In this paper, the impact of various system parameters such as the parameters of actual demand process, the review periods and the lead times, under each combination of inventory policies and information sharing, on the long run average inventory cost per period incurred at each participant in a supply chain, is considered. For this purpose, numerical studies are conducted, from which some valuable information as to how sensitive our long run average inventory cost per period are as the model parameters change is gleaned, from which, in turn, some managerial insights are gleaned in order for industry practitioners to perform better in supply chain management.

Quantity discounts provide a practical foundation for supply chain inventory policies, improving the supplier's profit and reducing the buyer's inventory cost simultaneously. Traditional quantity-discount research, which deals with inventory coordination between a buyer and a supplier, is extended to a stationary stochastic environment. This research shows that the magnitude of the optimal discounts scheduled by the deterministic quantity discount models may not be large enough to cover the buyer's additional inventory stocking risks under uncertain conditions. As a result, the buyer's total inventory cost may often increase rather than decrease. In contrast, the proposed model allows the supplier to identify the discount level, which shares the buyer's amplified risk associated with temporary overstocking and ensures that both buyer and supplier benefit economically. The performance of the proposed model was tested in the continuous review environments via numerical experiments. The experimental results support the proposed method as a feasible alternative in coordinating inventory decisions under stochastic demand.

Customer population management models can be classified into three categories: the first category includes the models that analyze the customer population at cohort level; the second one deals with the customer population at aggregate level; the third one has interest in the interactions among the customer populations in the competitive market. Our study proposes a model that can analyze the dynamics of customer population in consumer-durables market at aggregate level. The dynamics of customer population includes the retention curves from the purchase or at a specific duration time, the duration time expectancy at a specific duration time, and customer population growth or decline including net replacement rate, intrinsic rate of increase, and the generation time of customer population. For this study, we adopt mathematical ecology models, redefine them, and restructure interdisciplinary models to analyze the dynamics of customer population at aggregate level. We use the data of previous research on dynamic customer population management at cohort level to compare its results with those of ours and to demonstrate the useful analytical effects which the precious research cannot provide for marketers.

In this study, we present the exact methods of transforming the continuous solutions into the discrete ones for two types of bit-loading problem, marginal adaptive (MA) and rate adaptive (RA) problem, in multicarrier communication systems. While the computational complexity of existing solution methods for discrete optimal solutions depends on the number of bits to be assigned (R), the proposed method determined by the number of subcarriers (N), making ours be more efficient in most cases where R is much larger than N. Furthermore our methods have some strength of their simpler form to make a practical use.

Given an undirected network with a set of source-sink pairs, we are assumed to get a benefit if a pair of source and sink nodes are connected. The k-edge survivability of a network is defined as the total benefit secured after arbitrarily selected k edges are destroyed. The problem of computing k-edge survivability is known to be NP-hard and has applications of evaluating the survivability or vulnerability of a network. In this paper, we consider the k-edge survivability problem restricted to ring networks and develop an algorithm to solve it in O($n^3$|K|) time where n is the number of nodes and K is the set of source-sink pairs.

This paper considers the robust inventory control problem introduced by Bertsimas and Thiele [4]. In their paper, they have shown that the robust version of the inventory control problem can be solved by solving a nominal inventory problem which is formulated as a mixed integer program. As a proper generalization of the model, we consider the problem with non-stationary cost. In this paper, we show that the generalized version can also be solved by solving a nominal inventory problem. Furthermore, we show that the problem can be solved efficiently.