Jiyao Gao - Academia.edu (original) (raw)
Papers by Jiyao Gao
Computer Aided Chemical Engineering, 2016
Abstract In this work, we address the life cycle optimization of a shale gas supply chain coverin... more Abstract In this work, we address the life cycle optimization of a shale gas supply chain covering the well-to-wire life cycle of shale gas-generated electricity. A non-cooperative supply chain with multiple players is considered. Following the Stackelberg game, the power generation sector is identified as the leader in this game that takes action first and cares about both its own cost and the greenhouse gas emissions across the product life cycle. After the observation of power plants’ decisions, the follower as shale gas producer takes actions correspondingly to optimize its own profit. Both players need to make both design and operational decisions. The resulting problem is formulated as a multiobjective mixed-integer bilevel linear programming problem, which cannot be solved using any off-the-shelf solvers directly. Based on a case study of the Marcellus shale play, the levelized cost of electricity ranges from 75/MWhto75/MWh to 75/MWhto133/MWh, and the corresponding unit greenhouse gas (GHG) emissions range from 111 to 469 kg CO 2 -eq/MWh. The application of carbon capture and storage implies significant impacts on both economic and environmental performance. The non-cooperative supply chain has a 9% higher upstream GHG emissions compared with the cooperative one.
Chemical Engineering Transactions, 2017
In this paper, we propose a novel modeling framework to account for multiple strategic decisions ... more In this paper, we propose a novel modeling framework to account for multiple strategic decisions in the shale gas supply chain, including the well drilling schedule, water management, installation of gathering pipelines, allocation, capacity and design of processing plants, and selection of processing contract. We incorporate a leader-follower game theory and the life cycle optimization method into a holistic modeling framework, which enables us to simultaneously address the trade-offs between conflicting objectives as well as the interactions between different players. In this supply chain, the shale gas producer is identified as the leader. Due to the key role of leader in a game, the producer not only enjoys the priority to make decisions first, but senses the responsibility to mitigate the life cycle greenhouse gas (GHG) emission embedded in the final product. The midstream player shale gas processor is identified as the follower, who will take actions rationally according to th...
Chemical Engineering Transactions, 2017
This work addresses the life cycle economic and environmental optimisation of a supply chain netw... more This work addresses the life cycle economic and environmental optimisation of a supply chain network considering both design and operational decisions under uncertainty. A general modelling framework is proposed that integrates the functional-unit-based life cycle optimisation methodology and the two-stage stochastic programming approach for sustainable supply chain optimisation under uncertainty. a stochastic mixed-integer linear fractional programming (SMILFP) model is developed to tackle multiple uncertainties regarding feedstock supply uncertainty and product demand uncertainty. To address the computational challenge of solving large-scale SMILFP problems, an efficient solution algorithm that takes advantage of the efficiency of parametric algorithm and the decomposition-based multi-cut L-shaped method is used. A case study based on a spatially explicit model for the county-level hydrocarbon biofuel supply chain is presented in Illinois to demonstrate the applicability of the pr...
Chemical engineering transactions, 2018
In this paper, we address the sustainable design and operations of shale gas supply chains by pro... more In this paper, we address the sustainable design and operations of shale gas supply chains by proposing an integrated hybrid life cycle optimization (LCO) modelling framework. Unlike the traditional process-based LCO that suffers system truncation, the integrated hybrid LCO supplements the truncated system with a comprehensive economic input-output system. Meanwhile, the integrated hybrid LCO retains the precision in modelling major unit processes within the well-to-wire system boundary compared with the economic input-output-based LCO models. With the help of the integrated hybrid LCO framework, we can automatically identify the optimal sustainable alternatives in the design and operations of shale gas supply chains. To demonstrate the applicability, we present a case study of a well-to-wire shale gas supply chain located in the UK. According to the optimization results, the lowest levelized cost of electricity generated from shale gas is £51.8 /MWh, and the optimal life cycle GHG ...
