Vehicle routing is an important combinatorial optimization problem. In real transport networks,the travel speed and travel time of roads have large time-variability and randomness. The study of vehicle routing problem in time-dependent network has even more practical value than static network VRP problem. This paper combines the features of time-dependent networks and gives the mathematical models of the time-dependent vehicle routing problem. On this basis, the traditional ant colony optimization algorithm is improved. A new path transfer strategy of ants and new dynamic pheromone update strategy applicable to time-dependent network are proposed. Based on these strategies, the improved ant colony algorithm is given for solving the vehicle routing problem in time-dependent networks. The simulation results show that the algorithm can effectively solve the vehicle routing problem in time-dependent network and has better computational efficiency and convergence speed.

This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for Autonomous Transportation Systems (ATSs) in manufacturing systems. The TARP is a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW). In our former study, a cooperative algorithm, called the triple loop method, with autonomous distributed agents has been proposed. The Improving initial Task Assignment and Avoiding Deadlock method (ITAAD) is a faster algorithm than the triple loop method. In this paper, we propose a new vehicle routing method for the ITAAD. Results of computational experiments show effectiveness of the proposed routing method.

This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for Autonomous Transportation Systems (ATSs) in manufacturing systems. The TARP results in a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW). As an approach to this problem, a cooperative algorithm with autonomous distributed agents has been proposed. The algorithm is able to plan deadlock-free routes even though the buffer capacity is less, but includes reformability at the point that computation time of that case increases drastically. This paper proposes an initial task assignment method to reduce computation time on planning routes. Results of computational experiments show effectiveness of the proposed method.

Recently, there are so many researches on Autonomous Distributed Manufacturing Systems (ADMSs), where cooperation among agents is used to solve problems, such as the scheduling problem and the vehicle routing problem. We target ADMSs where an ADMS consists of two sub-systems: a Production System (PS) and an Autonomous Transportation System (ATS). This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for ATSs under conditions of given production schedule and finite buffer capacity. The TARP results in a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW), and this paper gives a mathematical formulation of the problem. This paper, also, proposes a cooperative algorithm to obtain suboptimal solutions in which no deadlocks and buffer overflows occur. By computational experiments, we will examine the effectiveness of the proposed algorithm. Computational experiments show that the proposed algorithm is able to obtain efficient and deadlock-free routes even though the buffer capacity is less.

The vehicle routing and facility location fields are well-developed areas in management science and operations research application. There is an increasing recognition that effective decision-making in these fields requires the adoption of optimization software that can be embedded into a decision support system. In this paper, we describe the implementation details of our software components for solving the vehicle routing and facility location problems.

In this paper, we propose a new cooperative search algorithm based on pheromone communication for solving the Vehicle Routing Problems. In this algorithm, multi-agents can partition the problem cooperatively and search partial solutions independently using pheromone communication, which mimics the communication method of real ants. Through some computer experiments the cooperative search of multi-agents is confirmed.