应用Agent理论的生产调度系数研究

生产调度问题，一般可根据生产流程的不同分为Job-shop调度和Flowshop调度两大类（也有学者认为，存在两者相结合的第三类－混合调度）。该文研究以最小化Makespan为目标的Flowshop调度问题。基于Agent理论，提出采用Flowshop复合代理体（Flowshop-Compond-Agent,FSCA)求解Flowshop调度问题的方法，在给出FSCA的结构及其实现的基础上，通过毛纺企业制度车间的实例说明了使用FSCA解决Flowhop调度问题的有效性。     

基于复合代理体（Agent）的纺纱车间调度系统研究

毛纺企业的纺纱车间生产调度问题是一种复杂的Flowshop调度问题，针对这类问题， 本文提出采用Flowshop复合代理体（FSCA）求解的方案，其中使用了GA算法．在讨论了FSCA 的结构、实现和详细调度算法的基础上，通过纺纱车间调度实例研究说明了使用FSCA解决Fl owshop调度问题的有效性．     

并行流程车间调度问题及其概率学习进化算法

并行Flowshop调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题.针对最小化最大完工时间目标函数,建立了一般并行Flowshop调度问题的整数规划模型.鉴于问题的求解复杂性,设计了基于概率学习的求解算法.对随机生成的测试问题进行求解,实验结果显示出该算法求解并行Flowshop调度问题的良好潜能.     

基于改进的RA算法的混合Flowshop调度问题的求解

针对混合Flowshop系统的最小化Makespan调度问题,提出基于改进的RA斜度指标的启发式算法来对工件进行排序,采用FAM算法来分配设备并给出其最优值的下界检验该算法。仿真结果表明该方法优于目前最好的启发式算法能较好地解决混合Flowshop的调度问题。     

具有线性恶化加工时间的调度问题

讨论了工件具有线性恶化加工时间的调度问题.在这类问题中,工件的恶化函数为线性 函数.对单机调度问题中目标函数为极小化最大完工时间加权完工时间和,最大延误以及最大费 用等问题分别给出了最优算法.对两台机器极小化最大完工时间的Flowshop问题,证明了利用 Johnson规则可以得到最优调度.对于一般情况,如果同一工件的工序的加工时间均相等,则 Flowshop问题可以转化为单机问题.     

基于改进的RA逄法的混合Flowshop调度问题的求解

针对混合Flowshop系统的最小化akespan调度问题，提出基于改进的RA斜度指标的启发式算法来对工件进行排序，采用FAM算法来分配设备，并给出最优值的下界检验该算法。仿真结果表明，该方法优于目前最好的启发式算法，能较好地解决混合Flowshop的调度问题。     

基于遗传算法的某航空发动机装配车间优化调度

航空发动机装配车间装配生产线的调度问题,是一类比较典型的混合Flowshop问题,同时还带有工件可重人等特点,这就区别于一般的Flowshop和Jobshop调度问题,因此,将可重入混合车间调度问题划为第三类调度问题。关于重入式混合车间生产调度的优化问题通常来说都是属于NP难问题。文中通过某航空发动机装配车间生产线的研究,以最小化最大完工时间为目标函数,借助随机矩阵的编码方式和改进的交叉方法与变异方法,提出了基于遗传算法的调度优化方法。最后实验结果表明,文中提出的改进算法能够有效地实现装配车间调度的优化。     

基于联姻遗传算法的混合FloWshop提前/拖期调度问题

混合流水车间(Flowshop)提前／拖期调度问题的目标是4~_r-件的提前／拖期惩罚成本最小，这是一个NP完全问题，很难用一般的方法解决。文中首先给出了问题的数学模型，然后采用联姻遗传算法求解该问题。仿真结果表明此算法能有效地解决该类复杂调度问题。     

