A practical model of routing problems for automated guided vehicles with acceleration and deceleration

Abstract

We consider an optimization of conflict-free routing problems for automated guided vehicles (AGV) with acceleration and deceleration. A continuous time model is developed to represent the dynamics of vehicles. In the proposed model, the transportation model is discretized into several regions. A network model is created by taking into account the acceleration and deceleration motions. The acceleration and deceleration are represented at curve locations. Column generation heuristic is used to find a near-optimal solution. In this algorithm, we construct a heuristic rule to generate a feasible solution with acceleration and deceleration of vehicles after the column generation. The pricing problem is represented by a resource constrained shortest path problem, which is effectively solved by a labeling algorithm. The dominance relation for acceleration and deceleration is addressed. In the proposed model, the dynamics of real speed AGV model are reflected into the routing problems. By comparing the performance of the conventional method, the effectiveness of the proposed method is demonstrated. IntroductionIn transportation systems such as semiconductor plants, container terminals, and flexible manufacturing systems (FMS), multiple automated guided vehicles (AGVs) are widely used. Transportation tasks are performed at loading points and/or unloading points. In practice, it is required to generate conflict-free routes for multiple vehicles, which minimizes the total transportation time.In previous research, there are a number of studies on vehicle scheduling problems (Bunte and Kliewer, 2009). A branch-and-cut-and-price algorithm for vehicle routing problem using column generation approach was proposed by Bettinelli, et al. (2011). Reveliotis and Roszkowska (2011) regarded the vehicle route planning problem as a resource allocation system and they developed a deadlock avoidance policy. In (2003) applied a column generation approach for routing problems of AGVs. By utilizing the column generation approach, a tight lower bound can be derived by solving the restricted master problem which is a linear programming problem with huge number of columns. In previous studies, for simplicity, the vehicle speed is constant. However, in practical transportation systems, each vehicle has its acceleration and deceleration at traveling, stopping and curving. It is therefore important to take into account the acceleration and

Automated Guided Vehicle (AGV) Scheduling in Automated Container Terminals (ACTs) Focusing on Battery Swapping and Speed Control

“…Some scholars' focus on AGV speed control in AGV scheduling is also worth noting. Nishi et al (2014) studied the optimization of collision-free paths for AGVs with acceleration and deceleration. By discretizing the transportation area into several regions, a continuous time model was established to represent the dynamic changes in the vehicle's state [28].…”

“…Nishi et al (2014) studied the optimization of collision-free paths for AGVs with acceleration and deceleration. By discretizing the transportation area into several regions, a continuous time model was established to represent the dynamic changes in the vehicle's state [28]. Adamo et al (2018) determined the AGV path and speed on each arc to prevent conflicts, focusing on time windows and minimizing the total energy consumption [29].…”

Improved Ant Colony Algorithm-based Automated Guided Vehicle Path Planning Research for Sensor-aware Obstacle Avoidance

“…(18) Nishi et al considered path planning for obstacle avoidance under AGV acceleration or deceleration conditions, established a continuous-time model, and proposed a heuristic algorithm based on column generation. (19) Duan et al proposed an operator for finely tuning paths to make path fragments shorter and avoid obstacles, and realized dynamic path planning based on a GA. (20) Ahmed et al proposed a collision prediction method based on vertex attributes and real time location information combined with graph theory, established a MIP model, and proposed an improved particle swarm optimization (PSO) algorithm suitable for optimizing collision avoidance decisions of multi-AGV systems. (21) Hu et al established a MIP model by analyzing the obstacles between sections and nodes and proposed an induced ant colony particle swarm algorithm.…”

“…The feasibility of joint IACA and the scroll window is shown. Here, the operating environment of the simplified AGV is a 20 × 20 grid unit, and the starting grid and ending grid coordinates are initialized to (1,19) and (19,1), respectively. The parameter settings are the same as those of the IACA-ADPA approach in the static environment.…”