The ant colony optimization meta-heuristic
New ideas in optimization
Heuristic Techniques for Single Line Train Scheduling
Journal of Heuristics
A Savings Based Ant System For The Vehicle Routing Problem
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Ant Colony Optimization
Train Traffic Deviation Handling Using Tabu Search and Simulated Annealing
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 3 - Volume 03
IEEE Computational Intelligence Magazine
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Operations Research and Information Systems
Meta Heuristic Approach for Automatic Forecasting Model Selection
International Journal of Information Systems and Supply Chain Management
Metaheuristic Approaches for Vehicle Routing Problems
International Journal of Information Systems and Supply Chain Management
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This study addresses the problem of car allocation to different routes under certain restrictions with the objective of reducing the excess cost of transportation. A meta-heuristic approach based on Ant Colony Optimisation (ACO) algorithm is proposed and implemented to schedule the cars efficiently along the routes under the existing logistics. A mathematical model with two objectives is formulated for this purpose and solved in two phases. In the first phase, sequences of allocated cars are determined while in the second, car allocation scheme for each trip is determined using ACO algorithm. The simulation study is carried out from the empirical distributions of distance and time, followed by the sensitivity analysis on the basis of their stochastic behaviour. The cost benefit analysis shows a projected savings in terms of reduction of cost of travel, both with respect to distance and time, through the solutions obtained.