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

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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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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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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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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Joint coded caching and BS sleeping strategy to reduce energy consumption in 6G edge networks

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INTERNET OF THINGS 2023年 24卷

作者： Yang, Liming Hu, Honglin Zhou, Ting Xu, Tianheng Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Shanghai Univ Sch Microelect Shanghai Peoples R China Shanghai Frontier Innovat Res Inst Shanghai Peoples R China

In the coming sixth-generation mobile communication era, the intensive deployment of Internet of Things (IoT) devices and cellular networks is an irresistible trend, leading to system energy consumption and network traffic increasing sharply. Fortunately, edge caching as a promising technology to reduce system energy consumption and transmission latency is attracting wide attention. Although simply deploying cache in edge network and merely shutting down the idle base stations (BSs) during the idle periods can save certain energy to a certain extent, in this case, the contents with important mission cached in idle BSs cannot be accessed by users that will affect users' experience. In this paper, we employ coded caching encoded by maximum distance separable (MDS) codes at the network edge, and we propose a joint coded caching and BS sleeping strategy, which utilizes the reconstruction feature of MDS codes to alleviate the impact of BS sleeping. Furthermore, the problem of minimizing energy consumption is studied, and we also design a discrete particle swarm optimization (DPSO) algorithm that is suitable to solve this mixed integer nonlinear programming problem. Simulation results reveal that energy consumption of the joint coded caching and BS sleeping strategy can be significantly decreased over 15.2% when compared with the current state-of-art strategy. Meanwhile, our proposed strategy can also improve the cache hit rate up to a maximum 11.1% compared with the existing strategies.

关键词： Coded caching Base station sleeping Energy consumption mixed integer nonlinear programming Discrete particle swarm optimization

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Decisions on sailing frequency and ship type in liner shipping with the consideration of carbon dioxide emissions

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REGIONAL STUDIES IN MARINE SCIENCE 2022年 52卷

作者： Wang, Chuanxu Yu, Shanshan Xu, Lang Shanghai Maritime Univ Sch Econ & Management Shanghai Peoples R China Shanghai Maritime Univ Coll Traff & Transportat Shanghai Peoples R China

To find the optimal sailing frequency and optimal ship type of container liner operation under low carbon environment, a mixed integer nonlinear programming model is proposed, in which the ship load capacity constraint is considered. The objective of the proposed model is to minimize voyage total cost consisting of carbon dioxide emissions cost, voyage shipping cost(fuel costs, port charges, operating costs) as well as shipper's inventory cost in a certain operation period. It is shown from theoretical analysis that the proposed model can be solved by Kuhn-Tucker condition and Enumeration method. A numerical example is given to demonstrate the effectiveness of the proposed model, and analyze the impacts of carbon tax levels, fuel prices, ship size and the sailing frequency on total cost and environmental cost. The results show that as the size of the ship increases, the ship total cost decreases first and then increases, the percentage of carbon emission cost in total ship cost increases. As the sailing frequency increases, the ship total cost decreases first and then increases, the percentage of carbon emission cost in total ship cost decreases. The results indicate that the shipping companies can improve economic performance and environment performance simultaneously by employing reasonable sailing frequency and ship type decisions.(C) 2022 Elsevier B.V. All rights reserved.

关键词： Container Low carbon mixed integer nonlinear programming Sailing frequency

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REACTIVE OPERATING THEATRE SCHEDULING

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Bulletin of the Australian Mathematical Society 2018年第3期98卷 520-521页

作者： BELINDA SPRATT School of Mathematical Sciences Queensland University of Technology 2 George St Brisbane 4000 Australia email b.spratt@qut.edu.au

The article highlights information on solution for several problems with operating theatre (OT) department in a hospital including OT planning problem, a dynamic OT planning problem and a real-time reactive OT scheduling problem. Topics discussed include implementation of mathematical techniques to reduce elective surgery waiting lists; innovative hyper and hybrid metaheuristic techniques in short amounts of computation time; and views on reducing the number of cancellations.

关键词： operating theatre planning and scheduling mixed integer nonlinear programming metaheuristics reactive robust

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Optimal inventory policies for finite horizon deterministic inventory models for non-instantaneous deteriorating items and permissible-delay in payment

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2023年第1期10卷 Article: 2235268页

作者： Aladwani, Rashid. M. Benkherouf, Lakdere Almathkour, Fatmah Kuwait Univ Coll Sci Dept Stat & Operat Res Safat Kuwait Kuwait Univ Coll Sci Dept Stat & Operat Res POB 5969 Safat 13060 Kuwait

This paper is concerned with the search for the optimal inventory policy for a finite horizon inventory model with time-varying demand, non-instantaneous deteriorating items, and permissible delay in payment. The optimal inventory policy consists of the determination of the number and timing of replenishment schedules that minimise some total inventory costs. The search for such policy is formulated as a mixed integer nonlinear programming problem (MINLP). It turns out that the non-instantaneous deterioration phenomenon coupled with permissible delay in payment introduce non-smoothness in the objective function of the MINLP. This leads to failure of direct applications of known solution methods to the MINLP. To circumvent this problem, a methodology is proposed for solving fully the MINLP problem. It is shown, through careful mathematical analysis, that earlier results on similar problems can be adapted to solve this problem. Conditions under which the solution of the MINLP is unique are identified. Moreover, convexity with respect to the number of replenishment orders is established. This makes the search for the optimal inventory policy handy. Numerical experiments are also conducted to test the applicability, to identify the key elements and to provide managerial insights to the model.

关键词： Inventory model finite time horizon non-instantaneous deteriorating items optimal schedule permissible delay in payment mixed integer nonlinear programming submodular function

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A hybrid imperialist competitive algorithm for solving economic lot and delivery scheduling problem in a four-lestage supply chain

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ADVANCES IN MECHANICAL ENGINEERING 2017年第2期9卷

作者： Kia, Hamidreza Ghodsypour, Seyed Hassan Davoudpour, Hamid Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

In this article, we study the economic lot and delivery scheduling problem for a four-stage supply chain that includes suppliers, fabricators, assemblers, and retailers. All of the parameters such as demand rate are deterministic and production setup times are sequence-dependent. The common cycle time and integer multipliers policies are adapted as replenishment policies for synchronization throughout the supply chain. A new mixed integer nonlinear programming model is developed for both policies, the objective of which is the minimization of inventory, transportation, and production setup costs. We propose a new hybrid algorithm including a modified imperialist competitive algorithm which is purposed to the assimilation policy of imperialist competitive algorithm and teaching learning-based optimization which is added to improve local search. A hybrid modified imperialist competitive algorithm and teaching learning-based optimization is applied to find a near-optimum solution of mixed integer nonlinear programming in large-sized problems. The results denoted that our proposed algorithm can solve different size of problem in reasonable time. This procedure showed its efficiency in medium-and large-sized problems as compared to imperialist competitive algorithm, modified imperialist competitive algorithm, and other methods reported in the literature.

关键词： Supply chain management mixed integer nonlinear programming scheduling coordination imperialist competitive algorithm teaching learning-based optimization

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