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This study presents a class-based approach developed to evaluate the financial performance of companies that have undergone public listing on the stock market. By employing both statistical analysis and machine learning methods, the models consider two important determinants, which are the company's internal capability determinants prior to going public and the amount of funds raised through the Initial Public Offering (IPO) to predict the company’s financial performance after joining the stock market. The study demonstrates that the machine learning method (random forest algorithm) outperforms the statistical method (logistic regression) in predicting financial performance. The findings also reveal that certain determinants significantly influence the predicted financial performance in a specific period. Furthermore, the study examines the impact of IPO funds on financial performance and observes that while the first year after listing does not exhibit a significant effect, a subsequent positive correlation emerges in the subsequent two to three years, up to a certain threshold, with excessive funds potentially leading to adverse effects. Overall, the predictive models provide valuable insights for companies, enabling them to prioritize resources towards significant determinants in a specific relative year, make informed decisions, and enhance their long-term success in the stock market.

Decision-making for shelter location-allocation influences the success of disaster response and affects the security of victims. This paper proposes a multi-objective optimization model for determining shelter location-allocation in response to humanitarian relief logistics. Three objective functions are formulated to improve both efficiency and effectiveness. The first objective is to minimize total costs, including fixed costs for opening the shelters, transportation costs, and service costs. The second objective is to minimize the total time for evacuating victims from all affected areas to allocated shelters. The third objective is to minimize the number of shelters required to provide thorough service to victims. The Epsilon Constraint method (EC) and Goal Programming (GP) are employed for solving the proposed model. The applicability of the proposed model is validated through a case study of flooding in Surat Thani, Thailand. The Pareto efficiency obtained from solving the proposed model is compared with current shelter location-allocation plans determined by the government sector. The comparisons reveal that the results obtained from solving the proposed model outperform current shelter location-allocation plans. Furthermore, the results of this study could provide an advantage to decision-makers considering appropriate strategies for disaster response.

In general, an experienced operator or expert must deliver face-to-face industrial manual assembly (I-MA) training. There is a limited number and availability of expert trainers, making training less accessible to trainees. Industrial virtual training systems (I-VTS) from research communities and commercial systems are focused on providing task demonstrations with rich multimedia immersive content. These systems can significantly lessen the reliance on the expert, but they still require an expert to observe and provide feedback. This study presents the “EXpert Independent Manual AsseMbly Virtual Trainer” (EXAMINER), addressing these limitations by integrating vision-based digitization of I-MA skills, comparison of digitized operators, and providing augmented feedback assessing the training outcomes automatically. This approach enhances trainee access to training without expert dependency. The study discusses the rationale and implementation process for the EXAMINER framework and a simulated case study evaluating the EXAMINER framework’s ability to digitize and provide appropriate extrinsic augmented terminal feedback based on participant performance, marking a significant advancement in industrial training technology.

Ensuring the well-being of patients is paramount for the effective functioning of healthcare systems. This research focuses on developing a comprehensive framework aimed at improving hospital systems with a primary emphasis on enhancing patient satisfaction, specifically addressing Length of Stay (LOS) and physician assignment issues. To achieve this objective, a multi-faceted approach was employed. The data collection involved a combination of surveys, observations, and interviews, which enabled a thorough understanding of the hospital environment. A unique formula was derived to compute the satisfaction scores from the gathered survey data. Subsequently, a simulation model was developed. The research applies two distinct multi-objective optimization approaches, that is, a weighted max-min for fuzzy multi-objective optimization and the Torabi-Hassini (TH) approach to balance the importance of various objectives. The application of a multi-criteria decision guideline further streamlines the decision-making process. The outcomes of this study provide suggestions for tailored improvements for hospitals of different sizes. For large hospitals, this study indicates enhancements that positively impact both LOS and physician assignment satisfaction. Medium and small-sized hospitals are presented with two distinct choices: improvements geared toward LOS satisfaction or those focused on physician assignment satisfaction. While this study is based on a case analysis of the Ophthalmology Department at Thammasat University Hospital in Thailand, its implications extend beyond this specific department. This study offers an adaptable framework that can be applied to other hospital departments, promoting an integrated approach to healthcare system enhancement.

Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method’s effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations.

