This paper introduces the planar storage location assignment problem (PSLAP) that no research has attempted to mathematically solve. The PSLAP can be defined as the assignment of the inbound and outbound objects to the storage yard with aim of minimizing the number of obstructive object moves. The storage yard allows only planar moves of objects. The PSLAP usually occurs in the assembly block stockyard operations at a shipyard. This paper formulates the PSLAP using a mathematical programming model, but which belongs to the NP-hard problems category. Thus this paper utilizes an efficient genetic algorithm (GA) to solve the PSLAP for real-sized instances. The performance of the proposed mathematical programming model and developed GA is verified by a number of numerical experiments.

As the number of personal computers installed in vehicles increases, a touchpad often used in a labtop computer can be used for the control of an in-vehicle information system (IVIS). Using a touchpad to control the system allows the user to select among large amount of information with a single touch of dragging. For safety and convenience of a driver, the touchpad could be placed on a steering wheel. This research is designed to calculate the most efficient Control-Response Ratio (C/R ratio) for the menu interaction of a touchpad on a steering wheel. Since the menu pointer's rate of movement and proper C/R ratio is determined by the amount of selected information, the amount of displayed information and the movement of a menu pointer was chosen to be independent variables. The dependent variables are a user's preference and task completion time. Two factor full factorial within subject design was used 16 subjects. The investigation revealed that the amount of selected information increased with increasing C/R ratio. The movement of the pointer became slower as the amount of information increased. The best C/R ratio was calculated for each amount of information and preference regression of the user's preference was drawn accordingly. Through this research, the automobile interior designer can benefit from the guidelines suggested for the touchpad control.

The heterogeneous fleet vehicle routing problem(HVRP) is a variant of the classical vehicle routing problem in which customers are served by a heterogeneous fleet of vehicles with various capacities, fixed costs and variable costs. We propose a new conceptual HVRPCR(HVRP with customer restriction) model including additional customer restrictions in HVRP. In this paper, we develop hybrid particle swarm optimization(HPSO) algorithm with 2-opt and node exchange technique for HVRP. The solution representation is a n-dimensional particle for HVRP with N customers. The decoding method for this representation starts with the transformation of particle into a priority list of customer to enter route and limit of vehicle to serve each customer. The vehicle routes are then constructed based on the customer priority list and limit of vehicle to serve. The proposed algorithm is tested using 8 benchmark problems and it consistently produces high-quality solutions, including new best solutions. The numerical results show that the proposed algorithm is robust and efficient.

This paper studies a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up under time windows(VRPSDP-TW). The objective of this paper is to minimize the total travel distance of routes that satisfy both the delivery and pick-up demand. We propose a heuristic algorithm for solving the VRPSDP-TW, based on the ant colony system(ACS). In route construction, an insertion algorithm based ACS is applied and the interim solution is improved by local search. Through iterative processes, the heuristic algorithm drives the best solution. Experiments are implemented to evaluate a performance of the algorithm on some test instances from literature.

This study presents the switching strategy between admission control and pricing control policies and clarifies the properties of the switching strategy in an M/M/1/K queueing system. In addition, this study demonstrates that employing the switching strategy can significantly improve the maximum total expected profit.

The Nuclear Power Plant(NPP) industry in Korea has been making efforts to reduce the human errors which have largely contributed to about 150 nuclear reactor trips since 2001. Recently, the Crew Resource Management(CRM) training has risen as an alternative countermeasure against the nuclear reactor trips caused by human errors. The effectiveness of CRM training in NPP industry, however, has not been proven to be significant yet. In this study a return on investment(ROI) model is developed to measure the effectiveness of CRM training for the operators in Korean NPP. The model consists of mathematical expressions including multiple variables affecting the CRM training impacts and nuclear reactor trips. Implication of the model is discussed further in detail.