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Abstract-This study aims at the availability optimization problem for n-stage standby system under different resource and design configuration constraints by applying Tabu-GA combination method. From the point of view of logistics engineering, availability optimization applied in the initial system development period, plays a key role to affect system reliability, system maintenance planning, logistics requirements, and related costs during system planned life cycle. In general, system availability can be improved by increasing component reliability level, component repair rate or the number of components in each subsystem. This proposed model combines Tabu method with Genetic Algorithm to solve system availability optimization problem. Through our method applied in the initial phase of system design and development, we can find the optimal allocation of component redundancy number, reliability and maintainability levels to minimize the total system cost under different configuration constraints such as weight, volume, and system availability requirements. The proposed numerical examples are demonstrated based on different system constraint requirements and parameter values. Through a simulation study of 30 times of calculations by applying this proposed combination method, stable results are clearly showed. Consequently, all numerical results and simulation studies clearly show that the proposed availability optimization model and Tabu-GA combination method developed in this paper can help solving system availability optimization problem in the initial system design and development period.

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This study looks at the system availability optimization problem under different resource and design configuration constraints by applying Tabu-GA combination method. From the point of view of logistics engineering, availability optimization applied in the initial system development period, plays a key role to affect system reliability, system maintenance planning, logistics requirements, and related costs during system planned life cycle. Generally, system availability can be improved by increasing component reliability level, component repair rate or the number of components in each subsystem.This proposed model is to combine Tabu method with Genetic Algorithm to solve system availability optimization problem. Through our method applied in the initial phase of system design and development, we can find the optimal allocation of component redundancy number, reliability and maintainability levels to minimize the total system cost under different configuration constraints such as weight, volume, and system availability requirements.The proposed numerical examples are demonstrated based on different system constraint requirements and parameter values. Through a simulation study of 30 times of calculations by applying this proposed combination method, stable results are clearly showed. We also perform the sensitivity analysis based on the cost parameters associated with reliability level and maintenance rate to provide very helpful information for system design and development process. Finally, the performance of this proposed Tabu-GA combination method is compared with SA-GA combination method and it turns out to be very good results in many aspects.

The goal of this paper is to spot out factors affecting the intention to use mobile payment service plan in Vietnam. This study attempts to analyze the impact of various variables extracted from mobility, convenience, compatibility, M-payment knowledge, ease to use, usefulness, risk, trust, and safe to use on intention to use mobile payment.Quantitative questionnaire is used to measure responses of participants. The statistical analysis method employed in this study is to apply Structural Equation Modeling to test all hypotheses. The results indicate that the strong predictors of the intention to use M-payment are perceived ease of use and perceived usefulness. All respondents show that they do not care about risk when they have intention to use mobile payment services. Convenience of mobility, compatibility, and mobile payment knowledge have impacts on ease to use and usefulness. Among of them, compatibility has the most significant impact on ease to use and usefulness in the opinion of those surveyed.Specially, it proved that trust of safe to use has no significant impact on usefulness, but instead has direct impact on intension to use mobile payment services. The outcomes of this research have important connotations for the improvement and development of mobile payment services in Vietnam. Therefore, at the end of this paper, some suggestions based on research results are given for the future development of mobile payment service business in Vietnam.Keywords: convenience of mobility, compatibility, M-payment knowledge, ease to use, usefulness, trust of safe to use, intention to use mobile payment, Vietnam

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Purpose The purpose of this paper is to develop a decision support system to consider geographic information, logistics information and greenhouse gas (GHG) emission information to solve the proposed green inventory routing problem (GIRP) for a specific Taiwan publishing logistics firm. Design/methodology/approach A GIRP mathematical model is first constructed to help this specific publishing logistics firm to approximate to the optimal distribution system design. Next, two modified Heuristic-Tabu combination methods that combine savings approach, 2-opt and 1-1 λ-interchange heuristic approach with two modified Tabu search methods are developed to determine the optimum solution. Findings Several examples are given to illustrate the optimum total inventory routing cost, the optimum delivery routes, the economic order quantities, the optimum service levels, the reorder points, the optimum common review interval and the optimum maximum inventory levels of all convenience stores in these designed routes. Sensitivity analyses are conducted based on the parameters including truck loading capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs and unit transport costs to support optimal distribution system design regarding the total inventory routing cost and GHG emission level. Originality/value The most important finding is that GIRP model with reordering point inventory control policy should be applied for the first replenishment and delivery run and GIRP model with periodic review inventory control policy should be conducted for the remaining replenishment and delivery runs based on overall simulation results. The other very important finding concerning the global warming issue can help decision makers of GIRP distribution system to select the appropriate type of truck to deliver products to all retail stores located in the planned optimal delivery routes depending on GHG emission consumptions.

In this research we consider the group replacement problem for an M/M/N production/service system. The servers are unreliable with identically exponentially distributed failure times. The repair cost consists of a fixed cost and a variable cost proportional to the number of repaired machines. In addition there is a holding cost for each customer in the system per unit of time. We develop a specific class of m-failure group replacement policy where the repair is started as soon as the number of failed machines reaches a predetermined level m. In the repair process, we assume the positive repair time and allow server failures during replacement. Finally, we formulate a matrix-geometric model to perform the steady state analysis and to obtain the expected average number of customers and the expected average cost. Besides the mathematical analysis, we numerically demonstrate the properties of the optimal policy for various sets of parameter values.