Post weld heat treatment, or PWHT, is often used to improve the mechanical properties of materials that have been welded. Several publications have investigated the effects of the PWHT process using experimental designs. However, the modeling and optimization using the integration of machine learning (ML) and metaheuristics have yet to be reported, which are fundamental steps toward intelligent manufacturing applications. This research proposes a novel approach using ML techniques and metaheuristics to optimize PWHT process parameters. The goal is to determine the optimal PWHT parameters for both single and multiple objective perspectives. In this research, support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF) were ML techniques employed to obtain a relationship model between PWHT parameters and mechanical properties: ultimate tensile strength (UTS) and elongation percentage (EL). The results show that the SVR demonstrated superior performance among ML techniques for both UTS and EL models. Then, SVR is used with metaheuristics such as differential evolution (DE), particle swarm optimization (PSO), and genetic algorithms (GA). SVR-PSO shows the fastest convergence among other combinations. The final solutions of single-objective and Pareto solutions were also suggested in this research.

Tourism is a dynamic industry that significantly contributes to the global economy, driven by the increasingly diverse preferences of tourists. Addressing these preferences requires sophisticated decision-making models capable of handling the uncertainty and subjectivity of human judgments. This study proposes sustainable models for effectively capturing and evaluating individual tourist preferences using the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). These models leverage the strengths of the AHP to construct a flexible decision-making framework that adapts to diverse tourist preferences, offering personalized recommendations. In this study, three main criteria are considered: types of tourism, tourism facilities, and tourism areas. Tourists are encouraged to provide their preferences for these criteria and sub-criteria, enabling the AHP and Fuzzy AHP to recommend suitable destinations. An analysis was conducted with 30 respondents providing pairwise comparisons of the tourism criteria, which were then used to generate tourist attraction recommendations using both the AHP and Fuzzy AHP. The study assessed respondents’ satisfaction with the recommendations, finding that both methods were effective, with a slight preference for the Fuzzy AHP due to its ability to better capture individual preferences. The results underscore the potential of these models in sustainably enhancing decision support systems in the tourism industry, offering tailored recommendations that align more closely with tourist expectations.

A novel mathematical model of stochastic internal task scheduling in cross docking is proposed herein for minimizing the total tardiness of customer orders. The model aims to simultaneously assign internal cross-dock working teams and transportation equipment to obtain the optimal internal task schedule in a single unloading activity. Stochastic parameters are considered to yield a more realistic problem. In this problem, the processing times of breaking down incoming containers and building up customer orders, and the due dates of customer orders are assumed as random variables subjected to normal and uniform distributions, respectively. The problem was formulated using chance-constrained programming to minimize the total tardiness. An experiment was performed for comparing solutions between stochastic and deterministic scheduling environments. Computational experiment using a LINGO optimization solver showed that the total tardiness obtained from the stochastic model with chance-constraint programming was higher than that from the deterministic model because of uncertainties in terms of processing times and due dates. However, the internal task scheduling problem in cross docking is NP-hard. The exact method provides an optimal solution within a reasonable time for small problems. Therefore, future research should consider metaheuristic approaches to addressing complexities in real-world practices.

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.