This research paper investigates the adoption of the sharing economy model in the advanced technology sector through infrastructure sharing. Aimed at maximizing the use of high-tech equipment, infrastructure sharing promotes sustainable industry development. Despite scant literature on infrastructure sharing applications in this field, our study addresses this gap by presenting three detailed case studies. The methodology involves two principal roles: infrastructure providers, who offer technological assets, and infrastructure takers with authentic requirements concerning specific equipment. A dedicated platform supports the infrastructure sharing process, enhancing access to advanced technologies for a variety of organizations. The case studies underscore the benefits of infrastructure sharing, including better resource management, increased technology usage, cost reductions, and stimulated innovation, thereby fostering sustainability in the technology sector.

Integrating remote Internet of Things (IoT) laboratories into project-based learning (PBL) in higher education institutions (HEIs) while exploiting the approach of technology-enhanced learning (TEL) is a challenging yet pivotal endeavor. Our proposed approach enables students to interact with an IoT-equipped lab locally and remotely, thereby bridging theoretical knowledge with practical application, creating a more immersive, adaptable, and effective learning experience. This study underscores the significance of combining hardware, software, and coding skills in PBL, emphasizing how IoTRemoteLab (the remote lab we developed) supports a customized educational experience that promotes innovation and safety. Moreover, we explore the potential of IoTRemoteLab as a TEL, facilitating and supporting the understanding and definition of the requirements of remote learning. Furthermore, we demonstrate how we incorporate generative artificial intelligence into IoTRemoteLab’s settings, enabling personalized recommendations for students leveraging the lab locally or remotely. Our approach serves as a model for educators and researchers aiming to equip students with essential skills for the digital age while addressing broader issues related to access, engagement, and sustainability in HEIs. The practical findings following an in-class experiment reinforce the value of IoTRemoteLab and its features in preparing students for future technological demands and fostering a more inclusive, safe, and effective educational environment.

Innovation models are key to fostering technology-focused entrepreneurship in higher education institutions (HEIs). These models create dynamic environments that encourage collaboration, creativity, and problem-solving skills among students and faculty. HEIs face several challenges in fostering entrepreneurship, including allocating sufficient financial and human resources, integrating entrepreneurship education across disciplines, and managing intellectual property. Overcoming these challenges requires HEIs to cultivate an entrepreneurial culture and establish strong partnerships with industry stakeholders. To achieve these goals, HEIs must adopt successful innovation models proven to work. This article presents an international case study highlighting such models and the factors contributing to their success. This study explores the implementation and impact of innovation models, specifically IDEATION and DEETECHTIVE, within HEIs to foster technology-focused entrepreneurship. By implementing numerous actions focusing on online education integration and the Quintuple Helix Innovation Model, these models support shifting engineering students’ mindsets toward entrepreneurship. This research highlights the importance of academia–industry collaboration, international partnerships, and the integration of entrepreneurship education in technology-focused disciplines. This study presents two models. The first, IDEATION, focuses on open innovation and sharing economy aspects. This model underwent rigorous testing and refinement, evolving into the second model, DEETECHTIVE, which is more comprehensive and deep tech-focused. These models have been validated as effective frameworks for fostering entrepreneurship and innovation within HEIs. This study’s findings underscore the potential of these models to enhance innovation capacity, foster an entrepreneurial culture, and create ecosystems rich in creativity and advancement. Practical implications include the establishment of open innovation-oriented structures and mechanisms, the development of specialized curriculum components, and the creation of enhanced collaboration platforms.

Multi-agent task allocation in physical environments with spatial and temporal constraints, are hard problems that are relevant in many realistic applications. A task allocation algorithm based on Fisher market clearing (FMC_TA), that can be performed either centrally or distributively, has been shown to produce high quality allocations in comparison to both centralized and distributed state of the art incomplete optimization algorithms. However, the algorithm is synchronous and therefore depends on perfect communication between agents. We propose FMC_ATA, an asynchronous version of FMC_TA, which is robust to message latency and message loss. In contrast to the former version of the algorithm, FMC_ATA allows agents to identify dynamic events and initiate the generation of an updated allocation. Thus, it is more compatible for dynamic environments. We further investigate the conditions in which the distributed version of the algorithm is preferred over the centralized version. Our results indicate that the proposed asynchronous distributed algorithm produces consistent results even when the communication level is extremely poor.

