As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulator...
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As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym environment, and those that do usually lack depth in modeling Vehicle-to-Grid (V2G) scenarios. To address the aforementioned issues, this paper introduces EV2Gym, a realistic simulator platform for the development and assessment of small and large-scale smart charging algorithms within a standardized platform. The proposed simulator is populated with comprehensive EV, charging station, power transformer, and EV behavior models validated using real data. EV2Gym has a highly customizable interface empowering users to choose from pre-designed case studies or craft their own customized scenarios to suit their specific requirements. Moreover, it incorporates a diverse array of RL, mathematical programming, and heuristic algorithms to speed up the development and benchmarking of new solutions. By offering a unified and standardized platform, EV2Gym aims to provide researchers and practitioners with a robust environment for advancing and assessing smart charging algorithms.
Circular economy has the potential to counter sustainability challenges by moving towards a circular flow that enhances the efficiency of resource use. Government directives and stakeholders' pressures are forcing...
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Circular economy has the potential to counter sustainability challenges by moving towards a circular flow that enhances the efficiency of resource use. Government directives and stakeholders' pressures are forcing organisations to redesign their global supply chain network to fit sustainability and circularity aims. To fulfil this need, many models are proposed to integrate sustainability objectives in supply chain optimisation. However, designing a sustainable supply chain within the circular economy framework is lacking. This is a barrier for organisations to provide a circular and sustainable product. To fill this gap, descriptive and content analysis are used in this paper to scrutinise literature and develop a conceptual framework incorporating sustainability objectives and circular economy strategies to assist supply network design. The developed framework is a useful tool that helps in a better transition from a linear to a sustainable circular economy. Based on the results, research opportunities are identified and suggestions for future research are proposed. The findings are useful for researchers and managers to implement circular economy practices in a sustainable supply chain.
Traditionally, heat exchanger network synthesis has focused on cost optimization. However, pursuing a cost- optimal solution can often lead to complex and tightly integrated structures, compromising the controllabilit...
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Traditionally, heat exchanger network synthesis has focused on cost optimization. However, pursuing a cost- optimal solution can often lead to complex and tightly integrated structures, compromising the controllability and safety of the system. On the other hand, the adopted superstructure optimization method poses computational challenges due to the NP-hard nature of these problems, particularly for large-scale instances. This study addresses these difficulties by developing an efficient two-stage method to generate cost-effective HENs with multiple subnetworks. A modified transportation-based model and a temperature interval generation procedure are proposed for cost estimating and predicting suitable heat matches for constructing reduced superstructures before detailed HEN design. Integer cuts are derived to allocate heat matches in subnetworks. A progressive network decomposition strategy is devised to enhance problem-solving efficiency. The effectiveness is demonstrated through three examples of varying sizes, showcasing alternative solutions with different levels of integration intensity to facilitate decision-making.
Electrification is a key priority of the European Union, focusing on saving energy resources and mitigating carbon emissions through enhancing restrictions on relative policies and initiatives. For such goals to be ac...
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Electrification is a key priority of the European Union, focusing on saving energy resources and mitigating carbon emissions through enhancing restrictions on relative policies and initiatives. For such goals to be achieved, investing in renewable energy technologies on large- and small-scale projects is promoted. These efforts were implemented in the building sector too, highlighting the importance of optimal decisions in improving the energy performance of buildings, from an economic, energy and environmental perspective. In this context, this paper aims to elaborate a decision-making methodology for building thermal design, considering the optimal selection and operation of multi-energy systems focused on renewable technologies. Solar thermal collectors, photovoltaic systems and heat pumps were included in an Energy Hub for meeting the heating, cooling and domestic hot water energy demand. Optimal decisions were achieved by formulating mathematical programming models in GAMS, for minimizing economic, energy and environmental parameters of the systems under a life cycle perspective. The proposed methodology was implemented in a residential building case study. Results show that combining heat pumps with photovoltaics is preferable for all of the examined criteria, while a sensitivity analysis of the economic, energy and environmental parameters, influencing the energy mixture, leads to optimal solutions with the participation of different energy systems.
The paper is a supplement for an article recently published in this journal. That paper proved that if the sign-preservation requirement is dropped then the solution of the so-called improved normalized squared differ...
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The paper is a supplement for an article recently published in this journal. That paper proved that if the sign-preservation requirement is dropped then the solution of the so-called improved normalized squared differences (INSD) two-directional matrix adjustment model is the same as the result of the 'additive correction iteration algorithm' which the author has been using successfully for decades. It also argued that if the sign-preservation requirement is dropped then the iteration procedure suggested by the authors of the INSD-model boils down to the same algorithm. Since the formal proof of this statement was not available at the time, in this paper the author duly publishes the three-and-a-half pages long proof he elaborated. In the conclusion the merit of this and similar, yet barely rigorously analysed iteration algorithms and the possible useful extensions are also outlined.
Drones have recently become more popular due to technological improvements that have made them useful in many other industries, including agriculture, emergency services, and military operations. Coordination of commu...
