Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems

The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

融合粗糙数据推理的离散麻雀搜索算法求解HFSP问题

针对麻雀搜索算法(SSA)易陷入局部最优、无法求解离散优化问题等不足,提出了一种改进离散麻雀搜索算法(IDSSA)。抽象原始麻雀搜索算法的位置更新公式,针对个体的不同身份设计新的离散化启发式位置更新策略,并针对混合流水车间调度问题(HFSP)设计了编码与解码方式;引入粗糙数据推理理论,通过数学证明解释了引入理论的可行性与合理性,为算法提供理论支撑,提高可解释性;利用上近似的性质扩大搜索空间,提高种群多样性,避免算法早熟,结合划分及粗糙数据推理提出3种策略,促进种群间信息共享,调节种群的开发能力与探索能力,降低算法陷入局部最优的概率;使用改进离散麻雀搜索算法求解混合流水车间调度问题,对3个小规模实例与10个Liao经典测试集进行仿真实验,验证了改进离散麻雀搜索算法求解混合流水车间调度问题的可行性,通过与遗传算法、差分进化算法等经典算法的对比实验,证明了所提算法的优越性与改进策略的有效性。

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

To solve the distributed hybrid flow shop scheduling problem(DHFS)in raw glass manufacturing systems,we investigated an improved hyperplane assisted evolutionary algorithm(IhpaEA).Two objectives are simultaneously considered,namely,the maximum completion time and the total energy consumptions.Firstly,each solution is encoded by a three-dimensional vector,i.e.,factory assignment,scheduling,and machine assignment.Subsequently,an efficient initialization strategy embeds two heuristics are developed,which can increase the diversity of the population.Then,to improve the global search abilities,a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions.Furthermore,a local search heuristic based on three parts encoding is embedded to enhance the searching performance.To enhance the local search abilities,the cooperation of the search operator is designed to obtain better non-dominated solutions.Finally,the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms.The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

Differential evolution algorithm for hybrid flow-shop scheduling problems

Aiming at the hybrid flow-shop （HFS） scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution （DE） is proposed. By using a special encoding scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the HFS problems. Simulation results based on some typical problems and comparisons with some existing genetic algorithms demonstrate the proposed algorithm is effective, efficient and robust for solving the HFS problems.

Minimizing makespan in a two-stage hybrid flow shop scheduling problem with open shop in one stage

This paper considers a scheduling problem in two-stage hybrid flow shop, where the first stage consists of two machines formed an open shop and the other stage has only one machine. The objective is to minimize the makespan, i.e., the maximum completion time of all jobs. We first show the problem is NP-hard in the strong sense, then we present two heuristics to solve the problem. Computational experiments show that the combined algorithm of the two heuristics performs well on randomly generated problem instances.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

Integrated Production and Transportation Scheduling Method in Hybrid Flow Shop

The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem(IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem(HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles(AGVs)is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation(including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.

基于遗传算法的混合Flow-shop调度方法

混合Flow-shop调度问题 (Hybrid flow-shop scheduling problem, HFSP),是一般Flow-shop调度问题的推广,由于在某些工序上存在并行机器,所以比一般的Flow-shop调度问题更复杂。本文提出了遗传算法求解混合Flow-shop调度问题的方法,给出了一种新的编码方法,设计了相应的交叉和变异操作算子,能够保证个体的合法性,同时又具有遗传算法本身所要求的随机性。最后给出了某汽车发动机厂金加工车间的生产调度实例,表明了此算法的有效性。

基于局部性修复的HFS机器故障重调度

针对机器故障下的HFS重调度问题,提出了重调度前后调度方案在时间安排和机器指派上的一致性度量方法,从约束变化的角度建立其动态约束满足模型。模型能够反映工件对重调度前后调度方案的一致性具有不同要求的实际情况。针对问题的模型及其特点,提出了基于局部性修复的重调度算法。仿真实验表明,本文提出的模型和算法是有效的。

基于自适应遗传算法混合Flow-shop的调度与仿真

通过对柔性制造系统中混合流水车间生产调度问题的分析和研究,开发了基于遗传算法的生产调度方法,调度目标为最小化工件的最大完工时间。采用了一套新的染色体编码方法以保证个体的合法性与计算的方便性,设计了相应的交叉和变异操作算子,并生成最优的排序计划。仿真结果表明,改进后的顺序自适应交叉遗传算法更能有效地解决混合流水车间调度问题,并采用VB软件编程实现了调度过程的动态仿真。

基于准时制的零等待混合 Flow Shop 调度问题

讨论了具有零等待混合ＦｌｏｗＳｈｏｐ调度问题，其目标是最小化提前／拖期总成本．这是一个ＮＰ难题．给出了问题的数学模型，同时将启发式算法和求解线性规划相结合，提出了这一调度模型的求解方法．最后给出了实验结果和结论．

