The integrated supply chain of business partners for e-Commerce in cyber space is defined as Logistics Chain if the cooperative activities are logistics-related. Logistics Chain could be managed effectively and efficiently by cooperative technologies of logistics chain execution. In this paper, we propose a routing and scheduling algorithm based on the Tabu search by adding geographical information into existing constraint for pick-up and delivery process to minimize service time and cost in logistics chain. And, we also consider an uncertainty processing for the tracing of moving object to control pick-up and delivery vehicles based on GPS/GIS/ITS. Uncertainty processing is required to minimize amount of telecommunication and database on vehicles tracing. Finally, we describe the Logistics Chain Execution (LCE) system to perform plan and control activities for postal logistics chain. To evaluate practical effects of the routing and scheduling system, we perform a pretest for the performance of the tabu search algorithm. And then we compare our result with the result of the pick-up and delivery routing plan generated manually by postmen.

In this paper, the cost impact of incorrect assumptions about the demand process in a supply chain in which there are two participants, a retailer and a manufacturer, is considered. When participants in the supply chain do not notice serial correlation in the demand process, they would turn to a simple inventory model based on an i.i.d. demand assumption. A mathematical model that allows us to quantify the cost incurred by each participant in the supply chain, when they implement inventory policies based on correct or incorrect assumptions about the demand process, is developed. This model enables us to identify how much it differs from the optimal costs.

In this study, a mathematical model that shows the optimal payout policy is developed. The model is new and unique in the sense that not only continuous-time framework is used, but also both partial differential equation (PDE) and real-option approach are utilized in the derivation of optimal payouts for the first time. In the model building, non-linear demand curve for dividend payouts in the competitive capital markets is assumed. From the sensitivity analysis using traditional comparative static analysis, some useful managerial implications which are consistent with famous previous studies are derived under realistic conditions. All results in this study, however, are valid under the assumption that the opportunity costs follow geometric Brownian motion, which is widely used in economic science and finance literature.

Automotive and aircraft assembly process rely on fixtures to support and coordinate parts and subassemblies. Fixture layout in multi-station panel assemblies has a direct dimensional effect on final products and thus presents a quality problem. This paper describes a methodology for fixture layout design in multi -station assembly processes. An optimal fixture layout improves the robustness of a fixture system against environmental noises, reduces product variability, and eventually leads to manufacturing cost reduction. One of the difficulties raised by multi-station fixture layout design is the overwhelmingly large number of design alternatives. This makes it difficult to find a global optimality and, if an inefficient algorithm is used, may require prohibitive computing time. In this paper, simulated annealing is adopted and appropriate parameters are selected to find good fixture layouts. A four-station assembly process for a sport utility vehicle (SUV) side frame is used throughout the paper to illustrate the efficiency and effectiveness of this methodology.

In this paper, we deal with a processor assignment problem that minimizes the total traffic load of an ATM switch controller by optimally assigning processors to ATM interface units. We develop an integer programming (IP) model for the problem, and devise an effective tabu search heuristic. Computational results reveal the efficacy of the proposed tabu search procedure, finding a good quality solution within 5% of optimality gap.

$\epsilon$-sensitivity analysis is a kind of methods for performing sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little computation. Although $\epsilon$-sensitivity analysis was proposed several years ago, there have been no studies on its relationship with other sensitivity analysis methods. In this paper, we discuss the relationship between $\epsilon$-sensitivity analysis and sensitivity analysis using an optimal basis. First. we present a property of $\epsilon$-sensitivity analysis, from which we derive a simplified formula for finding the characteristic region of $\epsilon$-sensitivity analysis. Next, using the simplified formula, we examine the relationship between $\epsilon$-sensitivity analysis and sensitivity analysis using optimal basis when an $\epsilon$-optimal solution is sufficiently close to an optimal extreme solution. We show that under primal nondegeneracy or dual non degeneracy of an optimal extreme solution, the characteristic region of $\epsilon$-sensitivity analysis converges to that of sensitivity analysis using an optimal basis. However, for the case of both primal and dual degeneracy, we present an example in which the characteristic region of $\epsilon$-sensitivity analysis is different from that of sensitivity analysis using an optimal basis.