The Theory of Incentives I: The Principal-Agent Model

Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

Metaheuristic research: a comprehensive survey

Supply chain coordination with revenue sharing contracts

A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm

Meta-heuristics are problem-independent optimization techniques which provide an optimal solution by exploring and exploiting the entire search space iteratively. These techniques have been successfully engaged to solve distinct real-life and multidisciplinary problems. A good amount of literature has been already published on the design and role of various meta-heuristic algorithms and on their variants. The aim of this study is to present a comprehensive analysis of nature-inspired meta-heuristic utilized in the domain of feature selection. A systematic review methodology has been used for synthesis and analysis of one hundered and seventy six articles. It is one of the important multidisciplinary research areas that assist in finding an optimal set of features so that a better rate of classification can be achieved. The concept of feature selection process along with relevance and redundancy metric is briefly elucidated. A categorical list of nature-inspired meta-heuristic techniques has been presented. The major applications of these techniques are explored to highlight the least and most explored areas. The area of disease diagnosis has been extensively assessed. In addition, the special attention has been given on highlighting the role and performance of binary and chaotic variants of different nature-inspired meta-heuristic techniques. The summary of nature-inspired meta-heuristic methods and their variants along with datasets, performance (mean, best, worst, error rate and standard deviation) is also depicted. In addition, the detailed publication trend of meta-heuristic feature selection approaches has also been presented. The research gaps have been identified for the researcher who inclines to design or analyze the performance of divergent meta-heuristic techniques in solving feature selection problem.

A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem

With the development of e-commerce, manufacturers have opportunities to sells products to consumers through direct channels in addition to independent retailers. Although manufacturers encroachment upon the retailing market of traditional goods has been extensively studied, there is a lack of research for manufacturers encroachment in a pharmaceutical supply chain. This paper analyzes a pharmaceutical manufacturer encroachment strategy by taking into account the distinct features of drugs, in which drugs expenses are covered by medical insurance only in physical channel and patients' channel preference is also considered. We find that under certain circumstance, the manufacturer encroachment may lead to a win-win outcome for both the manufacturer and the retailer, contradicting the conventional wisdom that manufacturer's encroachment into retail space is detrimental to the independent retailer. Additionally, the manufacturer is more likely to encroach when the incurred extra cost is sufficiently small, the perceived quality of the drug selling through the direct channel is sufficiently high and the out-of-pocket percentage cost of the drug selling in the physical channel is high. We further study the impact of manufacturer encroachment on social welfare and find that the introduction of encroachment decreases the social welfare when patients' perceived quality of the drug obtained through the online store is low and improves the social welfare when the perceived quality exceeds a certain threshold.

Yanfei Lan

Papers

The effects of medical insurance and patients’ preference on manufacturer encroachment in a pharmaceutical supply chain