To deal with the underlying heterogeneous law enforcement problem (LEPH), one needs to allocate police officers to dynamic tasks whose locations, arrival times, and importance levels are unknown a priory. Addressing this challenge and inspired by real police logs, this research aims to solve the LEPH problem by using and comparing three methods: Fisher market-based FMC_TAH+, swarm intelligence HDBA, and Simulated Annealing SA algorithms. The three methods were compared in this study for the performance measures that are commonly used by law enforcement authorities. The results indicate an advantage for FMC_TAH+ both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TAH+ leads to 34% and 32% higher team utility in the highest shift workload.

Police officers conduct routine patrols and perform tasks in response to reported incidents. The importance of each task varies from low (e.g. noise complaint) to high (e.g. murder). The workload associated with each task, indicating the amount of work to be completed for the incident to be processed, may vary as well. Multiple officers with heterogeneous skills may work together on important tasks to share the workload and improve response time. To deal with the underlying law enforcement problem (LEPH), one needs to allocate police officers to dynamic tasks whose locations, arrival times, and importance levels are unknown a priori. Addressing this challenge and inspired by real police logs, this research aims to solve the LEPH problem by using and comparing three methods: Fisher market-based FMC_TA(H+), swarm intelligence HDBA, and Simulated Annealing SA algorithms. FMC_TA(H+) is implemented, using agents as buyers and tasks as goods, to compute fair allocations (i.e. envy-free), and efficient (i.e. Pareto-optimal) in a polynomial or pseudo-polynomial time. FMC_TA(H+) allocations are heuristically scheduled, considering inter-agent constraints on shared tasks. HDBA, a probabilistic swarm intelligence algorithm inspired by the emergent behavior of social bees, was previously implemented to allocate agents to tasks based on agent performance, task priorities, and distances between agents and task-execution locations. SA is a meta-heuristic for approximating the global optimums in large optimization problems. The three methods were compared in this study for five different performance measures that are commonly used by law enforcement authorities. The results indicate an advantage for FMC_TA(H+) both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TA(H+) leads to 34% and 32% higher team utility in the highest shift workload.

Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly emphasized the needs for constantly adapting curricula. The sensible socioeconomic changes generated by the COVID-19 pandemic have induced significant challenges to society in general and industry. Higher education, specifically when dealing with Industry 4.0, must take these new challenges rapidly into account. Modernization of the industrial engineering curriculum combined with its migration to a blended teaching landscape must be updated in real-time with real-world cases. The COVID-19 crisis provides, paradoxically, an opportunity for dealing with the challenges of training industrial engineers to confront a virtual dematerialized work model which has accelerated during and will remain for the foreseeable future after the pandemic. The paper describes the methodology used for adapting, enhancing, and evaluating the learning and teaching experience under the urgent and unexpected challenges to move from face-to-face university courses distant and online teaching. The methodology we describe is built on a process that started before the onset of the pandemic, hence in the paper we start by describing the pre-COVID-19 status in comparison to published initiatives followed by the real time modifications we introduced in the faculty to adapt to the post-COVID-19 teaching/learning era. The focus presented is on Industry 4.0. subjects at the leading edge of the technology changes affecting the industrial engineering and technology management field. The manuscript addresses the flow from system design subjects to implementation areas of the curriculum, including practical examples and the rapid decisions and changes made to encompass the effects of the COVID-19 pandemic on content and teaching methods including feedback received from participants.