This paper introduces the demand-driven assembly line rebalancing problem (DDALRP) and proposes a non-linear, multi-objective, combinatorial optimization model to solve it. A DDALRP arises whenever the production output of the assembly line (AL) must be continuously readjusted along a planning horizon in order to satisfy as much as possible a given demand forecast; thus, dealing not with a one-time rebalance, but with a multi-period rebalance, fact that exponentially increases the complexity and combinatorial nature of the problem. Adapting or regulating the production output of the AL to a particular demand forecast or production plan is a relatively new idea in the assembly line balancing (ALB) / rebalancing (ALR) literature; and the novelty of this work is the rebalancing mechanism employed to solve the problem: we address the problem by reallocating workers to stations, taking into consideration their learning and forgetting (L&F) curves. Our proposed model was solved by implementing a genetic algorithm (GA) in 162 cases (three problem instances under 54 scenarios each), which produced useful insights about the dynamics of worker reallocation under different situations: optimistic, most-likely, pessimistic L&F coefficients; experienced and inexperienced workers; and different demand scenarios.

This paper addresses the problem of fuzzy clustering for categorical data. During the last two decades, many attempts have been made to extend the $k$ -means algorithm, making it applicable to clustering for categorical data, due to its simplicity and efficiency. However, as $k$ -means-like algorithms are local optimization methods, their clustering results are highly sensitive to initialization. In this paper, we propose to use Locality-Sensitive Hashing (LSH) to reduce the categorical data dimensions and predict the initial fuzzy clusters in low-dimensional space. Different from the existing cluster initialization optimization methods that aim to create only crisp initial clusters, the proposed method aims at predicting ‘high quality’ fuzzy clusters at the initialization step before proceeding in the $k$ -means-like fashion. The numerical results show that the proposed method yields relatively accurate results on 16 UCI datasets and outperforms all other related approaches in terms of both crisp and fuzzy clustering effectiveness.

This paper addresses the problem of multi-criteria recommendation in the hotel industry. The main focus is to analyze user preferences from different aspects based on multi-criteria ratings and develop a new multi-criteria collaborative filtering method for hotel recommendations. Particularly, the proposed recommendation system integrates matrix factorization into a deep learning model to predict the multi-criteria ratings, and then the evidential reasoning approach is adopted to model the uncertainty of those ratings represented as mass functions in Dempster-Shafer theory of evidence. Finally, Dempster’s rule of combination is utilized to aggregate those multi-criteria ratings to obtain the overall rating for recommendation. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness and efficiency of the proposed method compared with other multi-criteria collaborative filtering methods.

Currently, the number of deliveries handled by transportation logistics is rapidly increasing because of the significant growth of the e-commerce industry, resulting in the need for improved functional vehicle routing measures for logistic companies. The effective management of vehicle routing helps companies reduce operational costs and increases its competitiveness. The vehicle routing problem (VRP) seeks to identify optimal routes for a fleet of vehicles to deliver goods to customers while simultaneously considering changing requirements and uncertainties in the transportation environment. Due to its combinatorial nature and complexity, conventional optimization approaches may not be practical to solve VRP. In this paper, a new optimization model based on reinforcement learning (RL) and a complementary tree-based regression method is proposed. In our proposed model, when the RL agent performs vehicle routing optimization, its state and action are fed into the tree-based regression model to assess whether the current route is feasible according to the given environment, and the response received is used by the RL agent to adjust actions for optimizing the vehicle routing task. The procedure repeats iteratively until the maximum iteration is reached, then the optimal vehicle route is returned and can be utilized to assist in decision making. Multiple logistics agency case studies are conducted to demonstrate the application and practicality of the proposed model. The experimental results indicate that the proposed technique significantly improves profit gains up to 37.63% for logistics agencies compared with the conventional approaches.

Route selection strategy has become the main aspect in the multimodal transportation system. The transport cost and time as well as the inherent risks must be considered when determining a corrective design plan. The selection of a multimodal transportation network route is a complex multi-objective decision problem. Therefore, considering the impact factors such as the transport cost, time, and comprehensive risk assessment model were further created. This paper develops a decision support model using an analytic hierarchy process (AHP) and zero-one goal programing (ZOGP) to determine an optimal multimodal transportation route. AHP is employed to determine weights of each factor, which rely on expert judgments. The significant weights of criteria obtained from AHP can be integrated in the objective function of ZOGP which is used to generate the optimal route. The empirical case study of coal manufacturing is conducted to demonstrate the proposed model. This methodology can provide a guidance for effectively determining the multimodal transportation routes to improve performance of logistics systems.