Pub. online:1 Jan 2012Type:Research ArticleOpen Access
Volume 23, Issue 2 (2012), pp. 225–246
A logistics system that involves a supplier who produces and delivers a single product and a buyer who receives and sells the product to the final customers was analyzed. A mathematical model was developed to describe the behavior of the system and to derive the optimal cycle length and order-up-to levels for the two parties. An analysis of the obtained results revealed that the methods were able to determine the optimal control parameters for each party in a short time frame. A coordination mechanism based on the optimal policies was ultimately proposed so that each party benefits more than if they use their own optimal control policy.
Pub. online:1 Jan 2006Type:Research ArticleOpen Access
Volume 17, Issue 3 (2006), pp. 381–392
This paper discusses the determination of the spare inventory level for a multiechelon repairable item inventory system, which has several bases and a central depot with emergency lateral transshipment capability. Previous research is extended by removing a restrictive assumption on the repair time distribution. A mathematical model that allows a general repair time distribution, as well as an algorithm to find a solution of the model, is developed. Thus, the main focus of this study is to improve the accuracy of previous models and to estimate the gain in accuracy from use of the current methodology. Computational experiments are performed to estimate the accuracy improvement and to determine the managerial implications of the results.
Volume 16, Issue 1 (2005), pp. 93–106
Portfolio optimization is to find the stock portfolio minimizing the risk for a required return or maximizing the return for a given risk level. The seminal work in this field is the m ean-variance model formulated as a quadratic programming problem. Since it is not computationally practical to solve the original model directly, a number of alternative models have been proposed.
In this paper, among the alternative models, we focus on the Mean Absolute Deviation (MAD) model. More specifically, we derive bounds on optimal objective function value. Using the bounds, we also develop an algorithm for the model. We prove mathematically that the algorithm can solve the problem to optimality. The algorithm is tested using the real data from the Korean Stock Market. The results come up to our expectations that the method can solve a variety of problems in a reasonable computational time.