Effective mitigation of fine particulate matter (PM2.5) and ozone (O-3) pollution necessitates collaborative regional emission control of major pollutants. In this study, we propose an approach based on source-recepto...
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Effective mitigation of fine particulate matter (PM2.5) and ozone (O-3) pollution necessitates collaborative regional emission control of major pollutants. In this study, we propose an approach based on source-receptor relationships (SRRs) and a mathematical programming (MP) model to quantify the regional atmospheric environmental capacity (AEC) constrained by certain air quality goals for PM2.5 and O-3. We apply this method to optimize emission control strategies addressing a springtime case of concurrent high PM2.5 and O-3 pollution in the Yangtze River Delta (YRD) region of eastern China in May 2014. Our analysis of SRRs reveals that O-3 pollution is more contributed by regional transport compared to PM2.5, thereby limiting the potential for meeting the O-3 target through anthropogenic emission reductions. Imposing various constraints on air quality goals and upper limits of emission reduction ratios yields significant variations in potential control pathways specific to source sectors and regions. To effectively mitigate PM2.5 pollution, substantial reductions in sulfur dioxide (SO2) and primary PM2.5 emissions are required, whereas ammonia (NH3) control demonstrates less effectiveness. Differentiated and coordinated efforts in controlling nitrogen oxide (NOx) and volatile organic compounds (VOCs) emissions are necessary to simultaneously achieve the desired PM2.5 and O-3 targets. Evaluation of potential control pathways further emphasizes the effectiveness of implementing control measures on major precursor emissions to reduce PM2.5. However, meeting the O-3 target remains challenging due to the complex nonlinearity involved in O-3 formation. To attain the air quality goals for PM2.5 (<50 mu g m(-3)) and daily maximum 1-h average (MDA1) O-3 (<160 mu g m(-3)) across the entire YRD, the estimated AEC values for SO2, NOx, NH3, VOCs, and primary PM2.5 are approximately 8.3, 79.4, 102.7, 186.9, and 13.0 kt mon(-1), respectively, with corresponding emission reduction
In this paper, we consider the class of semi-α-preinvex functions. Under certain conditions, we provide a new criteria for differentiable semi-α-preinvex functions, and four new criteria for nondifferentiable semi-...
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Public health emergencies will pose an enormous challenge to healthcare service systems. As COVID-19 rage across the globe, we realize that COVID-19 exposes the problem of inadequate research on the dispatch of emerge...
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This research addresses the staff scheduling problem for an inbound call center. Manpower planning is a common challenge in call center optimization. The uncertain arriving call volumes and limited available resources...
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Integrated sensing and communication (ISAC) is an emerging technology for next generation communication networks. In this paper, we propose to design the ISAC system by jointly considering radar sensing and ultra-reli...
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The Capacitated Clustering Problem (CCP) involves the definition of capacity-constrained weighted individuals' sets such that the maximum similarity with respect to the cluster center value is granted among points...
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We introduce an approach to formulate and solve the multi-class user equilibrium traffic assignment as a mixed-integer linear programming (MILP) problem. Compared to simulation approaches, the analytical MILP formulat...
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We introduce an approach to formulate and solve the multi-class user equilibrium traffic assignment as a mixed-integer linear programming (MILP) problem. Compared to simulation approaches, the analytical MILP formulation makes the solution of network assignment problems more tractable. When applied in a multi -class context, it obviates the need to assume a symmetrical influence between classes and thereby allows richer traffic behavior to be taken into account. Also, it integrates naturally in optimization problems such as maintenance planning and traffic management. We develop the model and apply it for the Sioux Falls network, showing that it outperforms the traditional Beckmann-based and MSA approaches in smaller-scale problems. Further research opportunities lie in developing extensions of MILP-based assignment, with different variants of user equilibrium or dynamic assignment, and in improving the model and solution algorithms to allow large-scale application.
Fuzzy set theory has been extensively employed in mathematical programming, especially in linear programming problems. As a generalization of fuzzy sets, a hesitant fuzzy set is a very useful tool in places where ther...
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Fuzzy set theory has been extensively employed in mathematical programming, especially in linear programming problems. As a generalization of fuzzy sets, a hesitant fuzzy set is a very useful tool in places where there are some hesitations in determining the membership of an element to a set. There are few studies on hesitant fuzzy linear programming problems;therefore, in this paper, we have studied such problems. For this purpose, at first, the motivation of this paper is explained;then, types of hesitant fuzzy linear programming models are introduced. Since it is not easy to examine all of the hesitant fuzzy models for the linear programming problems in one paper, we have restricted ourselves to symmetric and right-hand-side hesitant fuzzy linear programming problems with the flexible approach and then proposed two new approaches to solve them. Finally, to illustrate the applicability of the proposed approaches, three examples under hesitant fuzzy information are given.
Over the last four decades Lake Koronia, part of the Mygdonia Basin, operates under a negative water balance due to poor resource management and planning decisions. Lake Koronia is a Ramsar site in northern Greece tha...
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Over the last four decades Lake Koronia, part of the Mygdonia Basin, operates under a negative water balance due to poor resource management and planning decisions. Lake Koronia is a Ramsar site in northern Greece that has experienced pronounced ecosystem degradation over the past 30 years associated with water level reduction and nutrient loading from agricultural and industrial activities. The objective of the present study is the optimal design of an environmental policy for theoretical and potentially in practice return to a sustainable state of the watershed of Lake Koronia and recommend a rational water resource management plan for the area to promote and support development. The use of mathematical modelling tools can assist in making the right decisions with respect to the water management. The increased complexity of simply managing ecosystems, due to many overlapping factors that affect the water balance, impedes the derivation of the optimal policy to address the problems. This paper presents an optimisation model that takes into account all potential investment options that will allow the restoration of the lake and surrounding area to a sustainable level, and determines the optimal operating policy to allow the ecosystem to recover while maintaining the financial stability of the area. Investment options include the transfer of water from larger water sources, creation of irrigation networks and canals, provision of subsidies to promote alternative land use for agriculture and others. The restoration of a sustainable positive water balance for the basin is possible even if future climatic conditions become more arid than the current. Critical aspects are crop manipulation, irrigation networks and a policy to manage water as a commodity rather than an unlimited resource.
Energy management systems are becoming increasingly important to utilize the continuously growing curtailed renewable energy. Promising energy storage systems, such as batteries and green hydrogen, should be employed ...
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Energy management systems are becoming increasingly important to utilize the continuously growing curtailed renewable energy. Promising energy storage systems, such as batteries and green hydrogen, should be employed to maximize the efficiency of energy stakeholders. However, optimal decision-making, i.e., planning the leveraging between different strategies, is confronted with the complexity and uncertainties of large-scale problems. A sophisticated deep reinforcement learning methodology with a policy-based algorithm is proposed to achieve real-time optimal energy storage systems planning under the curtailed renewable energy uncertainty. A quantitative performance comparison proved that the deep reinforcement learning agent outperforms the scenario-based stochastic optimization algorithm, even with a wide action and observation space. A robust performance, with maximizing net profit and a stable system, confirmed the uncertainty rejection capability of the deep reinforcement learning under a large uncertainty of the curtailed renewable energy. Action mapping was performed to visually assess the action the deep reinforcement learning agent took according to the state. The corresponding results confirmed that the deep reinforcement learning agent learns how the deterministic solution performs and demonstrates more than 90% profit accuracy compared to the solution.
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