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Auto tuning for new energy dispatch problem: A case study

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2016年 54卷 501-506页

作者： Wang, Jue Liu, Chun Huang, Yuehui Chinese Acad Sci Comp Network Informat Ctr Supercomp Ctr Beijing 100190 Peoples R China China Elect Power Res Inst Renewable Energy Dept Beijing 100192 Peoples R China

The new energy dispatch problem has aroused more and more attention. In this paper, we investigate the problem of determining the optimal usage of generating power during a scheduling period. A set of MIP formulations are adopted for precise modeling of the variety of power systems (different power generation units) and the actual situation in china. Based on these formulations, we construct a new energy dispatch model which includes many MIP sub-problems. An auto-tuning MIP solver CMIP is given to effectively improve the performance of solving the proposed model. The CMIP focuses on optimizations for presolver, the LP solver for corresponding relaxation problem, and the primal heuristics. Actual predict data is used in performance experiments. Computational results conform to the viability of optimization. Our optimizations further reduce 27.6% of the average execution time compared to CPLEX. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Auto tuning mixed-integer programming Presolver Heuristics method New energy dispatch problem

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The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2012年第4期14卷 600-617页

作者： Ayvaci, Mehmet U. S. Alagoz, Oguzhan Burnside, Elizabeth S. Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA Univ Wisconsin Madison Dept Radiol Madison WI 53792 USA

We develop a finite-horizon discrete-time constrained Markov decision process (MDP) to model diagnostic decisions after mammography where we maximize the total expected quality-adjusted life years (QALYs) of a patient under resource constraints. We use clinical data to estimate the parameters of the MDP model and solve it as a mixed-integer program. By repeating optimization for a sequence of budget levels, we calculate incremental cost-effectiveness ratios attributable to consecutive levels of funding and compare actual clinical practice with optimal decisions. We prove that the optimal value function is concave in the allocated budget. Comparing to actual clinical practice, using optimal thresholds for decision making may result in approximately 22% cost savings without sacrificing QALYs. Our analysis indicates short-term follow-ups are the immediate target for elimination when budget becomes a concern. Policy change is more drastic in the older age group with the increasing budget, yet the gains in total expected QALYs related to larger budgets are predominantly seen in younger women along with modest gains for older women.

关键词： Markov decision processes linear programming mixed-integer programming constrained MDPs breast cancer diagnostic decisions cost-effectiveness medical decision making mammography service operations

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Theoretical framework for formulating MIP scheduling models with multiple and non-uniform discrete-time grids

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COMPUTERS & CHEMICAL ENGINEERING 2015年 72卷 233-254页

作者： Velez, Sara Maravelias, Christos T. Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA

We present a framework for the formulation of MIP scheduling models based on multiple and nonuniform discrete time grids. In a previous work we showed that it is possible to use different (possibly nonuniform) time grids for each task, unit, and material. Here, we generalize these ideas to account for general resources, and a range of processing characteristics such as limited intermediate storage and changeovers. Each resource has its own grid based on resource consumption and availability allowing resource constraints to be modeled more accurately without increasing the number of binary variables. We develop algorithms to define the unit-, task-, material-, and resource-specific grids directly from problem data. Importantly, we prove that the multi-grid formulation is able to find a schedule with the same optimal objective as the discrete-time single-grid model with an arbitrarily fine grid. The proposed framework leads to the formulation of models with reduced number of binary variables and constraints, which are able to find good solutions faster than existing models. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： mixed-integer programming Process optimization Supply chain management

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Modeling formulation and a new heuristic for the railroad blocking problem

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APPLIED MATHEMATICAL MODELLING 2018年 56卷 304-324页

作者： Hasany, Reza Mohammad Shafahi, Yousef Sharif Univ Technol Dept Civil Engn Azadi AvePOB 11155-8639 Tehran Iran

The railroad blocking problem is an important issue at the tactical level of railroad freight transportation. This problem consists of determining paths between the origins and destinations of each shipment to minimize the operating and user costs while satisfying the railroad supply and demand restrictions. A mixed-integer program (MIP) is developed to find the optimal paths, and a new heuristic is developed to solve the proposed model. This heuristic decomposes the model into two sub-problems of manageable size and then provides feasible solutions. We discuss the performance of the proposed heuristic for a set of instances with up to 90 stations. A comparison with the CPLEX MIP solver shows that the heuristic gives the exact solution for 10 out of 15 instances. For the remaining instances, the heuristic obtained solutions within a tolerance of 0.03-0.84%. Furthermore, compared with the CPLEX MIP solver, the heuristic reduced the run time by an average of 85% for all 15 instances. Finally, we present the computational results of the heuristic applied to Iranian railroads. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Railroad blocking problem Decomposition method mixed-integer programming Heuristic

