heuristic policy for dispatching security agents among the frontier segments based on the evolving signals. Simulation experiments demonstrate that the proposed heuristic outperforms other scheduling policies. Border crossing is just one example of a viable appl...

The control and treatment of dyslipidemia is a major public health challenge, particularly for patients with coronary heart diseases. In this paper we propose a framework for survival analysis of patients who had a major cardiac event, focusing on assessment of the effect of changing LDL-cholesterol level and statins consumption on survival. This framework includes a Cox PH model and a Markov chain, and combines their results into reinforced conclusions regarding the factors that affect survival time. We prospectively studied 2,277 cardiac patients, and the results show high congruence between the Markov model and the PH model; both evidence that diabetes, history of stroke, peripheral vascular disease and smoking significantly increase hazard rate and reduce survival time. On the other hand, statin consumption is correlated with a lower hazard rate and longer survival time in both models. The role of such a framework in understanding the therapeutic behavior of patients and implementing effective secondary and primary prevention of heart diseases is discussed here.

Within the complex and competitive semiconductor manufacturing industry, lot cycle time (CT) remains one of the key performance indicators. Its reduction is of strategic importance as it contributes to cost decreasing, time-to-market shortening, faster fault detection, achieving throughput targets, and improving production-resource scheduling. To reduce CT, we suggest and investigate a data-driven approach that identifies key factors and predicts their impact on CT. In our novel approach, we first identify the most influential factors using conditional mutual information maximization, and then apply the selective naive Bayesian classifier (SNBC) for further selection of a minimal, most discriminative key-factor set for CT prediction. Applied to a data set representing a simulated fab, our SNBC-based approach improves the accuracy of CT prediction in nearly 40% while narrowing the list of factors from 182 to 20. It shows comparable accuracy to those of other machine learning and statistical models, such as a decision tree, a neural network, and multinomial logistic regression. Compared to them, our approach also demonstrates simplicity and interpretability, as well as speedy and efficient model training. This approach could be implemented relatively easily in the fab promoting new insights to the process of wafer fabrication.

Based on a simulated non volatile memory fab, we employ data-mining to identify and quantify the apparent causes of work in process bubbles along the process. The chosen bubble formalization methods proved able to detect the phenomenon and enabled its occurrence frequency to be forecasted. In the chosen environment, bubbles seem to be highly correlated with the utilization patterns of the process segment considered.

We suggest a data-driven methodology to identify key factors of the cycle time (CT) in a semiconductor manufacturing plant and to predict its value. We first extract a data set from a simulated fab and describe each operation in the set using 182 features (factors). Then, we apply conditional mutual information maximization for feature selection and the selective naive Bayesian classifier for further selection and CT prediction. Prediction accuracy of 72.6% is achieved by employing no more than 20 features. Similar results are obtained by neural networks and the C5.0 decision tree.

Background Hyperlipidemia remains a major cause of morbidity in Western countries. The objective of this study was to document the percentage of adults who underwent periodical LDL measurement, and the percentage of patients with diabetes and post-angioplasty who were treated to goal. Methods Using a national database, data were obtained on the percentage of adults who had an LDL performed and the percentage of adults with an LDL at pre-specified levels. We also assessed the attainment of target LDL levels in diabetic and post-angioplasty patients. Data were also collected from patients with an acute coronary syndrome (ACS) admitted to seven hospitals within a 5 year period (2000–2004). Results Primary prevention: In 2005, 64.6% of the total population of 754,910 aged 35–44 had at least one record of LDL cholesterol measurement documented. This figure was 79.6% in the 717,617 adults aged 45–54. Secondary prevention: Of 253,233 diabetics in 2005, 220,023 (86.9%) have undergone at least one annual LDL measurement. The percentage of patients on statin therapy 3 and 12 months after an ACS admission increased significantly during the years 2000–2004 and reached 87%. Of the 42,292 patients who underwent PTCA during 2005, 34,346 (81.2%) have purchased at least 3 prescriptions of statins during 2005, 35,261 (83.4%) have performed at least one LDL measurement and 57.8% attained an LDL level of Conclusions We have shown an improvement in primary and secondary preventions of CV disease as documented by LDL measured and attainment of treatment goals, but further efforts are needed.

