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Christodoulos A. Floudas - One of the best experts on this subject based on the ideXlab platform.
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Operational Planning of large scale industrial batch plants under demand due date and amount uncertainty ii conditional value at risk framework
Industrial & Engineering Chemistry Research, 2010Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:A novel framework based on conditional value-at-risk theory has been applied to the problem of Operational Planning for large-scale industrial batch plants under demand due date and amount uncertainty. The nominal Planning with production disaggregation model has been extended by means of conditional value-at-risk theory to address the objectives of providing a daily production profile that not only is a tight upper bound on the production capacity of the plant but also is immune to the various forms of demand uncertainty. An industrial case study was conducted to demonstrate the viability of the proposed approach, which involves the novel application of conditional value-at-risk theory to the problem of Operational Planning under demand due date and amount uncertainty. A comparative study that juxtaposes the proposed Operational Planning model and the robust Operational Planning with production disaggregation model presented in part I of this series of articles (Verderame and Floudas Ind. Eng. Chem. Res....
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Operational Planning of large scale industrial batch plants under demand due date and amount uncertainty i robust optimization framework
Industrial & Engineering Chemistry Research, 2009Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:The Operational Planning of a large-scale industrial batch plant typically occurs over a time horizon of several months with the goal of providing daily production targets and raw material requirements for the plant in question. Due to the length of the time horizon, demand uncertainty should be taken into account in order to ensure that the Operational Planning model provides reliable production targets and/or raw material requirements. A robust novel Operational Planning model has been developed in order to address the objective of providing a reliable daily production profile which is immune to various forms of demand uncertainty. The ability of the proposed Planning model to address the aforementioned objectives of an Operational Planning model has been validated through an industrial case study of a large-scale, multiproduct, and multipurpose batch plant having the capability of producing hundreds of different products over a time horizon of three months.
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Operational Planning framework for multisite production and distribution networks
Computers & Chemical Engineering, 2009Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:The Operational Planning of a multisite production and distribution network, which entails making Operational-level decisions for efficient production facility utilitization and customer-order fulfillment over a time horizon of several months, is of great importance but has received considerably less attention compared to Operational Planning approaches for a single production site. The inherent complexities of simultaneously optimizing the allocation of production tasks at each facility, as well as the interplay between several production and distribution centers make Operational Planning of a multisite production and distribution network challenging especially when addressing large-scale, industrial applications. The proposed multisite Planning with production disaggregation model (Multisite-PPDM) has been formulated in order to address industrially relevant supply chains and determine both the production and shipment profiles for the supply chain of interest.
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Integrated Operational Planning and Scheduling under Uncertainty
Computer Aided Chemical Engineering, 2009Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:Abstract An integrated Operational Planning and medium-term scheduling framework which takes into account various forms of market and process uncertainty will be presented. The framework entails the integration of a novel and robust Planning with Production Disaggregation Model with the robust counterpart of an industrially validated mediumterm scheduling model through a forward rolling horizon scheme. The Operational Planning and scheduling under uncertainty framework has been applied to an industrial case study in order to demonstrate that it can address relevant Planning and scheduling problems.
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integrated Operational Planning and medium term scheduling for large scale industrial batch plants
Industrial & Engineering Chemistry Research, 2008Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:The Operational Planning and the medium-term scheduling of a multipurpose and multiproduct batch chemical plant are inter-related activities that involve the allocation of plant resources. Because of their disparate time scales, however, the effective integration of Planning and scheduling has proven to be a formidable task. The lack of an integrative framework for Planning and scheduling will invariably cause the Planning model to provide unrealistic production targets to the scheduling level, leading to the misallocation of plant resources. In response to this issue, a novel framework for the integration of Planning and scheduling for a multipurpose and multiproduct batch plant is presented. The framework entails integrating a novel Planning with production disaggregation model with a medium-term scheduling model through a forward-rolling horizon approach.
