Abstract

Abstract
In the dynamic realm of nowadays manufacturing, integrating digital technologies has become paramount for enhancing operational efficiency and decision-making processes. This article presents a novel system architecture that integrates a Simulation-based Digital Twin (DT) with emerging trends in manufacturing to enhance decision-making, accompanied by a detailed technical approach encompassing protocols and technologies for each component. The DT leverages advanced simulation techniques to model, monitor, and optimize production processes in real time, facilitating both strategic and operational decision-making. Complementing the DT, trending technologies such as artificial intelligence, additive manufacturing, collaborative robots, autonomous vehicles, and connectivity advancements are strategically integrated to enhance operational efficiency and facilitate the adoption of the Manufacturing as a Service (MaaS) paradigm. A case study within a MaaS supplier context, deployed in an industrial laboratory with advanced robotic systems, demonstrates the practical application of optimizing dynamic job-shop configurations using Simulation-based DT, showcasing strategies to improve operational efficiency and resource utilization. The results of the industrial experiment were highly encouraging, underscoring the potential for extension to more intricate industrial systems, with particular emphasis on incorporating sustainability and remanufacturing principles.

Abstract
A new generation of manufacturing systems is emerging through the adoption of new policies to overcome future crises highlighted by constant social, environmental, and economic concerns. The rise of so-called smart manufacturing is noticeable. However, new risks to humankind are being introduced, and, more than ever, science and technology are required to guarantee the future sustainability and resilience of our manufacturing systems. This research presents a Digital Twin approach resorting to simulation models with embedded intelligence to transform efficient manufacturing systems and react to complex and unpredictable circumstances. The methodology covers production scheduling incorporating flexible robots, internal logistics supervision contemplating planning and control of mobile robots, and capacity management. The method demonstrates the potential of integrating Additive Manufacturing technologies to quickly react to production needs. The developed strategy was enforced and assessed in an industrial experiment, exhibiting its robustness and promising application. The attained results were very encouraging, highlighting its potential extension to more complex industrial systems.

Abstract
In this work a hybrid simulation-optimization approach is presented to support decision-making towards improved resiliency and sustainability in pharmaceutical supply chain (PSC) operations. In a first step, a simulation model is used to assess the PSC performance under a set of disruptive scenarios to select the best inventory-based strategy for enhanced resiliency. Disruptions addressed in this work are mainly related to unpredicted medium-term production stoppages due to unexpected high-impact events such as accidents in production and transportation, or natural disasters. In a second step, a multi-objective mixed integer linear programming (MO-MILP) model is developed to optimize the selected inventory-based strategy regarding the economic, social, and environmental dimensions. In particular, the social and environmental aspects are introduced by anticipating the expected waste generation of close to expire medicines, redirecting them into a donation scheme. The proposed approach is applied to a representative PSC, with preliminary results showing the relevance of this tool for decision-makers to assess the trade-offs associated to the economic and social dimensions, as well as their impacts on waste generation.

Abstract
Warehouses and distribution centres play a key role in any Supply Chain, particularly in the retail sector, where a network of stores needs to be replenished in a highly dynamic and increasingly uncertain context. In this regard, companies need to improve their intralogistics systems daily to ensure long-term competitiveness and sustainable growth. This is especially true in picking-by-line systems where many time-consuming and manual tasks are usually involved. This study introduces a new decision support tool based on simulation methods to aid the decision-making process in a picking-by-Line system, aimed to improve the overall picking operations efficiency, through human-centric perspective. A Discrete-Event-Simulation model is proposed to assess a set of parameters under several scenarios, driving a more informed decision-making process towards cost-effective strategies. The proposed approach was validated through an empirical case study showing its effectiveness in assisting operational planning decisions related to capacity and resource allocation. The system demonstrates promising versatility for application across varied warehouse environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem.