Yield management, which originated from the U.S. service industry, uses pricing techniques and information systems to make demand management decisions. Demand uncertainty is an important factor in the area of demand management. A key strategy to reduce the effects of demand uncertainty is substitution. The most generally known type of substitution is inventory-driven substitution, in which consumers substitute an out-of-stock product by buying a similar or other type of product. Another type of substitution is the price-driven substitution, which occurs as a result of price changes. In this research, we consider two market segments that have unique perishable products. We develop yield management optimization models with stochastic demand based on the newsvendor model where inventory-driven and price-driven substitutions are allowed between products in the two market segments. The most significant contribution of this research is that it develops analytical procedures to determine optimal solutions and considers both types of substitution. We also provide detailed theoretical analysis and numerical examples.

Clustering is one of the leading methods to analyze big data and is used in many different fields. This study deals with Clustering Validity Index (CVI) to verify the effectiveness of clustering results. We compare the performance of CVIs with business applications of various field. In this study, the used CVIs for comparing performance are DU, CH, DB, SVDU, SVCH, and SVDB. The first three CVIs are well-known ones in the existing research and the last three CVIs are based on support vector data description. It has been verified with outstanding performance and qualified as the application ability of CVIs based on support vector data description.

This paper proposes a new approximate approach to calculate the order fill rate and the probability of filling an entire customer order immediately from the shelf in a business environment under purchase dependence characterized by customer purchase patterns observed in such areas as marketing, manufacturing systems, and distribution systems. The new approximate approach divides customer orders into item orders and calculates fill rates of all order types to approximate the order fill rate. We develop a greed iterative search algorithm (GISA) based on the Gauss-Seidel method to avoid dimensionality and prevent the solution divergence for larger instances. Through the computational analysis that compares the GISA with the simulation, we demonstrate that the GISA is a dependable algorithm for deriving the stationary joint distribution of on-hand inventories in the type-K pure system. We also present some managerial insights.

Static wireless multi-hop networks, such as wireless mesh networks and wireless sensor networks have proliferated in recent years because of they are easy to deploy and have low installation cost. Two key measures are used to evaluate the performance of a multicast tree algorithm or protocol : end-to-end delay and the number of transmissions. End-to-end delay is the most important measure in terms of QoS because it affects the total throughput in wireless networks. Delay is similar to the hop count or path length from the source to each destination and is directly related to packet success ratio. In wireless networks, each node uses the air medium to transmit data, and thus, bandwidth consumption is related to the number of transmission nodes. A network has many transmitting nodes, which will cause many collisions and queues because of congestion. In this paper, we optimize two metrics through a guaranteed delay scheme. We provide an integer linear programming formulation to minimize the number of transmissions with a guaranteed hop count and preprocessing to solve the aforementioned problem. We extend this scheme not only with the guaranteed minimum hop count, but also with one or more guaranteed delay bounds to compromise two key metrics. We also provide an explanation of the proposed heuristic algorithm and show its performance and results.

Under a rapidly changing environment, firms have attempted to co-operate with organizations that possess high technologies to improve their technical capabilities. Most countries have worked towards developing a suitable strategy for international technology co-operation based on the diagnostic results of their technological capabilities and indices. These diagnostic results indicate the level and degree of international technology co-operation that have been developed in academia. However, identifying the overall conditions of international technology co-operation based on existing indices is a difficult task. Hence, to address this problem, indices that can measure diverse perspectives of core technology co-operation activities need to be developed. This research aims to develop a framework for identifying the current state of international technology co-operation in diverse perspectives at the national level. We develop indices based on relevant literature and formulate an analytical framework for international technology co-operation in diverse perspectives using DEA analysis. The results are expected to contribute to effective technology planning at the national level by diagnosing capabilities for international technology co-operation from diverse perspectives.

Much effort has been exerted to analyze online texts and understand how empirical results can help improve sales performance. In this research, we aim to extend this stream of research by decomposing online texts based on text sources, namely, companies and consumers. To be specific, we investigate how online texts driven by companies differ from those generated by consumers, and the extent to which both types of online texts have different effects on online sales. We obtained sales data from one of the biggest game publishers and merged them with online texts provided by companies using news articles and those created by consumers in user communities. The empirical analyses yield the following findings. Word visualization and topic analyses show that firms and consumers generate different contexts. Specifically, companies spread word to promote their own events whereas consumers produce online words to share winning strategies. Moreover, online sales are influenced by consumer-generated community topics whereas firm-driven topics in news articles have little to no effect. These findings suggest that companies should focus more on online texts generated by consumers rather than spreading their own words. Moreover, online sales strategies should take advantage of specific topics that have been proven to increase online sales. In particular, these findings give startup companies and small business owners in variety of industries the advantage when they use the online channel for distribution and as a marketing platform.

Irrational ordering decisions of supply chain members have been gaining growing importance in the area of supply chain management. Irrational ordering behaviors that deviate from the profit maximizing decisions in the newsvendor settings have observed with human experiments in recent research. These behaviors can be modeled with several typical decision bias elements. This bias in ordering decisions affects the performance of supply chain contracts designed based on the assumption that the supply chain members make optimal decisions, making it necessary to design supply chain contracts by considering the irrationality. The purpose of this research is to derive a method to design the revenue sharing contract that considers human irrationality in ordering decisions. This research considers a simple two-echelon supply chain consisting of one supplier and one retailer, where the supplier is assumed to be perfectly rational while the retailer making newsvendor type ordering decisions displays irrational ordering behaviors. Under this environment, this research analytically models the revenue sharing contract to maximize the total supply chain profit or the supplier's own profits while considering the three decision bias patterns of the retailer, which include the pull-to-center effect, the prospect theory, and the increased subjective sensitivity to the revenue sharing ratio. Irrationality parameters are measured through human experiments based on which and through numerical simulations, we showed that significant improvements in the supply chain performance can be achieved.

This paper presented a robust ship scheduling model using the quadratic programming problem. Given a set of available carriers under control and a set of cargoes to be transported from origin to destination, a robust ship scheduling that can minimize the mean-variance objective function with the required level of profit can be modeled. Computational experiments concerning relevant maritime transportation problems are performed on randomly generated configurations of tanker scheduling in bulk trade. In the first stage, the optimal transportation problem to achieve maximum revenue is solved through the traditional set-packing model that includes all feasible schedules for each carrier. In the second stage, the robust ship scheduling problem is formulated as mentioned in the quadratic programming. Single index model is used to efficiently calculate the variance-covariance matrix of objective function. Significant results are reported to validate that the proposed model can be utilized in the decision problem of ship scheduling after considering robustness and the required level of profit.