Meta-Heuristic Planning Minimum Transportation Time Between Source and Destination Constrained by Actual Road Conditions

One of the main objectives of logistic business, which directly impacts the customers, is the timely deliveries to ensure the meeting of deadlines and to retain customers’ loyalty. In fact, a shortest traveling distance may not guarantee to in time delivery due to various unexpected road events during the transportation such as impaired road, accident, heavy traffic jam. This paper proposes an algorithm to find the transportation path with minimum traveling time under various constraints of road conditions. The studied problem is similar to the problem of traveling salesman problem in terms of visiting all cities but the objective of this study is the minimum transportation time instead of minimum total distance. A new contributed cost function defined in terms of time variables computed from various road conditions is introduced. To find the solution of minimum value of cost function, the methods of meta-heuristic algorithms were deployed. The actual data of road conditions were involved to construct the cost function. Some real road maps were experimented with different meta-heuristic algorithm. The results show that the OriginalABC model has the lowest average time cost.