Peter M. Verderame - One of the best experts on this subject based on the ideXlab platform.
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Operational Planning of large scale industrial batch plants under demand due date and amount uncertainty ii conditional value at risk framework
Industrial & Engineering Chemistry Research, 2010Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:A novel framework based on conditional value-at-risk theory has been applied to the problem of Operational Planning for large-scale industrial batch plants under demand due date and amount uncertainty. The nominal Planning with production disaggregation model has been extended by means of conditional value-at-risk theory to address the objectives of providing a daily production profile that not only is a tight upper bound on the production capacity of the plant but also is immune to the various forms of demand uncertainty. An industrial case study was conducted to demonstrate the viability of the proposed approach, which involves the novel application of conditional value-at-risk theory to the problem of Operational Planning under demand due date and amount uncertainty. A comparative study that juxtaposes the proposed Operational Planning model and the robust Operational Planning with production disaggregation model presented in part I of this series of articles (Verderame and Floudas Ind. Eng. Chem. Res....
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Operational Planning of large scale industrial batch plants under demand due date and amount uncertainty i robust optimization framework
Industrial & Engineering Chemistry Research, 2009Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:The Operational Planning of a large-scale industrial batch plant typically occurs over a time horizon of several months with the goal of providing daily production targets and raw material requirements for the plant in question. Due to the length of the time horizon, demand uncertainty should be taken into account in order to ensure that the Operational Planning model provides reliable production targets and/or raw material requirements. A robust novel Operational Planning model has been developed in order to address the objective of providing a reliable daily production profile which is immune to various forms of demand uncertainty. The ability of the proposed Planning model to address the aforementioned objectives of an Operational Planning model has been validated through an industrial case study of a large-scale, multiproduct, and multipurpose batch plant having the capability of producing hundreds of different products over a time horizon of three months.
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Operational Planning framework for multisite production and distribution networks
Computers & Chemical Engineering, 2009Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:The Operational Planning of a multisite production and distribution network, which entails making Operational-level decisions for efficient production facility utilitization and customer-order fulfillment over a time horizon of several months, is of great importance but has received considerably less attention compared to Operational Planning approaches for a single production site. The inherent complexities of simultaneously optimizing the allocation of production tasks at each facility, as well as the interplay between several production and distribution centers make Operational Planning of a multisite production and distribution network challenging especially when addressing large-scale, industrial applications. The proposed multisite Planning with production disaggregation model (Multisite-PPDM) has been formulated in order to address industrially relevant supply chains and determine both the production and shipment profiles for the supply chain of interest.
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Integrated Operational Planning and Scheduling under Uncertainty
Computer Aided Chemical Engineering, 2009Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:Abstract An integrated Operational Planning and medium-term scheduling framework which takes into account various forms of market and process uncertainty will be presented. The framework entails the integration of a novel and robust Planning with Production Disaggregation Model with the robust counterpart of an industrially validated mediumterm scheduling model through a forward rolling horizon scheme. The Operational Planning and scheduling under uncertainty framework has been applied to an industrial case study in order to demonstrate that it can address relevant Planning and scheduling problems.
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integrated Operational Planning and medium term scheduling for large scale industrial batch plants
Industrial & Engineering Chemistry Research, 2008Co-Authors: Peter M. Verderame, Christodoulos A. FloudasAbstract:The Operational Planning and the medium-term scheduling of a multipurpose and multiproduct batch chemical plant are inter-related activities that involve the allocation of plant resources. Because of their disparate time scales, however, the effective integration of Planning and scheduling has proven to be a formidable task. The lack of an integrative framework for Planning and scheduling will invariably cause the Planning model to provide unrealistic production targets to the scheduling level, leading to the misallocation of plant resources. In response to this issue, a novel framework for the integration of Planning and scheduling for a multipurpose and multiproduct batch plant is presented. The framework entails integrating a novel Planning with production disaggregation model with a medium-term scheduling model through a forward-rolling horizon approach.
