What is it about?
China’s booming food-delivery industry produces massive disposable-tableware waste, demanding efficient and low-carbon reverse logistics. Here, we studied a dynamic tableware collection routing problem with real-time order insertion: a recycling center serves preset recycling points, while new door-to-door requests appear during route execution. The objective is to minimize total cost, including vehicle dispatch fixed cost, distance-based depreciation, cleaning cost (including incremental cleaning for inserted orders), waiting and lateness penalties under soft time windows, and fuel plus carbon-emission costs. Routes must satisfy depot start/end, single-service requirements, vehicle capacity limits, feasible service-time propagation, and a minimum satisfaction threshold derived from the soft time-window function. To solve this NP-hard problem, we designed an improved genetic algorithm with time-window-based grouped initialization, natural-number encoding with depot separators, OX crossover, two-point mutation, and a destruction-repair local search using farthest insertion for reinsertion. Experiments indicated faster and more stable convergence than a basic GA. In an order-insertion case, inserting new orders into en-route tours significantly outperforms dispatching an additional vehicle (total cost about 75.7% higher). The proposed method offers implementable decision support for platforms and municipalities to run time-sensitive, low-carbon tableware recovery
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Why is it important?
With the rapid growth of food-delivery services, disposable tableware waste is generated in large quantities, especially during peak meal times. If it is not collected in time, recyclable tableware may be sent to landfill or incineration, causing additional environmental pressure. This study is important because it does not only discuss whether takeaway tableware should be recycled, but also addresses how recycling can be carried out more efficiently, at lower cost, and with lower carbon emissions. The paper combines fixed-point recycling with real-time door-to-door order insertion, and builds a route optimization model that considers vehicle costs, cleaning costs, time-window penalties, fuel consumption, and carbon-emission costs. By using an improved genetic algorithm, the study provides practical decision support for platforms, recycling centers, and city managers.
Perspectives
In my opinion, the main value of this paper lies in turning a real environmental problem into an operational and solvable optimization problem. Instead of only discussing green consumption or recycling policies in general terms, the study focuses on the actual challenges faced by delivery platforms and recycling systems: changing orders, limited vehicles, time requirements, and cost pressure. The dynamic order insertion strategy is especially meaningful because it allows new recycling requests to be added to vehicles already on route, reducing the need to dispatch extra vehicles. This can help balance economic efficiency, service quality, and environmental sustainability. In the future, the model could become even more practical if it incorporates real traffic data, weather conditions, and different vehicle types. Overall, this research offers a useful technical approach for improving takeaway tableware recycling.
Huang Dexin
Read the Original
This page is a summary of: Optimization of takeaway tableware recycling route considering order insertion, AIMS Mathematics, January 2026, Tsinghua University Press,
DOI: 10.3934/math.2026106.
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