Integrating occupational health and safety to operational risks in real life industrials environments: a mixed optimization approach

一个可持续发展的企业必须更加重视平衡，实际上是优化其绩效要求与员工福利。在可持续发展和创新的背景下，我们的业务现实包括不确定性和不断的适应和调整。企业必须具有弹性，即在条件可预见时保持稳健，在面对不确定性时保持敏捷。 可用于最佳跨学科和综合管理的运营和职业健康与安全（OHS）风险的模型是基于这样的假设，即所代表的业务系统的行为是可预测的，并且可以完全分解为其组成部分。这些模型还旨在将所有系统可变性限制在既定边界内。 为了理解真实的工业条件下的业务管理系统并支持决策制定，用于表示系统功能的模型随着时间的推移而向集成的系统和主动模型发展是至关重要的，这些模型允许组合不同的可变性来源。因此，拟议的研究活动的目的是通过应用新的研究战略，以提高对复杂的社会技术建模的基础知识，以满足这些科学挑战。 我们将采用基于综合设计的混合方法（定性和定量）。首先，将进行人为因素研究，以确定需要考虑的运营和职业健康安全风险。其次，使用弹性工程方法，将建立一个模型化的原型，然后进行验证。模型将捕捉可变性来源，以及每个来源如何影响模型的功能和影响运营与OHS风险之间的权衡。第三，构建风险等级网络。使用人工智能算法，将确定最具影响力的因素（操作和OHS）。最后，将对上述三个步骤的结果进行比较，以确定在研究的两个案例中，如何整合、平衡和优化运营和职业健康安全风险及其不确定性，从而加强向企业提出的建议。 这一建议将提供更好的工具，指导当今的决策者选择平衡和优化生产和职业健康安全的必要性的手段。预计加拿大的两个高度优先工业部门，即自然资源和航空航天，将取得科学进步。

A sustainable business must place increased emphasis on balancing, indeed optimizing its performance imperatives with the wellbeing of its workers. In a context of sustainable development and unbridled innovation, the reality of our businesses includes uncertainty and constant adaptation and adjustment. Businesses must be resilient, that is, robust when conditions are foreseeable, and agile in the face of uncertainty. The models available for optimal interdisciplinary and integrated management of operational and occupational health and safety (OHS) risks are based on the hypothesis that the business system represented behaves predictably and can be deconstructed entirely into its component parts. These models also aim to confine all system variability within established boundaries. To understand business management systems under real industrial conditions and support decision making, it is crucial that the models used to represent system functioning evolve over time towards integrated systemic and proactive models that allow combining diverse sources of variability. The aim of the proposed research activities is therefore to meet these scientific challenges by applying a new research strategy to improve fundamental knowledge on complex socio-technical modeling. We shall use a mixed approach (qualitative and quantitative) based on an integrated design. Firstly, a human factors study will be done to identify the operational and OHS risks to consider. Secondly, using a resilience engineering method, a modeled prototype will be constructed then validated. Variability sources will be captured by the model, as well as how each source affects the models’ functions and impacts trade-offs between operational and OHS risks. Thirdly, risk hierarchy networks will be constructed. Using an artificial intelligence algorithm the most influencing factors (operational and OHS) will be identified. Finally, the results from the three steps above will be compared to identify, for the two cases under study, how operational and OHS risks, as well as their uncertainties, can be integrated, balanced and optimized, thus strengthening the recommendations made to businesses. This proposal will provide improved tools to guide today’s decision-makers in choosing the means to balance and optimize the imperatives of production and OHS. Scientific advances are expected for two high-priority industrial sectors in Canada, namely natural resources and aerospace.