A modified PCF(Point Coordination Function) protocol with the optimal polling sequence is defined in detail and shown to improve the efficiency of the conventional PCF protocol in IEEE 802.11 wireless LAN standard. The problem for the optimal polling sequence is formulated as TSP(Travelling Salesman Problem) with the distance values of 1's or 0's. Numerical examples show that the optimal polling sequence increases the capacity of the real-time service such as VoIP(Voice over Internet Protocol).

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event simulation. The developed algorithm uses the configuration algorithm that can change decision variables and the stopping algorithm that can end simulation in order to satisfy the given objective value during single run. It tries to estimate an auto-regressive model for evaluating correctly the objective function obtained by a small amount of output data. We apply the proposed algorithm to M/M/s model, (s, S) inventory model, and known-function problem. The proposed algorithm can't always guarantee the optimal solution but the method gives an approximate feasible solution in a relatively short time period. We, therefore, show the proposed algorithm can be used as an initial feasible solution of existing optimization methods that need multiple simulation run to search an optimal solution.

Some issues which should be considered in an R&D project selection problem are as follows: First, quantitative analysis on the efficiencies of R&D projects is required to guarantee objective validity in the evaluation of the projects. For this reason, the methodology for selecting R&D projects should be based on mathematical models that perform quantitative analysis. Second, in general there are ordinal factors like Likert-scale in the data for evaluating R&D projects. Previous researches, however, couldn't suggest explicit methods incorporating these ordinal factors into models. Third, for the R&D project selection problems with limited resources like budget, it is necessary to decide the perfect ranking of the all projects. This paper develops a mathematical model that can be applicable to the problems of selecting R&D projects with the previous features. In this paper, we improve the original DEA model for evaluating efficiency to incorporate ordinal factors and suggest a new model which can decide the perfect ranking of all projects by merging the improved DEA model and AHP method. Furthermore a web-based R&D project selection system using the DEA/AHP model suggested in this paper is developed and illustrated.

As the quality and environmental problems become one of the most concerned issues of company, the rework policy needs to be efficiently designed and implemented to sustain the company's competitiveness. This paper examines three basic rework policies in multi-stage production systems and analyzes them in terms of costs of setup, production, inspection, and inventory. For analyzing the performance of the rework policies, we propose mathematical models which can compute total production cost and determine the optimal production batch size to minimize the total production cost. The rework policies are classified by the number and location of inspection stages. Using the mathematical models, we could analyze the performances of rework policies and suggest the optimal rework policy according to each manufacturing environment. The analyses show that the proposed model can be used to analytically evaluate and choose the rework policies.

The multi-criteria group decision making (MCGDM) problem is to determine the best compromise solution in a set of competing alternatives that are evaluated under conflicting criteria by decision maker (DM)s. In this paper, we propose a mixed-integer programming (MIP) model to solve MCGDM. The existing method based on minimizing a distance measure such as Median Approach can not guarantee the best compromise solution because the element of median point vector is defined with respect to each criteria separately. However, by considering all criteria simultaneously, we generate median point that is better for locating the best compromise solution. We also utilize the concept of spatial dispersion index (SDI) to produce a threshold value, which is used as a guideline to choose either the Utopian Approach or the Median Approach. And we suggest using CBITP (Convex hull of individual maxima Based Interactive Tchebycheff Procedure) to provide DMs with various Pareto-optimal solutions so that DMs have broad range of selection.

When the nature of measuring a performance variable is destructive or very expensive, it is attractive to use a surrogate variable which is highly correlated with the performance variable and less expensive to measure. In this paper, we propose rectifying screening procedures using the performance and surrogate variables. Two screening procedures are considered; a statistically-based screening procedure to reduce the current proportion of nonconforming items to a specified lower value after screening, and an economically-based screening procedure where the screening limit is determined so that the expected cost is minimized. It is assumed that the surrogate variable given the performance variable is normally distributed with known mean and standard deviation. For two screening procedures, methods of finding the optimal solutions are presented and numerical examples are also given.

This paper deals with traveling salesman problem(TSP) with the stochastic travel time. Practically, the travel time between demand points changes according to day and time zone because of traffic interference and jam. Since the almost pervious studies focus on TSP with the deterministic travel time, it is difficult to apply those results to logistics problem directly. But many logistics problems are strongly related with stochastic situation such as stochastic travel time. We need to develop the efficient solution method for the TSP with stochastic travel time. From the previous researches, we know that Q-learning technique gives us to deal with stochastic environment and neural network also enables us to calculate the Q-value of Q-learning algorithm. In this paper, we suggest an algorithm for TSP with the stochastic travel time integrating Q-learning and neural network. And we evaluate the validity of the algorithm through computational experiments. From the simulation results, we conclude that a new route obtained from the suggested algorithm gives relatively more reliable travel time in the logistics situation with stochastic travel time.