Jul 2026· International Journal of Innovation and Multidisciplinary Research· Vol 6, pp. 94· 0 citations
Abstract
Efficient transportation planning requires balancing economic performance, operational efficiency, and environmental responsibility. In real-life transportation systems, transportation planner frequently need to include more than one conflicting factors like minimizing transportation cost, minimizing transportation time, and environmental impact. Traditional single-objective models are not sufficient to handle these complex requirements. Therefore, the need for efficient multi-objective optimization techniques has become increasingly significant. The proposed study focuses to solve a "Multi-Objective Transportation Problem (MOTP)” by different optimization techniques namely, Weighted Sum Method and Goal Programming Method. For each method, the mathematical models have been formulated and solved using secondary data in similar scenarios for fair comparison of methods. For each method, the solutions vary based on the prioritization of objectives. The results show that the Weighted Sum Method gives different results when the weights vary, which means it is sensitive to weight selection. Goal Programming proves to be more flexible and consistent by assigning prioritization and targets. A comparative analysis has been performed by focusing on the ability to find compromise solution, effectiveness of each method, efficiency in getting optimal results and practical applicability of the methods. As seen from the analysis, the multi-objective optimization offers more realistic solution as compared to single-objective optimization techniques. This could help decision makers in transportation and logistic planning.
The findings demonstrate that metaheuristic techniques consistently outperform traditional algorithms in complicated, constraint-rich situations and emphasize the need of cost-effective, data-driven metaheuristic optimization in current logistics planning.
K. Khaw, C. Tan· International Journal on Rob...· 0 citations
This study develops improved approaches to solving freight transportation problems by considering three distinct transportation cases using real-world data. The main objective is to minimize total transportation costs from supply sources to demand destinations while determining optimal shipment quantities. The proposed models are designed to obtain efficient basic feasible solutions satisfying the required number of occupied cells, m+n−1, at the minimum possible cost. The models were implemented in MATLAB and LINGO and evaluated using a genetic algorithm (GA) and four statistical methods: the arithmetic mean (PAM), geometric mean (PGM), quadratic mean (PQM), and harmonic mean (PHM). The obtained results show that the proposed models can reduce total transportation costs while satisfying the relevant supply and demand constraints. Optimal shipment quantities were determined for each of the three transportation problems after balancing the corresponding supply and demand conditions. The results obtained using MATLAB, LINGO, the genetic algorithm, and the statistical methods were generally close, indicating the validity and effectiveness of the proposed models for solving balanced and unbalanced transportation problems at minimum total cost.
L. Mohammed, M. Zeidan, E. N. Nori Mahmood· Информатика. Экономика. Упра...· 0 citations
This paper investigates the scalability of a multi-criteria optimization for the Service Team Transport Scheduling (STTS) problem, minimizing total travel time, maximum vehicle worktime, and total vehicle engagement time. The mathematical model of the problem is provided. A simplified problem representation and two graph-based representations along with problem properties are formulated. Using these properties, we propose two complementary frameworks: a declarative Constraint Optimization Problem (COP) solved via Gurobi, and a metaheuristic Tabu Search (TS) incorporating a Constraint Satisfaction Problem (CSP) and an auxiliary greedy method for obtaining initial feasible solutions. Additionally, the population-based NSGA-II algorithm is implemented as an evolutionary baseline. The solving methods are evaluated under a strict 300-second operational limit using 280 realistic instances. The results reveal clear tractability thresholds. The COP solver obtains high-quality Pareto fronts but experiences a combinatorial collapse beyond 8 customer locations, hitting a 0% success rate for instances with size 15 or more. Conversely, the proposed hybrid CSP-TS framework demonstrate high robustness, maintaining a 90–100% operational success rate up to 50 locations. Specifically, the expansive large-neighborhood TS variant excels at medium scales (14–40 locations), while the compact small-neighborhood TS variant achieves the highest Hypervolume Indicator (HVI) at large scales (45 or more locations). Pure population-based approaches like NSGA-II are consistently outperformed by the specialized local search neighborhood structures. These quantitative findings define scale-aware algorithmic boundaries essential for real-time decision support systems.