Chemical engineering transactions, 2015
In this work, we address the functional-unit based life cycle optimisation of the design and oper... more In this work, we address the functional-unit based life cycle optimisation of the design and operations of shale gas supply chain networks considering both the economic and environmental performance. The system covers the well-to-wire life cycle of electricity generated from shale gas, consisting of a number of stages including freshwater acquisition, shale well drilling, fracking, and completion, shale gas production, wastewater management, shale gas processing, electricity generation as well as transportation and storage. The resulting problem is formulated as a multi-objective non-convex mixedinteger nonlinear programming (MINLP) problem. By solving this problem with a global optimisation algorithm, we obtain the Pareto-optimal frontier, which reveals the trade-off between the economic and environmental objectives. A case study based on the Marcellus shale play shows that the environmental impact of shale gas generated electricity ranges from 454 to 508 kg CO2eq/MWh, and the leve...
Computer Aided Chemical Engineering, 2016
Abstract This paper addresses the risk management for optimal design and operations of shale gas ... more Abstract This paper addresses the risk management for optimal design and operations of shale gas supply chains under uncertainty of estimated ultimate recovery (EUR). A multiobjective two-stage stochastic mixed-integer linear programming model is proposed to optimize the expected total cost and the financial risk. The latter criterion is measured by conditional value-at-risk (CVaR) and downside risk. In this model, both design and planning decisions are considered with respect to shale well drilling, shale gas production, processing, multiple end-uses, and transportation. In order to solve this computationally challenging problem, we integrate both the sample average approximation method and the L-shaped method. The proposed model and solution methods are illustrated through a case study based on the Marcellus shale play. According to the optimization results, the stochastic model provides a feasible design for all the scenarios with the lowest expected total cost. Moreover, after risk management, total expected cost increases but the risk of high-cost scenarios is reduced effectively, and the CVaR management shows its advantage over downside risk management in this specific case study.
Chemical engineering transactions, 2015
This paper addresses the optimal design and operations of water supply chain networks for shale g... more This paper addresses the optimal design and operations of water supply chain networks for shale gas production. We develop a mixed-integer linear fractional programming (MILFP) model with the objective to maximize profit per unit freshwater consumption, such that both economic performance and water-use efficiency are optimized. The model simultaneously accounts for the design and operational decisions for freshwater source selection, multiple transportation modes, and water management options. Water management options include underground injection, commercial centralized wastewater treatment (CWT), and different onsite treatment technologies. To globally optimize the resulting MILFP problem efficiently, we present three tailored solution algorithms: a parametric approach, a reformulation-linearization method, and a novel Branch-and-Bound & Charnes-Cooper transformation method. The proposed models and algorithms are illustrated through one case study based on Marcellus shale play, in...
Chemical engineering transactions, 2018
Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing disti... more Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing distinct objectives. However, most existing supply chain studies rely on centralized models and neglect the uncertain behaviors of stakeholders in the decision-making process. In this work, a novel game theory based stochastic model is proposed that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme. The aim is to address the optimization problem of decentralized supply chains considering multiple stakeholders under uncertainty. The resulting model is formulated as a stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover’s linearization method. To illustrate the applicability of proposed modeling framework, a case study of a large-scale shale gas supply chain is presented, which demonstrates the advan...
In this paper, we propose a non-cooperative life cycle optimization (LCO) framework chain based o... more In this paper, we propose a non-cooperative life cycle optimization (LCO) framework chain based on both the leader-follower Stackelberg game and life cycle optimisation framework. Instead of assuming a single stakeholder as in existing LCO models, we specifically consider the conflicting objectives regarding economic and environmental performance for different stakeholders. An application on the sustainable design and optimization of shale gas supply chains is considered, where the shale gas producer is identified as the leader optimizing its net present value (NPV) and the life cycle greenhouse gas (GHG) emissions. The shale gas processor is identified as the follower that takes actions after the leader to maximize its own NPV. The resulting problem is formulated as a multiobjective mixed-integer bilevel linear program and solved by an efficient projection-based reformulation and decomposition algorithm. Based on a case study of the Marcellus shale play, the non-cooperative model n...
