Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

SOLVING FLEXIBLE JOB SHOP SCHEDULING PROBLEM BY GENETIC ALGORITHM

The job shop scheduli ng problem has been studied for decades and known as an NP-hard problem. The fl exible job shop scheduling problem is a generalization of the classical job sche duling problem that allows an operation to be processed on one machine out of a set of machines. The problem is to assign each operation to a machine and find a sequence for the operations on the machine in order that the maximal completion time of all operations is minimized. A genetic algorithm is used to solve the f lexible job shop scheduling problem. A novel gene coding method aiming at job sh op problem is introduced which is intuitive and does not need repairing process to validate the gene. Computer simulations are carried out and the results show the effectiveness of the proposed algorithm.

多功能机床环境下的Job Shop问题研究

本文引入工序机的概念描述加工系统的资源 ,建立了面向多功能加工机床的JobShop作业计划模型 ,用遗传算法对所建的模型进行优化。在遗传算法优化搜索的基础上 ,利用工件、工序机和实际机床之间的动态调度使作业计划更趋合理。最后给出数值试验结果。

考虑有限缓存区的Job Shop加工与搬运集成调度

在Job Shop环境下,以最小化最大完工时间为目标,考虑了搬运设备与有限缓存区对加工过程的影响,建立了带有限缓存区的Job Shop加工与搬运集成调度模型,并设计了改进的人工蜂群优化算法求解此问题。首先,在算法中引入了引领蜂和跟随蜂角色互换的机制,可更好的兼顾全局广泛寻优和局部精确寻优。其次,基于问题的特殊性,工序既是加工任务也是搬运任务,所以在编码方式上采取基于工序编码,便于算法运行过程中解码计算。然后,在解码过程中,为提高算法运行效率,设计了如何确定解码加工任务和搬运任务的启发式信息。最后,通过标准测例实验比较,给出了本文G-ABC算法种群规模的建议取值范围,并证明了G-ABC算法的有效性,启发式信息的有效性以及缓存区容量设置对调度结果的影响。

Infeasibility test algorithm and fast repair algorithm of job shop scheduling problem

To diagnose the feasibility of the solution of a job-shop scheduling problem（JSSP）,a test algorithm based on diagraph and heuristic search is developed and verified through a case study.Meanwhile,a new repair algorithm for modifying an infeasible solution of the JSSP to become a feasible solution is proposed for the general JSSP.The computational complexity of the test algorithm and the repair algorithm is both O（n） under the worst-case scenario,and O（2J＋M） for the repair algorithm under the best-case scenario.The repair algorithm is not limited to specific optimization methods,such as local tabu search,genetic algorithms and shifting bottleneck procedures for job shop scheduling,but applicable to generic infeasible solutions for the JSSP to achieve feasibility.

考虑工序相关性的动态Job shop调度问题启发式算法

提出一类考虑工序相关性的、工件批量到达的动态Job shop调度问题,在对工序相关性进行了定义和数学描述的基础上,进一步建立了动态Job shop调度问题的优化模型。设计了一种组合式调度规则RAN(FCFS,ODD),并提出了基于规则的启发式算法以及该类动态Job shop调度问题的算例生成方法。为验证算法和比较评估调度规则的性能,对算例采用文献提出的7种调度规则和RAN(FCFS,ODD)进行了仿真调度,对调度结果的分析表明了算法的有效性和RAN(FCFS,ODD)调度规则求解所提出的动态Job Shop调度问题的优越性能。

多目标柔性Job Shop调度问题的技术现状和发展趋势

首先概述了多目标柔性Job Shop调度问题的基本概念,包括问题定义、常用假设条件、性能指标和问题的分类,讨论了其复杂性;其次,分别从建模、优化方法和原型系统研究方面综述了其发展过程和研究现状,对一类更加通用的多目标柔性Job Shop问题进行了简单的文献综述;最后指出了现有研究存在的问题与不足,并对未来的发展趋势进行了探讨。

免疫模拟退火算法及其在柔性动态Job Shop中的应用

针对车间作业调度问题,在深入分析免疫算法和模拟退火算法的基础上,将两种算法巧妙结合,提出免疫模拟退火算法。该算法引入了免疫记忆、抽取疫苗和接种疫苗等免疫机制,有助于优良个体和基因的保留和利用,提高了算法收敛性,而且其基于概率突跳特性的爬山性能可以避免早熟现象。针对西安航空发动机(集团)有限公司的柔性动态Job Shop,分别用模拟退火算法、免疫算法和免疫模拟退火算法进行了仿真和比较,研究结果表明,免疫模拟退火算法比单一算法性能更优,是求解柔性动态Job Shop问题的有效实用算法。

基于多Agent的Job Shop调度方法研究

针对JobShop调度问题 ,提出基于多Agent的车间调度模型 ,实现调度甘特图的自动生成。在此基础上 ,设计了多Agent分组协作机制 ;实现了多目标优化调度 ,提高了调度优化算法的实用性和优化效果 ;分析了车间调度中各类干扰因素的特点 ,实现动态调度 ,提高了系统的适应性和健壮性。最后给出了实例验证。

