Multiprocessor open shop makes a generalization to classical open shop by allowing parallel machines for the same task. Scheduling of this shop environment to minimize the makespan is a strongly NP-Hard problem. Despite its wide application areas in industry, the research in the field is still limited. In this paper, the proportionate case is considered where a task requires a fixed processing time independent of the job identity. A novel highly efficient solution representation is developed for the problem. An ant colony optimization model based on this representation is proposed with makespan minimization objective. It carries out a random exploration of the solution space and allows to search for good solution characteristics in a less time-consuming way. The algorithm performs full exploitation of search knowledge, and it successfully incorporates problem knowledge. To increase solution quality, a local exploration approach analogous to a local search, is further employed on the solution constructed. The proposed algorithm is tested over 100 benchmark instances from the literature. It outperforms the current state-of-the-art algorithm both in terms of solution quality and computational time.

多处理器开放式商店通过允许并行机器执行相同的任务，对经典开放式商店进行了推广。调度这个车间环境以最小化完工时间是一个强烈的 NP-Hard 问题。尽管其在工业中的应用领域广泛，但该领域的研究仍然有限。在本文中，考虑了任务需要独立于工作身份的固定处理时间的比例情况。针对该问题开发了一种新颖的高效解决方案表示。提出了一种基于这种表示的蚁群优化模型，其目标是最小化完工时间。它对解空间进行随机探索，并允许以较少耗时的方式搜索良好的解特征。该算法充分利用了搜索知识，它成功地融入了问题知识。为了提高解决方案的质量，在构建的解决方案上进一步采用类似于局部搜索的局部探索方法。所提出的算法在文献中的 100 多个基准实例中进行了测试。它在解决方案质量和计算时间方面都优于当前最先进的算法。