An interesting puzzle in business practices is that although many researchers emphasize the benefits of a revenue-sharing contract, a wholesale contract has remained to be the most common contractual form. By introducing the concept of unobservable efforts, we examine the contractual efficiency of a wholesale contract and a revenue-sharing contract. The multi-task agency model and experimental design approach are used to analyze the relationship between the contractual efficiency and parameters. A major finding of our study is that a wholesale contract coordinates unobservable efforts, while it fails to coordinate the order quantity decision. Because unobservable efforts have mixed effects on the contractual efficiency, the superiority of contract type depends on parameters. This finding implies that a wholesale contract can be a competitive contract, especially when unobservable efforts are heavily involved. Our conclusion is that the current popularity of a wholesale contract is manager's rational response to complex supply chain environments rather than irrational behaviors.

In this paper, we analyze the relationship between implementation of information technology and inventory turnover as a measure of operational efficiency. We build a regression model including a dummy variable that indicates whether a firm has been nominated for being good at implementing information technology. By using publicly available data, we have conducted an extensive empirical analysis and found that firms' using information technology is not likely to affect inventory turnover significantly. This result implies that we have to take careful consideration in adapting information technology.

Refuse collection network design, one of major decision problems in reverse logistics, is the problem of locating collection points and allocating refuses at demand points to the opened collection points. As an extension of the previous models, we consider capacity and maximum allowable distance constraints at each collection point. In particular, the maximum allowable distance constraint is additionally considered to avoid the impractical solutions in which collection points are located too closely. Also, the additional distance constraint represents the physical distance limit between collection and demand points. The objective is to minimize the sum of fixed costs to open collection points and variable costs to transport refuses from demand to collection points. After formulating the problem as an integer programming model, we suggest an optimal branch and bound algorithm that generates all feasible solutions by a simultaneous location and allocation method and curtails the dominated ones using the lower bounds developed using the relaxation technique. Also, due to the limited applications of the optimal algorithm, we suggest two heuristics. To test the performances of the algorithms, computational experiments were done on a number of test instances, and the results are reported.

We propose a war-game model that is appropriate for a raid-type warfare in which, a priori, the maneuver of the attacker is relatively certain. The model is based on a multi-weapon extention of the Lanchester's law. Instead of a continuous time dynamic game with the differential equations from the Lanchester's law, however, we adopt a multi-period model relying on a time-discretization of the Lanchester's law. Despite the obvious limitation that two players make a move only on the discrete time epochs, the pragmatic model has a manifold justification. The existence of an equilibrium is readily established by its equivalence to a finite zero-sum game, the existence of whose equilibrium is, in turn, well-known to be no other than the LP-duality. It implies then that the war-game model dictates optimal strategies for both players under the assumption that any strategy choice of each player will be responded by a best strategy of her opponent. The model, therefore, provides a sound ground for finding an efficient reinforcement of a defense system that guarantees peaceful equilibria.

This paper suggests a numerical method for valuation of European and American options under the two L$\acute{e}$vy Processes, Normal Inverse Gaussian Model and the Variance Gamma model. The method is based on approximation of underlying asset price using a finite-state, time-homogeneous Markov chain. We examine the effectiveness of the proposed method with simulation results, which are compared with those from the existing numerical method, the lattice-based method.

This paper studies an optimal consumption and portfolio choice problem for unemployed people who have an option to work. Our problem is to find optimal consumption, risky investment, and workforce re-entry strategies for the unemployed. We find a closed form of the critical wealth level to re-enter the workforce. We show that the unemployed with a higher disutility of labor or a larger relative risk aversion are more reluctant to re-enter the workforce.

A Markov-chain Monte Carlo sampling algorithm samples a new point around the latest sample due to the Markov property, which prevents it from sampling from multi-modal distributions since the corresponding chain often fails to search entire support of the target distribution. In this paper, to overcome this problem, mode switching scheme is applied to the conventional MCMC algorithms. The algorithm separates the reducible Markov chain into several mutually exclusive classes and use mode switching scheme to increase mixing rate. Simulation results are given to illustrate the algorithm with promising results.