Aug 2026· International Research Journal on Advanced Engineering and Management (IRJAEM)· 0 citations· 15 references
Abstract
This paper presents a comprehensive comparative study of classical and heuristic shortest path algorithms, with a specific focus on their integration into the classical Transportation Problem (TP). In the conventional TP, the unit transportation cost between a supply node and a demand node is treated as a fixed constant, independent of the underlying network. In real logistics systems, however, this cost arises from the shortest route through a network of intersections and links. To bridge this gap, the present work models the transportation infrastructure as a directed weighted graph and redefines each TP cost coefficient, c_ij’ as the cost of a shortest path between node ‘i’ and node ‘j’. Dijkstra’s algorithm is implemented as the primary engine for computing these network-derived costs and is compared against the Bellman–Ford algorithm and the A* search algorithm under varying network sizes and densities. Simulation experiments on random and grid-based networks with 100–5000 nodes provide a mathematically grounded and empirically validated analysis of time complexity, scalability, and runtime variability. The results show that Dijkstra’s algorithm remains the benchmark for non-negative edge-weight networks, while A* can be faster when an admissible spatial heuristic is available. Bellman–Ford offers flexibility in handling negative weights but becomes computationally prohibitive for large-scale cost-matrix generation. The study establishes a route-aware TP modelling framework and provides practical guidance on algorithm selection for large-scale logistics and transportation planning.
Comparative analysis against A* search Algorithm, ALT, ALT (A* search Landmarks and Triangle inequality), and Arc Flags algorithms demonstrated that Dijkstra consistently achieved faster computation times, lower memory overhead, and higher accuracy in path selection, resulting in significant reductions in delivery and planning time.
O. Uchechi, Dennis Mary Chinonye, Oparauwah Nnaemeka Macdonald· International journal of res...· 0 citations
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 paper proposes an integrated routing framework for liner shipping networks in which the routing decision concerns the movement of one or more containers from an origin to a destination, jointly addressing topological feasibility, temporal consistency, and cost–time trade-offs. The methodology combines a label-setting routing algorithm with a post-processing phase that enables multi-criteria analysis and clustering of origin–destination pairs. Within this framework, each container route explicitly accounts for service schedules, frequencies, dwell time, transshipment constraints, and port-specific handling costs, thereby ensuring the generation of temporally feasible routes over large-scale liner shipping networks. Two optimality criteria are considered for the container routing problem: time and cost. Computational experiments on a real-inspired network demonstrate the scalability of the proposed approach and highlight the difference between optimal time and cost-routing choices for containers. Further insights are obtained through clustering analyses, which reveal heterogeneous routing profiles and distinct trade-off patterns across origin–destination pairs, providing additional management insights beyond aggregate performance indicators. Overall, the proposed procedure offers a flexible and extensible tool for analyzing container movements within liner shipping services and supports advanced decision-making in maritime network design and service planning.
M. Di Gangi, O. M. Belcore, A. Polimeni· Future Transportation· 0 citations
: This paper studies the impact of edge-based uncertainty in the Set Orienteering Problem, where travel costs are modeled as random variables instead of deterministic values. In this problem, a single traveler departs from and returns to a depot and selects a sequence of sets so that the total travel cost remains within a predefined budget while maximizing the collected profit. Unlike classical SOP formulations that assume fixed edge costs, we incorporate stochastic travel times using Gamma and Normal distributions to better capture real-world variability arising in applications such as logistics and transportation. An integer linear programming formulation based on expected travel costs is proposed and solved using GAMS/CPLEX. To evaluate the effect of uncertainty, a scenario-based analysis is performed on benchmark instances derived from the Generalized Traveling Salesman Problem. The obtained solutions are evaluated under multiple stochastic scenarios to analyze their feasibility and variability in collected profit. Computational results indicate that uncertainty affects route structure and profit stability, even for small instances. In particular, solutions derived under deterministic assumptions may become infeasible when variability in travel costs is considered. These findings highlight the limitations of deterministic planning and motivate the need for incorporating uncertainty in clustered routing problems.
Ravi Kant, A. Mishra· International Conference on...· 0 citations
This study investigates the applicability of three well-established graph-based algorithms, namely Dijkstra, Bellman-Ford, and A*, within an emergency transportation framework inspired by the urban road network of Skopje, and results indicate that all investigated algorithms successfully identify shortest feasible routes when transportation paths exist.
Aleksandra Stojanova Ilievska, N. Stojkovikj, L. Lazarova et al.· International Journal of Adv...· 0 citations
The Traveling Salesperson Problem (TSP) is one of the best-known problems in computer science and arises in many engineering applications, such as smart vehicles and intelligent transportation systems. In the"Euclidean"case, each node is defined by its coordinates in the plane and distances are computed using the Euclidean metric. In the Constraint Programming (CP) literature, the Euclidean TSP is typically addressed by computing the full distance matrix and treating it as a general case; however this approach ignores the geometric information carried by the points'coordinates. In this work, we propose new filtering algorithms, implemented in Constraint Logic Programming (CLP), that exploit such geometric information to achieve stronger constraint propagation than existing approaches. Moreover, we show how this methodology can be extended to other Euclidean variants of the TSP, including the Euclidean Generalized Traveling Salesperson Problem (EGTSP), which is relevant in practical routing and logistics applications. Experimental results demonstrate the computational advantages of the proposed approach.
A. Bertagnon, Marco Gavanelli· 0 citations
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