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.

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.

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.

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.

Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm

The distributed hybrid flow shop scheduling problem(DHFSP),which integrates distributed manufacturing models with parallel machines,has gained significant attention.However,in actual scheduling,some adjacent machines do not have buffers between them,resulting in blocking.This paper focuses on addressing the DHFSP with blocking constraints(DBHFSP)based on the actual production conditions.To solve DBHFSP,we construct a mixed integer linear programming(MILP)model for DBHFSP and validate its correctness using the Gurobi solver.Then,an advanced iterated greedy(AIG)algorithm is designed to minimize the makespan,in which we modify the Nawaz,Enscore,and Ham(NEH)heuristic to solve blocking constraints.To balance the global and local search capabilities of AIG,two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed.Additionally,each factory is mutually independent,and the movement within one factory does not affect the others.In view of this,we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective.Finally,two shaking strategies are incorporated into the algorithm to mitigate premature convergence.Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances,and experimental results illustrate that the makespan and the relative percentage increase(RPI)obtained by AIG are 1.0%and 86.1%,respectively,better than the comparative algorithms.

Decomposition-Based Multi-Objective Optimization for Energy-Aware Distributed Hybrid Flow Shop Scheduling with Multiprocessor Tasks

This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks(EADHFSPMT)by considering two objectives simultaneously,i.e.,makespan and total energy consumption.It consists of three sub-problems,i.e.,job assignment between factories,job sequence in each factory,and machine allocation for each job.We present a mixed inter linear programming model and propose a Novel MultiObjective Evolutionary Algorithm based on Decomposition(NMOEA/D).We specially design a decoding scheme according to the characteristics of the EADHFSPMT.To initialize a population with certain diversity,four different rules are utilized.Moreover,a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors.To enhance the quality of solutions,two local intensification operators are implemented according to the problem characteristics.In addition,a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence,which can adaptively modify weight vectors according to the distribution of the non-dominated front.Extensive computational experiments are carried out by using a number of benchmark instances,which demonstrate the effectiveness of the above special designs.The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.

THE EFFECT OF WORKER LEARNING ON SCHEDULING JOBS IN A HYBRID FLOW SHOP： A BI-OBJECTIVE APPROACH

This paper studies learning effect as a resource utilization technique that can model improvement in worker＇s ability as a result of repeating similar tasks. By considering learning of workers while performing setup times, a schedule can be determined to place jobs that share similar tools and fixtures next to each other. The purpose of this paper is to schedule a set of jobs in a hybrid flow shop （HFS） environment with learning effect while minimizing two objectives that are in conflict： namely maximum completion time （makespan） and total tardiness. Minimizing makespan is desirable from an internal efficiency viewpoint, but may result in individual jobs being scheduled past their due date, causing customer dissatisfaction and penalty costs. A bi-objective mixed integer programming model is developed, and the complexity of the developed bi-objective model is compared against the bi-criteria one through numerical examples. The effect of worker learning on the structure of assigned jobs to machines and their sequences is analyzed. Two solution methods based on the hybrid water flow like algorithm and non-dominated sorting and ranking concepts are proposed to solve the problem. The quality of the approximated sets of Pareto solutions is evaluated using several performance criteria. The results show that the proposed algorithms with learning effect perform well in reducing setup times and eliminate the need for setups itself through proper scheduling.

Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment

Smart manufacturing in the“Industry 4.0”strategy promotes the deep integration of manufacturing and information technologies,which makes the manufacturing system a ubiquitous environment.However,the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers.To deal with this challenge,this study focuses on the real-time hybrid flow shop scheduling problem(HFSP).First,the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed,and its scheduling problem is described.Second,a real-time scheduling approach for the HFSP is proposed.The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the real-time status in the hybrid flow shop.With the scheduling rule,the priorities of the waiting job are calculated,and the job with the highest priority will be scheduled at this decision time point.A group of experiments are performed to prove the performance of the proposed approach.The numerical experiments show that the real-time scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances.Therefore,the contribution of this study is the proposal of a real-time scheduling approach,which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.

Q-Learning-Based Teaching-Learning Optimization for Distributed Two-Stage Hybrid Flow Shop Scheduling with Fuzzy Processing Time

Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings.However,the distributed two-stage hybrid flow shop scheduling problem(DTHFSP)with fuzzy processing time is seldom investigated in multiple factories.Furthermore,the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP.In the current study,DTHFSP with fuzzy processing time was investigated,and a novel Q-learning-based teaching-learning based optimization(QTLBO)was constructed to minimize makespan.Several teachers were recruited for this study.The teacher phase,learner phase,teacher’s self-learning phase,and learner’s self-learning phase were designed.The Q-learning algorithm was implemented by 9 states,4 actions defined as combinations of the above phases,a reward,and an adaptive action selection,which were applied to dynamically adjust the algorithm structure.A number of experiments were conducted.The computational results demonstrate that the new strategies of QTLBO are effective;furthermore,it presents promising results on the considered DTHFSP.

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.

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.

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。

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。

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。

基于DDE技术混合Flow-shop调度的求解及其系统设计

利用DDE技术对混合流水车间生产调度的数据交换系统进行了设计。采用VB软件编程求解了基于遗传算法的混合Flow-shop调度。对加工信息的编码与解码过程进行了阐述,开发了基于组态王混合Flow-shop调度的监控界面。

基于改进分布估计算法的带并行机模糊混合Flow Shop调度

针对处理时间不确定情况下带并行机的混合Flow Shop调度问题,基于模糊规划理论,采用一种模糊数排序的方法建立了调度模型;以最小化加权模糊最大完工时间的平均值和不确定度作为调度目标,提出一种改进分布估计算法(IEDA)求解上述问题。IEDA算法采用基于NEH(Nawaz-Enscore-Ham)和破坏重建策略的初始化方法,对较优个体进行变邻域局部搜索以提高算法的局部搜索能力,同时采用破坏重建策略增加种群多样性,在最优解连续若干代没有改进时对其进行基于破坏重建策略的变邻域局部搜索,增强算法跳出局部最优的能力,并用正交设计的方法调节算法参数。仿真实验结果验证了本文算法的优越性。