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In this article, we study the joint pricing and inventory control problem for perishables when a retailer does not sell new and old inventory at the same time. At the beginning of a period, the retailer makes replenishment and pricing decisions, and at the end of a period, the retailer decides whether to dispose of ending inventory or carry it forward to the next period. The objective of the retailer is to maximize the long‐run average profit. Assuming zero lead time, we propose an efficient solution approach to the problem, which is also generalized to solve three extensions to the basic model. A feature of the present study is that we consider explicitly the influence of perishability on the demand. Among the insights gathered from the numerical analysis, we find that dynamic pricing aids extending shelf life and when disposal incurs a lower cost, or even a positive salvage value, the retailer is induced to dispose earlier since the benefit of selling new inventory offsets the loss due to disposal. We also observe that the faster the perceived rate of deterioration, the lower the threshold of the ending inventory for disposal. Perhaps a bit counter‐intuitive, maximizing profits does not mean eliminating disposals or expirations.

Abstract. The container relocation problem, where containers that are stored in bays are retrieved in a fixed sequence, is a crucial port operation. Existing approaches using branch and bound algorithms are only able to optimally solve small cases in a practical time frame. In this paper, we investigate iterative deepening A* algorithms (rather than branch and bound) using new lower bound measures and heuristics, and show that this approach is able to solve much larger instances of the problem in a time frame that is suitable for practical application.

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Scholars, practitioners, and students in operations research and management science (OR/MS) often hesitate to use traditional approaches to evaluate journal quality because of the inconsistency of these approaches to perceptions and other associated biases. We introduce an alternative approach, which is based on Google's PageRank, to evaluate 31 OR/MS journals. This approach enhances the impact factor method by considering both citation quantity and quality. When we apply our method to OR/MS journals, the results match survey opinions and significantly outperform rankings based on impact factors. In addition, we provide an extensive discussion of other methods that have been used to assess journal quality. Compared to these methods, our method is better able to discern quality differences for journals that are perceived to be of relatively higher quality. Most importantly, we integrate our method into a Web-based journal-ranking system.