Abstract

In container terminals (CTs), the performance of quay cranes (QCs) is extremely impacted by their operators’ productivity, which makes the QC worker assignment a relevant element in the QC scheduling. Nevertheless, in the literature, this problem was tackled without considering the objectives of balancing operators’ workload and minimising the distance required to move between QCs, even though practitioners underline their importance in maintaining an efficient working environment. Accordingly, and based on a real-case study raised by a CT partner, this paper addresses a novel problem referred to as the travelling QC worker assignment and balancing problem. First, we propose a novel mathematical model with original constraints faced by the company to assign operators to QCs on a daily basis. Second, to find approximate solutions in a reasonable time, a constructive heuristic and an iterative local search (ILS) are proposed and tested on real datasets. We also propose a simulation-optimisation mechanism to evaluate the robustness of the solutions under real-time probabilistic perturbations. The proposed algorithms are integrated into a decision support tool, a network-linked application allowing users in a real CT to generate automatic assignments. The proposed simulation-optimisation procedure helps obtain balanced planning, avoid workload conflicts, and increase productivity.

摘要

在集装箱码头 (CTs) 中，码头起重机 (QCs) 的性能受到其操作员生产力的极大影响，这使得 QC 工人分配成为 QC 调度中的一个相关元素。然而，在文献中，这个问题的解决没有考虑平衡操作员的工作量和最小化 QC 之间移动所需距离的目标，尽管从业者强调了它们在保持高效工作环境中的重要性。因此，基于 CT 合作伙伴提出的真实案例研究，本文解决了一个新问题，称为旅行 QC 工作人员分配和平衡问题。首先，我们提出了一种新颖的数学模型，该模型具有公司面临的原始约束，以每天将操作员分配给 QC。第二，为了在合理的时间内找到近似解，提出了建设性启发式和迭代局部搜索 (ILS)，并在真实数据集上进行了测试。我们还提出了一种模拟优化机制来评估解决方案在实时概率扰动下的鲁棒性。所提出的算法被集成到决策支持工具中，这是一个网络链接的应用程序，允许真实 CT 中的用户生成自动分配。建议的模拟优化程序有助于获得平衡的规划，避免工作量冲突，并提高生产力。所提出的算法被集成到决策支持工具中，这是一个网络链接的应用程序，允许真实 CT 中的用户生成自动分配。建议的模拟优化程序有助于获得平衡的规划，避免工作量冲突，并提高生产力。所提出的算法被集成到决策支持工具中，这是一个网络链接的应用程序，允许真实 CT 中的用户生成自动分配。建议的模拟优化程序有助于获得平衡的规划，避免工作量冲突，并提高生产力。