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Facility Location-Network Design Problem: Reliability and Investment Budget Constraint

JOURNAL OF URBAN PLANNING AND DEVELOPMENT 2014年第3期140卷

作者： Shishebori, Davood Jabalameli, Mohammad Saeed Jabbarzadeh, Armin Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

The simultaneous consideration of combined facility location and network design is an attractive empirical topic for locating public and private facilities and for reducing transportation costs. Moreover, applying a reliable facility location problem is one of the most effective ways to protect against occasional failures of the system, because of considering unexpected failures and possible customer reassignment when locating facilities. Accordingly, this study concerns a reliable combined facility location-network design problem regarding the investment budget constraint and proposes a mixed integer nonlinear programming formulation to model this constraint. The proposed model is linearized by suitable techniques. Also, a practical case study subject to sustainable design in urban health care development is presented in detail to illustrate the application of the proposed mathematical model. Finally, a sensitivity analysis is conducted to provide insights into the behavior of the proposed model in response to the changes of key parameters of the problem. (C) 2014 American Society of Civil Engineers.

关键词： Facility location Network design Urban transportation planning Facility reliability Investment budget constraint Health care Case study mixed integer nonlinear programming

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Near-optimal time series sampling based on the reduced Hessian

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AICHE JOURNAL 2020年第7期66卷

作者： Chen, Weifeng Biegler, Lorenz T. Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China Carnegie Mellon Univ Dept Chem Engn Ctr Adv Proc Decis Making 5000 Forbes Ave Pittsburgh PA 15213 USA

A model-based experimental design strategy is developed to select a minimal set of measured points from a time series of experiments. Based on reduced Hessian information embedded within a mixed integer nonlinear program, an optimal time series is determined that leads to well-fitted models, with ratios of parameter standard deviation to estimated value within acceptable bounds. Demonstrated on three examples, the proposed approach is effective and efficient especially for time-consuming and expensive measurements.

关键词： experimental design mixed integer nonlinear programming nonlinear optimization parameter estimation

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Mathematical programming Methods for Pressure Management in Water Distribution Systems

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Procedia Engineering 2015年 119卷 937-946页

作者： Filippo Pecci Edo Abraham Ivan Stoianov InfraSense Labs Department of Civil and Environmental Engineering Imperial College London South Kensington Campus London SW7 2AZ UK.

In this paper, we survey mathematical programming methods for the management of pressure in water distribution systems through optimal placement and operation of control valves. The optimization framework addresses the minimization of average zone pressure under multiple demand scenarios, enforcing hydraulic equations as nonlinear constraints. Binary variables are used to model the placement of valves. The derived nonlinear optimization problem is a non-convex MINLP. We implement and evaluate a direct MINLP solver and two different reformulation methods for MINLPs that solve sequences of regular NLPs. Moreover, we investigate the solutions under different design and operation loading conditions.

关键词： Water distribution systems valve placement mathematical optimization mixed integer nonlinear programming pressure management.

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A numerical study of MIDACO on 100 MINLP benchmarks

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OPTIMIZATION 2012年第7期61卷 873-900页

作者： Schlueter, Martin Gerdts, Matthias Rueckmann, Jan-J. Univ Birmingham Theoret & Computat Optimizat Grp Birmingham B15 2TT W Midlands England Univ Bundeswehr Muenchen Inst Math & Rechneranwendung D-85577 Neubiberg Muenchen Germany

This article presents a numerical study on MIDACO, a new global optimization software for mixed integer non-linear programming (MINLP) based on ant colony optimization and the oracle penalty method. Extensive and rigorous numerical tests on a set of 100 non-convex MINLP benchmark problems from the open literature are performed. Results obtained by MIDACO are directly compared to results by a recent study of state-of-the-art deterministic MINLP software on the same test set. Further comparisons with an established MINLP software is undertaken in addition. This study shows that MIDACO is not only competitive to the established MINLP software, but can even outperform those in terms of the number of global optimal solutions found. Moreover, the parallelization capabilities of MIDACO enable it to be even competitive to deterministic software regarding the amount of (serial processed) function evaluation, while the black-box capabilities of MIDACO offer an intriguing new robustness for MINLP.

