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We consider a periodic‐review perishable inventory system with multiple demand classes, each characterized by a different lost‐sales cost and the least freshness requirement. Demands of different classes in the same period could be correlated, while demands across periods are independent but not necessarily identical. In each period, the firm jointly makes the decisions regarding demand fulfillment, production/ordering, and disposal. The objective is to minimize the total discounted expected cost over the entire planning horizon including linear ordering cost, inventory holding/lost‐sales cost, expiration cost, and disposal cost. By establishing new properties of multimodularity, we explore some monotonicity and bounded sensitivity properties of the optimal policies. The optimality analysis enables us to propose a novel approximation approach, called adaptive approximation approach, which can be recursively calculated through a single‐dimension dynamic program. Numerical studies demonstrate that our proposed approximation approach is nearly optimal with the average optimality gap 0.30% and significantly outperforms the existing heuristics in the literature. [ABSTRACT FROM AUTHOR]

The present study investigates the benefit of joint decision making regarding whole blood collection and platelet production at a blood center. We consider a blood center that faces two types of platelet demands, differing in their freshness requirements and shortage penalty costs. We fully characterize the structure of the optimal policy regarding whole blood collection, platelet production, and inventory issuing, rationing and disposal. We find that the optimal platelet production quantity in each period is nonincreasing in the inventory levels of platelets and whole blood but that interestingly, the optimal blood collection effort may increase with the on‐hand platelet inventory level. We demonstrate with a real dataset that joint decision making leads to significant cost savings compared with separate decision making. The benefit is mainly derived from reduced blood collection and platelet production, better utilization of the collected whole blood, and reduced platelet shortage. For practical implementation, we develop a lookahead heuristic, which is shown to be very effective by numerical experiments. [ABSTRACT FROM AUTHOR]

This study reviews the literature on air cargo operations and compares theoretical studies with the practical problems of airlines, freight forwarders, and terminal service providers. In particular, we review studies in which mathematical models were used to identify the essential characteristics of air cargo operations, such as the intrinsic differences from passenger operations, and to explore the service processes in air cargo operations. The typical models used in previous studies are summarized. We then highlight the insightful findings from an industrial interview and present the gaps between previous research and practical realities. We finally discuss the new research opportunities of air cargo operations according to the gaps. [ABSTRACT FROM AUTHOR]

We develop an actionable data-driven approach to a periodic-review dual sourcing inventory management system in the presence of purchase price and demand uncertainties. The two supply sources differ in their lead times and prices due to, e.g., different transportation modes. We adopt robust optimization because the limited historical data is insufficient to construct meaningful distributions to characterize purchase price and demand fluctuations. Specifically, we build a robust rolling-horizon model and, in particular, construct the uncertainty sets from data and business insights. Using a real four-year data set, we show that our approach can yield significant cost savings compared to the other popular methods. Our experiments echo the earlier theoretical finding that a firm may incur a lower cost under a more volatile purchase price process. However, we find that under data-driven decision-making, in comparison with the theoretical results that assume complete distributional information, some interesting results arise. For example, first, considering a longer planning horizon may backfire. Second, some feasible region-reducing business constraints such as limited inventory capacity may lead to unintended benefits. These are consequences of protecting model performance from sampling error and our practically limited forecast ability, as almost always, to characterize uncertainties. Our research therefore calls for prudence in extending theoretical insights to data-driven decision-making scenarios. • Present a robust rolling-horizon model for dual sourcing inventory systems under purchase price and demand uncertainties • Develop a data-driven procedure to construct uncertainty sets and select parameters • A longer planning horizon may backfire under data-driven decision making. • Limited inventory capacity may lead to unexpected benefits under data-driven decision making. [ABSTRACT FROM AUTHOR]

Using relatively simple and widely used models, we illustrate how carbon emission concerns could be integrated into operational decision-making with regard to procurement, production, and inventory management. We show how, by associating carbon emission parameters with various decision variables, traditional models can be modified to support decision-making that accounts for both cost and carbon footprint. We examine how the values of these parameters as well as the parameters of regulatory emission control policies affect cost and emissions. We use the models to study the extent to which carbon reduction requirements can be addressed by operational adjustments, as an alternative (or a supplement) to costly investments in carbon-reducing technologies. We also use the models to investigate the impact of collaboration among firms within the same supply chain on their costs and carbon emissions and study the incentives firms might have in seeking such cooperation. We provide a series of insights that highlight the impact of operational decisions on carbon emissions and the importance of operational models in evaluating the impact of different regulatory policies and in assessing the benefits of investments in more carbon efficient technologies. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved filtering algorithm for regular constraint, which demand that the sequence of all assignments for variables can be accepted by a Deterministic Finite Automaton (DFA) or a regular language. Regular constraint is a type of global constraint in Constraint Satisfaction Problems (CSPs) and a natural expression of deterministic patterns in planning and decision making, and it utilizes the properties of DFA to pre-specify the optional paths for multiple variables. Thus, it is sensible to improve the filtering algorithm that enforces Generalized Arc Consistency (GAC) for regular constraint in order to minimize memory and time consumption. With the goal in mind, we encode DFA using bit vectors and then devise a bit operations algorithm to maintain the domain consistency for regular constraint. Lastly, we carry out an experiment to compare to the state-of-the-art filtering algorithm. The experimental results demonstrates that the proposed algorithm significantly improves both memory and time. • The improved filtering algorithm improves efficiency for regular constraints. • Our filtering algorithm uses a large number of bit operations. • The proposed algorithm maintains generalized arc consistency. • The experimental results show that our algorithm provides a significant improvement. [ABSTRACT FROM AUTHOR]