As public awareness of environmental hazards increases, a growing concern for corporations is the potential negative environmental impact of their products and the chemicals these products contain. In this study, we analyze the optimal decisions of a firm when a substance within its product is identified as potentially hazardous. Although the substance is not currently regulated, regulation may occur in the future. Therefore, the firm must devise a strategy for the development and implementation of a replacement substance. In an environment where replacement costs can be millions of dollars, regulation is uncertain, and both consumer and non‐governmental organization pressures exist, a carefully developed plan that balances costs and risks is critical for a firm. Our results demonstrate that as long as a threat of regulation exists, a firm should always dedicate resources toward developing a replacement substance. However, it is not always optimal for a firm to implement a developed replacement. Regarding competitive dynamics, we find that competition between firms can offset a low chance of a shift in consumer perception about a substance and compel firms to replace; however, competition can lead to inefficient outcomes in which firms incur avoidable costs to implement ahead of potential regulation.

In this chapter, we provide an overview of the state-of-the-art in sustainable product development and manufacturing and of the challenges in ubiquitous adoption of sustainable development practices in business. Environmental and business sustainability are examined in a holistic framework underscoring their interdependence on both spatial and temporal scales. We review the evolutionary rise in sustainability awareness including the development of methodologies for the assessment and development of sustainable products/manufacturing.

In order to satisfy customer demands despite unique specifications or schedule constraints, manufacturers have realised the critical need for real-time Available-To-Promise (ATP). In this paper, we present a model for real-time order promising in a mixed make-to-order, make-to-stock manufacturing environment. Each fulfilment source is considered as a separate module. Consistent with the real-time nature of the problem, this model considers a snapshot view of the enterprise at the moment the customer order enters the system. Relevant values from potential fulfilment sources are passed to the ATP optimisation engine. Following an instantaneous decision to accept or reject the order, the newly pegged resources are updated in the system. The flexibility in this modular structure allows the model to adapt to the most fragmented IT system or leverage the benefits of a highly integrated ERP system. We found that order acceptance levels and costs are most sensitive to capacity utilisation. Other factors that showed significant effects in our real-time ATP environment were demand variability, number or orders per day and the magnitude of these orders.

A new nomogram developed by researchers at the Baylor College of Medicine, Houston, US, may help physicians to provide more appropriate treatment for patients with prostate cancer. This aid to treatment decision-making could help reduce costs and improve outcomes, according to a report at the University of Texas MD Anderson Cancer Center’s 5th Annual Genitourinary Oncology Conference on Advances in the Biology and Therapy of Prostate Cancer [ San Antonio, Texas; March 1997 ].

We describe how the frequency of rescheduling permitted in Material Requirements Planning (MBP) systems influences the impact of safety stock on system costs, and its effectiveness in maintaining desired levels of customer service. The results are obtained from approximate analytical models and simulation studies of a single product with stochastic demand and a two-level product structure. The results indicate that in some cases it may be more economical to reschedule infrequently and use safety stock as protection against demand variations. They also indicate that the effect of changing safety stock levels is much more predictable when rescheduling is infrequent, and that increasing safety stock may actually result in degraded performance when rescheduling is frequent.

This paper describes a set of relatively simple procedures that are useful for solving a number of nonlinear programming problems. These problems are characterized by objective and constraint functions that are what we call “derivative decomposable”. Starting with a relaxed problem, we show how derivative decomposability yields a simple solution procedure. Then we demonstrate how slightly modified procedures can solve a variety of more complex problems displaying derivative decomposability. The solution procedures are easily understood in terms of their graphical representations. Furthermore, their simplicity and flexibility promise significant computational advantages for a variety of applications.

ABSTRACT We develop and test ways for modifying the Silver‐Meal and part‐period‐balancing lot‐sizing procedures to include the costs of schedule changes in response to changes in demand estimates. These modifications tend to reduce MRP system nervousness. We compare the performance of these approaches with the modified Wagner‐Whitin algorithm, which we have shown is optimal for static conditions. We find that the modified SilverMeal approach is only slightly more costly than the modified Wagner‐Whitin technique and that the modified part‐period‐balancing approach performs both poorly and erratically.

Instead of solving fixed horizon production scheduling problems with specified terminal inventory conditions, we use forecasting to extend the problem horizon until stopping rule conditions are met. Major questions for this procedure relate to how to provide data for the extended problem and when to stop the process. We provide extensive computational results indicating that relatively simple methods perform quite well.

ABSTRACTIn recent work, Baker [1] has investigated the use of rolling schedules for multiperiod production scheduling problems. One of his results is that the longest possible forecast horizon is not necessarily the best. He also found that the rolling schedules' effectiveness fluctuates widely depending upon the length of the forecast horizon. To smooth these fluctuations we have investigated use of forecasting to extend the problem horizon. Some of our results confirm the previous conclusions, but other results support the position that “the more information the better.”

ABSTRACT An important problem facing the manager of an outpatient health care clinic involves determining the best combination of services, facilities, and personnel to maximize profits and simultaneously achieve acceptable measures of the clinic's daily performance. This problem is often attacked using either linear programming or computer simulation. This paper uses a recursive optimization‐simulation approach which takes advantage of the best features of both optimization and simulation while minimizing the disadvantages of each method used alone. Results from a hypothetical setting using data from several actual settings demonstrate the value of the recursive method.

Recent work dealing with planning of complex facilities has proposed use of a recursive optimization-simulation approach. This technique takes advantage of the best features of both methods while minimizing the disadvantages of each method used alone. Here a mixed integer program generates staffing and facility plans and a simulation model evaluates their day-to-day acceptability. Then a linear regression uses the simulation results to generate non-cost constraints to be added to the optimization model. Results from a hypothetical health care setting demonstrate the value of the recursive method.