The Job Shop Scheduling Problem (JSP) is a pivotal challenge in operations research and is essential for evaluating the effectiveness and performance of scheduling algorithms. Scheduling problems are a crucial domain in combinatorial optimization, where resources (machines) are allocated to job tasks to minimize the completion time (makespan) alongside other objectives like energy consumption. This research delves into the intricacies of JSP, focusing on optimizing performance metrics and minimizing energy consumption while considering various constraints such as deadlines and release dates. Recognizing the multi-dimensional nature of benchmarking in JSP, this study underscores the significance of reference libraries and datasets like JSPLIB in enriching algorithm evaluation. The research highlights the importance of problem instance characteristics, including job and machine numbers, processing times, and machine availability, emphasizing the complexities introduced by energy consumption considerations. An innovative instance configurator is proposed, equipped with parameters such as the number of jobs, machines, tasks, and speeds, alongside distributions for processing times and energy consumption. The generated instances encompass various configurations, reflecting real-world scenarios and operational constraints. These instances facilitate comprehensive benchmarking and evaluation of scheduling algorithms, particularly in contexts of energy efficiency. A comprehensive set of 500 test instances has been generated and made publicly available, promoting further research and benchmarking in JSP. These instances enable robust analyses and foster collaboration in developing advanced, energy-efficient scheduling solutions by providing diverse scenarios.
The Job Shop Scheduling Problem (JSP) is central to operations research, primarily optimizing energy efficiency due to its profound environmental and economic implications. Efficient scheduling enhances production metrics and mitigates energy consumption, thus effectively balancing productivity and sustainability objectives. Given the intricate and diverse nature of JSP instances, along with the array of algorithms developed to tackle these challenges, an intelligent algorithm selection tool becomes paramount. This paper introduces a framework designed to identify key problem features that characterize its complexity and guide the selection of suitable algorithms. Leveraging machine learning techniques, particularly XGBoost, the framework recommends optimal solvers such as GUROBI, CPLEX, and GECODE for efficient JSP scheduling. GUROBI excels with smaller instances, while GECODE demonstrates robust scalability for complex scenarios. The proposed algorithm selector achieves an accuracy of 84.51% in recommending the best algorithm for solving new JSP instances, highlighting its efficacy in algorithm selection. By refining feature extraction methodologies, the framework aims to broaden its applicability across diverse JSP scenarios, thereby advancing efficiency and sustainability in manufacturing logistics.
Car manufacturers offer their customers an enormous number of configuration options. In the process, the variance is also increasing in the provided software. For exclusion of incompatible configurations, complex control systems are created using propositional logic. To check the feasibility of an order, all these constructability conditions must be satisfied. This check is called a "constructability check" and is carried out with the help of SAT solvers. Originally intended only for checking hardware configurations, the existing systems have been extended to also include software components. For software dependencies, however, Satisfiability Modulo Theory (SMT) solvers seem more appropriate in modern versioning approaches as they can also handle numerical domains. We propose a model in Quantifier-Free Integer Difference Logic (QF-IDL) to describe hardware/software configurations in the automotive context. Using our model, we demonstrate how to solve software installation and software update problems.
The increasing attention and investments in augmented reality (AR), virtual reality (VR), and mixed reality (MR) further highlight the importance of graphic representations as communication tools. However, numerous online configurators lack advanced visualization and very few utilize virtual reality. Considering the expense associated with advanced visualizations, it becomes crucial to understand the incremental utility of such visualizations within the configuration process. This positioning paper aims to call for and pave the way towards a deeper understanding of the role and value of visualization in configurators, not limiting to AR, VR, and MR but considering all forms of visualization.
The architecture, engineering, and construction (AEC) industry is increasingly exploring the potential of mass customization and its impact on digitalization. However, developing digital tools can be challenging in terms of defining, delimiting, and structuring a construction product platform. To address this, a suitable information model is crucial to translate the information from the real world into a subset of data that a configurator can handle. This research aims to identify the common characteristics of construction product platforms to enhance their deployment into an information model, the so called product variant master (PVM) model. The study adopts a case methodology approach, typifying product platforms in three construction companies, and evaluates the applicability of the PVM model. Based on the findings, a systemic framework is proposed for depicting construction product platforms within the PVM model. he research concludes that by adopting this framework, the industry can streamline the modeling process, facilitate collaboration, and pave the way for effective digitalization in the AEC sector.
Smart governance systems have different needs depending on the type of organization, which, together with their inherent complexity, make them difficult to configure. However, we have not found solutions that facilitate the configuration of these systems of great interest in the public sector. We propose a configurability solution compounds of a software framework-based multi-level configuration architecture, and a feature model (FM) to represent the variability in a compact way through the configuration of the different levels of the same software. Thus, the FM we present allows for obtaining a line of customized services for different organizations. On a first level, the variability of the typical collaboration processes is managed, on a second level the different collaboration models are handled, and on a third and fourth level the general smart governance system is configured, and the one adapted to the specific needs of the organization. In this way, while facilitating configuration, a high degree of accuracy is achieved regarding the specific and varying needs of the different stakeholders.
