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Mathematical modeling and hybrid evolutionary algorithm to schedule flexible job shop with discrete operation sequence flexibility

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COMPUTERS & OPERATIONS RESEARCH 2025年 176卷

作者： Yuan, Shuai Zhu, Xiaomin Cai, Wei Gao, Jinsheng Zhang, Runtong Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing Peoples R China North China Elect Power Univ Sch Econ & Management Beijing Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing Peoples R China

In actual industrial production, several operations of a job may not have precedence relationships and can be placed at any point in the process route. However, traditional flexible job shop scheduling problems (FJSP) often assume that all operations of each job must be processed in strict linear order. Therefore, this research addresses the FJSP with discrete operation sequence flexibility (FJSPDS) with the objective of minimizing the makespan. Based on existing models, two novel mixed-integer linear programming (MILP) models are formulated by improving the description methods of variables and constraints, significantly enhancing the models' performance. Additionally, a hybrid evolutionary algorithm (HEA) is proposed to solve large-scale instances through the following three aspects. An improved encoding method is proposed, which makes the search space of the HEA and solution space of the problem more compatible and reduces the possibility of optimal solutions being missed. A special neighborhood structure is designed according to the characters of sequence-free operations, and an iterative local search method is introduced to improve the quality of the solution. A knowledge-driven reinitialization operator is developed, which generates new individuals based on the features of the historical elite population, guiding the evolution of populations, avoiding premature convergence while also avoiding falling into local optima. Finally, a total of 110 benchmark problem instances are utilized to verify the superior effectiveness of the MILP models and the HEA in solving FJSPDS.

关键词： Flexible job shop scheduling Mixed-integer linear programming model Discrete operation sequence flexibility hybrid evolutionary algorithm Knowledge-driven reinitialization Iterative local search

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hybrid evolutionary algorithm with Hermite radial basis function interpolants for computationally expensive adjoint solvers

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2008年第1期39卷 97-119页

作者： Ong, Y. S. Lum, K. Y. Nair, P. B. Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Natl Univ Singapore Temasek Labs Singapore 117508 Singapore Univ Southampton Sch Engn Sci Computat Engn & Design Grp Southampton SO17 1BJ Hants England

In this paper, we present an evolutionary algorithm hybridized with a gradient-based optimization technique in the spirit of Lamarckian learning for efficient design optimization. In order to expedite gradient search, we employ local surrogate models that approximate the outputs of a computationally expensive Euler solver. Our focus is on the case when an adjoint Euler solver is available for efficiently computing the sensitivities of the outputs with respect to the design variables. We propose the idea of using Hermite interpolation to construct gradient-enhanced radial basis function networks that incorporate sensitivity data provided by the adjoint Euler solver. Further, we conduct local search using a trust-region framework that interleaves gradient-enhanced surrogate models with the computationally expensive adjoint Euler solver. This ensures that the present hybrid evolutionary algorithm inherits the convergence properties of the classical trust-region approach. We present numerical results for airfoil aerodynamic design optimization problems to show that the proposed algorithm converges to good designs on a limited computational budget.

关键词： hybrid evolutionary algorithm Hermite radial basis function gradient-based approximation computationally expensive adjoint solver

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hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第9-10期36卷 959-968页

作者： Chen, Yu-Wang Lu, Yong-Zai Yang, Gen-Ke Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

This paper presents a hybrid evolutionary algorithm with marriage of genetic algorithm (GA) and extremal optimization (EO) for solving a class of production scheduling problems in manufacturing. The scheduling problem, which is derived from hot rolling production in steel industry, is characterized by two major requirements: (i) selecting a subset of orders from manufacturing orders to be processed;(ii) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties, earliness/tardiness (E/T) penalties, etc. A combinatorial optimization model is proposed to formulate it mathematically. For its NP-hard complexity, an effective hybrid evolutionary algorithm is developed to solve the scheduling problem through combining the population-based search capacity of GA and the fine-grained local search efficacy of EO. The experimental results with production scale data demonstrate that the proposed hybrid evolutionary algorithm can provide superior performances in scheduling quality and computation efficiency.