Chemical engineering transactions, 2019
This work develops a novel dynamic material flow analysis (MFA)-based optimisation modelling fram... more This work develops a novel dynamic material flow analysis (MFA)-based optimisation modelling framework for sustainable design of shale gas energy systems. This dynamic MFA-based framework provides high-fidelity modelling of complex material flow networks with recycling options, and it enables detailed accounting of time-dependent life cycle material flow profiles. Moreover, by incorporating a dimension of resource sustainability, the proposed modelling framework facilitates the sustainable supply chain design and operations with a more comprehensive perspective. The resulting optimisation problem is formulated as a mixed-integer linear fractional program and solved by an efficient parametric algorithm. To illustrate the applicability of the proposed modelling framework and solution algorithm, a case study of Marcellus shale gas supply chain is presented. The optimisation results help to identify clear trade-offs among economic, environmental, and resource performances in the shale g...
Modular manufacturing is identified with great potential in the exploitation of shale gas resourc... more Modular manufacturing is identified with great potential in the exploitation of shale gas resource. In this work, we propose a novel mixed-integer nonlinear fractional programming model, where design and operational decisions regarding both the conventional processing plants and modular manufacturing devices are considered. The allocation, capacity selection, installment, moving, and salvage decisions of modular manufacturing devices are modeled with corresponding integer variables and logic constraints. To systematically evaluate the full spectrum of environmental impacts, an endpoint-oriented life cycle optimization framework is applied that accounts for up to 18 midpoint impact categories and three endpoint impact categories. Total environmental impact scores are obtained to evaluate the comprehensive life cycle environmental impacts of shale gas supply chains. A tailored global optimization algorithm is also presented to efficiently solve the resulting computationally challengin...
Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing disti... more Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing distinct objectives. However, most existing supply chain studies rely on centralized models and neglect the uncertain behaviors of stakeholders in the decision-making process. In this work, we propose a novel game theory based stochastic model that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme. The aim is to address the optimization problem of decentralized supply chains considering multiple stakeholders under uncertainty. The resulting model is formulated as a stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover’s linearization method. To illustrate the applicability of proposed modeling framework, a case study of a largescale shale gas supply chain is presented, which demonstrates the advanta...
Computers & Chemical Engineering, 2019
Abstract This paper investigates the influences of uncertainty in multi-stakeholder non-cooperati... more Abstract This paper investigates the influences of uncertainty in multi-stakeholder non-cooperative supply chains, and the corresponding optimal strategies based on game theory to hedge against uncertainty in design and operations of such decentralized supply chains. We propose a novel game-theory-based stochastic model that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme for optimizing decentralized supply chains under uncertainty. Both the leader's and the followers’ uncertainties are considered, which directly affect their design and operational decisions regarding infrastructure development, contracts selection, price setting, production profile, transportation planning, and inventory management. The resulting model is formulated as a two-stage stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover's linearization method. An illustrative example of flight booking under uncertain flight delays and a large-scale application to shale gas supply chains are presented to demonstrate the applicability of the proposed framework.
Journal of Global Optimization, 2018
We propose an extended variant of the reformulation and decomposition algorithm for solving a spe... more We propose an extended variant of the reformulation and decomposition algorithm for solving a special class of mixed-integer bilevel linear programs (MIBLPs) where continuous and integer variables are involved in both upper-and lower-level problems. In particular, we consider MIBLPs with upper-level constraints that involve lower-level variables. We assume that the inducible region is nonempty and all variables are bounded. By using the reformulation and decomposition scheme, an MIBLP is first converted into its equivalent single-level formulation, then computed by a column-and-constraint generation based decomposition algorithm. The solution procedure is enhanced by a projection strategy that does not require the relatively complete response property. To ensure its performance, we prove that our new method converges to the global optimal solution in a finite number of iterations. A large-scale computational study on random instances and instances of hierarchical supply chain planning are presented to demonstrate the effectiveness of the algorithm.
ACS Sustainable Chemistry & Engineering, 2017
This paper analyzes the life cycle environmental impacts of shale gas by using an integrated hybr... more This paper analyzes the life cycle environmental impacts of shale gas by using an integrated hybrid life cycle analysis (LCA) and optimization approach. Unlike the process-based LCA that suffers system truncation, the integrated hybrid LCA supplements the truncated system with a comprehensive economic input-output system. Compared with the economic input–output-based LCA that loses accuracy from process aggregation, the integrated hybrid LCA retains the precision in modeling major unit processes within the well-to-wire system boundary. Three environmental categories, namely, life cycle greenhouse gas emissions, water consumption, and energy consumption, are considered. Based on this integrated hybrid LCA framework, we further developed an integrated hybrid life cycle optimization model, which enables automatic identification of sustainable alternatives in the design and operations of shale gas supply chains. We applied the model to a well-to-wire shale gas supply chain in the UK to illustrate the applicab...
