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Reliability optimization and redundancy allocation for fire extinguisher drone using hybrid PSO-GWO

SOFT COMPUTING 2023年第20期27卷 14819-14833页

作者： Bhandari, Ashok Singh Kumar, Akshay Ram, Mangey Shri Guru Ram Rai Univ Sch Basic & Appl Sci Dept Math Dehra Dun India Graph Era Hill Univ Dept Math Dehra Dun Uttarakhand India Graph Era Univ Dept Math Comp Sci & Engn Dehra Dun Uttarakhand India

Reliability-redundancy allocation problem (RRAP) plays a vital role in reliability improvement and designing of systems which depend on the arrangement of components, reliability of the components, and redundancy allocation for the components. Higher reliability is the primary requisite for essential systems such as fire extinguisher drones (FEDs) which are very valuable for firefighters in tackling emergencies in non-reachable areas. In this work, a FED is considered with the aim of system designing for maximum reliability while considering the limited availability of resources such as volume, cost, and weight of the system. A total of five possible arrangements of the redundant components are investigated, and a mixed-integer nonlinear programming problem is solved for system reliability optimization. For optimization purposes, a recently developed metaheuristic hybrid particle swarm grey wolf optimizer (HPSGWO) is implemented. The HPSGWO is a powerful fusion of PSO's exploitation property and GWO's exploration property. Solving RRAP by using HPSGWO provides 99% reliability of the proposed FED under the limited availability of resources. To validate the superiority of the HPSGWO, a comparative study is explained.

关键词： Reliability-redundancy allocation problem Fire extinguisher drone Optimization mixed-integer nonlinear programming Hybrid PSO-GWO

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Classifier surrogates to ensure phase stability in optimisation-based design of solvent mixtures

DIGITAL CHEMICAL ENGINEERING

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DIGITAL CHEMICAL ENGINEERING 2025年 14卷

作者： Karia, Tanuj Chaparro, Gustavo Chachuat, Benoit Adjiman, Claire S. Imperial Coll London Inst Mol Sci & Engn Sargent Ctr Proc Syst Engn Dept Chem Engn London SW7 2AZ England

The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAMbD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAMbD optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the in silico design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.

关键词： Computer-aided mixture/blend design Phase stability mixed-integer nonlinear programming Surrogate-based optimisation Classifier Artificial neural network

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Hybrid Machine Learning-Mathematical programming Approach for Optimizing Gas Production and Water Management in Shale Gas Fields

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ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2023年第15期11卷 6043-6056页

作者： Lopez-Flores, Francisco Javier Lira-Barragan, Luis Fernando Rubio-Castro, Eusiel El-Halwagi, Mahmoud M. Ponce-Ortega, Jose Maria Univ Michoacana Chem Engn Dept Morelia 58060 Michoacan Mexico Univ Autonoma Sinaloa Chem & Biol Sci Dept Culiacan 80010 Sinaloa Mexico Texas A&M Univ Chem Engn Dept College Stn TX 77843 USA

This paper presents a novel mathematical program-ming approach that simultaneously incorporates a mixed-integer nonlinear programming formulation with machine learning models to determine the operating conditions, gas production, and optimal water management for the completion phase in shale gas fields. The dataset for the development of an artificial neural network model has been collected from the Eagle Ford Texas formation. The total cumulative gas production and flowback water generated in shale gas wells are selected as output variables. The mathematical optimization model considers machine learning models for each well, mass balances, treatment, storage, reuse, and disposal options as well as well location selection and associated costs and revenues from the sale of the shale gas produced. A case study has been used to illustrate the benefits of the proposed approach. The compromise solution offers a water consumption per produced energy of 6.71 L/GJ in addition to the fact that 27% of the total fracture water required can be obtained by reusing the flowback water with attractive economic indicators.

关键词： machine learning flowback water shale gas production hydraulic fracturing artificial neural network mixed-integer nonlinear programming

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A column generation algorithm for solving energy system planning problems

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OPTIMIZATION AND ENGINEERING 2023年第1期24卷 317-351页

作者： Muts, Pavlo Bruche, Stefan Nowak, Ivo Wu, Ouyang Hendrix, Eligius M. T. Tsatsaronis, George Hamburg Univ Appl Sci Hamburg Germany Tech Univ Berlin Berlin Germany Univ Malaga Malaga Spain

