ArticlePloS one2025
Centralized scheduling, decentralized scheduling or demand scheduling? How to more effectively allocate and recycle shared takeout lunch boxes.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling.Scientific reports · 2025Article
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Authors and funding
3 authors.
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Abstract
Efficient scheduling of shared takeaway containers plays a significant role in the sharing economy system. An effective scheduling system ensures the maximization of container reuse, reducing resource waste and environmental pollution. To explore the applicability of different scheduling models for shared takeaway containers, this paper constructs differential game models for three modes: centralized scheduling, decentralized scheduling, and demand-based scheduling. The equilibrium outcomes are compared and analyzed. The research findings indicate that when the revenue from scheduling takeaway containers is low, decentralized scheduling can yield the maximum benefit for takeaway platforms; conversely, when the revenue is high, centralized scheduling offers the greatest benefit. For restaurant enterprises, when the revenue from scheduling is low, if the cost of scheduling is also low, demand-based scheduling can provide the maximum benefit; however, if the cost is high, decentralized scheduling is more advantageous; otherwise, centralized scheduling can maximize the benefits for restaurant enterprises.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.