Reshaping Yard Management Systems with Efficient Container Placement
- Technology : Artificial Intelligence, Machine Learning
- Industry : Logistics & Supply Chain
Business Problem
- The yard management system must account for diverse criteria, including but not limited to size, occupancy, import/export type, temperature requirements, and staying durations, making the optimization process highly intricate
- Efficiently assigning optimum positions to incoming containers in a vast yard poses a significant challenge
- With over 25,000 container locations, manually comparing and optimizing each one is impractical and time-consuming
- The primary objective is to minimize moving time by strategically placing containers closer to the gate
- Placing containers with the same shipment ID in close proximity is a critical goal for efficient yard management
- Prioritizing the placement of containers with early departure times on the top levels aims to minimize shuffling and streamline operations
- Containers, even with the same ID, exhibit different staying durations in the yard, complicating the optimization task
- Managing data for each yard position, including occupancy status, size, import/export type, temperature considerations, and more, adds complexity to the optimization process
Solution
- Employing GA (Genetic Algorithm) as a solution leverages the concept of survival of the fittest, akin to Charles Darwin's evolutionary theory
- GA acts as a dynamic simulation of the evolution process, adapting and optimizing solutions over time
- The algorithm is rooted in the mechanics of natural selection and genetics, ensuring an organic and adaptable approach
- GA's central theme revolves around achieving robustness and a delicate balance between efficiency and efficacy
- Unlike traditional derivative-based optimization, GA introduces a paradigm shift by addressing several limitations
- GA is a stochastic algorithm due to its stochastic rules, contrasting with the deterministic nature of traditional algorithms
- The algorithm iterates through multiple generations, applying crossover, mutation, and population replacement steps in each iteration
Result
- Genetic Algorithm (GA) successfully optimized the placement of containers, considering diverse criteria such as proximity to the gate, shipment ID grouping, and prioritization of early departures
- Containers strategically placed closer to the gate resulted in a substantial reduction in moving time, contributing to a more efficient yard management process
- GA demonstrated adaptability to changing conditions and requirements, consistently refining container placement strategies over multiple generations
- Leveraging information from the objective function, GA enhanced decision-making, providing a more informed basis for optimizing container positions
Benefits
- The implementation of GA significantly enhanced overall yard management efficiency by dynamically adapting to complex and evolving optimization criteria
- GA's central theme of robustness enabled effective yard management across diverse environmental conditions, showcasing its versatility.
- GA addressed the limitations of traditional derivative-based optimization methods, providing a more comprehensive and effective solution for yard management
- A balance between efficiency and efficacy resulted in a well-rounded yard management system that excelled in various operational scenarios
- GA's iterative generational approach streamlined yard operations by continuously refining container placement strategies
- Enabled the algorithm to evolve and adapt to changing yard management requirements
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