基于Pareto迭代局部搜索的多目标Flow Shop调度算法改进
Improvements for multi-objective flow shop scheduling by Pareto Iterated Local Search
摘要：
介绍了一种求解多目标优化问题的局部搜索元启发式算法的提出和应用。它是基于两个主要原则的启发式搜索，通过可变的邻域，通过扰动和连续迭代的搜索空间的有利区域的多样化的集约化。这个概念在多目标下的置换流水车间调度问题上得到了成功的验证，并与其他局部搜索方法进行了比较。虽然所取得的成果在质量方面令人鼓舞，但该方法的另一个积极特点是它的简单性，因为它只需要设定很少的参数。
英文摘要：
The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multiple objectives and compared to other local search approaches. While the obtained results are encouraging in terms of their quality, another positive attribute of the approach is its simplicity as it does require the setting of only very few parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