ACS Sustainable Chemistry & Engineering, 2018
We propose a novel modeling framework integrating the dynamic material flow analysis (MFA) approa... more We propose a novel modeling framework integrating the dynamic material flow analysis (MFA) approach with life cycle optimization (LCO) methodology for sustainable design of energy systems. This dynamic MFA-based LCO framework provides high-fidelity modeling of complex material flow networks with recycling options, and it enables detailed accounting of time-dependent life cycle material flow profiles. The decisions regarding input, output, and stock of materials are seamlessly linked to their environmental impacts for rigorous quantification of environmental consequences. Moreover, by incorporating an additional dimension of resource sustainability, the proposed modeling framework facilitates the sustainable supply chain design and operations with a more comprehensive perspective. The resulting optimization problem is formulated as a mixed-integer linear fractional program and solved by an efficient parametric algorithm. To illustrate the applicability of the proposed modeling framework and solution algori...
AIChE Journal, 2018
This paper aims to leverage the big data in shale gas industry for better decision making in opti... more This paper aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second-stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models are investigated as well.
ACS Sustainable Chemistry & Engineering, 2017
Modular manufacturing is identified to have great potential in the exploitation of shale gas reso... more Modular manufacturing is identified to have great potential in the exploitation of shale gas resource. In this work, we propose a novel mixed-integer nonlinear fractional programming model to investigate the economic and environmental implications of incorporating modular manufacturing into well-to-wire shale gas supply chains. Both design and operational decisions regarding modular manufacturing are considered, including modular plant allocation, capacity selection, installment planning, moving scheduling, and salvage operation, as well as other decisions for shale gas supply chain design and operations, such as drilling schedule, water management, and pipeline network construction. To systematically evaluate the full spectrum of environmental impacts, an endpoint-oriented life cycle optimization framework is applied that accounts for up to 18 midpoint impact categories and three endpoint impact categories. Total environmental impact scores are obtained to evaluate the comprehensive life cycle environmen...
Computer Aided Chemical Engineering, 2016
Abstract In this work, we address the life cycle optimization of a shale gas supply chain coverin... more Abstract In this work, we address the life cycle optimization of a shale gas supply chain covering the well-to-wire life cycle of shale gas-generated electricity. A non-cooperative supply chain with multiple players is considered. Following the Stackelberg game, the power generation sector is identified as the leader in this game that takes action first and cares about both its own cost and the greenhouse gas emissions across the product life cycle. After the observation of power plants’ decisions, the follower as shale gas producer takes actions correspondingly to optimize its own profit. Both players need to make both design and operational decisions. The resulting problem is formulated as a multiobjective mixed-integer bilevel linear programming problem, which cannot be solved using any off-the-shelf solvers directly. Based on a case study of the Marcellus shale play, the levelized cost of electricity ranges from 75/MWhto75/MWh to 75/MWhto133/MWh, and the corresponding unit greenhouse gas (GHG) emissions range from 111 to 469 kg CO 2 -eq/MWh. The application of carbon capture and storage implies significant impacts on both economic and environmental performance. The non-cooperative supply chain has a 9% higher upstream GHG emissions compared with the cooperative one.
Chemical Engineering Transactions, 2017
In this paper, we propose a novel modeling framework to account for multiple strategic decisions ... more In this paper, we propose a novel modeling framework to account for multiple strategic decisions in the shale gas supply chain, including the well drilling schedule, water management, installation of gathering pipelines, allocation, capacity and design of processing plants, and selection of processing contract. We incorporate a leader-follower game theory and the life cycle optimization method into a holistic modeling framework, which enables us to simultaneously address the trade-offs between conflicting objectives as well as the interactions between different players. In this supply chain, the shale gas producer is identified as the leader. Due to the key role of leader in a game, the producer not only enjoys the priority to make decisions first, but senses the responsibility to mitigate the life cycle greenhouse gas (GHG) emission embedded in the final product. The midstream player shale gas processor is identified as the follower, who will take actions rationally according to th...
