This study proposes an enhanced artificial bee colony algorithm (ENABC) for flexible job shop scheduling with multiple assembly precedence constraints. The objective is to minimize the total penalty cost (TPC), defined as the sum of tardiness, earliness, flow time, and setup costs. ENABC enhances the classical artificial bee colony algorithm through five mechanisms: a three-parameter encoding scheme with efficient decoding heuristics, a dual food-source exploration strategy, a modified precedence-preserving crossover operator, a modified tabu search strategy, and a tabu list to prevent revisiting solutions. The proposed algorithm is evaluated using 10 large-scale industrial problem instances. Compared with benchmark approaches based on the TLSAPVNS and SCEGWO frameworks, ENABC achieved average TPC reductions of 12.96% and 10.90%, respectively. Relative to the factory's existing scheduling practice, the proposed approach reduced the average TPC by 49.93%. Furthermore, it required less computation time than all competitors.
Accurate dosing of chemicals is essential for coagulation and disinfection in water treatment systems. Conventional jar testing is time-consuming and may not respond quickly to changing raw water quality. This study compares artificial neural network architectures for predicting polyaluminum chloride and chlorine dosages jointly using a multiple output (MO) model or separately using single output (SO) models. MO models may capture shared dosing patterns, while SO models allow target-specific specialization. Operational data from an industrial water treatment plant in Pathum Thani, Thailand were used as a case study. Twelve growing window experiments with grid search tuning were conducted under temporally ordered and seasonally varying conditions. Results show that MO and SO models achieved comparable accuracy, although SO models had slightly lower error variance. The framework may be adapted to other process control applications with related outputs. However, predictions represent historical dosing by operators rather than optimized chemical use.
The ambiguity and deep concentration associated with programming work, along with perceived stereotypes about programmers, require effective human resource (HR) practices to sustain motivation. This study explores the relationships among the nature of programming work, programmer stereotypes, HR practices, and motivation. A conceptual model based on self-determination and social identity theories is developed, and eight hypotheses are presented. Data were collected from 254 participants across six industry sectors and analyzed using regression techniques. The results suggest that perceived programmer stereotypes stem either from the nature of programming work or from self-selection, whereby those who hold such stereotypes are often drawn to the field of programming. The findings also indicate that HR practices are not aligned with the nature of programming work or with stereotypes. Although HR practices and programmer stereotypes influence motivation, the nature of programming work can lead to demotivation. Therefore, HR practices should be better aligned with both programming work and programmer stereotypes to promote motivation.
The ambiguity and deep concentration associated with programming work, along with perceived stereotypes about programmers, require effective human resource (HR) practices to sustain motivation. This study explores the relationships among the nature of programming work, programmer stereotypes, HR practices, and motivation. A conceptual model based on self-determination and social identity theories is developed, and eight hypotheses are presented. Data were collected from 254 participants across six industry sectors and analyzed using regression techniques. The results suggest that perceived programmer stereotypes stem either from the nature of programming work or from self-selection, whereby those who hold such stereotypes are often drawn to the field of programming. The findings also indicate that HR practices are not aligned with the nature of programming work or with stereotypes. Although HR practices and programmer stereotypes influence motivation, the nature of programming work can lead to demotivation. Therefore, HR practices should be better aligned with both programming work and programmer stereotypes to promote motivation.
The rapid emergence of artificial intelligence (AI) chatbots has resulted in revolutionary transformations to the tourism and hospitality industry, fundamentally reshaping how services are delivered and experienced. In this study, the authors present a systematic review of 253 peer-reviewed publications indexed in Scopus and Web of Science from 2013 to 2025, offering a comprehensive overview of the academic landscape in this field. Employing descriptive statistics and bibliometric techniques, including co-citation analysis, bibliographic coupling, and keyword co-occurrence mapping via VOSviewer 1.6.20, the authors mapped the intellectual structure of existing literature and identified research gaps. Their research revealed four main thematic clusters: user interaction, technical foundations, adaptability of conversational AI across languages and cultures, and the strategic role of chatbots in tourism ecosystems. The authors outline future research directions and provide practical insights to advance AI chatbot development and implementation in tourism and hospitality.