Jarosław Rudy, G. Radzki· IEEE Access· 0 citations
Commonly used in math to discover the optimal solution to a problem with straight-line goals and limits is the technique of linear programming (LP). One of its first and most important applications is the Transport Problem (TP), which aims to find the best distribution strategy that meets supply and demand without sacrificing cost or time. The issue of transportation are balanced when supply meets demand and imbalanced otherwise. The Time-Minimizing Transportation Problem (TMTP) aims to reduce time spent on transportation. The literature suggests several ways to find an Initial Basic Feasible Solution. However, the quality of these solutions varies across methods and problem instances. Some approaches are computationally simple but often yield poor-quality solutions in terms of minimizing total transportation time. Others require slightly more effort yet provide better results, while a few methods can generate near-optimal or even optimal solutions but involve higher computational complexity. Importantly, no single method guarantees optimality for all transportation problems. In this research, we propose new, efficient algorithms for finding initial basic feasible solutions in both balanced and unbalanced transportation problems, with the primary objective of minimizing transportation time. A comparative study of results obtained by the proposed heuristics against existing methods demonstrates that our approach consistently achieves more efficient and reliable outcomes. The findings indicate that the proposed methods can serve as strong alternatives to traditional approaches, offering both computational efficiency and improved solution quality.
Jagannath University Journal of Science, Volume 12, Number 1, Jun. 2025, pp. 143−158
Farhana Rashid, Naeem Hossain, Jannatul Ferdous Jeba et al.· Jagannath University Journal...· 0 citations
The assignment problem is one of the most fundamental optimization models in operations research, focusing on the efficient allocation of limited resources to specific tasks while minimizing total cost or maximizing overall effectiveness. Because of its mathematical simplicity and computational efficiency, the model has become an essential decision – support tool across manufacturing, logistics, healthcare, education, transportation and many other industries. This paper presents a comprehensive examination of the assignment problem through a practical case study approach. It begins with an overview of the historical evolution of the assignment problem, followed by a review of relevant literature and a discussion of its theoretical foundations. The paper further distinguishes the assignment problem from other optimization techniques, including transportation and linear programming models. To illustrate its practical applicability, a real-world-inspired machine-to-job allocation problem is formulated and solved systematically using the Hungarian Method. Each stage of the solution process is explained with appropriate tables and interpretations to enhance conceptual understanding. The study also highlights the diverse applications of assignment models across multiple industries and discusses emerging research directions involving artificial intelligence, machine learning, fuzzy optimization, and dynamic decision-making. The findings demonstrate that the assignment problem remains a powerful analytical tool for improving operational efficiency and supporting evidence-based managerial decisions in increasingly complex organizational environments.
Dr. Karunasree Padala, Vijaya Sree Vignatha Vangala· International Journal of Lat...· 0 citations
Combinatorial optimization problems involve identifying the best solution from a wide set of alternatives; these often arise in logistics, scheduling, and resource allocation. These problems, such as the Traveling Salesman Problem (TSP) and the Multi-Depot Vehicle Routing Problem (MDVRP), are generally NP-hard, with solution spaces that increase exponentially, making brute-force methods impractical. We address a specific application of the MDVRP: the Electricity Technician Dispatch Problem (ETDP), which involves planning and optimizing technician routes for maintenance services to customers at various geographical locations while satisfying specific constraints and objectives. We focus on a variant of the ETDP that optimizes multiple objectives, including economic, environmental, and social. Economic objectives aim to reduce operational costs, such as fuel costs and technician wages. Environmental objectives focus on sustainability, for example, by minimizing gas emissions. Social objectives include fairness of workload and customer satisfaction. We will explore the ETDP problem in single- and multi-objective contexts and solve it using nature-inspired techniques.
H. Zaidi, Sifat E. Jahan, Malek Mouhoub· International Conference on...· 0 citations