This article explains how education in the Internet of Things (IoT) area is introduced into the training by the Department of Technology Management at the Holon Institute of Technology, Israel. The article demonstrates the feasibility of teaching technical subjects in a management program. —Peter Marwedel, TU Dortmund

Realistic multi-agent team applications often feature dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents. However, when such problems include temporal and spatial constraints that require tasks to be executed sequentially by agents, they are NP-hard, and thus are commonly solved using general and specifically designed incomplete heuristic algorithms. We propose FMC_TA, a novel such incomplete task allocation algorithm that allows tasks to be easily sequenced to yield high-quality solutions. FMC_TA first finds allocations that are fair (envy-free), balancing the load and sharing important tasks among agents, and efficient (Pareto optimal) in a simplified version of the problem. It computes such allocations in polynomial or pseudo-polynomial time (centrally or distributedly, respectively) using a Fisher market with agents as buyers and tasks as goods. It then heuristically schedules the allocations, taking into account inter-agent constraints on shared tasks. We empirically compare our algorithm to state-of-the-art incomplete methods, both centralized and distributed, on law enforcement problems inspired by real police logs. We present a novel formalization of the law enforcement problem, which we use to perform our empirical study. The results show a clear advantage for FMC_TA in total utility and in measures in which law enforcement authorities measure their own performance. Besides problems with realistic properties, the algorithms were compared on synthetic problems in which we increased the size of different elements of the problem to investigate the algorithm’s behavior when the problem scales. The domination of the proposed algorithm was found to be consistent.

Introducing up-to-date Industrial Internet of Things and small robots, in other words Industry 4.0, related concepts to students of a Bachelor of Science degree in Industrial Engineering and Technology Management. The degree until now mainly focused on Industry 3.0 using Programmable Logical Controllers and only support software for class exercises without automated teaching robots. At the Holon Institute of Technology, in Israel, the Faculty of Technology Management is dealing with this subject matter and upgrading its curriculum. The paper presents the needs for preparing the students to technology challenges in the industrial environment. It relates the local experience current results and future expected continuous improvements.

Introducing IoT concepts in an existing B.Sc. degree at the Department of Technology Management in HIT has proven to be challenging. This Work in Progress paper will describe the challenges, the progress and the expected results of this initiative.

Market Clearing is an economic concept that features attractive properties when used for resource and task allocation, e.g., Pareto optimality and Envy Freeness. Recently, an algorithm based on Market Clearing, FMC_TA, has been shown to be most effective for realistic dynamic multi agent task allocation, outperforming general optimization methods, e.g., Simulated annealing, and dedicated algorithms, specifically designed for task allocation. That been said, FMC_TA was applied to a homogeneous team of agents and used linear personal utility functions for representing agents' preferences. These properties limited the settings on which the algorithm could be applied.In this paper we advance the research on task allocation methods based on market clearing by enhancing the FMC_ TA algorithm such that it: 1) can use concave personal utility functions as its input and 2) can apply to applications which require the collaboration of heterogeneous agents, i.e. agents with different capabilities. We demonstrate that the use of concave functions indeed encourages collaboration among agents. Our results on both homogeneous and heterogeneous scenarios indicate that the use of personal utility functions with small concavity is enough to achieve the desired incentivized cooperation result, and on the other hand, in contrast to functions with increased concavity, does not cause a severe delay in the execution of tasks.

Realistic multi-agent team applications often feature dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents, but finding the optimal allocation is NP-hard because tasks must be executed sequentially by agents. We propose a novel task allocation algorithm that finds allocations that are fair (envy-free), balancing the load and sharing important tasks between agents, and efficient (Pareto optimal) by using a Fisher market based on a simplified problem model. Such allocations can be easily sequenced to yield high quality solutions, as shown empirically on problems inspired by real police logs.

Realistic multiagent team applications often feature distributed dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents, but finding the optimal allocation is NP-hard due to temporal and spatial constraints that require tasks to be executed sequentially by agents. We propose a novel task allocation algorithm that allows tasks to be easily sequenced to yield high quality solutions by finding allocations that are fair (envyfree), balancing the load and sharing important tasks between agents, and efficient (Pareto optimal). We compute such allocations in polynomial time using a Fisher market with agents as buyers and tasks as goods, then sequence the allocations by maximizing utility at each step. We empirically compare our algorithm to two state-of-the-art incomplete methods on synthetic problems and on realistic law enforcement problems inspired by real police logs. The results show a clear advantage for our algorithm in measures commonly used by law enforcement authorities.