Yoshikazu Fukuyama - One of the best experts on this subject based on the ideXlab platform.
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Integration of Optimal Operational Planning of Energy Plants and Optimal Production Planning for Actual Reduction of Energy Costs in Factories
IFAC-PapersOnLine, 2018Co-Authors: Shuhei Kawaguchi, Yoshikazu FukuyamaAbstract:Abstract This paper proposes integration of optimal Operational Planning of energy plants and optimal production Planning for actual reduction of energy costs in factories. Conventionally, fixed loads of the various tertiary energies have been utilized for solving optimal Operational Planning of energy plants so far. On the contrary, in this paper, the loads of the various tertiary energies are calculated according to candidates of production Planning. The proposed method is applied to 10 jobs and 10 machines problem and it is verified that it can minimize the secondary energy cost and production time simultaneously.
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Dependable multi-population differential evolutionary particle swarm optimization for optimal Operational Planning of energy plants
2017 19th International Conference on Intelligent System Application to Power Systems (ISAP), 2017Co-Authors: Norihiro Nishimura, Yoshikazu Fukuyama, Tetsuro MatsuiAbstract:This paper proposes dependable multi-population differential evolutionary particle swarm optimization (DEEPSO) for optimal Operational Planning of energy plants. The problem can be formulated as a mixed integer nonlinear optimization problem (MINLP). Optimal Operational Planning of numbers of energy plants are calculated simultaneously in a data center. Therefore, the problem is required to generate optimal Operational Planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for this challenge is speeding up by parallel and distributed processing (PDP). However, PDP utilizes numbers of processes and countermeasures for various faults of the processes should be considered. The problem requires successive calculation at every control interval for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality are required even if some of the calculation results cannot be returned from distributed processes. The results indicate that the proposed method can improve solution quality compared with the conventional parallel DEEPSO based method using a master-slave model even if some of the calculation results cannot be returned from distributed processes.
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Optimal Operational Planning of Energy Plants by Multi-population Differential Evolutionary Particle Swarm Optimization
Advances in Swarm Intelligence, 2017Co-Authors: Norihiro Nishimura, Yoshikazu Fukuyama, Tetsuro MatsuiAbstract:This paper presents optimal operation Planning of energy plants by multi-population differential evolutionary particle swarm optimization (DEEPSO). The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques such as particle swarm optimization (PSO) and differential evolution (DE) have been applied. However, solution quality can be improved. Multi-population is known as one of a way of increasing solution quality. This paper applies multi-population DEEPSO for optimal Operational Planning of energy plants in order to improve solution quality.
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the e constrained differential evolution approach for optimal Operational Planning of energy plants
Congress on Evolutionary Computation, 2010Co-Authors: Ryohei Suzuki, Tetsuro Matsui, S. Kitagawa, K. Matsumoto, Fukiko Kawai, Donghui Xiang, Yoshikazu FukuyamaAbstract:This paper introduces optimal Operational Planning of energy plants via the e constrained differential evolution. In order to generate optimal Operational Planning for energy plants, startup/shutdown status and/or input/output values of the facilities at each control interval should be determined. The problem can be formulated as a large-scale mixed-integer nonlinear problem (MINLP). Metaheuristics (MHs) is one of the solutions for MINLP. If the formulated MINLP has various equality and inequality constraints, it remains difficult to solve it without parameters tuning. In this paper, to overcome the difficulty, we propose an improved differential evolution approach using the e constrained method for MINLP. Results show the effectiveness of the proposed method compared with conventional methods.
