Antonella Meneghetti

The growing need to decarbonize urban energy systems and to exploit low-carbon heat sources is increasing interest in integrated district heating and cooling solutions. In this context, industrial waste heat can represent a valuable resource, provided that suitable tools are available to evaluate alternative system configurations under multiple and potentially conflicting stakeholder objectives. This study proposes a multi-objective decision-support framework for the design and assessment of district heating and cooling systems integrating industrial waste heat recovery. Three cooling strategies are compared: user-side absorption chillers, centralised absorption chillers connected to a district cooling network, and a hybrid solution based on organic Rankine cycle (ORC) electricity generation coupled with centralised electric chillers. The framework combines hourly simulation, multi-objective genetic-algorithm optimization, and multi-criteria decision-making, while considering both design and pricing variables. Four objective functions are adopted: maximization of district operator net present value, minimization of industrial stakeholder payback period, maximization of users’ cooling cost saving, and maximization of CO2 emission reduction. The methodology is applied to a case study in Udine, Italy, where waste heat recovered from a nearby steel plant is used to support an urban district energy system. Results show that no single strategy is optimal under all decision priorities: the ORC-based configuration provides the most balanced overall performance, centralised absorption cooling favours operator profitability, and user-side absorption chillers maximize end-user benefit. The proposed approach can support planning and negotiation processes for integrated district heating and cooling systems based on industrial waste heat recovery.

In a dynamic global economic landscape, logistics companies have to be able to respond quickly and flexibly to changes in demand. This is where the concept of On-Demand Warehousing (ODW) comes in; an emerging approach that promises to revolutionize the way companies manage their warehouse space. This approach allows companies with temporary excess capacity to offer their space to others, who want to cover short-term demand peaks. By this, this concept provides advantages over traditional models, such as dedicated storage facilities or long-term leasing. However, the dynamic nature of this system presents unique challenges, especially in terms of matching customer requests with available storage in real time. Unlike offline models, where future demands are known or estimated, the Online ODWP requires decisions to be made without prior knowledge of upcoming requests. Our work addresses online ODWP by proposing an innovative methodology that integrates Machine Learning methods with sequential stochastic optimization to enhance decision making processes in real time. In an extensive computational study, we show that the newly proposed approach outperforms state-of-the-art heuristics and yields near optimal solutions within very short run times. Detailed algorithmic analyses as well as managerial insights are derived. We, for instance, provide decision guidelines for platform providers facing acceptance or rejection decisions on dynamically arriving storage requests.

The emerging concept of Industry 5.0 is fostering companies to consider the three pillars of human-centricity, sustainability, and resilience. How such a new perspective can be effectively declined and practically guide the introduction of new technologies is a challenge to be addressed. This study proposes a framework to support companies when introducing new solutions to feed production lines by adopting an Industry 5.0 perspective. For each fundamental pillar, critical points to focus on have been highlighted and operational checklists have been developed to effectively support the analysis and implementation of new solutions. The application of the framework and related operational checklists to a case study regarding the integration of an Autonomous Mobile Robot system has proved its validity. Following the human-centricity checklist, full acceptance of the new technology by workers has been gained, together with a safer workplace. Energy savings for material handling and recycling have been supported about the sustainability pillar, while redundancy and backup systems have increased the resilience of the feeding system.

Warehouses are key elements of supply chain networks, and great attention is paid to increase their efficiency. Highly volatile space requirements are enablers of innovative resource sharing concepts, where warehouse capacities are traded on online platforms. In this context, our paper introduces the on-demand warehousing problem from the perspective of platform providers. The objective prioritises demand–supply matching with maximisation of the number of transactions. If there is a tie, the secondary objective maximises the number of suppliers matched with at least one customer and the number of customers that have matches within a specific threshold with respect to the minimum achievable cost. Besides the mathematical integer programming formulation, a myopic list-based heuristic and an efficient matheuristic approach are presented and benchmarked against the performance of a commercial optimisation solver. The impact of several parameters on the platform's objective is analysed. A particularly relevant finding is that the pricing flexibility on the demand side does not necessarily imply higher payments to the supply side. All data instances are made available publicly to encourage more researchers to work on this timely and challenging topic.

The Human-Centric SMED (H-SMED) is an evolution of the classical methodology introduced by Shingo to manage the setup process, which is particularly suitable for companies where the setup involves activities with high human content, that can be hardly transformed into external operations to be performed during the machine uptime. The H-SMED integrates Industry 4.0 tools, as the MES Data Analysis, Lean Management and Ergonomics with a new attention to the centrality of workers, in order to guide the transition towards the next Industry 5.0. The H-SMED methodology is developed into four phases, devolved to: (1) understanding the changeover process; (2) identifying potential improvements; (3) moving from proposal to practice; (4) training and control, in a continuous improvement cycle. The case study of a world-wide known company in the fashion eyewear industry is proposed to validate the framework, reporting a 44% reduction of setup duration.

Decarbonizing the cold chain is a priority for sustainability due to the increasing demand for chilled/frozen food and pharmaceutics. Refrigerated transport requires additional fuel for refrigeration other than for traction. Photovoltaic panels on the vehicle rooftop, a battery bank, and a power conversion system can replace the diesel engine driving the transport refrigerated unit. In long-haul deliveries, vehicles cross zones with different climate conditions, which affect both refrigeration requirements and photovoltaic energy conversion. Mandatory driver’s breaks and rest also affect delivery timing and energy consumption. A multiperiod, multizone optimization model is developed to size the onboard photovoltaic system, based on features of the delivery tour. The model is applied to a palletized chilled food delivery from North-Eastern Italy, showing a payback time of around four years, which can drop under two years for expected reduction of component costs. Economic and environmental performances can be increased by also allowing refrigerated products on-board during the return journey, leading to more fuel savings. Photovoltaic-integrated long-haul delivery for frozen products is not convenient at current market costs. Different climate conditions are tested, showing the model ability to act as a decision support tool to foster renewable energy penetration into the cold chain.