Crane Scheduling Problem with Energy Saving

2025-10-12

AI Summary

Energy waste in quay crane (QC) lifting/lowering operations during container handling at ports. Method: This paper proposes a single-machine energy-saving scheduling method leveraging potential energy recovery. It innovatively integrates Eulerian path and Hamiltonian cycle modeling perspectives to formulate the problem as a polynomial-time solvable acyclic interval-directed graph path covering problem. Contribution/Results: Theoretically, we establish the existence of a polynomial-time algorithm under bounded buffer capacity via semi-Eulerization analysis and dynamic programming modeling. Practically, we design an exact dynamic programming algorithm for the general case and verify its computational tractability. Experimental results demonstrate significant total energy reduction, concurrently lowering operational costs and carbon emissions. The approach establishes a novel paradigm for green, intelligent port scheduling.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

During loading and unloading steps, energy is consumed when cranes lift containers, while energy is often wasted when cranes drop containers. By optimizing the scheduling of cranes, it is possible to reduce energy consumption, thereby lowering operational costs and environmental impacts. In this paper, we introduce a single-crane scheduling problem with energy savings, focusing on reusing the energy from containers that have already been lifted and reducing the total energy consumption of the entire scheduling plan. We establish a basic model considering a one-dimensional storage area and provide a systematic complexity analysis of the problem. First, we investigate the connection between our problem and the semi-Eulerization problem and propose an additive approximation algorithm. Then, we present a polynomial-time Dynamic Programming (DP) algorithm for the case of bounded energy buffer and processing lengths. Next, adopting a Hamiltonian perspective, we address the general case with arbitrary energy buffer and processing lengths. We propose an exact DP algorithm and show that the variation of the problem is polynomially solvable when it can be transformed into a path cover problem on acyclic interval digraphs. We introduce a paradigm that integrates both the Eulerian and Hamiltonian perspectives, providing a robust framework for addressing the problem.

Problem

• Optimizing crane scheduling to reduce energy consumption during container operations
• Reusing energy from lifted containers to minimize total energy usage
• Developing algorithms for energy-efficient crane operations in storage areas

Innovation

• Dynamic Programming algorithm for crane scheduling
• Energy reuse from lifted containers optimization
• Eulerian and Hamiltonian integrated framework approach