Consider a fashion supply chain comprising a supplier, a contract manufacturer and a fashion brand, we examine the fashion brand’s profit performances when the contract manufacturer is either an OEM (having no design capability) or an ODM (having design capability). Regarding OEM, the fashion brand designs the products, outsources the manufacturing function, and has the option of outsourcing procurement function. Regarding ODM, the fashion brand buys unlabeled products from the ODM, which is charge of designing and manufacturing. In this case, buy-back contract is widely adopted so as to share the risk of demand uncertainty between the ODM and the fashion brand. We solve the wholesale pricing problems via sequential/simultaneous optimization, and derive the buy-back price via generalize Nash bargaining. We find that, fashion brand prefers contracting with an ODM when its bargaining power in buy-back negotiation is larger than a threshold, although the fashion brand’s order size under ODM is always larger than that under OEM. Interestingly, we find that the buy-back price is decreasing in the fashion brand’s bargaining power. We further analyze the supply chain sustainability in both ODM and OEM scenarios, finding that the supply chain might achieve both environmental sustainability and economic sustainability in OEM scenario when the fashion brand’s bargaining power in buy-back negotiation is small.

By far, the researches on how to distribute blood products among different departments in hospital have not been further studied, though the problem of blood shortage and wastage that caused by improper blood allocation is severe, which may endanger patient’s lives and impose considerable costs on hospitals. In order to solve this problem, this paper mainly studies on how to distribute the blood items among different departments within a hospital and investigates the allocation approach with the novel management method by centralizing the inventory of several different departments. By integrating the blood inventory requirements of some departments, the hospital could reduce the rate of blood shortage and wastage effectively, release the pressure of the occupancy of resources and reduce the bullwhip effect of blood products. This paper illustrates the centralization principle in hospital and formulates the mixed integer programming model to work out the optimal allocation network scheme and the optimal inventory setting for every department. And the results of the numerical example demonstrate that this centralization method could considerably reduce blood shortage and wastage in hospital by about 72% and 90% respectively. Furthermore, it could decrease the total cost by about 108,540 RMB a month on blood supply chain management in the hospital and improve the effect of some certain surgeries by transfusing the fresh blood to patients.

Online reviews play an important role in consumer purchasing behavior when shopping online, and in turn affect pricing strategies of sellers. We consider a supply chain consisting of three competitive manufacturers and an e-tailer, which sells multiple substitutable products procured from different manufacturers. Considering reviews in terms of quality information and fit information, we study the effect of online reviews on consumers’ purchasing decisions as well as pricing strategies of manufacturers and the e-retailer. Through modelling customer choice with online reviews based on neo-Hoteling model and solving Stackelberg game composed by manufacturers and e-tailer, we obtain the optimal equilibrium prices of manufacturers and the e-tailer. And we distinguish two kinds of effect from online reviews: effect of the opinion bias in product quality and effect of the match informativeness. Furthermore, compared to the case without online reviews, we find online reviews improve a manufacturer’s optimal wholesale price, profit as well as the retailer’s optimal retailing price, when the adjusted quality revealed by reviews is high; however, online reviews can improve the e-tailer’s optimal profit, only when the standard deviation of products’ quality revealed by reviews is large enough. Last, we get that the increase of competing manufacturers’ quantity would change the dominance between two kinds of effect from reviews on supply chain members.

Uncovering shilling attackers hidden in recommender systems is very crucial to enhance the robustness and trustworthiness of product recommendation. Many shilling attack detection algorithms have been proposed so far, and they exhibit complementary advantage and disadvantage towards various types of attackers. In this paper, we provide a thorough experimental comparison of several well-known detectors, including supervised C4.5 and NB, unsupervised PCA and MDS, semi-supervised HySAD methods, as well as statistical analysis methods. MovieLens 100K is the most widely-used dataset in the realm of shilling attack detection, and thus it is selected as the benchmark dataset. Meanwhile, seven types of shilling attacks generated by average-filling and random-filling model are compared in our experiments. As a result of our analysis, we show clearly causes and essential characteristics insider attackers that might determine the success or failure of different kinds of detectors.

Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics’ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint (HSOC), which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.

This paper develops manufacturer’s channel selection models considering carbon emission reduction and remanufacturing. The models are based on the cap-and-trade system and consumer’s preference for low-carbon product. We study the strategies of pricing and carbon emission reduction under three channel structures: pure online channel, pure offline channel and dual channel. The results show that a higher recovery rate of remanufacturing can bring a higher reduction in carbon emission level and a lower price for consumers in the three channel structures. Compared to the pure online channel and pure offline channel, the dual channel is the optimal selection for a for-profit manufacturer. However, for a retailer, the pure offline channel is more profitable than the dual channel. From the perspective of carbon emission reduction, the dual channel and the pure online channel generate the same effect; the pure offline channel makes the highest reduction in carbon emission when consumers’ acceptance for online channel is low. More managerial insights are provided in sensitivity analysis.

With increasing demand diversification and short product lifecycles, industries now encounter challenges of demand uncertainty. The Japanese seru production system has received increased attention owing to its high efficiency and flexibility. In this paper, the problem of seru production system formation under uncertain demand is researched. A multi-objective optimization model for a seru production system formation problem is developed to minimize the cost and maximize the service level of the system. The purpose of this paper is to formulate a robust production system that can respond efficiently to the stochastic demand. Sample average approximation (SAA) is used to approximate the expected objective of the stochastic programming. The non-dominated sorting genetic algorithm II (NSGA-II) is improved to solve the multi-objective optimization model. Numerical experiments are conducted to test the tradeoff between cost and service level, and how the performance of the seru production system varies with the number of product types, mean and deviation of product volume, and skill-level-based cost.