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A continuous-time supply-driven inventory-constrained routing problem

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年第0期92卷 102151-000页

作者： Fokkema, Jan Eise Land, Martin J. Coelho, Leandro C. Wortmann, Hans Huitema, George B. Univ Groningen Fac Econ & Business Dept Operat POB 800 NL-9700 AV Groningen Netherlands Univ Laval Fac Adm Sci Operat & Decis Syst Dept Quebec City PQ Canada

We address an inventory routing problem (IRP) in which routing and inventory decisions are dictated by supply rather than demand. Moreover, inventory is held in containers that act as both a storage container and a movable transport unit. This problem emanates from logistics related to biogas transportation in which biogas is transported in containers from many suppliers to a single facility. We present a novel and compact formulation for the supply-driven IRP which addresses the routing decisions in continuous-time in which inventory levels within the containers are continuous. Valid inequalities are included and realistic instances are solved to optimality. For all experiments, we found that the total transportation time is minimized when the storage capacity at each supplier is larger than or equal to the vehicle capacity. These routes are characterized by tours in which mostly single suppliers are visited. In 95% of the instances, the average content level of the exchanged containers exceeded 99.6%. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Inventory routing Continuous time mixed-integer programming Supply-driven

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A Technology Selection and Operation (TSO) optimisation model for distributed energy systems: Mathematical formulation and case study

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APPLIED ENERGY 2016年第0期180卷 491-503页

作者： Alvarado, Dagoberto Cedillos Acha, Salvador Shah, Nilay Markides, Christos N. Imperial Coll London Dept Chem Engn London SW7 2AZ England

This paper presents a model which simultaneously optimises the selection and operation of technologies for distributed energy systems in buildings. The Technology Selection and Operation (TSO) model enables a new approach for the optimal selection and operation of energy system technologies that encompasses whole life costing, carbon emissions as well as real-time energy prices and demands;thus, providing a more comprehensive result than current methods. Utilizing historic metered energy demands, projected energy prices and a portfolio of available technologies, the mathematical model simultaneously solves for an optimal technology selection and operational strategy for a determined building based on a preferred objective: minimizing cost and/or minimizing carbon emissions. The TSO is a comprehensive and novel techno-economic model, capable of providing decision makers an optimal selection from a portfolio of available energy technologies. The current portfolio of available technologies is composed of various combined heat and power (CHP) and organic Rankine cycle (ORC) units. The TSO model framework is data-driven and therefore presents a high level of flexibility with respect to time granularity, period of analysis and the technology portfolio. A case study depicts the capabilities of the model;optimisation results under different temporal arrangements and technology options are showcased. Overall, the TSO model provides meaningful insights that allow stakeholders to make technology investment decisions with greater assurance. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： CHP Optimisation Distributed energy systems mixed-integer programming Modelling On-site generation MILP

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Efficient scheduling method of complex batch processes with general network structure via agent-based modeling

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AICHE JOURNAL 2013年第8期59卷 2884-2906页

作者： Chu, Yunfei Wassick, John M. You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA Dow Chem Co USA Midland MI 48674 USA

A novel efficient agent-based method for scheduling network batch processes in the process industry is proposed. The agent-based model is based on the resource-task network. To overcome the drawback of localized solutions found in conventional agent-based methods, a new scheduling algorithm is proposed. The algorithm predicts the objective function value by simulating another cloned agent-based model. Global information is obtained, and the solution quality is improved. The solution quality of this approach is validated by detailed comparisons with the mixed-integer programming (MIP) methods. A solution close to the optimal one can be found by the agent-based method with a much shorter computational time than the MIP methods. As a scheduling problem becomes increasingly complicated with increased scale, more specifications, and uncertainties, the advantages of the agent-based method become more evident. The proposed method is applied to simulated industrial problems where the MIP methods require excessive computational resources. (c) 2013 American Institute of Chemical Engineers AIChE J, 59: 2884-2906, 2013

关键词： agent-based modeling efficient scheduling batch processes general network structure mixed-integer programming

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Multi-objective optimization of complex scheduling problems in low-volume low-variety production systems

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2019年 208卷 1-16页

作者： Russell, Arya Taghipour, Sharareh Ryerson Univ 350 Victoria St Toronto ON M5B 2K3 Canada

