Tractable Consideration Set Structures for Assortment Optimization and Network Revenue Management

Discrete‐choice models are widely used to model consumer purchase behavior in assortment optimization and revenue management. In many applications, each customer segment is associated with a consideration set that represents the set of products that customers in this segment consider for purchase. The firm has to make a decision on what assortment to offer at each point in time without the ability to identify the customer's segment. A linear program called the Choice‐based Deterministic Linear Program (CDLP) has been proposed to determine these offer sets. Unfortunately, its size grows exponentially in the number of products and it is NP‐hard to solve when the consideration sets of the segments overlap. The Segment‐based Deterministic Concave Program with some additional consistency equalities (SDCP+) is an approximation of CDLP that provides an upper bound on CDLP's optimal objective value. SDCP+ can be solved in a fraction of the time required to solve CDLP and often achieves the same optimal objective value. This raises the question under what conditions can one guarantee equivalence of CDLP and SDCP+. In this study, we obtain a structural result to this end, namely that if the segment consideration sets overlap with a certain tree structure or if they are fully nested, CDLP can be equivalently replaced with SDCP+. We give a number of examples from the literature where this tree structure arises naturally in modeling customer behavior.     

On the construction of orthogonal factorial designs of resolution 11

A new method of construction of orthogonal resolution IV designs for symmetrical and asymmetrical factorials has been presented. Many new series of orthogonal factorial designs of resolution IV can be obtained by the above general method.     

Analysing exponential family based messy data

Recently exponential family based random effects models have received considerable attention. These models usually arise from an unobservable random process added to the independent exponential family models. An unobservable correlated process, however, would cause correlations among the exponential family based data. This paper, first, develops an asymptotically optimal test for testing the appropriateness of a fixed effects model for the exponential family based independent data versus a random effects model for the exponential family based independent or correlated data. The paper, then, provides a general framework on regression analysis for the exponential family based data generated under the random effects models.     

Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates – an application to Arctic data analysis

In this article, we propose a family of bounded influence robust estimates for the parametric and non-parametric components of a generalized partially linear mixed model that are subject to censored responses and missing covariates. The asymptotic properties of the proposed estimates have been looked into. The estimates are obtained by using Monte Carlo expectation–maximization algorithm. An approximate method which reduces the computational time to a great extent is also proposed. A simulation study shows that performances of the two approaches are similar in terms of bias and mean square error. The analysis is illustrated through a study on the effect of environmental factors on the phytoplankton cell count.     

Determinants of Interoutsourcing: An Analytical Approach

Interoutsourcing is a round‐way process in which the vendor is its customer's customer and the customer is its vendor's vendor. While interoutsourcing is emerging as a prominent outsourcing strategy in many industries, there are no rigorous analytical studies focusing on this mechanism. In this article, we analytically demonstrate the efficacy of interoutsourcing by comparing it with normal outsourcing. Our results show that, compared with normal outsourcing, interoutsourcing acts as a self‐enforcer of vendor firms' behaviors toward increasing outsourcing service value. However, in situations where there is a mismatch of outsourcing activities, a high degree of incentive that is based on outsourcing service value, and a high cost of capital, interoutsourcing is not preferred to normal outsourcing. We discuss these results in detail and provide managerial implications for firms involved in interoutsourcing decisions.     

Multiproduct,Multicriteria Model for Supplier Selection with Product Life‐Cycle Considerations

In industrial purchasing contexts firms often procure a set of products from the same suppliers to benefit from economies of scale and scope. These products are often at different stages of their respective product life cycles (PLCs). Firms consider multiple criteria in purchasing such products, and the relative importance of these criteria varies depending on the PLC stage of a given product. Therefore, a firm should select suppliers and choose sourcing arrangements such that product requirements across multiple criteria are satisfied over time. The extant models in sourcing literature for evaluating and selecting suppliers for a portfolio of products have not considered this important and practical issue faced by firms. This article proposes a mathematical model that effectively addresses this issue and contributes to the sourcing literature by demonstrating an approach for optimally selecting suppliers and supplier bids given the relative importance of multiple criteria across multiple products over their PLC. The application of the model on a hypothetical data set illustrates the strategic and tactical significance of such considerations.     

Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that utilizes information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, referred to as BDSAcgh (Bayesian Detection of Shared Aberrations in aCGH), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alteration that correspond to local false discovery rates. We evaluate the operating characteristics of our method via simulations and an application using a lung cancer aCGH data set.