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We consider serving moving customers that follow a structured route through a system. Demand for resources by these customers occurs during their time in the system and is met at discrete points. Demand can be probabilistic and multiple service attempts may be desired as customers move through the system. The decision of interest is how to meet demand with mobile servers. We develop a model to structure the problem, answer the research question, and determine when a server should move between locations as well as how long the server should stay in any one location.

An important difference between both manufacturing and wholesaling vs. retail is the information available concerning inventory. Typically, far less information characterizes retail. Here, an extreme environment of information shortfall is examined. The environment is technically termed "unattended points of sale," but colloquially called vending machines. Once inventory is loaded into a machine, information on demand and inventory level is not observed until the scheduled reloading date. Technological advances and business process changes have drawn attention to the value of information (VOI) in retail inventory in many venues. Moreover, technology is now available that allows unattended points of sale to report inventory information. Capturing the value of this information requires changes in current business practice. We demonstrate the value of capturing information analytically in an environment with restrictive demand assumptions. Experiments in an environment with realistic demand assumptions and parameter values show that the VOI depends greatly on operating characteristics and can range from negligible effects to increasing profitability 30% or more in actual practice.

We consider a single product, single level, stochastic master production scheduling (Mps) model where decisions are made under rolling planning horizons. Outcomes of interest are cost, service level, and schedule stability. The subject of this research is the Mps control system: the method used in determining the amount of stock planned for production in each time period. Typically, Mps control systems utilize a single buffer stock. Here, two Mps dual-buffer stock systems are developed and tested by simulation. We extend the data envelopment analysis (dea) methodology to aid in the evaluation of the simulation results, where Dea serves to increase the scope of the experimental design. Results indicate that the dual-buffer control systems outperform existing policies.

Data envelopment analysis (dea) has become an increasingly popular method to measure performance for service firms with multiple sites. DEA is superior to many traditional methods for firms that have multiple goals. The promise of DEA is that the complex, multi-objective problem of performance measurement can be reduced to a single number. Unfortunately, the practice of DEA often belies the promise. Misconceptions concerning the purpose and implementation of DEA can cause DEA applications to be less than successful. Here, the technique is explained, and a guide to the implementation of DEA is proposed, utilizing DEA studies of retail bank branches.

Technological advances and changes in supply chain management practices have combined to draw attention to the value of information sharing in inventory replenishment. Academic research has produced seemingly conflicting results due to differences in the type of information that is shared, the supply chain structure, and the selection and parameterization of performance goals. This research provides a framework to help explain apparent differences in the extant literature. Our purpose is to understand what determines the value of information. With this specific view, we establish a set of research questions and suggest directions for future research. We introduce a research framework organized around the current literature and established theory. This framework is then evaluated by collectively classifying and coding results from the literature. Using a split-sample, least squares regression analysis, our results provide tentative empirical evidence that supports the framework, but also indicate that there are additional complex relationships among modeling parameters and assumptions that present opportunities for future research.

Capacity limitations for manufacturers of seasonally demanded goods create difficult problems for both practitioners and researchers. Empirical data suggest that practitioner response is far from optimal. Optimal solutions, however, are precluded for realistic problems due to computational complexity. Here, the structure of optimal policies is explored and heuristics based on myopic policies are developed. For simple problems, the best heuristic deviates from optimality by an average of 2.5% over a variety of conditions. Heuristics are also compared under more realistic business conditions by simulation.

We consider a production/inventory problem with stochastic seasonal demand. A scarce resource limits production in each time period, and setup time is negligible. Linear and stationary costs are assessed for production, holding inventory, and stock-outs. The calculation of optimal solutions is difficult so heuristics are used. The heuristics used in business practice are shown to cost an average of 30% above optimal policy costs. A superior heuristic is constructed utilizing an analytic approximation for optimal policies that costs an average of 2% over optimal policy costs.

Production-inventory systems that experience significant demand variations from period to period often use deterministic lot-sizing techniques to schedule production runs. If demand is uncertain, safety stock is included to buffer against stockouts. In this paper we propose a 'dual-buffer' production scheduling policy and show that it has superior performance characteristics to the single buffer policies currently in use. The second buffer is a simple calculation based on prior work in stochastic inventory theory. Methodologically, data envelopment analysis (DEA) is used to analyze results. This represents an unusual use of DEA

Transportation decisions and production decisions can usually be made separately. Under certain conditions, however, there are important interactions between transportation and production. An example of a combined production and routeing environment exists in the United States Postal Service (USPS). The daily movement and sorting of first class letter mail requires balancing transportation and mail sorting production costs, while considering the time dependent accumulation of mail during the day. The focus of this paper is on the practical application of a combined production/routeing model to a specific USPS division and the use of insights from this model to re-design the operation. A mixed integer linear program using candidate route integer variables is solved to near optimality. Specific routes and production schedules detailed by the model indicate that the division could save up to 5% of their combined production and routeing costs, amounting to $298000 per year. More importantly, the model provides insight on potential heuristics to choose routes under these circumstances.