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• Bike repositioning problem with service requirements for rental and return requests was studied. • A mathematical model with the goal of minimizing the travel and unsatisfied costs was developed. • An efficient two-phase heuristic approach based on linear programming was proposed. • The proposed approach performs better than CPLEX optimizer and a genetic algorithm-based method. • The effects of various system parameters on the system were also investigated. An approach to overcome the bike imbalance problem is to transfer excess bikes to branches with bike shortages. This study develops a constrained mathematical model to deal with a multi-vehicle bike-repositioning problem, and aims to minimize the sum of transportation and unmet demand costs over a planning horizon through bike-transfer strategies under a minimum service requirement. A two-phase heuristic based on linear programming was proposed to solve the problem and produce compromising solutions. In the first phase, the paper developed a linear programming model to quickly develop decisions related to bike inventory, unloading, and loading for all stations for each time slot. In the second phase, this paper proposed an iterative approach through two parameter sensitive mathematical models to sequentially reduce the problem scale to develop decisions related to bike transfers. Computational results show that the proposed approach is superior to a CPLEX optimizer and a hybrid heuristic based on a genetic algorithm. The proposed approach was used to analyze the bicycle system in Taiwan. The impacts of various system parameters on the system were also investigated. [ABSTRACT FROM AUTHOR]

Abstract: Due to the scientific and economic growth of recent years, the on-going innovation in electrical products has enabled people to live more comfortably and conveniently. The standby power function is one such innovation. Unfortunately, as an increasing number of products are equipped with standby power, more energy is wasted and more carbon dioxide is emitted into the atmosphere. This paper combines a two-step proportional stratified sampling technique with systematic random sampling to select representative households in Taiwan. We adopted the measurement method of the International Electro technical Commission (IEC) in measuring the overall use of standby power in Taiwanese households. We then proposed a model for the estimation of standby power consumption, before calculating annual standby power consumption among all households in Taiwan. This paper could serve as a reference for the government and relevant authorities in the establishment of corresponding standards, providing suggestions for the minimization of standby power consumption and the unnecessary waste of energy. [Copyright &y& Elsevier]

Abstract: Global enterprises require extended logistics operations that integrate channel intermediaries into the network. The method for deriving integrated models for logistics hubs that improve the efficiency of manufacturers’ global operations is the focus of this paper. Under government sponsorship, six industrial logistics hubs were implemented by leading manufacturing sectors across Taiwan over a five year period from 2004 until 2008. Each industry sector had unique industry characteristics, business strategies, and logistics models. Therefore, this paper describes how generalized and quick to implement integrated logistics hubs are developed by studying the successful reference models and systems used by six independent industrial sectors. The research results provide a field tested method for deriving integrated logistics hub models in different manufacturing economies with notes that provide sufficient methodological detail for repeating the construction of logistics hubs in other manufacturing economies. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, six univariate forecasting models for the container throughput volumes in Taiwan’s three major ports are presented. The six univariate models include the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey model, the hybrid grey model, and the SARIMA model. The purpose of this paper is to search for a model that can provide the most accurate prediction of container throughput. By applying monthly data to these models and comparing the prediction results based on mean absolute error, mean absolute percent error and root mean squared error, we find that in general the classical decomposition model appears to be the best model for forecasting container throughput with seasonal variations. The result of this study may be helpful for predicting the short-term variation in demand for the container throughput of other international ports. [Copyright &y& Elsevier]

Abstract: This paper presents the results of modeling the ionospheric effect of the seismogenic electrostatic field (SEF) seen at the earth’s surface as a perturbation of the vertical atmospheric electrostatic field in the earthquake preparation zone. The SEF distribution at ionospheric altitudes is obtained as an analytical solution of the continuity equation for the electric current density. It is shown that at night, the horizontally large scale SEF can efficiently penetrate into the ionosphere and produce noticeable changes in the horizontal distribution of the F region electron density. The results suggest that the seismogenic electrostatic field could be a possible source for the ionospheric variations observed over Taiwan before the strong Chi Chi earthquake of September 21, 1999. [Copyright &y& Elsevier]

Abstract: In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) method for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experimental results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively. [Copyright &y& Elsevier]

Abstract: This paper introduced a stochastic programming model to address the air freight hub location and flight routes planning under seasonal demand variations. Most existing approaches to airline network design problems are restricted to a deterministic environment. However, the demand in the air freight market usually varies seasonally. The model is separated into two decision stages. The first stage, which is the decision not affected by randomness, determines the number and the location of hubs. The second stage, which is the decision affected by randomness, determines the flight routes to transport flows from origins to destinations based upon the hub location and realized uncertain scenario. Finally, the real data based on the air freight market in Taiwan and China is used to test the proposed model. [Copyright &y& Elsevier]

Southern Taiwan has experienced severe PM10 problems for over a decade. The present paper describes the establishment of a simulation model for the daily average PM10 concentrations at Ta-Liao, southern Taiwan. The study used a regression with time series error models (RTSE models) (multivariate ARIMA time series model), including an explanatory variable resulting from principal component analyses to complete the PM10 simulation. Factor 1 estimated from the factor analyses explained the variance of 44–49%, which indicated the important contribution from the neighbor-city PM10 at Mei-Nung, Lin-Yuang, Zuoying, Chao-Chou, local ozone and NOx. Factor 1 can be interpreted with regional PM10 plus photochemical reactions. To improve the predictability of extremely high PM10, different results from the principal component analysis were introduced to the RTSE models. We constructed four kinds of RTSE models: RTSE model without PC, with PC4S (PM10 at Mei-Nung, Lin-Yuang, Zuoying, and Chao-Chou), with PCTL (meteorological variables and co-pollutants at Ta-Liao), and with PCTL4S (the combination of the above two) and evaluated the statistics model performance. Ozone, dew point temperature, NOx, wind speed, wind directions, and the PC trigger were the significant variables in the RTSE models most of time. When the neighbor-city PM10 was included in the PC trigger, the predictability was apparently improved. The closeness of fit with the inclusion of PC4S and PCTL4S was improved by reducing SEE from 0.117 to 0.092. Using the RTSE models with PC4S or PCTL4S, POD was improved by an increase of 33%, FAR was reduced 30%, and CSI was increased 39%, when simulating the daily average PM10 >150μgm−3. Evidently we need to survey source impacts prior to establishing a simulation model. Factor analysis is a useful method to investigate sources that contributed PM10 to a target site prior to establishing a simulation model. [Copyright &y& Elsevier]