We quantify the impact of work in process (WIP) bubbles on a semiconductor fab. As a preliminary step, we formalize the concept of WIP bubbles by decomposing them into local events of relatively acute and temporary WIP congestion. The local bubble is empirically identified and its impact on local waiting time distribution is assessed. We then estimate its marginal impact on the overall line waiting time and cost. Finally, a novel visualization tool for the bubble's progression is proposed.

Every organization controls its investments in the development and maintenance of internal knowledge (IK) as opposed to outsourcing this effort, namely, consuming external knowledge (EK). A number of factors involved in this decision, such as the IK learning curve, its associated holding cost, value deterioration rate, value of future IK or cost of purchasing EK. This study proposes a dynamic optimal control model for examining the properties of this problem. Optimal control strategies and steady-state conditions are identified for a number of special cases. Some insightful observations are obtained by studying the solution sensitivity to the underlying assumptions.

One would expect a continuous production planning and control system to be more sensitive to information inaccuracy than a periodic one. In practice, a major source of information inaccuracy is the updating rate of the perceived system state. This phenomenon is particularly significant in global firms that must employ appropriate technology and procedures for updating the corporate information throughout its branches. The study compares 'Push' and 'Pull' strategies for production planning and control under different updating rates for inventory information. For this purpose, we propose a Pull control mechanism for an environment characterized by inaccurate information. Then we construct a simulation model for a specific flow-shop production process that can be controlled by either the Pull or Push approach. The simulation results show a significant domination of the Pull strategy over Push. The results confirm that the Pull approach protects the flow of materials against machine and market variations, whi...

The paper deals with the layout and operation of an inspection system used for detecting malfunctioning processors in a multistage production system. This problem involves three inter-related decisions: (i) the overall inspection capacity; (ii) the assignment of inspection tasks to inspectors; and (iii) the scheduling of the inspector's tasks. These decisions require a trade-off between the cost of inspectors and the loss associated with non-conforming products. A hierarchical heuristic solution procedure is proposed to support these three related decisions. Numerical experiments demonstrate the performance of the heuristic, showing that solution criteria are very close to their lower bounds. Although we use production terminology, the results might be applicable to any organization, which inspects and maintains a variety of characteristics of its branches or activities.

Inventory management involves determination of shortage policy. It specifies the conditions for losing or backordering a demand. Alternative policies include pure backorder, pure lost sales, and partial backorder (using a single backorder control limit). When the backorder-cost is time dependent it makes sense to modify the backorder-limit over time. Thus, a new form of partial backorder policy (PB2) with two-segment backorder control limits is introduced. The traditional policies mentioned above, are special cases of PB2. Hence, we provide a unified framework for studying different policies that deal with shortage. The PB2 problem is formulated and solved as a discrete time, stochastic constrained control problem. Its performance is numerically compared with the simpler alternative policies. In some cases its cost savings, versus the best of PB and PL, exceeds 15%, and 7% versus a single backorder limit policy. The economical advantage is significant over a wide range of the problem parameters. Scope and purpose This paper develops an expanded framework for modeling shortages in inventory management. It recognizes that optimal backordering strategy may change over time during an “out-of-stock” period. The paper is motivated by experience in the chemical industry in which, the cost of backordering is highly time related. Inventory managers, in this industry, consider to lose sales initially (once they run out of stock) and begin to backorder demand later as they approach the replenishment time. A two-segment partial backorder (r,Q) model is introduced and solved. Pure backorder (PB), pure lost sales (PL), and partial backorder (using a single backorder limit), are all special cases of the proposed model. The problem is formulated and solved as a discrete time, stochastic constrained control problem. Its performance is numerically compared with the simpler alternative policies. In some cases its cost savings, versus the best of PB and PL, exceeds 15%, and 7% versus a single backorder limit policy. The economical advantage is significant over a wide range of the problem parameters. The partial backorder policy we propose is not only different from those in the literature, but it provides new control flexibility.

