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A Mathematical Model for the Vehicles Routing Problem with Multiple Depots, Considering the Possibility of Return Using the Tabu Search Algorithm

Ahmed Alim Al Ayub, Singhal Saurabh, Prakaash A. S., Dayupay Johnry, Rahadi Irwan, Marhoon Haydar Abdulameer, Iswanto A. Heri, Abbas Saja Fadhil, Aravindhan Surendar

Foundations of Computing and Decision Sciences

2022

Vol. 47, No. 4

359--370

The current study examines an essential type of vehicle routing problem (VRP). This type is one where customers are served by limited-capacity vehicles from multiple depots and is known as Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP). The novelty of this study is that in the present case, cars, after Leaving the Depot, can go back to any other depot. Those issues seem to occur in most real-world issues where information, messages, or news are sent electronically from somewhere. The purpose of the problem is to minimize the costs associated with routing. Regarding the literature on such issues, it has been proven in previous studies and research that these problems are among the hard-NP problems, and to solve them using the metaheuristic method, the exact methods justify it. After changing the basic model, this study developed a Tabu Search (TS) algorithm. The study results demonstrate that if the equipment can return to any depot, the cost is significantly reduced in contrast to the case where equipment is forced to return to their depot.

Ship Collision Avoidance by Distributed Tabu Search

Kim D., Hirayama K., Okimoto M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2015

Vol. 9, no. 1

23--29

More than 90% of world trade is transported by sea. The size and speed of ships is rapidly increasing in order to boost economic efficiency. If ships collide, the damage and cost can be astronomical. It is very difficult for officers to ascertain routes that will avoid collisions, especially when multiple ships travel the same waters. There are several ways to prevent ship collisions, such as lookouts, radar, and VHF radio. More advanced methodologies, such as ship domain, fuzzy theory, and genetic algorithm, have been proposed. These methods work well in one-on-one situations, but are more difficult to apply in multiple-ship situations. Therefore, we proposed the Distributed Local Search Algorithm (DLSA) to avoid ship collisions as a precedent study. DLSA is a distributed algorithm in which multiple ships communicate with each other within a certain area. DLSA computes collision risk based on the information received from neighboring ships. However, DLSA suffers from Quasi-Local Minimum (QLM), which prevents a ship from changing course even when a collision risk arises. In our study, we developed the Distributed Tabu Search Algorithm (DTSA). DTSA uses a tabu list to escape from QLM that also exploits a modified cost function and enlarged domain of next-intended courses to increase its efficiency. We conducted experiments to compare the performance of DLSA and DTSA. The results showed that DTSA outperformed DLSA.