Chemical Engineering Transactions, 2017
This work addresses the life cycle economic and environmental optimisation of a supply chain netw... more This work addresses the life cycle economic and environmental optimisation of a supply chain network considering both design and operational decisions under uncertainty. A general modelling framework is proposed that integrates the functional-unit-based life cycle optimisation methodology and the two-stage stochastic programming approach for sustainable supply chain optimisation under uncertainty. a stochastic mixed-integer linear fractional programming (SMILFP) model is developed to tackle multiple uncertainties regarding feedstock supply uncertainty and product demand uncertainty. To address the computational challenge of solving large-scale SMILFP problems, an efficient solution algorithm that takes advantage of the efficiency of parametric algorithm and the decomposition-based multi-cut L-shaped method is used. A case study based on a spatially explicit model for the county-level hydrocarbon biofuel supply chain is presented in Illinois to demonstrate the applicability of the pr...
Chemical engineering transactions, 2018
In this paper, we address the sustainable design and operations of shale gas supply chains by pro... more In this paper, we address the sustainable design and operations of shale gas supply chains by proposing an integrated hybrid life cycle optimization (LCO) modelling framework. Unlike the traditional process-based LCO that suffers system truncation, the integrated hybrid LCO supplements the truncated system with a comprehensive economic input-output system. Meanwhile, the integrated hybrid LCO retains the precision in modelling major unit processes within the well-to-wire system boundary compared with the economic input-output-based LCO models. With the help of the integrated hybrid LCO framework, we can automatically identify the optimal sustainable alternatives in the design and operations of shale gas supply chains. To demonstrate the applicability, we present a case study of a well-to-wire shale gas supply chain located in the UK. According to the optimization results, the lowest levelized cost of electricity generated from shale gas is £51.8 /MWh, and the optimal life cycle GHG ...
Chemical engineering transactions, 2015
In this work, we address the functional-unit based life cycle optimisation of the design and oper... more In this work, we address the functional-unit based life cycle optimisation of the design and operations of shale gas supply chain networks considering both the economic and environmental performance. The system covers the well-to-wire life cycle of electricity generated from shale gas, consisting of a number of stages including freshwater acquisition, shale well drilling, fracking, and completion, shale gas production, wastewater management, shale gas processing, electricity generation as well as transportation and storage. The resulting problem is formulated as a multi-objective non-convex mixedinteger nonlinear programming (MINLP) problem. By solving this problem with a global optimisation algorithm, we obtain the Pareto-optimal frontier, which reveals the trade-off between the economic and environmental objectives. A case study based on the Marcellus shale play shows that the environmental impact of shale gas generated electricity ranges from 454 to 508 kg CO2eq/MWh, and the leve...
Computer Aided Chemical Engineering, 2016
Abstract This paper addresses the risk management for optimal design and operations of shale gas ... more Abstract This paper addresses the risk management for optimal design and operations of shale gas supply chains under uncertainty of estimated ultimate recovery (EUR). A multiobjective two-stage stochastic mixed-integer linear programming model is proposed to optimize the expected total cost and the financial risk. The latter criterion is measured by conditional value-at-risk (CVaR) and downside risk. In this model, both design and planning decisions are considered with respect to shale well drilling, shale gas production, processing, multiple end-uses, and transportation. In order to solve this computationally challenging problem, we integrate both the sample average approximation method and the L-shaped method. The proposed model and solution methods are illustrated through a case study based on the Marcellus shale play. According to the optimization results, the stochastic model provides a feasible design for all the scenarios with the lowest expected total cost. Moreover, after risk management, total expected cost increases but the risk of high-cost scenarios is reduced effectively, and the CVaR management shows its advantage over downside risk management in this specific case study.
Chemical engineering transactions, 2015
This paper addresses the optimal design and operations of water supply chain networks for shale g... more This paper addresses the optimal design and operations of water supply chain networks for shale gas production. We develop a mixed-integer linear fractional programming (MILFP) model with the objective to maximize profit per unit freshwater consumption, such that both economic performance and water-use efficiency are optimized. The model simultaneously accounts for the design and operational decisions for freshwater source selection, multiple transportation modes, and water management options. Water management options include underground injection, commercial centralized wastewater treatment (CWT), and different onsite treatment technologies. To globally optimize the resulting MILFP problem efficiently, we present three tailored solution algorithms: a parametric approach, a reformulation-linearization method, and a novel Branch-and-Bound & Charnes-Cooper transformation method. The proposed models and algorithms are illustrated through one case study based on Marcellus shale play, in...