关键词： hybrid evolutionary algorithm genetic algorithm extremal optimization production scheduling

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hybrid evolutionary algorithm for microscrew thread parameter estimation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2010年第4期23卷 446-452页

作者： Makeyev, Oleksandr Sazonov, Edward Moklyachuk, Mikhail Lopez-Meyer, Paulo Clarkson Univ Dept Elect & Comp Engn Potsdam NY 13699 USA Taras Shevchenko Natl Univ Kyiv Dept Probabil Theory Stat & Actuarial Math UA-01601 Kiev Ukraine

In this paper we propose a novel approach to the problem of microscrew thread parameter estimation based on a hybrid evolutionary algorithm that combines a stochastic evolutionary algorithm with the deterministic inverse parabolic interpolation. The proposed method uses a machine vision system utilizing a single web camera. The hybrid evolutionary algorithm was tested on a specially created image database of microscrews. Experimental results prove speed and efficiency of the proposed method and its robustness to noise in the images. This method may be used in automated systems of real-time non-destructive quality control of microscrews and has potential for parameter estimation of different types of microparts. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： evolutionary algorithm hybrid evolutionary algorithm Inverse parabolic interpolation Machine vision Parameter estimation

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hybrid evolutionary algorithm for job scheduling under machine maintenance

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APPLIED SOFT COMPUTING 2013年第3期13卷 1440-1447页

作者： Sarker, Ruhul Omar, Mohd Hasan, S. M. Kamrul Essam, Daryl Univ New S Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Univ Malaya Inst Math Sci Kuala Lumpur 50603 Malaysia

The job scheduling problem (JSP) belongs to the well-known combinatorial optimization domain. After scheduling, if a machine maintenance issue affects the scheduled processing of jobs, the delivery of jobs must be delayed. In this paper, we have first proposed a hybrid evolutionary algorithm (HyEA) for solving JSPs. We have then analyzed the effect of machine maintenance, whether preventive or breakdown, on the job scheduling. For the breakdown maintenance case, it is required to revise the algorithm to incorporate a rescheduling option after the breakdown occurs. The algorithm has been tested by solving a number of benchmark problems and thence comparing them with the existing algorithms. The experimental results provide a better understanding of job scheduling and the necessary rescheduling operations under process interruption. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Job scheduling Machine maintenance hybrid evolutionary algorithm

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hybrid evolutionary algorithm based solution for Register Allocation for Embedded Systems

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JOURNAL OF COMPUTERS 2008年第6期3卷 59-65页

作者： Mahajan, Anjali Ali, M. S. G H Raisoni Coll Engn Nagpur Maharashtra India Prof Ram Meghe Inst Technol & Res Amravati India

Embedded systems have an ever-increasing need for optimizing compilers to produce high quality codes with a limited general purpose register set. Either memory or registers are used to store the results of computation of a program. As compared to memory, accessing a register is much faster, but they are scarce resources and have to be utilized very efficiently. The optimization goal is to hold as many live variables as possible in registers in order to avoid expensive memory accesses. We present a hybrid evolutionary algorithm for graph coloring register allocation problem based on a new crossover operator called crossover by conflict-free sets(CCS) and a new local search function.

关键词： compilers compiler optimization register allocation hybrid evolutionary algorithm embedded systems

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hybrid evolutionary algorithm applied to Automated Floor Plan Generation

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2019年第3期17卷 260-283页

作者： Nisztuk, Maciej Myszkowski, Pawel B. Wroclaw Univ Sci & Technol Fac Architecture Dept Hist Architecture Art & Technol Ul Boleslawa Prusa 53-55 PL-50317 Wroclaw Poland Wroclaw Univ Sci & Technol Fac Comp Sci & Management Dept Computat Intelligence Wroclaw Poland

The article presents the application of hybrid evolutionary and Greedy-based algorithms to the problem of Automated Floor Plan Generation. The described optimization issue is part of a wider domain of Computer-Aided Architectural Design. The article covers the extensive description of the representation domain model (architectural canonical guidelines, user design requirements and constraints) and the explanation of proposed approach: problem representation, genetic algorithm operators, and fitness function definition. The research experimental procedures are based on real-world data: the architectural design guidelines being the design constraints and five real-world functional programs introduced and proposed as benchmarks. The article summarizes the implementation of the proposed approach, compares the hybrid evolutionary algorithm experimental results with the Greedy-based algorithm, and suggests possible extensions and future research directions.

关键词： Computer-Aided Architectural Design optimization in CAAD Automated Floor Plan Generation hybrid evolutionary algorithm optimization benchmark

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hybrid evolutionary algorithm for integrated supply chain network design with assembly line balancing 15

Hybrid evolutionary algorithm for integrated supply chain ne...