In this paper, a new approach for solving scheduling problems in low-volume low-variety production systems is proposed. Products assembled in such production systems follow a pre-defined processing order through a series of unique work centers, each budgeted with multiple classifications of resources, responsible to complete a pre-defined statement of work, over the span of an imposed takt-time. Aircraft, heavy aero-structures, and heavy mining and military equipment are examples of products assembled in such production systems. Despite prominent scholarly advancements in sequencing and scheduling optimization of a wide range of production systems, limited research has been reported on mathematical programming approaches for scheduling optimization of activities in low-volume low-variety production systems. This paper fills the gap in the current literature, through the formulation of a set of multi-objective mixed-integer linear mathematical programming models, developed for solving discrete-time work center scheduling problems in low-volume low-variety production systems. Three mathematical models are proposed in this paper, two of which are formulated for scheduling optimization of activities within a work center, differentiated by their objectives and underlying assumptions, reflective of two distinct industrial approaches to scheduling. Additionally, an alternative optimization model is proposed for evaluating a work center's maximum capacity given the complete saturation of resources, recommended for capacity studies and early detection of bottlenecks. The models proposed in this paper are validated and verified for compatibility and reliability through a real-world case study with a global leader in the aerospace industry.

关键词： Low-volume low-variety production systems Large-scale optimization Scheduling optimization Multi-objective programming mixed-integer programming Aerospace assembly line

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Optimal assignment for the single-household shared autonomous vehicle problem

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2020年 141卷 98-115页

作者： Cokyasar, Taner Larson, Jeffrey Argonne Natl Lab Energy Syst Div Argonne IL 60439 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA

Autonomous vehicles have the potential to transform the way people are transported. While driverless technology may mean fewer vehicles are required to transport people to and from their daily activities, such changes may result in increased congestion or total miles traveled. In this study, we solve the single-household shared autonomous vehicle problem to identify cost-optimal routings of vehicles throughout the day. Such a tool will be useful for consumers seeking to minimize cost and for regulators seeking to understand and predict how people may behave in different scenarios. We provide a thorough literature review and construct a mixed-integer linear program to minimize the daily travel cost of a household attending a given set of activities. Since solution time is a determinant for applicability of such a model, we present the model in a component-wise fashion. This approach allows us to understand which features most affect the problem complexity and solution time. We note that modeling carpooling is the feature that most increases time to find an optimal solution, and we therefore propose a novel modeling technique for carpooling two people. We illustrate the performance of our model by comparing it with other models from the literature and note that our model can solve significantly larger problem instances and in a time that is short enough to facilitate real-time scheduling. We also highlight the utility of our model for regulators, who can use it to analyze quickly produced optimal routes under different cost/tax scenarios. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Autonomous vehicle routing mixed-integer programming Operational decision-making

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Optimizing multiship routing and scheduling with constraints on inventory levels in a Brazilian oil company

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2018年第4期25卷 1163-1198页

作者： Stanzani, Amelia de Lorena Pureza, Vitoria Morabito, Reinaldo Virginio da Silva, Bruno Jensen Yamashita, Denise Ribas, Paulo Cesar Univ Fed Sao Carlos Dept Prod Engn Via Washington LuizKm 235 Sao Carlos SP Brazil Petrobras SA Res & Dev Ctr Ilha Fundao Ave Horacio de Macedo 950 Rio De Janeiro RJ Brazil

This study addresses a real-life multiship routing and scheduling application with inventory constraints that arises in pickup and delivery operations of different types of crude oil from various offshore oil rigs (platforms) to coastal terminals. Oil transportation largely results from the need to maintain inventories at each supply point (platform) between minimum and maximum levels, considering production rates in these operational points, and to meet demands of different oils in the terminals within the planning time horizon. Routing and scheduling of the available fleet aims to obtain solutions of minimum total costs, subject to various constraints such as the maximum volume of cargo carried on each ship, simultaneous cargo unloading in some terminals, conditions that rule ship docking in offshore platforms and terminal berths, among others. In this research, we modify and extend inventory constrained maritime routing and scheduling models to appropriately represent the problem of a case study at a Brazilian company and to solve small-to-moderate instances based on real data. We also present a matheuristic to deal with larger problem instances. Solution evaluation by company experts indicates that the model and this hybrid heuristic properly represent the problem and highlights the potential of their application in practice.

关键词： multiship routing and scheduling inventory constrained maritime routing problems crude oil transportation mixed-integer programming matheuristic oil industry OR practice

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