The Dead Sea Works Ltd extracts more than 2 million ton of potassium a year from the Dead Sea and ships it by surface and marine transportation to markets around the world. Product mix and market distribution changes have made it necessary to redesign the surface logistics system. This system consists of four sites with a total of more than 300,000 ton of roofed storage capacity. The purpose of the study was to suggest changes in the storage capacity of each product at each of the sites. We have designed a model that expresses the expected annual loss for the firm and have solved it under potential market scenarios and management and technological constraints. Sensitivity analyses were then carried out for several uncertain cost and reliability parameters. Based on the best system configuration for each scenario, alternative configurations were designed in detail and compared under potential market scenarios. With a clear understanding of the risks and expected benefits involved, the decision makers then selected the best configuration. The annual net benefit from implementing the proposed solution is expected to be 1.7 million dollars.

The reliability of a multi-attribute deteriorating production system is controlled using versatile identical inspection facilities. An attribute state is dichotomous (up designates proper function versus down). A product item is conforming if all the system attributes are up when it is produced. When a system attribute is detected as down it is restored back to an up state. Inspection of an attribute can rely on observations of the system, recently produced items, or both. Inspection policy determines the inspection capacity, frequency of inspecting each attribute and inspection schedule. These decisions involve a tradeoff between the cost of inspectors and the loss associated with the roportion of non-conforming items due to lack of adequate inspection. Three models are introduced, analyzed and solved. In the first model, inspection and restoration are perfect, product attribute is up (down) when the system attribute is up (down), and restoration is immediate. The assumptions of perfect inspection and restoration are relaxed in the second model. The third model relaxes in addition the assumption of immediate restoration. An efficient heuristic solution scheme is provided for solving these models. Sensitivity of the solution to system parameters is studied. Numerical experiments provide some insights regarding the combined effect of imperfect production, inspection and restoration, in various conditions of inspection and restoration durations. Copyright © 2001 John Wiley & Sons Ltd.

Two cases of the discrete time finite horizon technology replacement problem are solved. The first deals with two available machines, one in use and a better one that can be purchased to replace it. The second case considers, in addition, a third machine of future technology that will be available at some random future time. The maintenance level of each used machine is chosen for each period in order to economically control performance deterioration. For solving the model, we first derive an optimal preventive maintenance policy by showing that “bang-bang” (i.e., full or non), non-increasing, maintenance efforts are optimal. Employing the optimal maintenance policy, we reformat the problem and conduct a numerical search in order to derive the replacement policy that will maximize the expected net present profit. Numerical examples demonstrate that management may improve profitability by delaying (but not necessarily foregoing) replacement with an available better machine.

A new form of partial backorder policy (PB2) using two explicit backorder control limits and a intermediate time when dealing with shortages is introduced and parameterc analyses of various cost parameters of this policy are numerically explored. The traditional backorder policy, the lost sales policy, and a partial backorder policy using a single backorder control limit, called PB1, are special cases of PB2; hence a PB2 policy provides a unified frame work for studying different policies dealing with shortages. We provide an example where a PB2 policy produces a cost savings of 3.36% over the best of the pure backorder, the lost sales and the PB1 policies. Without the time dependent backorder cost, our PB2 policy will reduce to the PB1 policy. A cost savings of 6.63% is realized. This cost savings could be quite substantial for a firm with very large volume of business.

This paper deals with the decision making for resource sharing and scheduling for a class of computer-integrated manufacturing cells. Each such cell is characterized by a set of resources and it can be used for the cyclic production of several products. The production (processing) of each product requires a set of operations with sequence constraints among them. Each operation can be performed using alternative modes. Each mode defines the subset of resources needed for the operation and the timing of their use. Resource setup times are sequence-dependent. The problem is to choose the best mode for each operation and, accordingly, to allocate and schedule resources in order to minimize make-span time. A general (feasible) mixed 0–1 LP model is proposed. A customized Branch and Bound type solution algorithm is presented, which fathoms only feasible solutions and finds all optimum solutions.