Energy system optimization models are typically large models which combine sub-models which range from linear to very nonlinear. Column generation (CG) is a classical tool to generate feasible solutions of sub-models, defining columns of global master problems, which are used to steer the search for a global solution. In this paper, we present a new inner approximation method for solving energy system MINLP models. The approach is based on combining CG and the Frank Wolfe algorithm for generating an inner approximation of a convex relaxation and a primal heuristic for computing solution candidates. The features of this approach are: (i) no global branch-and-bound tree is used, (ii) sub-problems can be solved in parallel to generate columns, which do not have to be optimal, nor become available at the same time to synchronize the solution, (iii) an arbitrary solver can be used to solve sub-models, (iv) the approach (and the implementation) is generic and can be used to solve other nonconvex MINLP models. We perform experiments with decentralized energy supply system models with more than 3000 variables. The numerical results show that the new decomposition method is able to compute high-quality solutions and has the potential to outperform state-of-the-art MINLP solvers.

关键词： Decomposition method Parallel computing Column generation Nonconvex optimization Global optimization mixed-integer nonlinear programming

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Optimization and analysis of three-part tariff pricing strategies

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OR SPECTRUM 2023年第4期45卷 1223-1262页

作者： Lin, Shi-Woei Merdikawati, Silvia Wu, Shan-Fei Yeh, Ruey-Huei Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sec 4Keelung Rd Taipei City 106335 Taiwan Natl Taiwan Univ Sci & Technol Artificial Intelligence Operat Management Res Ctr Taipei City 106335 Taiwan Inst Teknol Indonesia Dept Ind Engn Tangerang Selatan Banten Indonesia

Service providers usually offer different pricing schemes to consumers, which can simultaneously influence consumers' tariff choices and their usage of the service and thus greatly affect the profits of the service providers or the utilization of the service system. However, little is known about the optimal design and management of three-part tariff pricing schemes because the models are generally not analytically tractable since the arbitrary population of heterogeneous consumers can yield an extremely complex profit function with multiple local optima. This study investigates how to design three-part tariffs, which are pricing plans that are widely used in transportation or telecommunications industries, by formulating a mixed-integer nonlinear programming optimization model. In particular, numerical analyses using GAMS/BARON and metaheuristic approaches including genetic algorithm, particle swarm optimization algorithm, and sine cosine algorithm were conducted to derive the optimal three-part tariffs under specific conditions and to compare several common tariff structures (e.g., the menus of the three-part tariff, single three-part tariff, two-part tariff, and flat rate). This study successfully identified key factors affecting the performance of the different pricing structures. Guidelines for determining the best timing of the use of different pricing structures were also derived.

关键词： Three-part tariffs mixed-integer nonlinear programming Metaheuristics Price mechanism nonlinear pricing

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Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems

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DIGITAL CHEMICAL ENGINEERING 2025年 14卷 100218页

作者： Nikkhah, Hasan Aghayev, Zahir Shahbazi, Amir Charitopoulos, Vassilis M. Avraamidou, Styliani Beykal, Burcu Univ Connecticut Dept Chem & Biomol Engn Storrs CT 06269 USA Univ Connecticut Ctr Clean Energy Engn Storrs CT 06269 USA UCL Sargent Ctr Proc Syst Engn Dept Chem Engn Torrington Pl London WC1E 7JE England Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA

Planning and scheduling are crucial components of enterprise-wide optimization (EWO). For the successful execution of EWO, it is vital to view the enterprise operations as a holistic decision-making problem, composed of different interconnected elements or layers, to make the most efficient use of resources in process industries. Among different layers of the operating decisions, planning and scheduling are often treated sequentially, leading to impractical solutions. To tackle this problem, integrated approaches, such as bi-level programming are utilized to optimize these two layers simultaneously. Nonetheless, the bi-level optimization of such interdependent and holistic formulations is still difficult, particularly when dealing with mixed-integer nonlinear programming (MINLP) problems, due to a lack of effective algorithms. In this study, we employ the Data-driven Optimization of bi-level mixed-integer nonlinear problems (DOMINO) framework, a data- driven algorithm developed to handle single-leader single-follower bi-level mixed-integer problems, to solve single-leader multi-follower planning and scheduling problems subject to MINLP scheduling formulations. We apply DOMINO to the continuous production of multi-product methyl methacrylate polymerization process formulated as a Traveling Salesman Problem and demonstrate its capability in achieving near-optimal guaranteed feasible solutions. Building on this foundation, we extend this strategy to solve a high-dimensional and highly constrained nonlinear crude oil refinery operation problem that has not been previously tackled in this context. Our study further evaluates the efficacy of using local, NOMAD (nonlinear Optimization by Mesh Adaptive Direct Search), and a global data-driven optimizer, ARGONAUT (AlgoRithms for Global Optimization of coNstrAined grey-box compUTational), within the DOMINO framework and characterize their performance both in terms of solution quality and computational expense. The results ind

关键词： Data-driven optimization Integrated planning and scheduling Bi-level programming mixed-integer nonlinear programming

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Bi-Objective Job-Shop Scheduling Considering Human Fatigue in Cobotic Order Picking Systems: A Case of an Online Grocer 5

Bi-Objective Job-Shop Scheduling Considering Human Fatigue i...

