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Response time reduction is a fundamental aspect of ambulance location management. To minimize patient mortality and disability, the response time of emergency medical services is critical. Therefore, real-time management is required to determine the location of an ambulance with a low response time or called or a dynamic allocation system. Dynamic allocation is moving the ambulance bases from low demand areas to high-demand areas that is useful in the operational level. However, the dynamic allocation model for real-time management requires re-allocation of ambulances, resulting in high costs and heavy workloads for the ambulance crews. This paper focuses on a covering model based on social media analysis. The model was used for developing an ambulance reallocation system. In addition to dynamic allocation, the proposed model considers real-time data from a social media application (Twitter) to minimize the response time and cost during emergencies and disasters. Twitter has been used in various ways to communicate during and manage emergencies. In this paper, we formulate the Maximal Covering Location Problem (MCLP), develop a solution procedure based on social media (Twitter application) and show the effect of the approach on the optimal solution by comparing it with the classical approach and also demonstrate our approach on Bangkok EMS.

A supply chain disruption is an unanticipated event that disrupts the flow of materials in a supply chain. Any given supply chain disruption could have a significant negative impact on the entire supply chain. Supply chain network designs usually consider two stage of decision process in a business environment. The first stage deals with strategic levels, such as to determine facility locations and their capacity, while the second stage considers in a tactical level, such as production quantity, delivery routing. Each stage’s decision could affect the other stage’s result, and it could not be determined individual. However, supply chain network designs often fail to account for supply chain disruptions. In this paper, this paper proposed a two-stage stochastic programming model for a four-echelon global supply chain network design problem considering possible disruptions at facilities. A modified simulated annealing (SA) algorithm is developed to determine the strategic decision at the first stage. The comparison of traditional supply chain network decision framework shows that under disruption, the stochastic solutions outperform the traditional one. This study demonstrates the managerial viability of the proposed model in designing a supply chain network in which disruptive events are proactively accounted for.

Currently, supply chain network design becomes more complex. In designing a supply chain network to withstand changing events, it is necessary to consider the uncertainties and risks that cause network disruptions from unexpected events. The current research related to the designing problem considers network disruptions using Monte Carlo Sampling (MCS) or Latin Hypercube Sampling (LHS) techniques. Both have a disadvantage that sample points or disruption locations are not scattered entirely sample space leading to high variation in objective function values. The purpose of this study is to apply a modified LHS or Improved Distributed Hypercube Sampling (IHS) techniques to reduce the variation. The results show that IHS techniques provide smaller standard deviation than that of the LHS technique. In addition, IHS can reduce not only the number of sample size but also and the computational time.

Appearance, colors and adhesion characteristics of chicken nugget are important to customer satisfaction and buying decision. These characteristics are generally inspected by hu-man, thus, the inspectors might incorrectly judge. In addition, the results are not quantitatively recorded for further analysis and improvement. Therefore, this study focuses on constructing a measurement instrument for detecting the qualities of chicken nugget, then gage repeatability and reproducibility (GR&R) study is used to ensure that the instrument is capable of dis-tinguishing nugget differences. Since, there are eleven characteristics of chicken nugget are analyzed. The principal component analysis is applied to reduce the number of characteristics from eleven dimensions to only four dimensions. The experiments and data analysis show that the dimension reduction is useful for rapidly detect the abnormality of nuggets and finally help practitioners to improve the process.

Stochastic programming is a mathematical tool for decision-making under uncertainty. However, it remains largely unused as a practical tool for decision making. One reason for its light use in practice may be that practical instances are of larger size than can be solved by available software tools on existing computational platforms. In this thesis, we focus on algorithms for solving large-scale two-stage stochastic programs on a parallel distributed computer platform known as a computational grid. An overarching theme of this work is to make use of hundreds of loosely-coupled processors over long time periods to effectively solve stochastic programs. We study how grid computing can be employed for effective solutions of stochastic programs in the following areas. First, we study a parallel implementation of the L-Shaped decomposition method for solving a sample average approximation(SAA) of large stochastic programs. We propose warm-starting methods for the L-Shaped decomposition method by using pre-sampling and scenario partitioning. Further, a method for managing cuts in the L-Shaped method is also discussed. Second, since evaluating the objective function is a computational task that can be easily and efficiently distributed on a computational grid, we study enhancements to traditional techniques that can help us exploit this fact. In particular, we propose a line search method and trust-region scaling methods to enhance the convergence rate. Finally, in order to estimate the bias of solution value estimates using the SAA method, we propose using a statistical technique known as the bootstrap method. The bootstrap estimate requires solution of a large number of bootstrap sample problems, a process that can be easily parallelized by using the power of grid computing. In all cases, extensive computational experiments were performed. We conclude with a case study demonstrating the application of our developed techniques to solve very large-scale instances.

Private hospitals offer an advanced appointment program that allows patients to receive medical care services at their convenient time. While the amount of callers has increased, many hospitals face difficulties to determine the number of operators to promptly respond the calls. Long waiting time may cause some callers to abandon their lines, which leads to the loss of opportunity. This chapter focuses on how to determine the optimal number of operators and their assignment in a service time horizon. An integrated framework is proposed using mixed-integer nonlinear programming to solve the staff planning and allocation problem. The result shows that the framework is viable.