The availability of digital infrastructures and the fast-paced development of accompanying revolutionary technologies have triggered an unprecedented reliance on Artificial intelligence (AI) techniques both in theory and practice. Within the AI domain, Machine Learning (ML) techniques stand out as essential facilitator largely enabling machines to possess human-like cognitive and decision making capabilities. This paper provides a focused review of the literature addressing applications of emerging ML tools to solve various Project Scheduling Problems (PSPs). In particular, it employs bibliometric and network analysis tools along with a systematic literature review to analyze a pool of 104 papers published between 1985 and August 2021. The conducted analysis unveiled the top contributing authors, the most influential papers as well as the existing research tendencies and thematic research topics within this field of study. A noticeable growth in the number of relevant studies is seen recently with a steady increase as of the year 2018. Most of the studies adopted Artificial Neural Networks, Bayesian Network and Reinforcement Learning techniques to tackle PSPs under a stochastic environment, where these techniques are frequently hybridized with classical metaheuristics. The majority of works (57%) addressed basic Resource Constrained PSPs and only 15% are devoted to the project portfolio management problem. Furthermore, this study clearly indicates that the application of AI techniques to efficiently handle PSPs is still in its infancy stage bringing out the need for further research in this area. This work also identifies current research gaps and highlights a multitude of promising avenues for future research.

In alignment with the ever-growing interest in adopting sustainable practices, this paper devises a cold supply chain (CSC) planning model that integrates the three pillars of sustainability into the decision-making process while accounting for the shift towards clean energy sources. Interrelated decisions pertaining to production-distribution strategy, backorder and inventory levels, choice of truck type, and selection of third-party logistics (3PLs) providers are jointly optimized. For global CSCs in specific, such decisions are particularly sensitive to the energy sources of the refrigerated facilities and the accompanying levels of CO₂ emissions generated. As such, a multi-objective mixed-integer non-linear programming (MINLP) model is developed and then solved via the weighted-sum method. In essence, the model seeks to operationalize sustainability goals by considering the rapidly evolving transition in energy sources across different regions when deciding on which 3PLs to engage in a contractual agreement with while adjusting the production and distribution strategy accordingly. The practical relevance of the model is illustrated using a case study drawn from the North American frozen food industry. The conducted trade-off analysis indicates the possibility of obtaining a drastic improvement of 86% in jobs’ stability levels (social measure) with a maximum cost increase of around 9% as compared to the economic measure. Furthermore, the analysis reveals that it is possible to reduce 71% of CO₂ emissions while attaining 63% reduction in worker variations at the expense of only 4.47% cost increase once compared to solely optimizing the economic objective.

The full integration of sustainability considerations into maintenance practices requires a close monitoring of maintenance impact through appropriate key performance indicators (KPIs) that take into account various sustainability aspects. However, there is a lack of a comprehensive framework of KPIs covering all the sustainability dimensions, and the few attempts to address this need are specific to certain industries and lack validation by experts in the field. Therefore, the aim of this research is to develop and validate a comprehensive and multidimensional framework of sustainability performance indicators for the assessment of maintenance impacts on the triple-bottom-line. To that end, an exhaustive list of general indicators is collected based on both international organizations standard-sets and published literature. Subsequently, this list is projected on the maintenance area to establish the pertinent indicators. Then, a validation phase is performed with a highly qualified expert panel through semi-structured questionnaires and follow-up discussions. The validated list of sustainable maintenance KPIs includes 63 indicators amongst which 34 belong to the environmental pillar and are split between various categories such as resource use, residuals, environmental compliance, etc. The social pillar comprises of 15 indicators related to occupational health and safety, training and development and customer satisfaction dimensions. Finally, the economic pillar includes 14 indicators split between economic performance and investment categories. The findings of this research provide guidance to practitioners in the field and set the foundations for conducting promising future works. A rich portfolio of future research based on the obtained results is proposed.

In today's competitive industrial environment, the implementation of maintenance digital technologies has become a necessity for organizations to enhance operational efficiency and ensure longevity of their assets. However, many organizations struggle with the successful integration of these technologies due to the lack of a structured approach for implementation. This paper proposes a comprehensive seven-stage framework for maintenance digital transformation (MDT) implementation based on a thorough review of the literature, industry best practices, and expert empirical validation. The proposed framework covers the entire MDT implementation process, from planning and technology selection to continuous improvement and sustaining success. It offers a systematic and practical approach to help organizations navigate through the complexities associated with MDT. The main contribution of this conceptual framework lies in its holistic approach, which encompasses various enablers such as leadership support and technology and data management, and its provision of a structured process for implementation. While the framework provides a practical guide for organizations and serves as a useful tool for maintenance professionals seeking to leverage MDT to achieve operational excellence and competitive advantage, there are still some limitations to its development. Future research is warranted to further refine and apply the framework in a real industrial setting.