A continuous quality improvement (CQI) project was conducted at Soroka Medical Center in Beer-Sheva, Israel, in an effort to identify and address causes of delays in thrombolytic therapy in patients arriving at a high-volume (160,000 patients per year) emergency department with acute myocardial infarction and thereby reduce the "door-to-needle time" (DTNT). The study had four phases: preintervention survey, peri-intervention process redesign, postintervention evaluation, and follow-up evaluation. CQI TEAM: The CQI team followed a seven-step protocol: problem definition, present-state screening, factors analysis, solution development, outcome evaluation, standardization, and conclusions.A DTNT of 45 minutes was considered acceptable for this data set, and accordingly, patients were divided into an "early" group (n = 50, DTNT45 minutes), and a "late" group (n = 50, DTNTor = 45 minutes). After the CQI intervention, the mean DTNT decreased from 61.8 +/- 32.5 (mean +/- standard deviation) to 47.6 +/- 18.5 minutes (p0.029). The prolonged DTNT time intervals of the late versus the early groups was primarily due to extended decision-making time (36.0 +/- 22.7 versus 13.6 +/- 6.7 minutes, p0.003), followed by time until therapy was initiated (26.2 +/- 14.2 versus 11.1 +/- 5.8 minutes, p0.002).Results suggest that the 30-minute DTNT suggested by the American College of Cardiology/American Heart Association is appropriate for patients with a clear diagnosis and no contraindications for thrombolysis, but when the risk-benefit ratio of thrombolytic therapy raises concerns, a 45- to 60-minute DTNT may still be acceptable. Further CQI projects should address technical triage of simple cases and clinical estimation of risk-benefit ratio in complicated patients.

Final product inspection of a multi-attribute product, such as in the electronic assembly industry, involves expensive facilities. The correlation among attributes may be used for reducing the efforts needed for screening the products; however, engineers without an appropriate statistical-economical analysis tools do not take risks, and they designate full inspection of each item. We propose a double stage inspection program for reducing inspection efforts. Assuming that the joint distribution is known, the conditional probability that a product is “good” may be evaluated conditional upon the observation of the product's first-stage inspected quality attributes. Then, an expected cost minimization is implemented in order to decide whether a second inspection stage is required or a classification should be based solely on the first inspection stage. The cost factors include inspection and false classification. The method is illustrated on a real data set from a particular electronic product of Motorola-Arad Ltd.

Consider a single inspection facility that can be quickly switched among multiple inspection tasks. It can be used (for example) for detecting malfunction (or down state) production stages in a multistage production system. We assume that a properly working (or up state) production stage moves to a down state in any period with fixed probability. The stage then stays down until it is inspected and immediately restored back to an up state. Our purpose is to schedule inspections among the different production stages so as to maximize the fraction of good items produced. An optimal inspection schedule for a two stage production system is provided. For the general case of more than two stages, four heuristics are compared. We conclude that the proposed dynamic schedule is easy to derive, always feasible, and outperforms the static schedules.

Previous research on the newsboy problem is based on the assumption that in case of a shortage, unsatisfied demand is lost. Such an assumption is inappropriate for items that have a close substitute. In this paper, we formulate a two-item newsboy problem with substitutability (TINPS). Upper and lower bounds on the optimal order quantities of the two items are derived. Since analytical solutions to the problem are difficult to obtain, a Monte Carlo simulation is used to identify the optimal solution to the TINPS. Order quantities identified by the simulation provide higher expected profit than would have been obtainable without considering substitutability.

This paper presents a two-stage model for industrial robot operation and selection in a multi-product manufacturing system. The quality of the production process is related to robot repeatability and the output rate is related to robot speed. Robot repeatability deteriorates with increased speed. In the second stage, a one-period robot operational model maximises the expected profit of a risk-neutral decision maker, leading to optimal robot speed and production quantity decisions. Using the optimal speeds and quantities of the operational model and taking into account demand uncertainties, the first stage robot selection model is solved.

