This study develops novel mathematical models to capture the complexities of international land logistics by extending the classical Traveling Salesman Problem (TSP) within a symmetry-aware optimization framework. A focused review of literature provides the theoretical basis for model formulation and highlights the limitations of conventional distance-only approaches. In international transport, shorter routes are often assumed to reduce energy use; however, this assumption overlooks the decisive influence of travel time and traffic variability. In this context, symmetry offers a useful analytical lens, as balanced relationships among distance, time, and fuel consumption can reveal more efficient logistics structures. Accordingly, two models are proposed: the Traditional Traveling Salesman Problem in terms of Distance Concentration (TTSPD), which minimizes route length, and the Extended Traveling Salesman Problem in terms of Distance and Time Concentration (ETSPDT), which jointly considers distance, travel time, and fuel consumption. Furthermore, TTSPD was employed to validate ETSPDT, since it is based on the traditional TSP. Both models are solved exactly using the Solver Add-in in Microsoft Excel 2024 with data derived from Google Maps. The results show that ETSPDT achieves superior energy efficiency and average speed, demonstrating the practical value of multidimensional, symmetry-informed optimization for sustainable supply chain and logistics management.

This study aims to examine the determinants influencing road freight transport costs in a middle-income ASEAN country. It utilizes secondary data from government sources concerning road freight transport costs, logistics sector employee expenses, export-import volume indices, transport energy prices, and road freight transport indices from 2015 to 2022 in Thailand to formulate the mathematical model. The analysis employs Correlation Analysis and Multiple Regression Analysis, with road freight transport costs as the dependent variable and other factors as independent variables. The results indicate that most variables have the positive impacts on road freight transport costs. However, factors such as Employee Cost, Import Volume Index, Road Freight Transport Index for lorries, Public Infrastructure Budget, Vehicle Equipment Price Index, and Communication Services exhibit the negative effects on transport costs, influenced by the specific national context and the COVID-19 pandemic. Notably, three significant predictors including Vehicle Price Index, Road Freight Transport Index for Hazardous Materials Trucks, and Public Infrastructure Budget emerge as critical areas for policymakers. Addressing these factors could help the policy makers optimize road freight transport costs in Thailand.

This study seeks to introduce the alternative mathematical models for addressing the courier transportation problem using personal vehicles. Initially, a review of relevant literature was conducted to inform the development of these models, particularly focusing on groups of courier deliveries characterized by special orders or small lot sizes, with an emphasis on minimizing costs associated with energy consumption. Subsequently, two mathematical models were formulated: one addressing the transportation problem based on distance with a fixed transport speed (TPD), and another that considers both distance and time (TPDT). These models were numerically analysed using the exact method facilitated by the Solver Add-in in Microsoft Excel 2021, incorporating secondary data sourced from Geographic Information System (GIS) data via Google Maps in Thailand, a middle-income country. The analysis revealed that the transportation model incorporating both distance and time proved to be a more effectiveness than the model based solely on distance with a fixed transport speed, particularly when evaluating the cost objective function. It was determined that focusing exclusively on transport distance is insufficient for minimizing costs in real-world courier delivery scenarios. Therefore, transport planners are encouraged to consider both distance and time for managing special orders or small lots, as this approach holds significant potential for cost savings in practical courier transport planning.

Cross-border land logistics in international supply chains presents a complex decision-making challenge, as a logistics provider must simultaneously allocate jobs to vehicles and determine efficient delivery routes under strict capacity constraints and customer time windows. In this study, the linked job assignment and multiple traveling salesmen problem (mTSP) is formulated as a multiple objective optimization model that minimizes total travel distance, total travel time, and the number of late deliveries. To solve this problem, an adaptive goal-constrained genetic algorithm (AGCGA) is developed. First, a time-window-aware K-means clustering approach is used to generate feasible initial job assignments and estimate the required vehicle fleet. Next, a genetic algorithm with an embedded goal-programming fitness function is applied, where aspiration levels and penalty weights are adaptively adjusted during evolution to better explore the Pareto frontier. The proposed method is evaluated using a real-world-inspired dataset of 34 cross-border delivery locations between Thailand and Malaysia, with distance and travel-time matrices derived from Google Maps via web platforms. The results show that AGCGA outperforms NSGA-II and a greedy heuristic in terms of hypervolume, goal achievement, and computational stability. Overall, the framework provides a robust decision-support tool for balancing cost, service time, and delivery punctuality in international logistics.

The Greater Mekong Subregion (GMS) has emerged as a strategic logistics hub due to rapid economic integration and the expansion of cross-border road transportation. However, international road logistics networks in the region must address several competing objectives, including minimizing transportation costs, reducing delivery times, and balancing transport distances among trading partners. To overcome these challenges, this study proposes an innovative hybrid computational framework that integrates the classical Transportation Problem with the Pythagorean methodology (TPPM). The proposed approach consolidates multiple transportation objectives into a unified performance metric based on the Pythagorean concept, thereby enabling simultaneous optimization under practical constraints. In addition, geographic inputs derived from Google Maps and Google Earth via web platforms, which are reliable open-source GIS tools, are incorporated into the transportation model to improve spatial accuracy. A simulated dataset comprising 35 suppliers and 42 customers, representing major logistics nodes in the GMS, is developed to evaluate the proposed method. The computational results indicate that the TPPM approach outperforms the conventional single-objective Classical Transportation Problem (CTP) by producing higher solution quality and more balanced performance. Overall, the findings demonstrate that the proposed hybrid method is a robust decision-support tool for sustainably enhancing the resilience of international logistics planning in emerging economic regions.

Matching surgical patients to intensive care beds in real time remains stubbornly difficult in healthcare business operation. Demand spikes. Capacity tightens. Yet, even brief admission delays might trigger avoidable complications and inflate operational costs. To navigate this bottleneck, this study proposes the model of Stochastic Programming and Queueing Theory-Based Decision Support for Dynamic Surgical Triage (SQDSS), a framework blending two-stage stochastic programming with multi-class priority queueing. It attempts to capture uncertain arrivals and shifting acuity, alongside the grim reality that waiting patients often deteriorate. This investigation tested the model across 48 synthetic scenarios. Subsequently, the testing results exhibited that SQDSS experimentally appears to outperform both first-come, first-served and fixed-threshold policies. Empirically, high-acuity waits seemingly dropped by roughly 23%, while bed utilization potentially climbed by 16%. Queue overflows and simulated complication rates also suggested notable declines, hovering near 28% and 18%, respectively. Interestingly, the most pronounced gains emerged during surge conditions, particularly when trauma cases dominated. While no mathematical abstraction perfectly mirrors clinical reality, these findings hint that merging stochastic optimization with queueing analytics could offer a robust foundation for dynamic triage. Ultimately, such hybrid approaches might just foster more resilient healthcare business operation and supply chain management.