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Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

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In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...
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Carbon-Efficient Scheduling in Fresh Food Supply Chains with a Time-Window-Constrained Deep Reinforcement Learning

Yuansu Zou1,2, Qixian Gao1, Hao Wu1

  • 1University of Electronic Science and Technology of China, Chengdu 611731, China.

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Summary

This study optimizes fresh food distribution routes using intelligent transportation systems (ITSs) and Internet of Things (IoT) to minimize costs and carbon emissions. A reinforcement learning model effectively manages logistics, considering time windows and cooling needs.

Keywords:
Intelligent Transportation Systemscarbon-efficient schedulingfresh food supply chainreinforcement learning

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Area of Science:

  • Intelligent Transportation Systems (ITSs)
  • Supply Chain Management
  • Operations Research

Background:

  • Intelligent Transportation Systems (ITSs) integrate Internet of Things (IoT) for enhanced vehicle, infrastructure, and user connectivity, optimizing traffic flow.
  • Fresh food supply chains face challenges in distribution, including time sensitivity, temperature control, and environmental impact.
  • Minimizing logistics costs and carbon emissions is crucial for sustainable fresh food distribution.

Purpose of the Study:

  • To develop an optimization model for fresh food supply chain distribution routes.
  • To minimize total distribution costs, including carbon emission costs and cooling expenses.
  • To enhance decision-making for optimizing transportation and distribution of fresh products.

Main Methods:

  • Constructed an optimization model incorporating carbon taxes for emission costs, time windows, and cooling expenses.
  • Utilized a graph attention network to represent node locations, paths, and data collection windows for path planning.
  • Integrated a time-window-constrained reinforcement learning model to solve for optimal distribution routes.

Main Results:

  • The proposed model effectively optimizes distribution routes for fresh food products.
  • Demonstrated significant reductions in logistics costs and carbon emissions.
  • Provided effective decision-making information for supply chain management under varying temperature conditions.

Conclusions:

  • The time-window-constrained reinforcement learning model offers an effective solution for optimizing fresh food distribution.
  • The model successfully balances cost reduction, time constraints, and environmental sustainability.
  • ITSs and IoT technologies are vital for improving the efficiency and environmental performance of fresh food logistics.