The Experts below are selected from a list of 32718 Experts worldwide ranked by ideXlab platform
Avishai Ceder - One of the best experts on this subject based on the ideXlab platform.
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optimal public transport operational strategies to Reduce Cost and vehicle s emission
PLOS ONE, 2018Co-Authors: Chunyan Tang, Avishai CederAbstract:Public transport passenger demand is inevitably made non-uniform because of spatial and temporal land use planning. This non-uniformity warrants the use of public transport operational strategies to attain Operating efficiency. The optimization of these strategies is commonly being done from the operator perspective, and indirectly from the user perspective. However, the environmental perspective of these strategies, in terms of vehicle’s emission, has not been investigated. This study proposed a methodology to analyze the benefits of using transit operational strategies to Reduce Operating Cost and eventually also to Reduce undesirable emissions. First, a strategy-based optimization model is established to minimize the number of transit vehicles required. Four candidate operational strategies are considered in this model, including full route operation (FRO), short turn, limited stop, and a combination of limited stop and short turn. Second, the pollutant emissions of transit vehicles are estimated by the MOVES emission model. The developed methodology is applied to a real life case study in Dalian, China. Results show that the use of operational strategies can not only significantly save the number of vehicles by 12.5%, but also Reduce emissions of pollutants (i.e., CO2, HC, CO, NOx, PM2.5) by approximately 13%, compared with applying FRO strategy exclusively. In addition, both benefits can be further enhanced through the use of an efficient payment mode (e.g., off-board or contactless card) or improving bus performance in deceleration/acceleration as well as doors opening and closing at a stop.
Lazaros G. Papageorgiou - One of the best experts on this subject based on the ideXlab platform.
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A combined optimization and agent-based approach to supply chain modelling and performance assessment
Production Planning and Control, 2001Co-Authors: Jonatan Gjerdrum, Nilay Shah, Lazaros G. PapageorgiouAbstract:The main objective of this paper is to give an example of how expert systems techniques for distributed decision-making can be combined with contemporary numerical optimization techniques for the purposes of supply chain optimization and to describe the resulting software implementation. In this paper, multi-agent modelling techniques are applied to simulate and control a simple demand-driven supply chain network system, with the manufacturing component being optimized through mathematical programming. The system measures supply chain performance and the effect of different parameters in the replenishment control system, and can be used to simulate the behaviour of a system that uses optimization for part of its decision-making. The objective of this supply chain network system is to Reduce Operating Cost, while maintaining a high level of customer order fulfilment.
Xiaohua Xia - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical model predictive control of Venlo-type greenhouse climate for improving energy efficiency and reducing Operating Cost
Journal of Cleaner Production, 2020Co-Authors: Dong Lin, Lijun Zhang, Xiaohua XiaAbstract:Abstract In this paper, a hierarchical control strategy for Venlo-type greenhouse climate control under South Africa climate is proposed to improve energy efficiency and Reduce Operating Cost. The proposed hierarchical control architecture includes two layers. The upper layer is to generate set points by solving different optimization problems. Three different strategies with different optimization objectives are studied. The meteorological data of a typical winter day is used. Strategy 1 is to minimize the energy consumption. Strategy 2 is to minimize the energy Cost under the time-of-use (TOU) tariff. Strategy 3 is to minimize the total Cost of energy consumption, ventilation and carbon dioxide ( CO 2 ) supply. The lower layer is to track the trajectories obtained from the upper layer. A closed-loop model predictive control (MPC) strategy is introduced to address model plant mismatch and reject system disturbances. Two performance indices, relative average deviation (RAD) and maximum relative deviation (MRD), are introduced to compare the tracking performance of the proposed MPC and an open loop control under three different levels of system disturbances (2%, 5%, 10%). Simulation results show that the proposed strategy can effectively Reduce the Operating Cost while keeping the temperature, relative humidity and CO 2 concentration within required ranges. Compared with Strategy 1 and Strategy 2, the total Cost of Strategy 3 is Reduced by 72.07% and 71.41% respectively. Moreover, the proposed MPC has better tracking performance than the open loop control. Therefore, the proposed hierarchical MPC strategy could be an effective way to improve greenhouse energy efficiency and achieve sustainable cleaner production.
Alessandro Romagnoli - One of the best experts on this subject based on the ideXlab platform.
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liquid air energy storage as a polygeneration system to solve the unit commitment and economic dispatch problems in micro grids applications
Energy Procedia, 2019Co-Authors: Stefano Mazzoni, Alessio Tafone, Emiliano Borri, Gabriele Comodi, Alessandro RomagnoliAbstract:Abstract Storage technologies play a crucial role in polygeneration plants that attempt to integrate power, thermal and cooling energy systems in order to maximize process efficiency and Reduce Operating Cost. With the increasing penetration of renewable energy into the plant, storage technologies help to dampen the intermittency problem in their energy supply whilst at the same time perform peak shaving to Reduce primary energy consumption, thus mitigating pollutant emission. Among the various storage technologies, Liquid Air Energy Storage (LAES) have gathered research interest due to its capability of simultaneously producing electrical and cooling power. Furthermore, unlike Electrochemical Energy Storage (EES) technologies, the LAES lifetime is not heavily dependent on its duty cycle, thus allowing for a calendar life twice or thrice that of EES. In this paper, the economic dispatch of an Eco-building in Singapore has been evaluated using a mixed-integer quadratic programming solver by comparing the adoption of EES and LAES within a capacity range of 300kWh-2000kWh. At the higher end of the capacity range, the LAES configuration results in a higher Net Present Value after 20 years and a shorter time period to obtain the Return of Investment compared to that of EES. At the lower capacity range, both technologies give similar financial returns. Analysis of the results show LAES to be a promising technology to compete with EES in the context of a polygeneration plant and further technology integration is discussed.
Jonatan Gjerdrum - One of the best experts on this subject based on the ideXlab platform.
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A combined optimization and agent-based approach to supply chain modelling and performance assessment
Production Planning and Control, 2001Co-Authors: Jonatan Gjerdrum, Nilay Shah, Lazaros G. PapageorgiouAbstract:The main objective of this paper is to give an example of how expert systems techniques for distributed decision-making can be combined with contemporary numerical optimization techniques for the purposes of supply chain optimization and to describe the resulting software implementation. In this paper, multi-agent modelling techniques are applied to simulate and control a simple demand-driven supply chain network system, with the manufacturing component being optimized through mathematical programming. The system measures supply chain performance and the effect of different parameters in the replenishment control system, and can be used to simulate the behaviour of a system that uses optimization for part of its decision-making. The objective of this supply chain network system is to Reduce Operating Cost, while maintaining a high level of customer order fulfilment.