In this paper, we consider the partial backorder policy used in conjunction with the traditional stochastic (r, Q) inventory system. The partial backorder control is modeled using a control variable, b, which limits the maximum number of backorders allowed to be accumulated in any given cycle. In traditional inventory control policies, the unsatisfied demands are either completely backordered or completely lost. The new control variable b provides an alternative to dealing with shortages, which is different from the two extremes policies: the pure backorder policy (which corresponds to b=∞) and the lost sales policy (b=0). We obtain the expected annual cost of the model and give a procedure to compute the optimal parameters, r∗, b∗ and Q∗. Numerical examples which demonstrate the advantage of implementing the partial backorder policy are presented and the percentage of cost savings depends on the fill rate.

A two-stage decision and control model for the operation of the power station in the Hazbani-Dan Water System (Israel) was developed and implemented. In this system, river water is stored and then either consumed by agricultural farms or released to a hydroelectric power station. Under management policy, agricultural consumption is taken as a constraint. The problem was solved via a two-stage procedure consisting of a long-term model (LTM) and a short-term model (STM). The LTM was presented by Rabinowitz et al. (1988). This paper reports on the development and analysis of the STM, in which operational flexibilities of the power station are taken into consideration, thereby improving the LTM solution. Our main contributions are aimed at: (1) describing the (nontraditional) systematic thinking process which transformed the mathematically expressed problem into an efficiently solvable model; (2) presenting the analysis of the model; and (3) reporting on a practical application of the model in a unique system.

Dyslipidemia remains underdiagnosed and undertreated in patients with coronary artery disease. The Computer-based Clinical Decision Support System provides an opportunity t close these gaps.To study the impact of computerized intervention on secondary prevention of CAD.The CDSS was programmed to automatically detect patients with CAD and to evaluate the availability of an updated lipoprotein profile and treatment with lipid-lowering drugs. The program produced automatic computer-generated monitoring and treatment recommendations. Adjusted primary clinics were randomly assigned to intervention (n=56) or standard care arms (n=56). Reminders were mailed to the primary medical teams in the intervention arm every 4 months updating them with current lipid levels and recommendations for further treatment. Compliance and lipid levels were monitored. The study group comprised all patients with CAD who were alive at least 3 months after hospitalization.Follow-up was available for 7448 patients (median 19.8 months, range 6-36 months). Overall, 51.7% of patients were adequately screened, and 55.7% of patients were compliant with treatment to lower lipid level. In patients with initial low density lipoprotein120 mg/dl, a significant decrease in LDL levels was observed in both arms, but was more pronounced in the intervention arm: 121.9 +/- 34.2 vs. 124.3 +/- 34.6 mg/dl (P0.02). A significantly lower rate of cardiac rehospitalizations was documented in patients who were adequately treated with lipid-lowering drugs, 37% vs. 40.9% (P0.001).This initial assessment of our data represent a real-world snapshot where physicians and CAD patients often do not adhere to clinical guidelines, presenting a major obstacle to implementing effective secondary prevention. Our automatic computerized reminders system substantially facilitates adherence to guidelines and supports wide-range implementation.

Market trends for applying “Systems of Systems” (SoS) compels developers who want to advance their business to be involved in SoS programs. Although the difficulties and challenges entailed in developing SoS are numerous, the integration of varied technologies into coherent systems, interconnected in a network, brings with it an immense potential for creating new spin-off products with correspondingly new market shares. We present a novel method, designated “Fan-Out”, to be used during SoS development. Application of the Fan-Out method together with the presented tool confers the ability to control, monitor, and direct spin-off potential on the developer, thus increasing future potential benefits. The model includes parameters affecting spin-off emergence that were mapped via the Delphi method. After running simulation cases with industry experts, the model was upgraded to fit the needs of future model users. Subsequent stages of the research will include validation of the model with real data gathered from industry.