关键词： mixed integer nonlinear programming ant colony optimization oracle penalty method parallel computing MIDACO MISQP BONMIN COUENNE

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Optimal design-for-control of self-cleaning water distribution networks using a convex multi-start algorithm

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WATER RESEARCH 2023年 231卷 119602页

作者： Jenks, Bradley Pecci, Filippo Stoianov, Ivan Imperial Coll London Dept Civil & Environm Engn London SW7 2BB England

The provision of self-cleaning velocities has been shown to reduce the risk of discolouration in water distri-bution networks (WDNs). Despite these findings, control implementations continue to be focused primarily on pressure and leakage management. This paper considers the control of diurnal flow velocities to maximize the self-cleaning capacity (SCC) of WDNs. We formulate a new optimal design-for-control problem where locations and operational settings of pressure control and automatic flushing valves are jointly optimized. The problem formulation includes a nonconvex objective function, nonconvex hydraulic conservation law constraints, and binary variables for modelling valve placement, resulting in a nonconvex mixed integer nonlinear programming (MINLP) optimization problem. Considering the challenges with solving nonconvex MINLP problems, we propose a heuristic algorithm which combines convex relaxations (with domain reduction), a randomization technique, and a multi-start strategy to compute feasible solutions. We evaluate the proposed algorithm on case study networks with varying size and degrees of complexity, including a large-scale operational network in the UK. The convex multi-start algorithm is shown to be a more robust solution method compared to an off-the-shelf genetic algorithm, finding good-quality feasible solutions to all design-for-control numerical experiments. Moreover, we demonstrate the implemented multi-start strategy to be a fast and scalable method for computing feasible solutions to the nonlinear SCC control problem. The proposed method extends the control capabilities and benefits of dynamically adaptive networks to improve water quality in WDNs.

关键词： Water quality Discolouration Self-cleaning capacity Design-for-control mixed integer nonlinear programming Convex optimization

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Optimizing the multi-product, multi-constraint, bi-objective newsboy problem with discount by a hybrid method of goal programming and genetic algorithm

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ENGINEERING OPTIMIZATION 2009年第5期41卷 437-457页

作者： Taleizadeh, Ata Allah Niaki, Seyed Taghi Akhavan Hoseini, Vahid Sharif Univ Technol Dept Ind Engn Tehran Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Islamic Azad Univ Dept Informat Technol Engn Qazvin Iran

This article points out the real-world prevalence of the multiple-product, multiple-constraint newsboy problem with two objectivesthe 'newsstand problem'in which there are total and incremental discounts on purchasing prices. The constraints are the warehouse capacity and the batch forms of the order placements. The first objective of this problem is to find the order quantities that maximize expected profit, and the second objective is maximizing the service rate. It is assumed that the holding and shortage costs, modelled by a quadratic function, occur at the end of the period. Moreover, the decision variables are integer. A formulation of the problem is presented and shown to be an integer nonlinear programming model. Finally, an efficient hybrid algorithm is provided to solve the model and the results are illustrated with a numerical example.

关键词： inventory control newsboy problem mixed integer nonlinear programming goal programming genetic algorithm

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Identification of best sets of actions in Influence Nets

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International Journal of Hybrid Intelligent Systems 2008年第1期5卷 19-29页

作者： Haider, Sajjad Zaidi, Abbas K. Levis, Alexander H. System Architectures Lab George Mason University Virginia 22030 USA

This paper presents a heuristic approach to solve the problem of best set of actions determination in Influence Nets. Influence Nets are a special instance of Bayesian Networks that model uncertain situations by connecting a set of desired effects to a set of actionable events through chains of probabilistic cause and effect relationships. Once an Influence Net is specified, a system analyst is often interested in identifying the set of action which has the highest probability of achieving a desired effect. The existing techniques to solve this problem, such as sensitivity analysis and exhaustive search, have limitations of their own. The proposed algorithm, named SAF, attempts to overcome these limitations. The paper also shows that the problem of best set of actions determination can be formulated as an instance of mixed integer Non Linear programming (MINLP). An empirical study is presented that compares the performance of sensitivity analysis, SAF, and MINLP.