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5th International Conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Vermin, Berry Lance Abbink, David Schulte, Frederik Delft Univ Technol Dept Cognit Robot Mekelweg 2 NL-2628 CD Delft Netherlands Dept Maritime & Transport Technol Mekelweg 2 NL-2628 CD Delft Netherlands

Increasing online retail has resulted in increased automation in order picking systems, leading to new challenges and opportunities in task scheduling. The job-shop scheduling problem is an optimization problem essential in such systems, but existing JSP literature often overlooks workplace fatigue, which harms employees' well-being and costs U.S. employers up to 127 billion annually. In this work, we propose fatigue consideration in the job-shop scheduling problem in a cobotic order picking system to mitigate its negative effects. We present a new bi-objective mixed integer nonlinear programming problem formulation that considers worker fatigue and productivity during schedule optimisation. To put the results of simulated optimisation in perspective, we experimentally validate the fatigue model and scheduling results in a real operation. The mathematical model finds solutions that conventional single-objective optimisation cannot, allowing fractional fatigue distribution improvements more than 4x larger than the decrease in productivity they require in 53% of the considered virtual cases. The experiments show that our predictive fatigue model has an average RMSE of 2.20 kcal/min in estimating energy expenditure rates compared to heart rate measurements. It also shows a low correlation, meaning it is unfit for application. On the other hand, fatigue-conscious schedules show no clear benefit regarding measured and perceived fatigue. However, the scheduling model could also use heart rate measurements that do not show these inaccuracies. Our study highlights the need to further develop and validate the mathematical formulation and fatigue model and extend to other human factors and indirect fatigue effects. (C) 2024 The Authors. Published by Elsevier B.V.

关键词： Order Picking Job-Shop Scheduling Problem Human Factors mixed-integer nonlinear programming Occupational Fatigue Cobotic Order Picking

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Optimal Sizing and Allocation of Distributed Energy Resources in Microgrids Considering Internal Network Reinforcements (Jul, 10.1007/s40313-022-00934-x, 2022)

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2023年第1期34卷 120-120页

作者： dos Santos, Kaio Vieira Higino Silva Santos, Luiza Banol Arias, Nataly Lopez, Juan Camilo Rider, Marcos J. da Silva, Luiz C. P. Univ Campinas UNICAMP Sch Elect & Comp Engn FEEC Dept Syst & Energy DSE Av Albert Einstein400 BR-13083852 Campinas Brazil Univ Twente Dept Elect Engn Math & Comp Sci EEMCS Enschede Netherlands

Microgrids have become valuable assets because they improve the reliability of consumers while integrating renewables via distributed energy resources (DERs). Thus, making them cost-efficient is essential to secure their proliferation. This paper proposes a new method for the optimal design of microgrids. The proposed two-stage method optimizes the size and the location of the DERs, i.e., the renewable energy sources (RESs), distributed generation (DG) units, and battery energy storage systems (BESSs). Furthermore, the overall operation of the microgrid is optimized using a stochastic scenario-based approach, considering grid-connected and unintentional islanded modes. The proposed method also considers internal network reinforcements. Thus, the first stage is an energy-based approach, formulated as a mixed-integer linear programming (MILP) problem, and it is used to size the DERs, whereas the second stage uses an optimal AC power flow (AC-OPF) to formulate a mixed-integer nonlinear programming (MINLP) model that allocates the DERs and selects the best conductor for each circuit. The multi-objective nature of the problem is addressed via Pareto optimization to analyze the trade-off between operational and capital costs. The MINLP model is linearized through piece-wise approximations and solved using commercial solvers. Furthermore, the impact of battery degradation is analyzed through a simple adaptation of the Stage 1 model. Results were obtained with data from the real university campus microgrid CampusGrid, located at the State University of Campinas (UNICAMP), in São Paulo, Brazil.