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Drones have recently become more popular due to technological improvements that have made them useful in many other industries, including agriculture, emergency services, and military operations. Coordination of communication amongst drones is often required for the efficient performance of missions. With an emphasis on building robust k-connected networks and restoration procedures, this paper investigates the relevance of connection in drone swarms. Specifically, we tackle the k-connectivity restoration problem, which aims to create k-connected networks by moving the drones as little as possible. We propose four novel approaches, including an integer programming model, an integer programming-based heuristic approach, a node converging heuristic, and a cluster moving heuristic. Through extensive measurements taken from various drone networking setups, we provide a comparative analysis of the proposed approaches. Our evaluations reveal that the drone movements produced by the integer programming-based heuristics are nearly the same as the original mathematical formulation, whereas the other heuristics are favorable in terms of execution time.
Process heat electrification offers the prospect of deep decarbonization of the chemical and allied industries. Replacing fossil fuel-fired boilers with electric units can reduce carbon emissions if the power mix has ...
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Process heat electrification offers the prospect of deep decarbonization of the chemical and allied industries. Replacing fossil fuel-fired boilers with electric units can reduce carbon emissions if the power mix has a large share of renewables. For multinational firms with plants in multiple locations, the electrification decisions should be scheduled based on grid carbon intensity projections and should also be coordinated among these subsidiaries. In addition, carbon credits can be traded among the multiple sites to allow lagging plants to reduce their carbon footprints. A novel mathematical model has been developed to optimize process heat electrification plans in multinational corporations. The model determines the optimal timing of electrification at each location, and also the necessary level of carbon trading among subsidiaries. An illustrative case study demonstrates how the model can be used to generate electrification plans that are superior to those based on simple heuristics.
Selecting third-party logistics (3PL) providers is a crucial strategic decision for organizations seeking to enhance supply chain performance in today's dynamic business environment. This study explores the advanc...
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Selecting third-party logistics (3PL) providers is a crucial strategic decision for organizations seeking to enhance supply chain performance in today's dynamic business environment. This study explores the advancement of decision-making approaches for 3PL outsourcing, analysing 121 research articles published from 2002 to 2024. Through a comprehensive systematic literature review supported by descriptive and keyword co-occurrence analyses, the study identifies three primary methodological approaches: Multi-Criteria Decision Making (MCDM), mathematical programming (MP), and Artificial Intelligence (AI), with MCDM emerging as the most preferred approach. These methodologies show promising advancements in addressing traditional limitations, adopting data-driven approaches, integrating hybrid methods, managing uncertainty, and refining criteria development processes. Based on an analysis of 1662 selection criteria from the literature, the study proposes an up-to-date and universally applicable three-layer criteria framework focusing on five key aspects: Business Strength and Prestige, Service Excellence, Cost and Pricing, Technology Capabilities, and Sustainability (measured by Environmental, Social, and Governance metrics). This framework, enhanced by insights from industry experts and top 3PL companies, reflects a shift toward more holistic 3PL evaluation and selection. The study contributes to decision science and management literature while providing valuable insights for supply chain professionals, concluding with suggestions for future research directions.
A unified objective optimization framework is developed for damage-coupled multisurface plasticity in the context of normal-dissipative media. The framework is shown to be advantageous in rock and soil mechanics appli...
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A unified objective optimization framework is developed for damage-coupled multisurface plasticity in the context of normal-dissipative media. The framework is shown to be advantageous in rock and soil mechanics applications to overcome difficulty associated with non-smoothness of the elastic domain due to the use of multiple intersecting yield-surfaces. The basic approach is one of mathematical programming, where the evolution of internal variables over a finite time step incrementally minimizes a suitable convex functional of the internal-energy and dissipative terms. A variant of the Broyden-Fletcher-Goldfarb-Shanno algorithm (BFGS) is employed to obviate the need for matrix inversion while constricting order of operations to O(n2). To demonstrate the effectiveness of the novel multi-surface model in modeling strength and damage behavior over a range of confining pressures, we provide validation against existing triaxial compression data for Tavel limestone. Model robustness and utility in damage-based element deletion is further demonstrated infinite element simulation of a projectile penetrating into limestone.
Data classification is considered a fundamental research subject within the machine learning community. Researchers seek the improvement of machine learning algorithms in not only accuracy, but also interpretability. ...
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Data classification is considered a fundamental research subject within the machine learning community. Researchers seek the improvement of machine learning algorithms in not only accuracy, but also interpretability. Interpretable algorithms allow humans to easily understand the decisions that a machine learning model makes, which is challenging for black box models. mathematical programming-based classification algorithms have attracted considerable attention due to their ability to effectively compete with leading-edge algorithms in terms of both accuracy and interpretability. Meanwhile, the training of a hyper-box classifier can be mathematically formulated as a Mixed Integer Linear programming (MILP) model and the predictions combine accuracy and interpretability. In this work, an optimisation-based approach is proposed for multi-class data classification using a hyper-box representation, thus facilitating the extraction of compact IF-THEN rules. The key novelty of our approach lies in the minimisation of the number and length of the generated rules for enhanced interpretability. Through a number of real-world datasets, it is demonstrated that the algorithm exhibits favorable performance when compared to well-known alternatives in terms of prediction accuracy and rule set simplicity.
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