Engineering and Technology Horizons

Hybrid Simulated Annealing for Multi–Objective Capacitated Vehicle Routing in School Milk Distribution

Peerapong Pakawanich — 2026-01-23

This study develops a Hybrid Multi–Objective Simulated Annealing (HMOSA) framework for solving the Multi–Objective Capacitated Vehicle Routing Problem in Thailand’s School Milk Program, where balancing efficiency and fairness is essential due to the manual unloading tasks performed by each delivery team. The model minimizes total travel distance and workload imbalance, quantified by the standard deviation of vehicle loads to better capture physical handling effort. The proposed HMOSA introduces two novel mechanisms: i) warm–start initialization using extreme seed solutions generated from Single–Objective SA (SOSA) and Weighted–Sum SA (WSSA), and ii) a guided neighborhood mechanism that selects promising neighbors using weighted scores to enhance search efficiency and diversity. These contributions improve convergence stability without relying on complex parameter tuning. Computational experiments on 10, 30, and 51–customer instances demonstrate that HMOSA consistently outperforms conventional MOSA and SA, and provides superior Pareto–front quality compared with Non-dominated Sorting Genetic Algorithm II NSGA–II. Performance was assessed using two widely adopted indicators: hypervolume (HV) for solution diversity and inverted generational distance (IGD) for convergence reliability. In the real–world 51–school case, small increases in total distance resulted in substantial improvements in workload equity, offering actionable compromise solutions between distance and fairness. Overall, HMOSA embeds fairness into routing decisions while maintaining scalability and robustness, serving as a practical decision–support tool for real routing applications where routing efficiency and equitable workload distribution are both essential.

Productivity Improvement of the Coconut Water Conveying System Using Simulation Modeling: A Case Study

Phoometh Sangrayub — 2026-01-23

This study examines the coconut water conveying system through a case study of the coconut company. The applicability of Discrete-Event Simulation (DES), implemented using FlexSim, is demonstrated as a decision-support tool for analyzing and improving the system. The existing operation is characterized by inefficiencies in manual handling and unbalanced workloads, which negatively affect system throughput. Various scenarios were evaluated to determine optimal staffing levels at each workstation. Simulation results indicated that scenario 6 achieved an optimal allocation of labor at each workstation, thereby maximizing average throughput and reducing average work-in-process compared with the current system. Furthermore, a comprehensive simulation model was developed to evaluate potential process improvements, identifying the shell cracking and separation operations as the primary bottleneck constraining overall system throughput. The optimized conveyor-based configuration increases average throughput by 45.89%, reduces average work-in-process by 59.70%, and decreases average waiting time by 49.86% compared with the current system.

Detection of Isolated Track Defects Using Axle Box Acceleration from Multibody Simulation and Recurrent Neural Network

Kritat Plodphai — 2026-02-18

Railway track inspection is typically conducted cyclically, which is slow, budget-intensive, and time-consuming, as it can only be performed during non-service periods when trains are not in operation. Currently, there are emerging concepts and research studies utilizing railway defect detection through acceleration measurements from axle box mounted on service trains, as this approach offers easier implementation, reduced costs, and time efficiency compared to conventional methods. However, the diagnostic process remains challenging due to the high complexity and large volume of data involved. This research presents a method for applying Recurrent Neural Network (RNN) that works with time-series data in diagnosing railway defects by classifying three specific types of isolated defects: squats and corrugation. The training samples for the neural network were derived from axle box acceleration data obtained through multibody simulation analysis that modeled various railway conditions, speeds, and defect characteristics. A total of 360 samples from the multibody model were used for training, validation, and testing. The study results showed that the Recurrent Neural Network (RNN) achieved an average defects classification accuracy of 91.67%. Furthermore, the study findings revealed that the developed RNN model can accurately predict the location and classify defects, with particularly high accuracy in predicting the location and defects caused by long-pitch corrugation. The study concluded that the developed RNN model can efficiently learn and classify defects from axle box acceleration data obtained from multibody simulation. This approach can be applied as a guideline for railway damage diagnosis in future maintenance applications.

Exploring Sustainability-Oriented Innovation in Thai Technology-Driven Enterprises: A Multiple-Case Study

Nattida Tachaboon — 2026-03-24

This study investigates how Thai technology-driven enterprises perceive and implement Sustainability-Oriented Innovation (SOI), with a focus on the economic, social, and environmental dimensions. Addressing the limited empirical evidence on SOI among SMEs in emerging economies, the study examines sectoral variations and contextual influences shaping sustainability adoption. A qualitative multiple-case study was conducted with twelve enterprises across diverse sectors, including healthcare, agriculture, food, digital services, and engineering systems. Data were collected through semi-structured interviews with founders and senior executives and analyzed using thematic coding, supplemented by secondary documentation.

Findings reveal that economic considerations dominate SOI adoption, with firms prioritizing market survival, financial stability, and competitive product or process innovation. Social initiatives are emerging but often instrumental, focusing on community engagement, health, and capacity building. Environmental practices are limited and usually pursued opportunistically or when aligned with product differentiation. Contextual factors, including founder generational experience, shape sustainability orientation and decision-making. Sectoral and stage-specific differences further influence SOI implementation, with agri-food and health enterprises demonstrating higher social and environmental engagement.

The study contributes to theory by highlighting how technology-driven enterprises in emerging economies selectively adopt SOI, emphasizing the interplay of technological capability, sector, development stage, and cultural context. Practical implications suggest the need for targeted policy support, entrepreneurial training, and ecosystem development to enhance comprehensive adoption of sustainability practices. These insights provide guidance for entrepreneurs, policymakers, and investors seeking to foster innovation that integrates economic, social, and environmental value.

