I propose new combined randomized methods for global optimization problems. These methods are based on the Nested Partitions(NP) method, a useful method for simulation optimization which guarantees global optimal solution but has several shortcomings. To overcome these shortcomings I hired various statistical selection methods and combined with NP method. I first explain the NP method and statistical selection method. And after that I present a detail description of proposed new combined methods and show the results of an application. As well as, I show how these combined methods can be considered in case of computing budget limit problem.

This paper is concerned with proving a conjecture that the two stage assembly system is reversible in deterministic makespan scheduling context. The reversibility means that a job sequence in the assembly system has the same makespan as that of its reverse sequence in the disassembly system which is the reversal of the assembly system. The proposed conjecture shows that the reversibility of serial flowshops can be extended to non-serial and synchronized shops.

We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.

We consider the product differentiation model of online channel competition and examine the strategies of hybrid firms in terms of efficiency. After measuring the social cost of online business strategies, we show that (i) online channel of hybrid firm under blockaded entry may increase the social cost if the firms' delivery cost is sufficiently smaller than the consumer's transportation cost, and (ii) online competition under free entry may increase the social cost if the firms' delivery cost is sufficiently larger than the consumer's transportation cost. Finally, we discuss the strategic incentive of hybrid firms to reduce delivery cost and investigate the effect of the Internet maturity on the social cost.

We investigate factors that influence the choice of high-share brands(HSBs) vs. low-share brands(LSBs) among various product and consumer characteristics related to brand-share perceptions. Specifically, using 8 product categories varying in terms of purchase decision involvement, we show how the influencing factors vary across the categories. At the general level that cover all the 8 categories, our hierarchical Bayesian regressions analysis shows that factors that favor high-share brands are purchase decision involvement, search goods, experience goods, price-quality relationship, positive network externalities, and price-prestige beliefs. Conversely, consumers who value variety seeking and need for uniqueness favor low-share brands. The effects of these factors, however, vary across product categories. The identification of these characteristics can help brand managers establish a more effective brand-share strategy in such areas as setting an optimal market share goal, extending a brand, and developing ad copy. Furthermore, our consumer segmentation analysis demonstrates the general market has two distinct segments - (1) a segment composed of HSB buyers(86%) and (2) a segment composed of LSB buyers(14%). The two segments are also shown to have different significant factors that explain their brand choice. Our segmentation analysis can help marketers establish a marketing strategy that targets a specific segment of interest.

In the previous research, we considered a logistics brokerage problem with the objective of minimizing total transportation lead time of freights in a logistics e-marketplace, in which a logistics brokerage agent intermediates empty vehicles and freights registered by car owners and shippers [7]. However, in the logistics e-marketplace, transportation due date tardiness is more important than the transportation lead time, since transportation service level is critically determined by whether the due date is met or not. Therefore, in this paper, we deal with the logistics brokerage problem with the objective of minimizing total tardiness of freights. Hungarian method based matching algorithms, real time matching(RTM), periodic matching(PM), and fixed matching(FM), are used for solving the problem considered in this paper. In order to test performance of the proposed algorithms, we perform computational experiments on a various problem instances. The results show that the waiting-and-matching algorithms, PM and FM, also give better performance than real time matching strategy, RTM, for the total tardiness minimization problem as the algorithms did for the total lead time minimization problem.

In [1], robust combinatorial optimization problem is introduced, where a positive integer $\Gamma$ is used to control the degree of robustness. The proposed algorithm needs solutions of n+1 nominal problems. In this paper, we show that the number of problems needed reduces to $n+1-\Gamma$.

Among many graphs, directed acyclic graph(DAG) attracts many researchers due to its nice property of existence of topological sort. In some classic graph problems, it is known that, if we use the aforementioned property, then much efficient algorithms can be generated. So, in this paper, new algorithm for the all-pairs shortest path problem in a DAG is proposed. While the algorithm performing the iteration, it identifies the set of essential arcs which requires in a shortest path. So, the proposed algorithm has a running time of $O(m^*n+m)$, where m, n and $m^*$ denote the number of arcs, number of node, and the number of essential arcs in a DAG, respectively.