A nonlinear decision-control model for real-time operation of a dual-purpose water system for electrical energy generation and irrigation is presented. Maximal return depends on the implemented flow-rate forecasting method and varying costs for energy sold to an external utility. The results of two forecasting models and the historical data are compared, indicating the importance of an accurate data base for supporting the decision towards optimal operation of time-dependent water systems.

We propose a mixed 0–1 linear programming model for repetitive scheduling of multirobot assembly and machining cells. The approach adopted is monolithic as opposed to hierarchical to avoid system suboptimization. The model permits any number of alternative ways (or modes) to perform each operation. A mode of an operation is determined by the required resources (facilities) and the duration of their use. The model incorporates facility changeover times. Robot collisions are avoided. Several objective functions are formulated to support different purposes. The scheduling problem of a multirobot assembly cell is formulated and solved by using commercially available mathematical programming software. Solutions under four different objective functions are reported. Acknowledging the complexity and considerable size of the formulation required, we prescribe and illustrate specific methods to achieve size reduction. Finally, for successful use of our model, an information processing schema is offered as a general guidance to help data management needed by the model.

Diabetes-related complications can be reduced by better control of glycemia, lipid abnormalities and blood pressure. In recent years, efforts at improving diabetes care in Israel have been made. This study aims to estimate mortality savings related to a national program for diabetes care in Israel.Total population data for Israel was projected to 2020. Current diabetes prevalence and disease management data were obtained from a national program of diabetes care. Projections of the program's effect were based on two models: improvement in glycemic control, reflected in Hb A1c levels, and improvement in overall diabetes care, reflected in the percentage with LDL100 mg/dl, a proxy for multi-factorial control. Potential years of life lost (PYLL) and quality-adjusted life years (QALYs) saved were calculated.A drop in average Hb A1c values from 8.13% at baseline to 7.36% in 2020 is expected, and as a result 4216 deaths from diabetes will be prevented over the period 2001-2020, saving around 47,773 life years or 34,342 QALYs. Overall diabetes care, reflected in improving the control rate of LDL levels to100 mg/dl from 36% in 2000 to 58% in 2020, is estimated to prevent around 4803 deaths from diabetes over the period 2001-2020., so the program will save around 47,127 PYLL or 32,862 QALYs.A nationwide program of diabetes care is estimated to result in significant reductions of overall, as well as CHD-related, mortality.

The Hazbani-Dan water system supplies gravitational water for both irrigation and hydroelectric energy production. An automatic control system for operating the energy plant while satisfying the irrigation needs was developed and applied. This was accomplished through an interdisciplinary research effort comprised of a feasibility study, formulation and analysis of a two level optimization model, development of forecasting models, and information system analysis. The value of such a control system goes beyond its direct economical benefits. It also provides tools for management decisions involving the evaluation of investments, and the determination of operating rules. This paper consolidates the narrow and technical scope of previous reports by presenting the overall research methodology, the relationships among the different parts, and a unique way of conducting applied research in an academic environment.

We propose a customized branch and bound (B&B) algorithm (which we refer to as B&B#2) to solve the resource-sharing and scheduling problem (RSSP). The RSSP has previously been formulated as a continuous-time mixed-integer linear programming model and was optimally solved using a branch-and-bound (B&B) algorithm (which we refer to as B&B#1). The RSSP considers the use of a set of resources for the production of several products. Producing each product requires a set of operations with precedence relationships among them. Each operation can be performed using alternative modes which define the subset of resources needed. An operation may share different resources simultaneously. The problem is to select a single mode for each operation (i.e., the allocation decision) and accordingly to schedule the resources (i.e., the sequencing decision) while minimizing the makespan time. The B&B algorithm we propose is based on a minimal branching process at the allocation decision level. We compare the runtime of B&B#2 algorithm versus B&B#1 algorithm on a set of problem instances. The results showed that the average runtime of the B&B#2 algorithm was the smallest.