In this paper, we study make-or-buy decisions with the consideration of retail-level competition, in which a supplier provides substitutable products to two retailers. One incumbent retailer is capable of producing the product in-house and makes the make-or-buy decision, while the rival retailer can only outsource from the supplier. Intuitively, the incumbent will not outsource if the wholesale price is higher than its production cost. However, we illustrate this may not be true when the supplier also supplies the retail rival. In this case, the incumbent may accept a high wholesale price to limit the suppliers incentive to serve the retail rival on particularly favorable terms. Moreover, under certain circumstances, the supplier may charge a wholesale price lower than its production cost to attract orders from the incumbent, which can generate for the supplier and the incumbent a higher total profit than the situation in which the incumbent makes the product in-house.

To lower pharmaceutical expenditure, the Chinese government has replaced the Fixed Percent Markup (FPM) policy with the policies of the Separation of Outpatient Pharmacies from Hospitals (SOPH) and the Zero Markup Drug (ZMD). We build a multistage game theoretic model comprising a hospital and a drugstore to analyze the policies’ impacts on the providers’ drug selection and pricing behaviors. By comparing the equilibrium outcomes, we draw the following conclusions: (i) FPM, especially for one imposing a strict margin ceiling, actually induces an expensive prescription given patients’ great compliance. (ii) Both SOPH and ZMD can conditionally lower patients’ expenditure, and their performances rely on the hospital’s selection. (iii) A proper rate of insurance coverage and a removal of drug rebate are helpful to improve the policies’ performance.

A huge volume of digitized clinical data is generated and accumulated rapidly since the widespread adoption of Electronic Medical Records (EMRs). These big data in healthcare hold the promise of propelling healthcare evolving from a proficiency-based art to a data-driven science, from a reactive mode to a proactive mode, from one-size-fits-all medicine to personalized medicine. This paper first discusses the research background - big data analytics in healthcare, the research framework of big data analytics in healthcare, analysis of medical process, and the literature summary of treatment pattern mining. Then the challenges for data-driven typical treatment pattern mining are highlighted, including similarity measure between treatment records, typical treatment pattern extraction, evaluation and recommendation, when considering the rich temporal and heterogeneous medical information in EMRs. Furthermore, three categories of typical treatment patterns are mined from doctor order content, duration, and sequence view respectively, which can provide a data-driven guideline to achieve the “5R” goal for rational drug use and clinical pathways.

Warranty claims forecasting plays an increasingly important role not only for preparing financial plans but also for optimizing warranty policy and improving after-sale services. In the case of new products, an important feature is that the new generation of products often has a close connection with the previous generations of products it replaces. Thus, the warranty claims data of the previous generations of products can be used for extracting reliability information of new products. In this context, we propose a warranty claims forecasting model considering usage rate for new products sold with a two-dimensional warranty. The accelerate failure time model is introduced to investigate the effect of usage rate on product degradation. The non-homogeneous Poisson process is used to model failure counts of repairable products and the constrained maximum likelihood estimation method is used to estimate model parameters. The results of data experiments based on both simulation and real data collected from an automobile manufacturer in China show that the proposed model considering the varying usage rate outperforms the traditional models in forecasting the number of warranty claims.

Due to the anonymous and free-for-all characteristics of online forums, it is very hard for human beings to differentiate deceptive reviews from truthful reviews. This paper proposes a deep learning approach for text representation called DCWord (Deep Context representation by Word vectors) to deceptive review identification. The basic idea is that since deceptive reviews and truthful reviews are composed by writers without and with real experience on using the online purchased goods or services, there should be different contextual information of words between them. Unlike state-of-the-art techniques in seeking best linguistic features for representation, we use word vectors to characterize contextual information of words in deceptive and truthful reviews automatically. The average-pooling strategy (called DCWord-A) and max-pooling strategy (called DCWord-M) are used to produce review vectors from word vectors. Experimental results on the Spam dataset and the Deception dataset demonstrate that the DCWord-M representation with LR (Logistic Regression) produces the best performances and outperforms state-of-the-art techniques on deceptive review identification. Moreover, the DCWord-M strategy outperforms the DCWord-A strategy in review representation for deceptive review identification. The outcome of this study provides potential implications for online review management and business intelligence of deceptive review identification.

Hazardous wastes pose increasing threats to people and environment during the processes of offsite collection, storage, treatment, and disposal. A novel game theoretic model, including two levels, is developed for the corresponding optimization of emergency logistics, where the upper level indicates the location and capacity problem for the regulator, and the lower level reflects the allocation problem for the emergency commander. Different from other works in the literature, we focus on the issue of multi-quality coverages (full and partial coverages) in the optimization of facility location and allocation. To be specific, the regulator decides the location plan and the corresponding capacity of storing emergency groups for multiple types of hazmats, so to minimizes the total potential environmental risk posed by incident sites; while the commander minimizes the total costs to provide an efficient allocation policy. To solve the bi-level programming model, two solution techniques, namely a KKT condition approach and a heuristic model, are designed and compared. The proposed model and solution techniques are then applied to a hypothetical case and a real-world case to demonstrate the practicality and provide managerial insights.

This research includes optimization of aggregate production of the stone crushing plant using fuzzy modelling. The investigation includes onsite aggregate testing and fuzzy logic implementation. Fuzzy modelling is a type of computerized reasoning used to simulate the real plant operation. In this work, a lot of agent degree information for crushers were reproduced using fuzzy logic. Fuzzy logic was then used to shape the information after a crusher process. Fuzzy logic is created to improve the final product gradation for the client. Strategies for utilizing the created fuzzy model is sketched out and could be utilized as a part of a representative preparing program or for informative purposes. About the tonnages anticipated by fuzzy, it is evident that the program does great in predicting the final product tonnage with an average of 13.7 % for just four samples.