Real-time optimization for WEEE recycling routing problem with multiple service types

Efficient waste electrical and electronic equipments (WEEEs) collection is crucial for controlling pollution and improving resource efficiency. Service requirements for recycling various types of WEEEs are different, such as small electronics require only recycling, whereas large ones need disassembly. In addition, dealing with real-time customer orders not only ensures timely service, but also effectively reduces customer loss. Therefore, this study focuses on a real-time vehicle routing problem for WEEEs recycling, which involves multiple service types. By considering customers’ time windows, vehicle capacity, and time constraints, an integer programming model with dynamically updating strategy is formulated to minimize the total cost for the real-time WEEEs recycling routing problem. For this purpose, a rolling horizon genetic algorithm is developed, which divides the planning horizon into several rolling periods to dynamically handle the real-time routing problem by optimizing decisions at each period. Additionally, some optimality properties are provided to simplify the computation. The well-known 21 benchmark instances are utilized to test the performance of the proposed approach, and the optimal solution can be obtained in 15 benchmark instances. Finally, a real-case analysis demonstrates the model’s validity and applicability.