In this paper, we consider a single-period model comprised of an original manufacturer (OM) who produces only new products and a remanufacturer who collects used products from consumers and produces remanufactured products. The OM and the remanufacturer compete in the product market. We examine the effects of government subsidy as a means to promote remanufacturing activity. In particularly, we consider three subsidy options: subsidy to remanufacturer, subsidy to consumers, and subsidy shared by remanufacturer and consumers. We find that the introduction of government subsidy on remanufacturer or consumers always increases remanufacturing activity. We also find that subsidy to remanufacturer is the best subsidy option, because subsidy to remanufacturer results in lower price of remanufactured products, thus leading to higher consumer surplus.

Societal risk classification is a fundamental and complex issue for societal risk perception. To conduct societal risk classification, Tianya Forum posts are selected as the data source, and four kinds of representations: string representation, term-frequency representation, TF-IDF representation and the distributed representation of BBS posts are applied. Using edit distance or cosine similarity as distance metric, four k-Nearest Neighbor (kNN) classifiers based on different representations are developed and compared. Owing to the priority of word order and semantic extraction of the neural network model Paragraph Vector, kNN based on the distributed representation generated by Paragraph Vector (kNN-PV) shows effectiveness for societal risk classification. Furthermore, to improve the performance of societal risk classification, through different weights, kNN-PV is combined with other three kNN classifiers as an ensemble model. Through brute force grid search method, the optimal weights are assigned to different kNN classifiers. Compared with kNN-PV, the experimental results reveal that Macro-F of the ensemble method is significantly improved for societal risk classification.

Efficient staff rostering and patient scheduling to meet outpatient demand is a very complex and dynamic task. Due to fluctuations in demand and specialist availability, specialist allocation must be very flexible and non-myopic. Medical specialists are typically restricted in sub-specialization, serve several patient groups and are the key resource in a chain of patient visits to the clinic and operating room (OR). To overcome a myopic view of once-off appointment scheduling, we address the patient flow through a chain of patient appointments when allocating key resources to different patient groups. We present a new, data-driven algorithmic approach to automatic allocation of specialists to roster activities and patient groups. By their very nature, simplified mathematical models cannot capture the complexity that is characteristic to the system being modeled. In our approach, the allocation of specialists to their day-to-day activities is flexible and responsive to past and present key resource availability, as well as to past resource allocation. Variability in roster activities is actively minimized, in order to enhance the supply chain flow. With discrete-event simulation of the application case using empirical data, we illustrate how our approach improves patient Service Level (SL, percentage of patients served on-time) as well as Wait Time (days), without change in resource capacity.

Hydrocarbon prospective zone is a certain layer in a reservoir which is estimated producing oil. The geologists use the qualitative analysis method to find the prospect layers. The research used five variables modeled by three fuzzy membership functions and eight rules of fuzzy logic. The rules cause insensitiveness in the working system. This study therefore was conducted by modeling each of input variables into different models using 36 rules. It aims to determine the existence of hydrocarbon prospective zone through a qualitative analysis in a reservoir using fuzzy inference system with Mamdani method. The data were taken from well log data in reservoir “X”. There were some steps in doing this study, including fuzzification, inference system, and defuzzification. The result showed 99 prospect layers from 3000 layers in reservoir “X” with 97.7% of accuracy.

China’s companies have attracted much attention due to the development of stock market in China. The listing status of listed Chinese companies becomes an important indicator which implies the potential risk of a stock. Thus predicting the status of listed Chinese companies is obviously crucial for stockholders and investors when they make further decisions. According to the four possible listing statuses for Chinese companies, researchers formulate the above issue as a classification problem which is typical in data mining area. Plenty of classification techniques have been implemented to predict the status of the listing Chinese companies based on their financial factors. Usually, there are more than 150 financial factors for each of the listed companies, and feature selection is needed before the implementation of classification methods. In the literature, researcher used t-test with variance inflation factor (VIF) analysis to select relevant factors. However, such method can not be applied in the high dimensional case. In this paper, we apply the idea of penalized regression to select the interested factors based on a logistic regression model, and then apply popular classification methods to predict the companies’ statuses. Our results show that the proposed method can find more representative factors and improves the prediction accuracy of the classification methods.

This paper focuses on an outpatient capacity allocation problem where the patient demand is quite higher than the supply. We study an adding capacity policy to mitigate the mismatch between supply and demand. Under this policy, the doctor is allowed to add capacity if all regular capacity have been booked. A capacity allocation model is formulated for both possible no-show routine patients and all show-up same-day patients. The purpose is to determine the number of capacity can be added and how to allocate regular capacity among routine patients and same-day patients, towards maximizing the expected profit, which is composed of the expected income minus the cost of weighted expected doctor’s overload work caused by the adding capacity policy and the cost of rejecting patients. To achieve the aims, we prove the expected profit monotonously decreases when the number of additional capacity exceeds a threshold, and present a two-tier enumeration search algorithm to find the global optimal solution based on the proof. Numerical results indicate that the proposed policy performs well under different levels of demand higher than supply. The optimal number of the additional capacity is hardly affected by varying total expected patient demand. Additionally, under the change of no-show rate, the number of regular capacity allocated to routine patients becomes more stable, compared with the optimal scheme without considering adding capacity policy.

The use of systems theory to attempt to determine which ''place in La Mancha'' was the one whose name Cervantes could not quite recall in his universal novel, appears to be ordained to change certain attitudes towards science held in Cervantine literature. The reason: after four centuries of literary analysis, systemic methodology has proven able to identify the famous ''place'' with acceptable accuracy. Nonetheless, certain reasonable doubts persist around the suitability of a strictly scientific analysis of literature. In light of those doubts, the present article aims primarily to facilitate critique both of the systemic approach adopted and its outcome.

This paper investigates a production system in which the desired system state is a reduction of emissions and the controlling action plan is altering carbon price. From the operations perspective, we develop a model to study how increasing carbon emission costs may affect the joint production and location decisions for a manufacturer across different locations. Specifically, our model incorporates economies of scale and explicitly links product demand, production costs and carbon emission levels to location decisions. The firm's decisions in production batch size and locations are then optimised. The system state of emission are analysed under different carbon prices. Finally we check the alignment of objectives in costs and emissions for the system. The results show that for a production system with economies of scale, the production allocation is transformed to a location choice problem after optimising the costs. Raising the carbon price reduces the carbon emissions but may not be able to induce the production to be placed in an emission efficient place. We propose a hybrid policy combining carbon price and free emission allowance to fully align the cost efficiency and emission efficiency and characterise the link between the emission target and the carbon price.