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15th IEEE International Conference on Automation Science and Engineering (IEEE CASE)

作者： Sun, Bin-qi Wang, Ling Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781728103563

This paper addresses a supply chain network design problem simultaneously considering facility capacity selection and assembly line balancing. The studied supply chain is composed of manufacturers, assemblers and customers, and the objective of the problem is to minimize the total cost including shipping cost, facility fixed cost and station fixed cost. To solve the problem, we propose a hybrid evolutionary algorithm (HEA), which fuses genetic algorithm (GA) and model-based approaches. The genetic algorithm is developed as the search framework to determine which facilities to be established. Model-based approaches are employed in the decoding stage to determine the shipping amount between facilities and the task allocation in each assembler. Moreover, a knowledge-based heuristic is designed to generate a good initial population, and a problem-specific local intensification is designed to enhance the algorithm's exploitation ability. The proposed HEA is tested on randomly generated instances from small to large scales. The computational results demonstrate the effectiveness of the HEA and the self-designed heuristics.

关键词： supply chain network design assembly line balancing hybrid evolutionary algorithm

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hybrid evolutionary algorithm for Residential Demand Side Management with a Photovoltaic Panel and a Battery

Hybrid Evolutionary Algorithm for Residential Demand Side Ma...

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International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Garroussi, Zineb Ellaia, Rachid Talbi, El-Ghazali Lucas, Jean-Yves Mohamed V Univ Rabat Lab Study & Res Appl Math LERMA Mohammadia Sch Engn BP 765Ibn Sina Ave Rabat Morocco Inria Lille Nord Europe DOLPHIN Project Team F-59650 Villeneuve Dascq France Univ Lille 1 UMR CNRS 8022 LIFL F-59655 Villeneuve Dascq France Elect France SA EDF R&D 1 Ave Gen Gaulle F-92140 Clamart France

ISBN: (纸本)9781509065363

Residential demand side management (DSM) is one of the most challenging topics in smart grids. In this paper, a multiobjective model for the residential DSM over a 24-hour horizon is presented. This model consists of appliances, a battery and a photovoltaic panel. The resolution of this model is based on combining a multiobjective evolutionary algorithm (NSGA-II) and an exact solver (CPLEX). Solutions in this hybrid approach are incompletely represented, and optimally the exact solver determines the missing parts of the encoding. In our case, hybridization involves solving a MILP sub-problem by CPLEX to manage the battery and the photovoltaic panel constraints. Through case studies, It is shown that the coordination between the photovoltaic panel and the battery is effective to reduce the total electricity cost, the discomfort and the standard deviation of power consumed especially in summer conditions.

关键词： Residential demand side management multiobjective optimization hybrid evolutionary algorithm photovoltaic panel battery

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Multiobjective Optimization for Train Speed Trajectory in CTCS High-Speed Railway With hybrid evolutionary algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2015年第4期16卷 2215-2225页

作者： Wei ShangGuan Yan, Xi-Hui Cai, Bai-Gen Wang, Jian Beijing Jiaotong Univ Sch Elect & Informat Engn State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Engn Res Ctr EMC & GNSS Technol Rail Tran Beijing 100044 Peoples R China

A speed trajectory profile indicating the authorized train speed at each position can be used to guide the driver or the automatic train operation (ATO) system to operate the train more efficiently, which is the most important part of the Chinese Train Control System (CTCS) and will decide the safety and efficiency of train operation. The efforts produced by the train to follow the speed trajectory will directly affect the evaluation of train operation. This paper studies the optimization approach for the speed trajectory of high-speed train in a single section. First, we take the energy consumption as the measure of satisfaction of the railway company, and the trip time is being regarded as the passenger satisfaction criterion;then, we present optimal speed trajectory searching strategies under different track characteristics by dividing the section into some subsections according to different speed limitations. After that, we develop a multiobjective optimization model for the speed trajectory, which is subject to the constraints such as safety requirement, track profiles, passenger comfort, and the dynamic performance. For obtaining the Pareto frontier of train speed trajectory, which has equal satisfaction degree on all the objects, a hybrid evolutionary algorithm is designed and applied to solve the model based on the differential evolution and simulating annealing algorithms. By showing some numerical results of simulations, the efficiency of the proposed model and solution methodology is illustrated.

关键词： Chinese train control system (CTCS) energy efficient high-speed railway hybrid evolutionary algorithm multiobjective optimization train speed trajectory

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