多阶段混合Flow Shop调度问题及其遗传求解算法

针对多阶段混合Ｆｌｏｗ Ｓｈｏｐ 调度问题的一般结构和不同的调度目标函数，提出混合整数规划模型，并基于问题的结构特点设计了遗传求解算法。计算实验结果表明。

蚁群系统结合指派规则求解HFS调度问题

以NP-难的最小化时间表长为目标的混合流水车间调度问题为研究对象。把工件在第1阶段开始加工的排序问题转化为旅行商问题,采用蚁群系统求得初始排序;在第1阶段后各阶段采用工件先到先服务规则选择工件、最先空闲机器优先规则选择机器以构建初始工件的机器指派与排序;充分利用已知的机器布局和工件加工时间特点,确定工件加工瓶颈阶段,并以此为基础对工件的机器指派与排序进行改进。用Carlier和Neron设计的Bench-mark算例仿真后与著名的NEH算法比较,表明这种算法是有效的。

一类Flow Shop排序问题的混合遗传算法

】描述了在平行顺序移动方式下ＦｌｏｗＳｈｏｐ排序问题的数学模型，构造了求解该问题的混合遗传算法．用计算机模拟计算的结果表明。

非标准HFS-B问题的设备协同调度研究

在同贝位边装边卸工艺的集装箱作业环境中,提出了一类非标准的HFS-B问题,即同时存在2种方向互逆的工件流、与紧前紧后机器相关的准备和处理时间.这类来自港口背景的非标准HFS-B问题,主要涉及空间位置决策、任务分配和设备调度等子问题的决策,目标是makespan最小.鉴于HFS-B的NP-hard特性,在分析设备顺序约束和协同关系基础上,基于Fill ratio启发式、表调度理论和设备的状态空间变换,设计了3阶段决策的启发式算法.基于阶段的下界理论和makespan的下界理论,对多个实际的大规模案例进行仿真分析,结果表明:算法的makespan与理论下界的最大偏差不大于7%,具有良好的实用性.

混合遗传算法求解含机器可利用约束的HFSP

针对含机器阻塞和可利用约束的混合流水车间调度优化问题,考虑工件运输时间,以最小化总加权完工时间为优化目标,建立混合整数规划模型,提出一种基于启发式规则的自适应混合遗传算法求解该模型。在传统遗传算法的基础结构上,引入五种启发式规则生成部分初始种群,从而改善部分初始解的质量;设计分段自适应交叉概率和变异概率计算公式,以加快算法收敛;利用局域搜索对得到的调度解进行再次优化,进一步提高算法搜索能力。对不同规模问题进行仿真实验,结果验证了该算法的可行性和有效性。

求解带有阻塞限制的HFSP的MILP模型与改进回溯搜索算法

针对带有阻塞限制的不相关并行机混合流水车间调度问题,以最小化最长完工时间为目标,依据不同的建模思想,建立了求解该问题的4个混合整数线性规划(MILP)模型;鉴于混合整数线性规划不适合求解中大规模问题,提出了一种改进的回溯搜索算法以求解中大规模问题,在该算法中,引入了轮盘赌选择策略以及变邻域搜索算法,以提高算法的收敛速度以及局部搜索能力。最后,对所提MILP模型以及算法进行了对比分析,通过对具体实例的求解验证了所提MILP模型以及算法的有效性及优越性。

基于混合离散人工蜂群算法的阻塞Flow Shop调度

针对带阻塞的Flow Shop调度问题,以最小化总流水时间为调度目标,提出了一种混合离散人工蜂群(Hybrid Discrete Artificial Bee Colony,HDABC)算法。HDABC算法采用基于NEH和NEH变体初始化,保证种群的质量和多样性。在雇佣蜂阶段采用差分进化策略产生邻域个体;在跟随蜂阶段采用锦标赛选择方法选择个体跟随,并对选择的个体采用优化插入操作产生新的邻域个体。此外,在侦查蜂阶段再一次采用锦标赛选择方法选择个体,并对较好的个体执行破坏重建操作,用产生的新个体代替原来较差的个体。用正交设计方法调节了该算法的参数。通过与其他两个算法的仿真实验结果比较,验证了本文算法的优越性。

基于置换Flow Shop调度问题的混合量子算法研究

安排合理有效的生产调度是生产活动能井然有序开展,生产资源得到最佳配置,运作过程简明流畅的有力保证。置换Flow Shop调度问题是流水车间的典型问题,同时也是NP-C难题。从问题出发,设计了由量子进化,最佳模式和其他优化技术所构成的混合量子算法(HQA)。HQA模仿量子行为迭代演化,将种群一分为二,种群1在量子作用和其他优化作用下,探索解空间。种群2保留最佳模式,提高了搜索的效率。经计算测试,验证了HQA在求解排序问题中的可行性,测试结果表明HQA具备了求解置换Flow Shop调度问题的能力。