Chemical engineering transactions, 2018
Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing disti... more Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing distinct objectives. However, most existing supply chain studies rely on centralized models and neglect the uncertain behaviors of stakeholders in the decision-making process. In this work, a novel game theory based stochastic model is proposed that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme. The aim is to address the optimization problem of decentralized supply chains considering multiple stakeholders under uncertainty. The resulting model is formulated as a stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover’s linearization method. To illustrate the applicability of proposed modeling framework, a case study of a large-scale shale gas supply chain is presented, which demonstrates the advan...
In this paper, we propose a non-cooperative life cycle optimization (LCO) framework chain based o... more In this paper, we propose a non-cooperative life cycle optimization (LCO) framework chain based on both the leader-follower Stackelberg game and life cycle optimisation framework. Instead of assuming a single stakeholder as in existing LCO models, we specifically consider the conflicting objectives regarding economic and environmental performance for different stakeholders. An application on the sustainable design and optimization of shale gas supply chains is considered, where the shale gas producer is identified as the leader optimizing its net present value (NPV) and the life cycle greenhouse gas (GHG) emissions. The shale gas processor is identified as the follower that takes actions after the leader to maximize its own NPV. The resulting problem is formulated as a multiobjective mixed-integer bilevel linear program and solved by an efficient projection-based reformulation and decomposition algorithm. Based on a case study of the Marcellus shale play, the non-cooperative model n...
Chemical engineering transactions, 2019
This work develops a novel dynamic material flow analysis (MFA)-based optimisation modelling fram... more This work develops a novel dynamic material flow analysis (MFA)-based optimisation modelling framework for sustainable design of shale gas energy systems. This dynamic MFA-based framework provides high-fidelity modelling of complex material flow networks with recycling options, and it enables detailed accounting of time-dependent life cycle material flow profiles. Moreover, by incorporating a dimension of resource sustainability, the proposed modelling framework facilitates the sustainable supply chain design and operations with a more comprehensive perspective. The resulting optimisation problem is formulated as a mixed-integer linear fractional program and solved by an efficient parametric algorithm. To illustrate the applicability of the proposed modelling framework and solution algorithm, a case study of Marcellus shale gas supply chain is presented. The optimisation results help to identify clear trade-offs among economic, environmental, and resource performances in the shale g...
Modular manufacturing is identified with great potential in the exploitation of shale gas resourc... more Modular manufacturing is identified with great potential in the exploitation of shale gas resource. In this work, we propose a novel mixed-integer nonlinear fractional programming model, where design and operational decisions regarding both the conventional processing plants and modular manufacturing devices are considered. The allocation, capacity selection, installment, moving, and salvage decisions of modular manufacturing devices are modeled with corresponding integer variables and logic constraints. To systematically evaluate the full spectrum of environmental impacts, an endpoint-oriented life cycle optimization framework is applied that accounts for up to 18 midpoint impact categories and three endpoint impact categories. Total environmental impact scores are obtained to evaluate the comprehensive life cycle environmental impacts of shale gas supply chains. A tailored global optimization algorithm is also presented to efficiently solve the resulting computationally challengin...
Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing disti... more Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing distinct objectives. However, most existing supply chain studies rely on centralized models and neglect the uncertain behaviors of stakeholders in the decision-making process. In this work, we propose a novel game theory based stochastic model that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme. The aim is to address the optimization problem of decentralized supply chains considering multiple stakeholders under uncertainty. The resulting model is formulated as a stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover’s linearization method. To illustrate the applicability of proposed modeling framework, a case study of a largescale shale gas supply chain is presented, which demonstrates the advanta...
Computers & Chemical Engineering, 2019
Abstract This paper investigates the influences of uncertainty in multi-stakeholder non-cooperati... more Abstract This paper investigates the influences of uncertainty in multi-stakeholder non-cooperative supply chains, and the corresponding optimal strategies based on game theory to hedge against uncertainty in design and operations of such decentralized supply chains. We propose a novel game-theory-based stochastic model that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme for optimizing decentralized supply chains under uncertainty. Both the leader's and the followers’ uncertainties are considered, which directly affect their design and operational decisions regarding infrastructure development, contracts selection, price setting, production profile, transportation planning, and inventory management. The resulting model is formulated as a two-stage stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover's linearization method. An illustrative example of flight booking under uncertain flight delays and a large-scale application to shale gas supply chains are presented to demonstrate the applicability of the proposed framework.
Journal of Global Optimization, 2018
We propose an extended variant of the reformulation and decomposition algorithm for solving a spe... more We propose an extended variant of the reformulation and decomposition algorithm for solving a special class of mixed-integer bilevel linear programs (MIBLPs) where continuous and integer variables are involved in both upper-and lower-level problems. In particular, we consider MIBLPs with upper-level constraints that involve lower-level variables. We assume that the inducible region is nonempty and all variables are bounded. By using the reformulation and decomposition scheme, an MIBLP is first converted into its equivalent single-level formulation, then computed by a column-and-constraint generation based decomposition algorithm. The solution procedure is enhanced by a projection strategy that does not require the relatively complete response property. To ensure its performance, we prove that our new method converges to the global optimal solution in a finite number of iterations. A large-scale computational study on random instances and instances of hierarchical supply chain planning are presented to demonstrate the effectiveness of the algorithm.
ACS Sustainable Chemistry & Engineering, 2017
This paper analyzes the life cycle environmental impacts of shale gas by using an integrated hybr... more This paper analyzes the life cycle environmental impacts of shale gas by using an integrated hybrid life cycle analysis (LCA) and optimization approach. Unlike the process-based LCA that suffers system truncation, the integrated hybrid LCA supplements the truncated system with a comprehensive economic input-output system. Compared with the economic input–output-based LCA that loses accuracy from process aggregation, the integrated hybrid LCA retains the precision in modeling major unit processes within the well-to-wire system boundary. Three environmental categories, namely, life cycle greenhouse gas emissions, water consumption, and energy consumption, are considered. Based on this integrated hybrid LCA framework, we further developed an integrated hybrid life cycle optimization model, which enables automatic identification of sustainable alternatives in the design and operations of shale gas supply chains. We applied the model to a well-to-wire shale gas supply chain in the UK to illustrate the applicab...
ACS Sustainable Chemistry & Engineering, 2018
We propose a novel modeling framework integrating the dynamic material flow analysis (MFA) approa... more We propose a novel modeling framework integrating the dynamic material flow analysis (MFA) approach with life cycle optimization (LCO) methodology for sustainable design of energy systems. This dynamic MFA-based LCO framework provides high-fidelity modeling of complex material flow networks with recycling options, and it enables detailed accounting of time-dependent life cycle material flow profiles. The decisions regarding input, output, and stock of materials are seamlessly linked to their environmental impacts for rigorous quantification of environmental consequences. Moreover, by incorporating an additional dimension of resource sustainability, the proposed modeling framework facilitates the sustainable supply chain design and operations with a more comprehensive perspective. The resulting optimization problem is formulated as a mixed-integer linear fractional program and solved by an efficient parametric algorithm. To illustrate the applicability of the proposed modeling framework and solution algori...
AIChE Journal, 2018
This paper aims to leverage the big data in shale gas industry for better decision making in opti... more This paper aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second-stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models are investigated as well.
ACS Sustainable Chemistry & Engineering, 2017
Modular manufacturing is identified to have great potential in the exploitation of shale gas reso... more Modular manufacturing is identified to have great potential in the exploitation of shale gas resource. In this work, we propose a novel mixed-integer nonlinear fractional programming model to investigate the economic and environmental implications of incorporating modular manufacturing into well-to-wire shale gas supply chains. Both design and operational decisions regarding modular manufacturing are considered, including modular plant allocation, capacity selection, installment planning, moving scheduling, and salvage operation, as well as other decisions for shale gas supply chain design and operations, such as drilling schedule, water management, and pipeline network construction. To systematically evaluate the full spectrum of environmental impacts, an endpoint-oriented life cycle optimization framework is applied that accounts for up to 18 midpoint impact categories and three endpoint impact categories. Total environmental impact scores are obtained to evaluate the comprehensive life cycle environmen...