The Effect of Demand Forecasting on Production Policies and Coordination in Periodic Review Production Systems.

We consider a sequential decision process in a periodic review production system. At the beginning of each period, marketing decides how much money to be invested in demand forecasting. After its decision, marketing obtains the forecast and passes the forecast to production, which then determines production quantity. Centralized and decentralized models are formulated. In the centralized model, the two divisions make their decisions by considering the total profit of the company. In the decentralized model, marketing pays production a unit transfer price for every product sold. Three types of policies about the unit transfer price are considered. In the company with the marketing-oriented policy, the unit transfer price is decided by marketing; in the company with the production-oriented policy, it is decided by production; and, in the company with the coordination policy, it is determined by considering the overall profit of the company. We find that, in the decentralized companies, production favors a larger unit transfer price, but marketing prefers a lower unit transfer price. In the company with the marketing-oriented policy, marketing tends to spend more money on forecasting, but production becomes less likely to start a run. However, in the production-oriented company, production is more likely to produce, but marketing spends less money on forecasting. The results in the company with the coordination policy can be considered as outcomes of cooperation, and it has the best performance among the decentralized companies. Furthermore, we find that the centralized company has a larger total profit than do the decentralized companies. Most of the time, it spends more money on forecasting, and it is more likely to produce. We develop two multi-period models. For the multi-period centralized model with a finite planning horizon, we find that the learning effect can induce the system to adopt a forecasting method with high cost and precision. As to the other multi-period model, we study a system with a fixed batch size, a fixed lead time, and an infinite planning horizon. The important result is that there exists a threshold inventory level, which property is similar to that of an (s, Q) inventory management system

The Day-of-the-Week Effect on the Shape of the Heavy-Tailed Distribution

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Case Experience of Radiofrequency Ablation for Benign Thyroid Nodules: From an Ex Vivo Animal Study to an Initial Ablation in Taiwan

AbstractRadiofrequency ablation (RFA) is a minimally invasive technique, used with ultrasound or computed tomography guidance, which can produce tissue coagulation necrosis in various kinds of tumors in the human body. In the past 10 years, numerous studies about RFA in benign thyroid nodules have been published. Reviewing these studies, we noticed that the effectiveness of ablation was higher when it was performed with the “moving-shot technique” via an internally cooled electrode. A consensus statement published from the Korean Society of Radiology also suggested the moving-shot technique as a standard ablation procedure for benign thyroid nodule ablation in Korea. In Taiwan, most symptomatic benign nodules are currently treated with surgical removal. RFA for mass lesions is primarily performed for the treatment of metastatic hepatic tumors. In our case, we have attempted to introduce RFA for benign thyroid nodules in Taiwan. Because endocrinologists in Taiwan were not familiar with this technique, we adopted a stepwise approach in learning how to perform RFA. We conducted ex vivo animal ablation exercises to gain experience in setting the radiofrequency generator for the right ablation mode and appropriate power output. The thyroid nodule volume reduction rate after 1 year of follow up was approximately 50% in this case. The most important thing we learned from this trial is that we confirmed the safety of thyroid nodule ablation. To the best of our knowledge, this is the first reported study of RFA of a thyroid nodule in Taiwan

The Selection Model for Compound or Portfolio Relationships Oriented in Supply Chain

As the environment changed, the inter-organizat ional in Supply chain has been transferred fro m simple relations to complex relations “Comp ound or Portfolio Relationships”. The main pur pose of this research is to integrate external/int ernal resource and maintain flexible volatility o f inter-organization for helping organizations/fir ms could increase the competitive advantage fo r them. To survey the current researches which discuss inter-organization in supply chain; it c ould be found that most literatures are focused on each simple relationship or portfolio relatio nship about their types and features. Our resear ch uses multiple relative theory and interviews to perform the research. To develop theory mo del and analyse the nature of relations about c ompound relationships oriented and portfolio rel ationships oriented. The theoretical framework model concerns the influence of the difference selection factors between inter-organizational in supply chain. We hope this research will cont ribute to further studies and provide some sugg estions for implementing management of the rel ationship between supply chains

Maximizing Friend-Making Likelihood for Social Activity Organization

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by modeling both existing friendships and the likelihood of new friend making. To find a set of attendees for socialization activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships and the objective function on the likelihood of friend making. We prove that HMGF is NP-Hard, and no approximation algorithm exists unless P = NP. We then propose an error-bounded approximation algorithm to efficiently obtain the solutions very close to the optimal solutions. We conduct a user study to validate our problem formulation and per- form extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm

Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

Expiration Effect of Leveraged and Inverse ETFs

[[abstract]]This study aims to examine whether there is a maturity effect in the leveraged and reverse ETFs on the maturity date of the underlying index futures or on the trading day before and after the maturity date and to investigate whether the maturity effect of different types of ETF commodities is more prominent issue. The sample is leverage and inverse ETFs tracking the Taiwan Stock Exchange Capitalization Weighted Stock Index from October 31, 2014 to January 15, 2018. Empirical model used bivariate GARCH model to captures the variations of maturity effects. The study shows that all ETFs present significant maturity effects before expiration. In particular, the volatility and trading volume present abnormal phenomenon.[[notice]]補正完