关键词： Bayesian Networks sensitivity analysis Influence Nets probabilistic reasoning combinatorial optimization mixed integer nonlinear programming

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Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem

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Modern Supply Chain Research and Applications 2020年第3期2卷 161-177页

作者： Ashish Dwivedi Ajay Jha Dhirendra Prajapati Nenavath Sreenu Saurabh Pratap Indian Institute of Technology Delhi New Delhi India Department of Transportation Management University of Petroleum and Energy Studies Dehradun India Mechanical Engineering IIITDM Jabalpur Jabalpur India Management Studies MANIT Bhopal India

Purpose Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain. Design/methodology/approach A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set. Findings The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time. Research limitations/implications In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries. Originality/value The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.

关键词： Agro-supply chain Sustainability mixed integer nonlinear programming Meta-heuristics

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Superstructure optimization of biodiesel production from microalgal biomass

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IFAC Proceedings Volumes 2013年第32期46卷 111-116页

作者： Muhammad Rizwan Jay H Lee Rafiqul Gani Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea (Tel: 0082-42-350-3966 Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea CAPEC Department of Chemical and Biochemical Engineering. Technical University of Denmark Dk-2800 Kgs Lyngby Denmark

In this study, we propose a mixed integer nonlinear programming (MINLP) model for superstructure based optimization of biodiesel production from microalgal biomass. The proposed superstructure includes a number of major processing steps for the production of biodiesel from microalgal biomass, such as harvesting of microalgal biomass, pretreatments including drying and cell disruption of harvested biomass, lipid extraction, transesterification, and post-transesterfication purification. The proposed model is used to find the optimal processing pathway among the large number of potential pathways that exist for the production of biodiesel from microalgae. The proposed methodology is tested by implementing on a specific case study. The MINLP model is implemented and solved in GAMS using a database built in Excel. The results from the optimization are analyzed and their significances are discussed.

关键词： Biodiesel microalgae superstructure optimization mixed integer nonlinear programming biorefinery

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Hybrid Genetic Algorithms and Heuristics for nonlinear Short-Term Hydropower Optimization: A Comparative Analysis

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Procedia Computer Science 2024年 246卷 282-291页

作者： M. Jafari Aminabadi S. Séguin I. Fofana Department of applied sciences Université du Québec à Chicoutimi Saguenay (Qc) Canada G7H 2B1 GERAD Montréal (Qc) Canada H3T 1J4 Department of computer science and mathematics Université du Québec à Chicoutimi Saguenay (Qc) Canada G7H 2B1 Canada Research Chair tier 1 in Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT)

In this paper, a mixed integer nonlinear programming (MINLP) for the short-term hydropower optimization problem considering operational constraints such as demand and startup costs, is presented. Since solving the MINLP is complicated and, in many cases, impossible, three methods are proposed based on reducing the complexity, which is hybridized with the exact solver. Method A, a binary genetic algorithm; method B, an iterative heuristic method; and method C, using the iterative heuristic method in the genetic algorithm. Based on computational results in a case study, method B converges to a solution very quickly and with few iterations, whereas methods A and C perform more efficiently. A comparison between methods A and C indicates that method C not only reduces the computational burden for convergence but also yields better results. The proposed methods are evaluated by comparing them with optimal solutions. The results indicate that the proposed methods are highly effective in achieving favorable results.

关键词： nonlinear Short-term Hydropower Optimization mixed integer nonlinear programming Genetic algorithm Heuristic algorithm Meta-heuristic algorithm

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