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Optimal Operational Planning considering uncertainties for energy plants
2009 IEEE Power & Energy Society General Meeting, 2009Co-Authors: S. Kitagawa, Tetsuro Matsui, K. Kikuchi, K. Matsumoto, Yoshikazu FukuyamaAbstract:In recent years, cogeneration systems (CGS) have been installed in various factories and buildings. In order to generate optimal Operational Planning for CGS, various load, for example, electric loads, air-conditioning loads, heating loads, and hot water loads, should be forecasted, and startup and shutdown status and input values for the facilities at each control interval should be determined using facility models. The authors have already developed optimal Operational Planning for CGS using particle swarm optimization (PSO), which is one of the meta-heuristic optimization methods. However, there have always been uncertainties such as load forecasting errors or sensor errors. Therefore, generated Operational Planning does not always be optimal considering uncertainties. This paper proposes optimal Operational Planning of energy plants by PSO considering load forecasting error. And this paper also introduces the sensor diagnosis functions based on the concept of power system state estimation. The proposed method is applied to a typical cogeneration system with promising results.
Fengji Luo - One of the best experts on this subject based on the ideXlab platform.
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an Operational Planning framework for large scale thermostatically controlled load dispatch
IEEE Transactions on Industrial Informatics, 2017Co-Authors: Fengji Luo, Zhao Yang Dong, Ke Meng, Junhao Wen, Haiming Wang, Junhua ZhaoAbstract:This paper proposes an Operational Planning framework for large-scale thermostatically controlled load (TCL) dispatch. The proposed framework consists of a day-ahead scheduling stage and a real-time operation stage. A thermal comfort model is employed to estimate the occupants’ thermal comfort degree. A self-adaptive TCL grouping method is proposed to group the TCLs based on the similarity of the TCL model parameters. Then, a hierarchical day-ahead scheduling model is proposed to make the optimal dispatch plan for the TCL aggregators based on the day-ahead forecasted information. In the real-time operation stage, a predictive control model is proposed for the TCL aggregators to make the real-time TCL dispatch decision based on the updated real-time information. The simulation results prove the efficiency of the proposed framework.
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short term Operational Planning framework for virtual power plants with high renewable penetrations
Iet Renewable Power Generation, 2016Co-Authors: Fengji Luo, Zhao Yang Dong, Ke Meng, Jing Qiu, Jiajia Yang, Kit Po WongAbstract:This study proposes a two-stage Operational Planning framework for the short-term operation of the virtual power plant (VPP). In the first stage, a stochastic bidding model is proposed for the VPP to optimise the bids in the energy market, with the objective to maximise its expected economic profit. The imbalance costs of the VPP are considered in the bidding model. In the second stage, a model predictive control (MPC)-based dispatch model is proposed to optimise the real-time control actions. In the real-time dispatch model, the real-time information of the resources is continuously updated, and the deviations between the actual energy output and the contracted energy over the MPC control horizon are minimised. The simulation results prove the efficiencies of the proposed method.
Paul I. Barton - One of the best experts on this subject based on the ideXlab platform.
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a short term Operational Planning model for natural gas production systems
Aiche Journal, 2008Co-Authors: Ajay Selot, Loi Kwong Kuok, Mark Robinson, Thomas L Mason, Paul I. BartonAbstract:A short-term Operational Planning model for natural gas production systems can help identify a consistent Operational policy that satisfies contractual rules and customer specifications. Formulating and solving such a model poses challenges due to nonlinear pressure-flowrate relations, a multicommodity network and complex production-sharing contracts (PSC). A production allocation model is presented that can be viewed as a contractual model superimposed on an infrastructure model. The infrastructure model incorporates nonlinear pressure-flowrate relationships for wells and pipelines, multiple qualities of gas in the trunkline network and models of facilities. The contractual model is a mathematical representation of the PSC and associated Operational rules. The model features are inspired by the Sarawak Gas Production System (SGPS) in East Malaysia. A case study similar to the Sarawak Gas Production System (SGPS) is presented. The final formulation is a nonconvex mixed-integer nonlinear program and is solved with GAMS/BARON to global optimality. A hierarchical multiobjective Operational study is also presented. © 2007 American Institute of Chemical Engineers AIChE J, 2008
