Abstract
Recent trends towards larger and more complex systems necessitate the use of heterogeneous and flexible automated guided vehicles (AGVs) to fulfill the transport demand within a factory. To operate the fleet of AGVs efficiently, it is also important to consider their limited battery capacity. In this context, we tackle the problem of scheduling transport requests on multi-load and multi-ability AGVs with battery management. Each AGV can carry more than one load at a time and have specific capabilities such as lift loads, tow loads, or handle loads with a mounted robot arm. Each request consists of a pickup and a delivery task associated with an origin, a destination, a soft time window, and a priority. Each transport request may also require different AGV capabilities, and the AGV batteries can be recharged partially under consideration of a critical battery threshold. The decisions involve assigning transport and charging requests to AGVs, sequencing these requests, and determining the arrival times and charging duration. A mixed-integer linear programming model is formulated. A hybrid adaptive large neighborhood search with an integrated local search method is proposed to find a feasible schedule with the aim to minimize the tardiness costs of requests and travel costs of AGVs. We illustrate the efficacy of the hybrid algorithm with an industry case study using real-world data. The computational results reveal a 20%–50% cost reduction in current practice by using our hybrid algorithm, and around 50% cost reduction with respect to a single-load AGV scheduling approach proposed in the literature.
Highlights
Automated guided vehicles (AGVs) are driverless vehicles that are commonly used for material handling operations in manufacturing plants, warehouses, distribution centers, and transshipment terminals (Le-Anh and De Koster, 2006)
(ii) We propose a hybrid adaptive large neighborhood search (ALNS) (Ropke & Pisinger, 2006) to solve industry-sized instances, which outperforms the mixed-integer linear programming (MILP) and the dispatching method currently used in practice
For larger scenarios with busier periods and more automated guided vehicles (AGVs), the performance of the hybrid Adaptive Large Neighborhood Search (ALNS) is only compared to the dispatching method, since the MILP cannot obtain a feasible solution within the time limit of the experiments
Summary
Automated guided vehicles (AGVs) are driverless vehicles that are commonly used for material handling operations in manufacturing plants, warehouses, distribution centers, and transshipment terminals (Le-Anh and De Koster, 2006). Using multi-load AGVs for material handling operations may reduce the number of AGVs needed or increase the system throughput (Le-Anh and De Koster, 2006). An example of a facility trying to implement an automated material handling system using a heterogeneous multi-load AGV fleet is the Brainport Industries Campus (BIC). The use of multi-load AGVs provides additional flexibility, since they can deviate from their routes to pick up additional loads. The downside of this increased flexibility is that managing multi-load AGVs becomes more complex.
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