The effect of dynamic scheduling and routing in a solid waste management system

Area of Science:

• Environmental Engineering
• Operations Research
• Computer Science

Background:

• Solid waste collection and hauling represent a substantial portion of total waste management costs.
• Modern waste management systems are increasingly adopting technological solutions for efficiency.
• Smart city initiatives are integrating IoT devices for public services like waste management.

Purpose of the Study:

• To evaluate the effectiveness of dynamic scheduling and routing policies for solid waste collection using real-time data.
• To compare the performance of dynamic policies against traditional static policies in waste management.
• To analyze the impact of real-time container data on operational costs and resource allocation.

Main Methods:

• Analytical modeling to develop general frameworks for scheduling and routing.
• Discrete-event simulation to model and test various waste collection scenarios.
• Empirical simulation study using data from a real-world recycling station in Malmoe, Sweden.
• Utilization of real-time data from 3300 smart recycling containers equipped with level sensors and wireless communication.

Main Results:

• Dynamic scheduling and routing policies demonstrated lower operating costs compared to static policies.
• Shorter collection and hauling distances were achieved with dynamic strategies.
• Reduced labor hours were observed when implementing dynamic waste collection routes.
• Simulation results were consistent with analytical models and operator experiences.

Conclusions:

• Dynamic scheduling and routing policies, leveraging real-time data, offer significant improvements in solid waste management efficiency.
• The adoption of smart container technology can lead to substantial cost savings and optimized resource utilization.
• Real-time data analytics provide a viable pathway to enhance traditional waste collection operations.