Abstract
背景介绍
The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints.
作为NP难问题，作业车间调度问题以其复杂性而众所周知。 我们已经考虑了JSSP，其目标是在满足许多硬约束的同时最小化完工时间。
方法
In this paper, we developed a memetic algorithm (MA) for solving JSSPs. Three priority rules were designed, namely partial re-ordering, gap reduction and restricted swapping, and used as local search techniques in our MA.
在本文中，我们开发了一种用于求解JSSP的模因算法（MA）。 设计了三个优先级规则，即部分重新排序，间隙减少和限制交换，并在我们的MA中用作本地搜索技术。
实验结果
We have solved 40 benchmark problems and compared the results obtained with a number of established algorithms in the literature. The experimental results show that MA, as compared to GA, not only improves the quality of solutions but also reduces the overall computational time.
我们已经解决了40个基准问题，并将所获得的结果与文献中的许多已建立的算法进行了比较。 实验结果表明，与GA相比，MA不仅提高了解决方案的质量，而且缩短了整体计算时间。
Conclution
Although JSSP is a very old and popular problem, still no algorithm can assure the optimal solution for all test problems, specifically for larger problems appearing in the literature. However, GAs and MAs are gaining popularity due to their effectiveness of solving optimization problems within a reasonable time period. In this paper, we have presented genetic and memetic algorithm based approaches to solve job-shop scheduling problems. After developing a traditional GA with different kind of operations, we have designed and implemented three local searches and three versions of memetic algorithms. All three memetic algorithms provided superior results than the GA for JSSPs.
虽然JSSP是一个非常古老和流行的问题，但仍然没有算法可以确保所有测试问题的最佳解决方案，特别是对于文献中出现的较大问题。然而，由于GA和MA在合理的时间段内解决优化问题的有效性，因此越来越受欢迎。在本文中，我们提出了基于遗传和模因算法的方法来解决作业车间调度问题。在开发了具有不同类型操作的传统GA之后，我们设计并实现了三个本地搜索和三个版本的模因算法。对于JSSP，所有三种模因算法都比GA提供了更好的结果。
We have solved 40 benchmark problems and have compared results with well-known algorithms appearing in the literature. Our memetic algorithm MA(GR-RS) clearly outperforms all the algorithms considered in this paper.We have also provided a sensitivity analysis of parameters and have also experimented with different parameters and algorithms for analyzing their contributions.Although our algorithm is performingwell, we feel that the algorithm requires further work to ensure consistent performance for a wide range of practical JSSPs. We intend to extend our research by introducing constraints such as, machine breakdown, dynamic job arrival, machine addition and removal, and due date restrictions. Moreover, we would also like to test the performance of our algorithm on large scale problems. However, the new memetic algorithm is a significant contribution to the research of solving JSSPs.
我们已经解决了40个基准问题，并将结果与文献中出现的众所周知的算法进行了比较。我们的模因算法MA（GR-RS）明显优于本文考虑的所有算法。我们还提供了参数的灵敏度分析，并且还尝试了不同的参数和算法来分析它们的贡献。虽然我们的算法表现良好，但我们感觉该算法需要进一步的工作，以确保广泛的实用JSSP的一致性能。我们打算通过引入约束来扩展我们的研究，例如机器故障，动态作业到达，机器添加和移除以及到期日限制。此外，我们还想测试我们的算法在大规模问题上的性能。然而，新的模因算法对解决JSSP的研究做出了重要贡献。
