Introduction of product designs and process innovation requires a company to evaluate complex cost and environmental tradeoffs. In the past, these have not included environmental costs. This paper describes the first known analytical approach to capture comprehensively measurable corporate environmental impact considerations for the product life cycle. A mixed integer programming model is developed to select product and process alternatives while considering tradeoffs of yield, reliability, and business-focused environmental impacts. Explicit constraints for environmental impacts such as material consumption, energy consumption, and process waste generation are modeled for specified assembly and disassembly periods. The constraint sets demonstrate a new way to define the relationship between disassembly configurations and assembly activities through take-back rates. Use of the model as an industry decision tool is demonstrated with an electronics assembly case study.

Manufacturing managers often address capacity and inventory decisions separately, thus ignoring the interaction between capacity and inventory within a manufacturing system. The separation of these two decisions can lead to an imbalance of capacity and inventory investment. We develop a model that simultaneously plans capacity investment, inventory investment, and the production schedule using return on assets as the objective to maximize. An algorithm is developed that optimizes a fractional objective function for a mixed-integer program. The model was applied at an electronics manufacturer and at a manufacturer of office supplies.

In the newsvendor problem, a decision maker facing random demand for a perishable product decides how much of it to stock for a single selling period. This simple problem with its intuitively appealing solution is a crucial building block of stochastic inventory theory, which comprises a vast literature focusing on operational efficiency. Typically in this literature, market parameters such as demand and selling price are exogenous. However, incorporating these factors into the model can provide an excellent vehicle for examining how operational problems interact with marketing issues to influence decision making at the firm level. In this paper we examine an extension of the newsvendor problem in which stocking quantity and selling price are set simultaneously. We provide a comprehensive review that synthesizes existing results for the single period problem and develop additional results to enrich the existing knowledge base. We also review and develop insight into a dynamic inventory extension of this problem, and motivate the applicability of such models.

Simulation experiments for analysing the steady-state behaviour of queueing systems over a range of traffic intensities are considered, and a procedure is presented for improving their design. In such simulations the mean and variance of the response output can increase dramatically with traffic intensity; the design has to be able to cope with this complication. A regression metamodel of the likely mean response is used consisting of two factors, namely, a low-degree polynomial and a factor accounting for the exploding mean as the traffic intensity approaches its saturation. The best choice of traffic intensities at which to make simulation runs depends on the variability of the simulation output, and this variability is estimated using analytical heavy traffic results. The optimal numbers of customers simulated at each traffic intensity are built up using a multistage procedure. The asymptotic properties of the procedure are investigated theoretically. The procedure is shown to be robust and to be more efficient than more naive procedures. A result of note is that even when the range of interest includes high traffic intensities, the highest traffic load simulated should remain well away from its upper limit; but the number of customers simulated should be concentrated at the higher traffic intensities used. Empirical results are included for simulations of a single server queue with different priority rules and for a complicated queueing network. These results support the theoretical results, demonstrating that the proposed procedure can increase the accuracy of the estimated metamodel significantly compared with more naive methods.

In stochastic dynamic programming (SDP) with continuous state and decision variables, the future value function is computed at discrete points in the state space. Interpolation can be used to approximate the values of the future value function between these discrete points. However, for large dimensional problems the number of discrete points required to obtain a good approximation of the future value function can be prohibitively large. Statistical methods of experimental design and function estimation may be employed to overcome this “curse of dimensionality.” In this paper, we describe a method for estimating the future value function by multivariate adaptive regression splines (MARS) fit over a discretization scheme based on orthogonal array (OA) experimental designs. Because orthogonal arrays only grow polynomially in the state-space dimension, our OA/MARS method is accurately able to solve higher dimensional SDP problems than previously possible. To our knowledge, the most efficient method published prior to this work employs tensor-product cubic splines to approximate the future value function (Johnson et al. 1993). The computational advantages of OA/MARS are demonstrated in comparisons with the method using tensor-product cubic splines for applications of an inventory forecasting SDP with up to nine state variables computed on a small workstation. In particular, the storage of an adequate tensor-product cubic spline for six dimensions exceeds the memory of our workstation, and the run time for an accurate OA/MARS SDP solution would be at least an order of magnitude faster than using tensor-product cubic splines for higher than six dimensions.