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Motivated by a real application that requires effective medium-term planning in intermodal transportation, we develop a mathematical programming model that jointly considers pricing and operations planning. Building on available work from marketing and transportation literature, we develop a new method for considering pricing, as well as develop distinctly different operations submodels that are appropriate for this context. We solve the resulting nonlinear optimization problem (with continuous and 0-1 variables)to optimality through a decomposition that exploits the structure of subproblems. Computational testing on industrial-sized problems shows that our algorithm is very quick, allowing for effective scenario testing and "what-if"analysis. [ABSTRACT FROM AUTHOR]

We address several aspects related to managing re-entrant lines in a unified manner capacity allocation, inventory management and production control. Our approach to study these systems is through simulation based optimization. Simulation offers the flexibility to model the complexities adequately while the gradient computation (via infinitesimal Perturbation Analysis (IPA)) helps identify good solutions quickly. Our framework is a discrete time capacitated multiple product production-inventory system operating under a base stock policy. We analyze several different production and capacity allocation rules. We develop expressions for and validate the appropriate derivatives. These derivatives can then be used in an optimization tool which enables the determination of optimal parameters. For the several policies proposed. We also present a summary of insights obtained through an extensive computational study. [ABSTRACT FROM AUTHOR]

We present an efficient solution method -- Direct Derivative Estimation (DDE) -- for computing optimal order-up-to levels for a discrete time non-stationary inventory control model. We generalize a number of non-stationary inventory control models (which underlie larger models of supply chains) that include forecast updates. seasonality, information sharing, and exchange-rate fluctuations. This procedure is different from the existing ones in that it computes, in a recursive manner, the derivative of the cost function and not the cost function itself. It can also handle a much wider variety of fluctuations in the problem parameters. In our computational testing it was found to be considerably faster than Dynamic Programming and Infinitesimal Perturbation Analysis. [ABSTRACT FROM AUTHOR]

The integration of product design and operations could potentially enhance the ability of manufacturers to provide large variety in the product line while keeping their operations competitive. At the plant level, an improved assembly sequence can lead to superior operational response to customer demands. While this has been intuitively accepted, there is a scarcity of models that can adequately reflect the operational benefits of a costlier yet better design. In this paper, we present integrated models applicable at a plant level that can provide quantification of certain operational benefits. Further, through a computational study, we also provide qualitative insights on several issues such as the effect of : (i) features in the product line; (ii) variance of demand for the different features on the optimal assembly design; (iii) set-up times; and (iv) life cycle of the product line. [ABSTRACT FROM AUTHOR]

Motivated by a real application, we consider the following aircraft scheduling problem. At any time, the aircraft are at different locations or are serving a customer and new customer requests arrive, each consisting of a departure location, departure time, and destination. Our objective is to satify these requests (by subcontracting extra aircraft if necessary) at minimum cost under additional constraints of maintenance requirements and previously scheduled trips. We show that the jet aircraft scheduling problem is NP complete and discuss three special cases. We show that the second and third special cases are also NP complete. We provide a polynomial time network flow based algorithm for the first special case and a pseudo-polynomial time dynamic programming algorithm for the second special case. We formulate the problem as a 0-1 integer program and observe that most small and medium size problems can be solved by Cplex. For larger and difficult instances, we provide a fast heuristic with good performance. [ABSTRACT FROM AUTHOR]

In an environment where a buyer procures capacity from a capacitated supplier through a wholesale price contract, the impact of timing of the decisions and of additional demand information on the supplier's pricing and the buyer's procurement decisions are investigated. As the selling season approaches, the buyer and the supplier have better information about the demand process. It is questioned as to what degree waiting until the demand uncertainty is resolved—given that such an option is feasible—is the best alternative for the supply chain partners. The main model is a two-period dynamic pricing/procurement game where the supplier sets wholesale prices dynamically; he sets the second period price after seeing the demand state as well as the buyer's first period procurement quantity. Two single-period models based on the information available to the players prior to their decisions are also studied. For each model, the subgame perfect Nash equilibrium is found in a closed form and the players' behaviors are analyzed to determine the impact of the additional information and trading periods on their welfare. The supplier's optimal capacity decision is also considered. Several additional insights are obtained through numerical experiments. [ABSTRACT FROM AUTHOR]

Summary: Reducing preventable hospital re‐admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retrospective study of electronic health records, we hypothesized Machine‐Learning (ML) algorithms may outperform standard re‐admission scoring systems (LACE and HOSPITAL indices). Participants (n = 446) included patients with SCD with at least one unplanned inpatient encounter between January 1, 2013, and November 1, 2018. Patients were randomly partitioned into training and testing groups. Unplanned hospital admissions (n = 3299) were stratified to training and testing samples. Potential predictors (n = 486), measured from the last unplanned inpatient discharge to the current unplanned inpatient visit, were obtained via both data‐driven methods and clinical knowledge. Three standard ML algorithms, Logistic Regression (LR), Support‐Vector Machine (SVM), and Random Forest (RF) were applied. Prediction performance was assessed using the C‐statistic, sensitivity, and specificity. In addition, we reported the most important predictors in our best models. In this dataset, ML algorithms outperformed LACE [C‐statistic 0·6, 95% Confidence Interval (CI) 0·57–0·64] and HOSPITAL (C‐statistic 0·69, 95% CI 0·66–0·72), with the RF (C‐statistic 0·77, 95% CI 0·73–0·79) and LR (C‐statistic 0·77, 95% CI 0·73–0·8) performing the best. ML algorithms can be powerful tools in predicting re‐admission in high‐risk patient groups. [ABSTRACT FROM AUTHOR]

Fractional aircraft ownership programs, where individuals or corporations own a fraction of an aircraft, have revolutionized the corporate aviation industry. Fractional management companies (FMC) manage all aspects of aircraft operations enabling the owners to enjoy the benefits of private aviation without the associated responsibilities. We describe here the development of a scheduling decision support tool for a leading FMC. We present mathematical models, exact and heuristic solution methods. Our computational results using real and randomly generated data indicate that these models are quite effective in finding optimal or near-optimal solutions. The first phase of the implementation of one of these models at the FMC led to a significant improvement in effective utilization of the aircraft, reduction of costs due to reduced empty moves, and hence increased profits. [ABSTRACT FROM AUTHOR]