This paper studies learning effect as a resource utilization technique that can model improvement in worker’s ability as a result of repeating similar tasks. By considering learning of workers while performing setup times, a schedule can be determined to place jobs that share similar tools and fixtures next to each other. The purpose of this paper is to schedule a set of jobs in a hybrid flow shop (HFS) environment with learning effect while minimizing two objectives that are in conflict: namely maximum completion time (makespan) and total tardiness. Minimizing makespan is desirable from an internal efficiency viewpoint, but may result in individual jobs being scheduled past their due date, causing customer dissatisfaction and penalty costs. A bi-objective mixed integer programming model is developed, and the complexity of the developed bi-objective model is compared against the bi-criteria one through numerical examples. The effect of worker learning on the structure of assigned jobs to machines and their sequences is analyzed. Two solution methods based on the hybrid water flow like algorithm and non-dominated sorting and ranking concepts are proposed to solve the problem. The quality of the approximated sets of Pareto solutions is evaluated using several performance criteria. The results show that the proposed algorithms with learning effect perform well in reducing setup times and eliminate the need for setups itself through proper scheduling.

This paper aim is to examine the optimal pricing and return policies for false failure returns in a dual-channel supply chain. Four prevailing return policies in which a manufacturer both operates an E-shop and sells its product through a brick-and-mortar retailer are analyzed, i.e. (I) the manufacturer handlings E-shop’s returns, while the retailer addresses brick-and-mortar store’s returns (NR); (II) the retailer tackles the whole (both E-shop’s and brick-and-mortar store’s) returns (ORR); (III) the manufacturer tackles the whole returns (ORM); and (IV) the manufacturer and the retailer are jointly responsible for the whole returns (RRM). Firstly, the optimal pricing and return policies comparing these four scenarios under uniform-pricing strategy are presented. Our conclusions show that the ORR is an optimal return policy. Compared with the NR, consumers will get a lower product pricing under the ORR and a higher product pricing under the ORM. With regard to the RRM, the product pricing is depended on consumer preference, return-rates of the E-shop and the brick-and-mortar store. Then, the optimal pricing and return policies are analyzed under differential-pricing strategy by conducting two-stage sequential games between the manufacturer and the retailer. The findings show that if consumers in the market prefer to purchase via the E-shop, the ORR is an optimal return policy. Otherwise, the NR is the optimal return policy. Compared with the NR, the ORR retailer’s product pricing will rely on the retailer’s and the manufacturer’s return-costs; the RRM retailer’s product pricing will depend on the return-costs of the retailer and the manufacturer, the return-rates of the E-shop and the brick-and-mortar store and so on. Finally, the influences of the manufacturer and the retailer establishing a Buy-back contract are discussed. Our results illustrated that the Buy-back contract doesn’t affect optimal pricing and return policies under both the uniform and the differential pricing strategies.

Due to the rapid development of applications of artificial intelligence and robotics in recent years, the necessity of reasoning and decision making with uncertain and inaccurate information is increasing. Since robots in the real world are always exposed to behavioral inaccuracies and uncertainty arising from recognition methods, they may occasionally encounter contradictory facts during reasoning on action decision.

Paraconsistent logic programming is promising to make appropriate action decisions even when an agent is exposed to such uncertain information or contradictory facts, but there has been no implementation of this programming to the best of our knowledge. We propose a resolution algorithm for the 3-valued paraconsistent logic programming system QMPT0 and its implementation on SWI-Prolog. We also describe an application of the 3-valued paraconsistent logic programming regarding agent decision making.

Maybe it is the most important medical reform for current China to adopt the bid-and-procurement scheme to correct the distortion of pharmaceutical resources allocation. Nevertheless, overpriced drugs often happen. This article aims to identify potential factors leading to the overpriced. Based on the mechanism rules of the scheme, it is found that unethical doctors are crucial to high price markup in the pricing pattern of drugs. Under China's actual conditions, asymmetric ownership is identified to be another cause of the overpriced by developing an asymmetric bidding model. How are the impacts of these two factors to be alleviated? Upon examination, several reform measures cannot effectively avoid the occurrence of the overpriced. Yet, the issue is very urgent for China because it is facing with the problem of population aging. Therefore, alternative options - a regulation-penalty tool and an investment subsidy policy - are proposed to improve China's health care.

Web 2.0 technologies have attracted an increasing number of people with various backgrounds to become active online writers and viewers. As a result, exploring reviewers’ opinions from a huge number of online reviews has become more important and simultaneously more difficult than ever before. In this paper, we first present a methodological framework to study the “purchasing-reviewing” behavior dynamics of online customers. Then, we propose a review-to-aspect mapping method to explore reviewers’ opinions from the massive and sparse online reviews. The analytical and experimental results with real data demonstrate that online customers can be sectioned into groups in accordance with their reviewing behaviors and that people within the same group may have similar reviewing motivations and concerns for an online shopping experience.