关键词： Distributed energy resources, Microgrids mixed-integer nonlinear programming Network reinforcements Optimal sizing and allocation of DERs

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Asynchronous optimization of part logistics routing problem

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JOURNAL OF GLOBAL OPTIMIZATION 2022年第4期82卷 803-834页

作者： Huang, Yaoting Chen, Boyu Lu, Wenlian Jin, Zhong-Xiao Zheng, Ren Fudan Univ Sch Math Sci Shanghai 200433 Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai Peoples R China Fudan Univ Shanghai Ctr Math Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Contemporary Appl Math Shanghai Peoples R China Fudan Univ Lab Math Nonlinear Sci Shanghai Peoples R China Fudan Univ Key Lab Computat Neurosci & Brain Inspired Intell Minist Educ Shanghai Peoples R China SAIC Motor Corp Ltd Artificial Intelligence Lab Shanghai Peoples R China SAIC Intelligent Technol Shanghai Corp Ltd Shanghai Peoples R China

The solution to the capacitated vehicle routing problem (CVRP) is vital for optimizing logistics. However, the transformation of real-world logistics problems into the CVRP involves diverse constraints, interactions between various routes, and a balance between optimization performance and computation load. In this study, we propose a systematic model originating from the part logistic routing problem (PLRP), which is a two-dimensional loading capacitated pickup-and-delivery problem that considers time windows, multiple uses of vehicles, queuing, transit, and heterogeneous vehicles. The newly introduced queuing and transit complicate the problem, and to the best of our knowledge, it cannot be solved using existing methods or the standard commercial optimizer. Hence, this problem has caused the existing research to develop, generalize, and extend into the two-dimensional CVRP (2L-CVRP). To solve this problem, we provide a framework that decouples the combination of 2L-CVRP and global optimization engineering and derives an efficient and realistic solver that integrates diverse types of intelligent algorithms. These algorithms include: (1) a heuristic algorithm for initializing feasible solutions by imitating manual planning, (2) asynchronous simulated annealing (SA) and Tabu search (TS) algorithms to accelerate the optimization of global routes based on novel bundling mechanics, (3) dynamic programming for routing, (4) heuristic algorithms for packing, (5) simulators to review associated time-related constraints, and (6) truck-saving processes to promote the optimal solution and reduce the number of trucks. Moreover, the performances of the SA and TS solver algorithms are compared in terms of various size scales of data to obtain an empirical recommendation for selection. The proposed model successfully established an intelligent management system that can provide systematic solutions for logistics planning, resulting in higher performance and lower costs compared to

关键词： Two-dimensional capacitated vehicle routing Logistics Asynchronous optimization mixed-integer nonlinear programming

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Iterative LP-Based Methods for the Multiperiod Optimal Electricity and Gas Flow Problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第1期37卷 153-166页

作者： Mhanna, Sleiman Saedi, Isam Mancarella, Pierluigi Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

In light of the increasing coupling between electricity and gas networks, this paper introduces two novel iterative methods for efficiently solving the multiperiod optimal electricity and gas flow (MOEGF) problem. The first is an iterative MILP-based method and the second is an iterative LP-based method with an elaborate procedure for ensuring an integral solution. The convergence of the two approaches is founded on two key features. The first is a penalty term with a single, automatically tuned, parameter for controlling the step size of the gas network iterates. The second is a sequence of supporting hyperplanes and halfspaces for controlling the convergence of the electricity network iterates. Moreover, the two proposed algorithms use as a warm start the solution from a novel polyhedral relaxation of the MOEGF problem, for a noticeable improvement in computation time as compared to a cold start. Unlike the first method, which invokes a branch-and-bound algorithm to find an integral solution, the second method implements an elaborate steering procedure that guides the continuous variables to take integral values at the solution. Numerical evaluation demonstrates that the two proposed methods can converge to high-quality feasible solutions in computation times at least two orders of magnitude faster than both a state-of-the-art nonlinear branch-and-bound (NLBB) MINLP solver and a mixed-integer convex programming (MICP) relaxation of the MOEGF problem. The experimental setup consists of five test cases, three of which involve the real electricity and gas transmission networks of the state of Victoria with actual linepack and demand profiles.

关键词： Manganese Pipelines programming Computational modeling Mathematical model Iterative methods Regulators Sequential linear programming polyhedral envelopes integrated electricity and gas systems piecewise linear approximations mixed-integer nonlinear programming mixed-integer second-order cone programming branch-and-bound

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