一种求解Job Shop问题的合作型协同进化算法

针对Job Shop调度问题,提出了一种改进的合作型协同进化算法。根据机器数量"自然"分割种群,每个种群对应一台机器,个体以机器前工件的优先列表为编码;将静态繁殖理论引入遗传算子,并通过三种共生伙伴选择方式,利用改进的基于优先列表的G&T算法解码来评价个体;最后采用一种更新技术和动态群体更新策略来加快算法收敛。通过对Job Shop基准问题的优化,该算法获得了比传统的遗传算法更好的结果。

基于遗传算法的Job Shop调度研究进展

Job Shop是典型的调度问题 ,遗传算法一直是计算智能的主要研究对象 ,因此基于遗传算法的Job Shop研究在学术界和工程界受到极大的关注。对近年来这方面的研究情况进行了较全面的综述 ,其中涉及编码、算法改进和比较、特征分析、混合算法、拓宽性、实际应用和调度器开发等 。

基于遗传算法求解Job Shop调度优化的新方法

针对Job Shop调度问题,提出了一种遗传算法编码新方法和矩阵解码方法。该方法根据问题的特点,采用一种按工序进行总体排序染色体编码方案,并采用矩阵解码,解码时体现了编码与调度方案一一对应,并且该编码方案有多种交叉操作算子可用,不需要专门设计算子。算例计算结果表明,基于该编码方案的遗传算法是有效的,能适用解决Job Shop调度问题,通过比较,用该编码方案的遗传算法优化Job Shop调度操作简单并且收敛速度快。

扰动环境下Job Shop瓶颈识别方法研究

针对Job Shop作业管理层面的瓶颈识别,改变传统将瓶颈识别独立于调度优化方案的做法,先进行瓶颈充分利用再进行瓶颈系统辨识,不仅保证了瓶颈的有效识别,而且保证了瓶颈的充分利用。笔者给出了工序级瓶颈识别指标,提出了瓶颈分级识别策略,采用遗传算法和优化仿真结合的方法实现瓶颈的充分利用,其中,利用遗传算法优化零件的投料顺序,采用Plant-Simulation建立模拟仿真模型,设置设备故障率、平均故障修复时间、缓冲容量等实际扰动,经过大量的生产过程仿真,基于瓶颈出现率进行瓶颈识别,并输出优化调度方案。算例验证表明了瓶颈识别方法的有效性。

解决JOB SHOP问题的粒子群优化算法

设计了2种解决Job shop问题的粒子群算法,即实数编码的粒子群调度算法和工序编码的粒子群调度算法。工序编码的粒子群调度算法更符合Job shop问题的特点,优化性能相对高。但粒子群调度算法容易陷入局部最优。为了提高优化性能,将粒子群算法和模拟退火算法结合,得到了粒子群-模拟退火混合调度算法。仿真结果表明了算法的有效性。

一类解决Job Shop问题的改进遗传算法

将遗传算法与模拟退火算法相结合,提出一种有效的混合调度算法。采用4-2选择代替传统的转轮选择方法,既保留了优秀个体又维持了群体的多样性;采用具有较强突跳能力的模拟退火算法代替传统遗传算法的变异算子,增强了全局探索能力,减小了陷入局部极小值的机会;采用基于关键路径的状态产生函数,缩小了搜索邻域,提高了算法的效率。仿真结果表明,该算法具有较高的求解质量和效率。

求解Job Shop调度问题的改进禁忌搜索算法

提出一种改进的禁忌搜索算法,解决传统禁忌搜索算法优化效果对运行次数和初始解依赖的不足,提高这类问题的求解质量。根据Job Shop调度问题解的特征,提出基于关键工序的邻域选择方法,采用此邻域选择方法构造禁忌搜索算法,当无邻域时,重新产生初始解进行禁忌搜索,将传统的禁忌搜索算法从单起始点搜索改进成多起始点搜索。采用改进的禁忌搜索算法对13个难的benchmarks问题进行10次求解,得到的平均值8个优于TSAB算法,得到的最优解6个优于TSAB算法、4个与TSAB算法相同。采用基于关键工序的邻域结构构造的改进TS算法具有较强的搜索能力。

免疫进化算法求解静态Job shop调度

基于克隆选择原理与细胞超变异思想构造了一种免疫进化算法CHIEA(Clonal selection and hyper mutations based immune evolution algorithm)求解静态JSP问题(Job shop scheduling problem)。随机混排变异算子的构造和抗体连续累积变异的实施丰富了细胞超变异的内容,基于优先列表编码方式的采用和免疫进化算子的构造提高了搜索效率,加速了算法收敛并提高了解的质量。通过与COELLO的AIS(Artificial immune system)算法的全面比较得出,CHIEA求解不同类型中小规模的静态JSP问题时具有更好的优化性能。

遗传算法求解柔性job shop调度问题

在分析柔性jobshop调度问题特点的基础上,提出一种新的求解该问题的遗传算法,即利用编码方法表示各工序的优先调度顺序及工序的加工机器,由此产生可行的调度方案,使得问题的约束条件在染色体中得以体现.所设计的遗传算子不仅能避免非法调度解的出现,保证后代的多样性,而且可使算法具有记忆功能.仿真结果证明了该算法的有效性.