The manufacturing industry is facing increasing challenges to improve sustainability performance by using Industry 4.0 technologies like big data analytics, the internet of things, digital twins considering their potentials. However, effectively integrating sustainability goals and Industry 4.0 technologies within manufacturing systems can be challenging due to real time attribute data of digital twins of machinery obtained by IoT tools and necessity to integrate them into production planning levels. This challenge can be addressed by developing a framework for optimizing the flow of operations in a manufacturing system while incorporating sustainability assessment and Industry 4.0 technologies effectively. This paper investigates the integration of sustainability assessment considering Industry 4.0 technologies and the use of Markov Decision Process capabilities. The framework utilizes Markov Decision Process to model the decision-making process of the manufacturing system and its decision-makers. It includes sustainability goals as constraints or objectives in the Markov Decision Process model. The use of Industry 4.0 technologies is integrated into the framework to gather data and optimize the decision-making process based on that data. The capabilities of the proposed framework are demonstrated through case studies of a single agent on a shop floor. The findings from the case study indicate that the proposed framework can effectively support decision-making at the top-tier level of the enterprise by integrating sustainability assessment and the Industry 4.0 paradigm.

This paper develops a new mathematical model to study a location-routing problem with simultaneous pickup and delivery under the risk of disruption. A remarkable number of previous studies have assumed that network components (e.g., routes, production factories, depots, etc.) are always available and can permanently serve the customers. This assumption is no longer valid when the network faces disruptions such as flood, earthquake, tsunami, terrorist attacks and workers strike. In case of any disruption in the network, tremendous cost is imposed on the stockholders. Incorporating disruption in the design phase of the network will alleviate the impact of these disasters and let the network resist disruption. In this study, a mixed integer programming (MIP) model is proposed that formulates a reliable capacitated location-routing problem with simultaneous pickup and delivery (RCLRP-SPD) services in supply chain distribution network. The objective function attempts to minimize the sum of location cost of depots, routing cost of vehicles and cost of unfulfilled demand of customers. Since the model is NP-Hard, three meta-heuristics are tailored for large-sized instances and the results show the outperformance of hybrid algorithms comparing to classic genetic algorithm. Finally, the obtained results are discussed and the paper is concluded.

To remain competitive in the current market, an enterprise must differentiate itself based on higher value propositions. For this purpose, since improving the product or service performance can reach some limits, one potential solution is to move towards new combinations of products and services. This evolution, called servitization, leads to the generation of Product Service Systems (PSS). Servitization requires not only a clear understanding of enterprise core business, but also a clear vision of the prevailing trends and challenges of PSS development from both the business and technological points of view. In addition, the evolution path should be aligned with the enterprise strategy. This paper first highlights the notion of symbiotic PSS where product systems and service systems, and their stakeholders, interoperate seamlessly based on a win-win approach. Then, it proposes a PSS Conceptual Framework (PSS-CF), which can be applied in the early stages of servitization to increase the understating of PSS dimensions and to facilitate the prioritization of the servitization investments. The framework dimensions were discussed in several iterations, from both the academic and industrial points of view, in the frame of a European research project. Moreover, the applicability of the framework was studied in four different industrial use-cases.

Compartmental models have gained significant attention not only in public health studies but also in fields such as Operations Research (OR), social sciences, and logistics, particularly following the COVID-19 pandemic. Their broad applicability in epidemiology and their utility in understanding, predicting, and controlling the global spread of infectious diseases have made them indispensable across various disciplines. The appeal of these models lies in their simplicity yet effectiveness in capturing the essential dynamics of disease transmission. This paper provides a comprehensive review of compartmental models, focusing on the Susceptible-Infectious-Recovered (SIR) models and the key aspects of their structure. The primary objective of this review is to enhance the ability of researchers and practitioners to understand and manage infectious disease outbreaks through a twofold approach: (1) an evaluation of the assumptions, equations, and methodologies used for estimating critical parameters in SIR models, and (2) an exploration of the relationship between SIR models and optimization models. Additionally, a systematic micro-level review has identified the most significant research gaps in the literature on compartmental models, leading to recommendations for future research. A key finding emphasizes the need to revisit various assumptions to clarify the connection between SIR models and optimization approaches, which is expected to offer valuable insights for epidemic disease modeling.

To remain competitive in the current market, an enterprise must differentiate itself based on higher value propositions. For this purpose, since improving the product or service performance can reach some limits, one potential solution is to move towards new combinations of products and services. This evolution, called servitization, leads to the generation of Product-Service Systems (PSS). Servitization requires not only a clear understanding of enterprise core business but also a clear vision on the prevailing trends and challenges of PSS development from both business and technological points of view. In addition, the evolution path should be aligned with the enterprise strategy. This paper first highlights the notion of symbiotic PSS where Product Systems and Service Systems, and their stakeholders, interoperate seamlessly based on a win-win approach. Then it proposes a PSS Conceptual Framework (PSS-CF) which can be applied in the early stages of servitization to increase the understating of PSS dimensions and to facilitate the prioritization of the servitization investments. The framework dimensions were discussed in several iterations, from both academic and industrial points of view, in the frame of a European research project. Moreover, the applicability of the framework was studied in four different industrial Use-Cases.