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.

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.

模糊生产系统中的Flexible Job-Shop调度模型

引入柔性生产系统下的调度过程中存在的不确定性问题,接着对存在模糊处理时间和模糊操作间隔的柔性工作车间调度问题进行描述,并给出基于模糊逻辑和遗传优化的调度模型,最后通过实例验证模型的有效性。

具有模糊加工时间的Flexible Job-Shop Scheduling问题的研究

采用梯形模糊数来表征柔性生产系统中的时间参数,并在此基础上对具有模糊加工时间的柔性作业车间最小化制造跨度调度问题进行了描述。然后给出了基于粒子群优化的柔性作业车间调度模型。最后通过实例验证了模型的有效性。

A Modi ed Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem(FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them di cult to code and not easy to reproduce. This paper proposes a modified iterated greedy(IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an e ective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.

A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems

Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.

Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

As a typical transportation tool in the intelligent manufacturing system,Automatic Guided Vehicle(AGV)plays an indispensable role in the automatic production process of the workshop.Therefore,integrating AGV resources into production scheduling has become a research hotspot.For the scheduling problem of the flexible job shop adopting segmented AGV,a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function,and an improved genetic algorithmis designed to solve the problem in this study.The algorithmdesigns a two-layer codingmethod based on process coding and machine tool coding and embeds the task allocation of AGV into the decoding process to realize the real dual resource integrated scheduling.When initializing the population,three strategies are designed to ensure the diversity of the population.In order to improve the local search ability and the quality of the solution of the genetic algorithm,three neighborhood structures are designed for variable neighborhood search.The superiority of the improved genetic algorithmand the influence of the location and number of transfer stations on scheduling results are verified in two cases.

A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.

Dynamic Scheduling of Flexible Job Shops

Aim of this research is to minimize makespan in the flexible job shop environment by the use of genetic algorithms and scheduling rules. Software is developed using genetic algorithms and scheduling rules based on certain constraints such as non-preemption of jobs, recirculation, set up times, non-breakdown of machines etc. Purpose of the software is to develop a schedule for flexible job shop environment, which is a special case of job shop scheduling problem. Scheduling algorithm used in the software is verified and tested by using MT10 as benchmark problem, presented in the flexible job shop environment at the end. LEKIN software results are also compared with results of the developed software by the use of MT10 benchmark problem to show that the latter is a practical software and can be used successfully at BIT Training Workshop.

An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.

具有平行批处理机的多目标FJSP问题研究

针对具有平行批处理机的多目标柔性作业车间调度问题,建立以最大完工时间、最大机器负荷和能耗为优化目标的数学模型,并提出了一种多种群MOEAD算法进行求解。算法结合了多种群策略的优势,三个子种群分别采用了WS、TE和BI三种聚合函数进行协同搜索,保持种群多样性的同时扩展了算法的搜索广度。设计了符合问题特性的进化算子和局部搜索策略,提高算法整体搜索性能。通过标准的FJSP算例和符合问题特性的实际数据,验证了所提算法的可行性与有效性。

基于柯西游走的改进灰狼算法求解FJSP

为了提高生产资源的利用率和调度效率,提出了一种基于柯西游走的灰狼优化算法,将其应用于求解柔性作业车间调度问题(FJSP)。在经典灰狼算法的基础上,加入柯西游走策略跳出局部最优;引入非线性收敛因子a控制算法的广度搜索与深度搜索程度;采用混合生成新解的种群更新策略适当增强种群多样性。通过在不同规模的测试用例上进行仿真实验和分析比较,实验结果表明,基于柯西游走的灰狼算法寻优性能稳定,在平衡算法的全局搜索和局部搜索程度方面表现较为出色。

融合随机重启爬山算子的改进遗传算法求解FJSP

针对传统遗传算法在求解柔性车间调度问题时,存在种群的动态适应能力差、容易陷入局部最优等问题,提出一种融合随机重启爬山算子的改进遗传算法。通过双种群交叉,增强种群间的信息交流能力。引入收敛准则,在维护种群多样性的同时防止种群的优良个体被过度破坏。结合随机重启爬山法的思想进行局部搜索,提升了算法的局部搜索能力。仿真实验表明,所提出的算法在不同规模的问题中,都有着明显的寻优能力。

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。

基于极限调度完工时间最小化的机器选择及FJSP求解

为提高柔性作业车间调度问题求解的遗传算法的初始群体质量,通过分析机器选择与调度完工时间的关系,提出一种基于极限调度完工时间(Climit)最小化的机器选择初始化方法。采用机器选择链和工序顺序链双链结构编码,初始化机器选择链时,宏观上采用全局选择和局部选择分别侧重于实现对最大机器负荷和最大工件加工时间指标的优化;微观上采用随机次序取代工件工艺顺序选择工序,在考虑可选机器负荷的基础上进一步比较加工时间选择机器,兼顾最大机器负荷和最大工件加工时间指标的优化。对基准算例机器选择结果进行分析和基于遗传算法求解,验证了所提方法的有效性。

多目标FJSP的一维编码粒子群优化求解方法

以单件小批量生产方式为主的柔性车间调度中,快速得到满足低生产成本、高生产效率,避免瓶颈发生的调度方案,是调度优化算法的设计目标。就此建立了以制造期、机床总负荷和单机最大负荷为综合目标的柔性车间调度问题(Flexible Job-shop Scheduling Problems,FJSP)优化模型;设计了一种以概率值为分量的一维粒子群优化算法,通过概率区间划分将连续粒子分量离散化,结合完工时间最早启发式规则,实现工序的排序与加工机床的选取。通过不同规模算例的比较,分析结果表明该方法在求解较大规模问题时具有一定的优势。

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.