Solution of Travelling Salesman Problem based on Metaheuristic Techniques

The traveling salesman problem is a classic problem in combinatorial optimization. This problem is to find the shortest path that a salesman should take to traverse through a list of cities and return to the origin city. The list of cities and the distance between each pair are provided. It is an NP-complete problem i.e., class of computational problem for which no efficient solution algorithm has been found, presently there is no polynomial solution available. In this paper, we try to solve this very hard problem using various heuristics such as Simulated Annealing, Genetic Algorithm to find a near-optimal solu-tion as fast as possible. We try to escape the local optimum, using these advanced heu-ristic techniques

The Eco-Friendly Intermodal Delivery Network

The design of the distribution process is a strategic issue for almost every company. As the use of advanced technology and automation increases in manufacturing and logistics, the implementation of autonomous and electrical transportation, such as driverless vehicles and electric trucks, has become an interesting topic of study within the last few years, with the main objective of minimizing distribution costs and delivery times. The purpose of this research is to prove that intermodal delivery networks, which may combine a train and several electric vehicles, are more efficient and environmentally friendly than unimodal networks for high volume and long haul transportation, regardless of the customers’ distribution. This is only applicable if demand does not fall within the capacity restriction of road transportation vehicles. To do so, this paper utilizes an optimization algorithm that consists of a feedback mechanism between K-means and a genetic algorithm, which finds the optimal routes between distribution centers and surrounding customers as a multiple traveling salesman problem (mTSP)

RoboTSP - A Fast Solution to the Robotic Task Sequencing Problem

In many industrial robotics applications, such as spot-welding, spray-painting or drilling, the robot is required to visit successively multiple targets. The robot travel time among the targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the order of visit of the targets, and by the robot configurations used to reach each target. Therefore, it is crucial to optimize these two elements, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a fast, near-optimal, algorithm to solve RTSP. The key to our approach is to exploit the classical distinction between task space and configuration space, which, surprisingly, has been so far overlooked in the RTSP literature. Second, we provide an open-source implementation of the above algorithm, which has been carefully benchmarked to yield an efficient, ready-to-use, software solution. We discuss the relationship between RTSP and other Traveling Salesman Problem (TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and show experimentally that our method finds motion sequences of the same quality but using several orders of magnitude less computation time than existing approaches.Comment: 6 pages, 7 figures, 1 tabl

Dynamic approach to solve the daily drayage problem with travel time uncertainty

The intermodal transport chain can become more e cient by means of a good organization of drayage movements. Drayage in intermodal container terminals involves the pick up and delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the di erent vehicles, often with the presence of time windows. This scheduling has traditionally been done once a day and, under these conditions, any unexpected event could cause timetable delays. We propose to use the real-time knowledge about vehicle position to solve this problem, which permanently allows the planner to reassign tasks in case the problem conditions change. This exact knowledge of the position of the vehicles is possible using a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution