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Spatially Explicit Multiobjective Optimization for the Strategic Design of First and Second Generation Biorefineries Including Carbon and Water Footprints

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2013年第22期52卷 7170-7180页

作者： Bernardi, Andrea Giarola, Sara Bezzo, Fabrizio Univ Padua Dept Ind Engn CAPE Lab Comp Aided Proc Engn Lab I-35131 Padua Italy Univ London Imperial Coll Sci Technol & Med CPSE Ctr Proc Syst Engn London SW7 2AZ England

Biofuels production has been promoted in the attempt to address global warming and oil dependence concerns. However, the environmental impact of biofuels is a very complex issue and cannot be represents by GHG (greenhouse gas) emissions only (carbon footprint). In particular, water consumption (water footprint) has been recognized as a key issue in renewable fuels production. This paper proposes a multiobjective mixed integer Liner programming modeling framework to optimize the environmental (i.e., the carbon and water footprints) and economic performances of bioethanol supply chains. Multiechelon, multiperiod, and spatially explicit features are embedded within the formulation to steer decisions and investments through a global approach. The strategic design and planning of corn- and stover-based bioethanol production networks is taken into account. A case study is presented referring to the emerging Italian ethanol production. Results show the effectiveness of mathematical programming-based tools to provide decision makers with a quantitative analysis assessing the economic and environmental performances of different design configurations.

关键词： ECOLOGICAL impact STRATEGIC planning GREENHOUSE gases WATER consumption mixed integer linear programming DECISION making

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Optimization model for sustainable materials selection using objective and subjective factors

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MATERIALS & DESIGN 2013年 46卷 310-321页

作者： Florez, Laura Castro-Lacouture, Daniel Georgia Inst Technol Sch Bldg Construct Atlanta GA 30332 USA

Materials selection is an area where designers can have a significant impact on the sustainable performance of a building. Objective factors such as cost constraints, design considerations, and environmental requirements can play a role in the selection of materials. However, there may be subjective factors that could also impact the selection and affect the achievement of sustainability goals. In order to help decision makers with the selection of appropriate materials, this study proposes a mixed integer optimization model that considers objective and subjective factors. Design, budget, and the number of points achieved under the Leadership in Energy and Environmental Design (LEED) account for objective factors while subjective factors comprehend user-based perceptions. An instrument further validated through a factor analysis approach is developed to measure perceptions of product sustainability in an attempt to capture subjective factors. To illustrate how objective and subjective factors influence decision making, this paper presents two cases of a building in which materials are selected using the optimization model proposed. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Selection for material properties Environmental performance mixed integer linear programming Building design practices Sustainability perceptions

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Global optimization of bilinear programs with a multiparametric disaggregation technique

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JOURNAL OF GLOBAL OPTIMIZATION 2013年第4期57卷 1039-1063页

作者： Kolodziej, Scott Castro, Pedro M. Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Unidade Modelacaoe Otimizacao Sistemas Lab Nacl Energia & Geol P-1649038 Lisbon Portugal

In this paper, we present the derivation of the multiparametric disaggregation technique (MDT) by Teles et al. (J. Glob. Optim., 2011) for solving nonconvex bilinear programs. Both upper and lower bounding formulations corresponding to mixed-integer linear programs are derived using disjunctive programming and exact linearizations, and incorporated into two global optimization algorithms that are used to solve bilinear programming problems. The relaxation derived using the MDT is shown to scale much more favorably than the relaxation that relies on piecewise McCormick envelopes, yielding smaller mixed-integer problems and faster solution times for similar optimality gaps. The proposed relaxation also compares well with general global optimization solvers on large problems.

关键词： Global optimization mixed integer linear programming mixed integer nonlinear programming Quadratic optimization Disjunctive programming

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Optimal source-sink matching in carbon capture and storage systems with time, injection rate, and capacity constraints

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2013年第2期32卷 411-416页

作者： Tan, Raymond R. Aviso, Kathleen B. Bandyopadhyay, Santanu Ng, Denny K. S. De La Salle Univ Ctr Engn & Sustainable Dev Res Dept Chem Engn Manila 1004 Philippines Indian Inst Technol Dept Energy Sci & Engn Bombay 400076 Maharashtra India

Carbon capture and storage (CCS) involves capturing relatively pure carbon dioxide (CO2) from gaseous combustion products and storing it in various reservoirs. In this work, a multiperiod mixed integer linear programming model focusing primarily on physical and temporal considerations of CO2 sourcesink matching is proposed. CO2 sources are assumed to be characterized by variable flow rates and fixed operating lives;on the other hand, CO2 sinks are characterized by finite injection rate and storage capacity limits, as well as earliest time of availability. The proposed approach takes into account important temporal issues that may be encountered in planning the CCS system, particularly when the operating lives of sources and sinks do not completely overlap. Two illustrative case studies are then solved to illustrate the use of the model to realistic CCS planning problems. (c) 2012 American Institute of Chemical Engineers Environ Prog, 32: 411-416, 2013

关键词： Carbon capture and storage sourcesink matching mixed integer linear programming

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Minimising makespan heuristics for scheduling a single batch machine processing machine with non-identical job sizes

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第12期51卷 3488-3500页

作者： Lee, Yoon Ho Lee, Young Hoon Yonsei Univ Dept Informat & Ind Engn Seoul 120749 South Korea

In this paper, the problem of minimising maximum completion time on a single batch processing machine is studied. A batch processing is performed on a machine which can simultaneously process several jobs as a batch. The processing time of a batch is determined by the longest processing time of jobs in the batch. The batch processing machine problem is encountered in many manufacturing systems such as burn-in operations in the semiconductor industry and heat treatment operations in the metalworking industries. Heuristics are developed by iterative decomposition of a mixed integer programming model, modified from the successive knapsack problem by Ghazvini and Dupont (1998, Minimising mean flow times criteria on a single batch processing machine with non-identical jobs sizes. International Journal of Production Economics 55: 273-280) and the waste of batch clustering algorithm by Chen, Du, and Huang (2011, Scheduling a batch processing machine with non-identical job sizes: a clustering perspective. International Journal of Production Research 49 (19): 5755-5778). Experimental results show that the suggested heuristics produce high-quality solutions comparable to those of previous heuristics in a reasonable computation time.

关键词： scheduling batch scheduling mixed integer linear programming

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The heterogeneous multi-factory production network scheduling with adaptive communication policy and parallel machine

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INFORMATION SCIENCES 2013年 219卷 181-196页

作者： Behnamian, J. Ghomi, S. M. T. Fatemi Amirkabir Univ Technol Dept Ind Engn Tehran *** Iran

Traditionally centralized manufacturing planning, scheduling, and control mechanisms are being found to be insufficiently flexible to respond to highly dynamic variations in the market requirements. In order to be competitive in today's rapidly changing business world, organizations have shifted from a centralized to a decentralized structure in many areas of decision making. Distributed scheduling is an approach that enables local decision makers to create schedules that consider local objectives and constraints within the boundaries of the overall system objectives. In this paper, we assumed that production takes place in several factories, which may be geographically distributed in different locations, in order to take advantage from the trend of globalization. In this approach, the factories that are available to process the jobs have different speeds in which each factory has parallel identical machine. The optimization criterion is the minimization of the maximum completion time or makespan among the factories. After proposing mixed integer linear programming model for the problem, we developed a heuristic and genetic algorithm. For the proposed genetic algorithm, at first, to represent the solutions, we suggested a new encoding scheme, and then proposed a local search based on the theorem developed in the paper. Finally, we compare the obtained solutions using the lower bound developed in this paper. The results show the proposed algorithms to be very efficient for different structures of instances. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Production network scheduling Distributed system Heterogeneous multi-factory mixed integer linear programming Lower bound Genetic algorithm

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A note on integer-valued radial model in DEA

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第1期66卷 199-200页

作者： Khezrimotlagh, Dariush Salleh, Shaharuddin Mohsenpour, Zahra Univ Teknol Malaysia Fac Sci Dept Math Johor Baharu 81310 Malaysia

This note shows that the input targets of proposed model by Kuosmanen and Kazemi Matin (2009) (http://***/10.1016/***.2007.09.040 and http://***/10.1016/***.2008.11.002) may not be less than the input targets of proposed model by Lozano and Villa (2006) (http://***/10.1016/***.2005.02.031). (C) 2013 Elsevier Ltd. All rights reserved.

关键词： DEA mixed integer linear programming integer values

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Formulating and solving a multi-mode resource-collaboration and constrained scheduling problem (MRCCSP)

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ANNALS OF OPERATIONS RESEARCH 2013年第1期206卷 311-339页

作者： Pinto, Gaby Ben-Dov, Yariv T. Rabinowitz, Gad Jerusalem Coll Engn JCE Dept Ind Engn & Management IL-91035 Jerusalem Israel Ben Gurion Univ BGU Negev Dept Ind Engn & Management IL-84105 Beer Sheva Israel

The main motivation of this study is to provide, for the first time, a formulation and solution for a class of production scheduling problems (as in cluster tools) characterized mainly by resource collaboration to perform an operation and while allowing batches and considering alternative production methods. We develop a formulation for the new problem and term it a multiple mode per operation, resource collaboration, and constrained scheduling problem (MRCCSP). Some of the important new characteristics we consider are: multiple products (families);multiple orders (jobs) per family;precedence restrictions among the operations that constitute a job;alternative modes for the performance of an operation (each of which needs a set of collaborating resources) may be defined;complementary and exclusive restrictions between operation-modes;batch production is allowed;and setup times may depend on sequence and batch-size. The objective of the MRCCSP is to minimize makespan. We formulate the MRCCSP as a mixed integer linear programming model, and acknowledging the considerable size of the monolithic formulation required, we prescribe a specific method to achieve size reduction. Finally, a customized branch and bound algorithm for optimally solving this problem is proposed and examined experimentally.

关键词： Resource-sharing and scheduling problem Resource constrained scheduling problem mixed integer linear programming Branch and bound algorithm

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Speed-up Benders decomposition using maximum density cut (MDC) generation

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ANNALS OF OPERATIONS RESEARCH 2013年第1期210卷 101-123页

作者： Saharidis, Georgios K. D. Ierapetritou, Marianthi G. Univ Thessaly Dept Mech Engn Volos 38334 Volos Greece Rutgers State Univ Dept Chem & Biochem Engn Piscataway NJ 08854 USA

The classical implementation of Benders decomposition in some cases results in low density Benders cuts. Covering Cut Bundle (CCB) generation addresses this issue with a novel way generating a bundle of cuts which could cover more decision variables of the Benders master problem than the classical Benders cut. Our motivation to improve further CCB generation led to a new cut generation strategy. This strategy is referred to as the Maximum Density Cut (MDC) generation strategy. MDC is based on the observation that in some cases CCB generation is computational expensive to cover all decision variables of the master problem than to cover part of them. Thus MDC strategy addresses this issue by generating the cut that involves the rest of the decision variables of the master problem which are not covered in the Benders cut and/or in the CCB. MDC strategy can be applied as a complimentary step to the CCB generation as well as a standalone strategy. In this work the approach is applied to two case studies: the scheduling of crude oil and the scheduling of multi-product, multi-purpose batch plants. In both cases, MDC strategy significant decreases the number of iterations of the Benders decomposition algorithm, leading to improved CPU solution times.

关键词： Benders decomposition mixed integer linear programming Multi-generation of cuts Covering cut bundle generation (CCB) Active constraints

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Operation Planning of Electrical Distribution Systems Using a mixed integer linear Model

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2013年第5期24卷 668-679页

作者： Goncalves, Rogerio R. Alves, Raiane Piacente Franco, John F. Rider, Marcos J. Mato Grosso State Univ UNEMAT Math Dept Sinop Mato Grosso Brazil Paulista State Univ UNESP Elect Engn Dept Ilha Solteira SP Brazil

This article presents a mixed integer linear programming model for the problem of operation planning of an electrical distribution system. The model defines the most appropriate adjustment of a set of control variables to minimize active energy losses of the system. The generation of active and reactive powers of distributed generators, the number of modules in operation of capacitor banks, and the number of tap steps for the voltage regulators are considered as the control variables. In the proposed formulation, the steady-state operation of the electrical distribution network is modeled mathematically, using linear expressions. The use of a mixed-integer linear model guarantees convergence to optimality using existing optimization software. The model was implemented in the mathematical modeling language AMPL and solved using the commercial solver CPLEX. A test system of 42 nodes was used to show the accuracy of the mathematical model as well as the efficiency of the proposed solution technique.

关键词： Capacitor banks Distributed generation Electrical distribution system mixed integer linear programming Operation planning Voltage regulators

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