Abstract
This research presents an attempt to solve a logistic company's problem of delivering petrol to petrol station in the state of Johor. This delivery system is formulated as a travelling salesman problem (TSP). TSP involves finding an optimal route for visiting stations and returning to point of origin, where the inter-station distance is symmetric and known. This real world application is a deceptive simple combinatorial problem and our approach is to develop solutions based on the idea of local search and meta-heuristics. As a standard problem, we have chosen a solution is a deceptively simple combinatorial problem and we defined it simply as the time spends or distance travelled by salesman visiting n cities (or nodes) cyclically. In one tour the vehicle visits each station just once and finishes up where he started. As standard problems, we have chosen TSP with different stations visited once. This research presents the development of solution engine based on local search method known as Greedy Method and with the result generated as the initial solution, Simulated Annealing (SA) and Tabu Search (TS) further used to improve the search and provide the best solution. A user friendly optimization program developed using Microsoft C++ to solve the TSP and provides solutions to future TSP which may be classified into daily or advanced management and engineering problems.
Highlights
The present society are built on infrastructure of information technology (IT) comprised of computers and systems of communication
One such combinatorial optimization problem is the distribution of petrol from the depot to various petrol modeled as a Travelling Salesman Problems (TSP)
We describe some details of the engines for TSP together with some computational results
Summary
The present society are built on infrastructure of information technology (IT) comprised of computers and systems of communication. In TSP, the salesman (say) must make a complete tour of a given set of stations in the order to minimize the total distances travelled. These problems are usually very difficult if we want to compute exact optimal solutions and we have to resort to approximate (or heuristic) algorithms to obtain good suboptimal solutions. It is generated randomly, but more sophisticated approaches such as greedy search heuristics are possible.
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