Optimal Sizing and Placement of a Battery Energy Storage System for Load Frequency Control using Particle Swarm Optimization

Sidtha Rattanakam — 2026-03-24

This article focuses on addressing key challenges in modern power distribution systems by presenting an appropriate approach for determining the size and location of Battery Energy Storage Systems (BESS) to improve Load Frequency Control (LFC) using the principles of Particle Swarm Optimization (PSO). The research modeled the standard IEEE 28-bus power distribution system to analyze and identify optimal locations for BESS installation, with the main goal of minimizing power losses, which directly support LFC. The simulation results show that using PSO allows for the identification of the optimal BESS location and size, which is the installation of a 1.16 MW BESS at bus 18. This configuration significantly reduces power losses by 72.03 % compared to the case without BESS installation. This result confirms the excellent ability of BESS to improve the efficiency and stability of the power system and is extremely beneficial for future power grid planning, which faces load and renewable energy source fluctuations.

Perception of Factors Influencing Labor Productivity of Skilled Workers in High-Rise Construction Projects in Bangkok, Thailand

Tewakun Chankampom — 2026-03-24

The construction industry, particularly high-rise construction projects in major urban areas, is a key driver of national economic growth; nevertheless, persistently low labor productivity continues to constrain project performance. This study investigates skilled construction workers’ perceptions of factors influencing labor productivity in high-rise construction projects in Bangkok, Thailand. A quantitative research design was employed using a structured questionnaire administered to skilled workers engaged in high-rise building projects across Bangkok and its metropolitan areas. A total of 346 valid responses were analyzed using multiple regression analysis (MRA). Labor productivity was specified as the dependent variable, while materials, tools, and equipment, health and safety, motivation, construction site area, and weather conditions were modeled as independent variables. The proposed model explained 77.2% of the variance in labor productivity. The results indicate that motivation (β = 0.786, p < 0.01) and health and safety (β = 0.130, p < 0.05) exert statistically significant positive effects on labor productivity. In contrast, the construction site area has a significant adverse effect (β = −0.154, p < 0.01). In contrast, materials, tools, and equipment, and weather conditions do not exhibit statistically significant relationships with labor productivity. The findings underscore the critical role of motivation-oriented management practices and proactive investment in occupational health and safety in enhancing skilled labor productivity, while highlighting productivity losses associated with spatial constraints and inefficient site layouts in high-rise construction projects. Departing from the predominantly managerial focus of prior studies, this research advances the literature by providing worker-centric empirical evidence from high-rise construction projects in Bangkok, Thailand, within a developing-country context. The results offer theoretically grounded, practically actionable insights for improving labor productivity through motivation-driven management, safety-oriented policy interventions, and practical construction site planning in urban high-rise developments.

Enhancing Industrial Machine Sound Anomaly Detection Using STFT Integrated with DWT and Autoencoder-Based Neural Networks

Tawan Mattanaweerapong — 2026-03-26

This study proposes a hybrid feature extraction approach that integrates the Discrete Wavelet Transform (DWT) with the Short-Time Fourier Transform (STFT) to improve the accuracy of anomalous sound detection in industrial machines. Conventional STFT-based methods, while effective in representing time–frequency characteristics, exhibit limitations in handling non-stationary noise and transient variations, which often lead to reduced anomaly detection performance in practical industrial environments. To address this problem, the proposed method incorporates multiresolution analysis through DWT, enhancing the system’s capability to capture both spectral and temporal information with improved noise robustness. The MIMII dataset (valve, -6 dB, ID02) was used to evaluate the model, where the DWT–STFT feature, representation was applied to an autoencoder for unsupervised anomaly detection. Experimental results demonstrate that the integration of DWT effectively enhanced noise robustness and improved classification metrics, achieving higher AUC and F1-scores compared to the baseline STFT-based approach. In conclusion, the proposed DWT–STFT fusion provides a more resilient and discriminative feature representation, making it a promising technique for practical industrial anomaly detection systems.

Optimization of Slow Pyrolysis Process for Avocado peel and Cashew Nutshell Biochar Production: Physicochemical Properties and Stability for Carbon Sequestration

Atchara Chaiya — 2026-03-24

This study examined the slow pyrolysis of avocado peel (AP) and cashew nutshell (CNS) to produce biochar for adsorption, solid fuel, and carbon sequestration. Experiments were conducted at 400–600°C with residence times of 30–120 min, with process optimization via Response Surface Methodology (RSM). A central finding was a “physicochemical decoupling” effect, whereby conditions maximizing yield and adsorption capacity differ fundamentally from those required for carbon stability and energy densification. RSM models predicted maximum yield for both feedstocks at 400°C and 30 min, while peak iodine adsorption was achieved at 600°C — at 30 min for AP (530.19 mg/g) and 120 min for CNS (552.95 mg/g). SEM and elemental analyses confirmed that higher temperatures promoted well-developed porous networks and elevated carbon content, with Higher Heating Value (HHV) reaching 26.82 MJ/kg for AP and 32.20 MJ/kg for CNS, comparable to commercial biomass fuel benchmarks. Notably, CNS biochar at 600°C and 120 min achieved an O/C ratio of 0.055, classifying it as IBI Class 1 with a predicted carbon sequestration half-life exceeding 1,000 years. These results establish a condition-selection framework: 400°C for adsorbent production and 600°C for solid fuel and long-term carbon sequestration.