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
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