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This study considers the two-fold dynamic ambulance allocation problem, which includes forecasting the distribution of Emergency Medical Service (EMS) requesters and allocating ambulances dynamically according to the predicted distribution of requesters. EMSs demand distribution forecasting is based on on-record historical demands. Subsequently, a multi-objective ambulance allocation model (MOAAM) is solved by a mechanism called Jumping Particle Swarm Optimization (JPSO) according to the forecasted distribution of demands. Experiments were conducted using recorded historical data for EMS requesters in New Taipei City, Taiwan, for the years 2014 and 2015. EMS demand distribution for 2015 is forecasted according to the on-record historical demand of 2014. Ambulance allocation for 2015 is determined according to the anticipated demand distribution. The predicted demand distribution and ambulance allocation solved by JPSO are compared with historic data of 2015. The comparisons verify that the proposed methods yield lower forecasting error rates and better ambulance allocation than the actual one.

In the past few years, the electronics industry has undergone an explosion in new products and technologies. This fierce global competition has resulted in the decision by many companies to outsource manufacturing in order to concentrate on research and development, marketing, and sales. This has given rise to a number of companies who solely provide electronics manufacturing services. Exchange of information between product developers and contract manufacturers, especially information about product demand, is critical to the success of this business model. In this paper, we analyze forecast data obtained from a local contract manufacturer in order to better understand forecast variability and its reasons. We find that forecast variability tends to increase as the production period approaches, despite common belief that forecasts get better with time. We also discuss the impact that chronic material unavailability and product mix have on forecast variability. Finally, we see that unmet demand pushed in the next production period can distort forecasts and result in unrealistic expectations.

In this paper, two problems that exist in parking spot layout of bike-sharing which need to be solved are addressed. One is the size of parking spot, while another is the location of parking spot. First, the generation and attraction model of traffic travel is used to predict the usage requirements of bike-sharing, to acquire the parking requirements of bike-sharing. Then, we establish the best station spacing model to calculate the optimal quantity of parking spots. Moreover, the method of shortest distance clustering is applied to cluster the parking demand spots. Finally, the location optimization of bike-sharing parking spot is carried out by using gravity location model. In summary, the layout of bike-sharing parking spot is finished based on the abovementioned four steps.