Development of an integrated scheduling algorithm for operations management in the analytical services sector

Actlabs是一家提供端到端测试服务的公司，采矿和勘探公司在矿山生命周期的每一步都需要这些服务，以便能够继续开发。在过去十年中，采矿和勘探公司对测试实验室的需求增加，延长了Actlabs客户的周转时间，增加了矿山开发和调试的时间轴，并导致采矿公司因分析结果无法及时获得而延迟或做出错误决策而导致成本高昂。虽然该行业的产能有所增加，但智能运营管理系统并没有跟上步伐，这导致效率和生产力略有提高，周转时间也略有改善。因此，必须开发一种更有效的方式来管理运营，缩短周转时间并降低采矿业的成本。 Actlabs与滑铁卢大学的研究人员合作开发了一种基于计算的基础设施，以改善其运营管理。该项目将开发一种调度算法，该算法考虑需求的随机性，过程（即随机质量故障）和测试结果，以及Actlabs提供的各种服务的技术人员的最佳分配。由于业务的性质，将制定新的方法，以说明可能只出现在分析服务设施中的具体业务特点。虽然随机调度和个人重新分配已被广泛研究，在文献中，这些技术还没有被应用到有效地调度业务管理的分析服务部门。该提案旨在应对这些新挑战，并在此过程中培训两名研究生，同时为Actlabs和加拿大采矿业带来直接利益。

Actlabs is a company that provides end-to-end testing services, required by mining and exploration companies at every step of a mine's lifecycle in order to be able to proceed with development. Increased demand placed on testing laboratories by the mining and exploration companies over the last decade has lengthened turnaround times for Actlabs' customers, increasing the timeline for mine development and commissioning and resulting in high costs for mining companies due to delays or bad decisions taken when analyses results are not available on time. While capacity has increased in the industry, smart operations management systems have not kept pace, which has led to marginal improvement in efficiency, productivity and only marginal turnaround time improvements. Thus, it is imperative to develop a more efficient way to manage operations, shorten turnaround times and reduce costs for the mining industry. Actlabs has teamed up with researchers from the University of Waterloo to develop a computational-based infrastructure to improve their operations management. A scheduling algorithm that considers the stochastic nature of the demands, the processes (i.e. random quality failures) and the test results, as well as the optimal allocation of technical staff to the various services provided by Actlabs will be developed in this project. Due to the nature of the operations, new approaches will be developed to account for specific operational characteristics that may only occur in analytical services facilities. Although stochastic scheduling and personal reallocation have been widely studied in the literature, these techniques have not been applied to efficiently schedule operations management in the analytical services sector. This proposal aims to tackle these new challenges and train two graduate students while doing so, all while generating direct benefits to Actlabs and hence to Canada's mining sector.