多机Flow shop加权完成时间调度问题的启发式算法分析

因实际生产中调度问题的规模很大，分析其近似算法的绝对性能比很难，有时甚至不可行，所以研究近似算法的渐近性能比就很有必要，本文针对多机Flowshop加权完成时间调度问题，使用单机松弛和概率分析方法，证明了基于加权最短处理时间需求的启发式算法是渐近最优的．     

基于遗传算法的混合Flowshop调度

混合Flowshop调度问题,是一个NP完全问题,很难用一般的方法解决,文章提出了遗传算法求解混合Flow-shop调度问题的方法,给出了一种染色体表示方法,设计了相应的交叉和变异操作算子,这两种算子很容易保证个体的合法性,同时又具有遗传算法本身所要求的随机性。最后给出了一个较大规模的计算实例,仿真结果表明此算法是有效的。     

两机无等待流水车间调度问题的性质

针对两机无等待流水车间调度问题,提出目标函数最大完工时间最小化的快速算法,并给出算法的复杂度。分析两机无等待流水车间调度问题的排列排序性质,证明了两机无等待流水车间调度问题的可行解只存在于排列排序中,排列排序的最优解一定是两机无等待流水车间调度问题的最优解。最后研究了同时包含普通工件和无等待工件的两机流水车间调度问题的复杂性,为进一步研究两机无等待流水车间调度问题提供了理论依据。     

An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.     

Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm

Production scheduling plays an important role in the intelligent decision support system and intelligent optimization decision technology. In the context of the globalization trend, the current production and management may extend from a single factory to a distributed production network. In this paper, we study the distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem in the distributed environment. Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder. The first five heuristics are developed based on the well-known NEH2 heuristic [B. Naderi, R. Ruiz, The distributed permutation flowshop scheduling problem, Computers & Operations Research, 37 (4) (2010) 754–768.] and the last heuristic is presented by extending the PW heuristic [Q.K. Pan, L. Wang, Effective heuristics for the blocking flowshop scheduling problem with makespan minimization, Omega, 40 (2) (2012) 218–229.] to DBFSP in an effective way. The composite heuristics that combining constructive heuristics and local searches are also studied. The proposed composite heuristics are chosen to generate an initial solution with a high level of quality. Keeping the simplicity of the IG algorithm, three local search procedures, two destruction procedures, an improved reconstruction procedure, and a simulated annealing-like acceptance criterion are well designed based on the problem-specific knowledge to enhance the IG algorithm. The computational experiments are carried out based on the 720 benchmark instances from the literature. The results show that the proposed heuristics are very effective for solving the problem under consideration and the presented IG algorithm performs significantly better than the other state-of-the-art metaheuristics from the literature.     

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on m machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard’s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling problems.     

A genetic algorithm with genes-association recognition for flowshop scheduling problems

In this paper, we consider a flowshop scheduling problem with a special blocking RCb (Release when Completing Blocking). This flexible production system is prevalent in some industrial environments. Genetic algorithms are first proposed for solving these flowshop problems and different initial populations have been tested to find which is best adapted. Then, a method is proposed for further improving genetic algorithm found solutions, which consists in marking out recurrent genes association occurrences in an already genetic algorithm optimized population. This idea directly follows Holland’s first statement about nature observations. Here, proposed idea is that populations well adapted to a problem have an adapted genetic code with common properties. We propose to mark out these properties in available genetic code to further improve genetic algorithm efficiency. Implementation of this method is presented and obtained results on flowshop scheduling problems are discussed.     

The three-stage assembly flowshop scheduling problem

We consider the three-stage assembly flowshop scheduling problem with the objective of minimizing the makespan. The three-stage assembly problem generalizes both the serial three machine flowshop problem and the two-stage assembly flowshop scheduling problem and is therefore strongly NP-hard. We analyze the worst-case ratio bound for several heuristics for this problem. We also analyze the worst-case absolute bound for a heuristic based on compact vector summation techniques and we point out that, for a large number of jobs, this heuristic becomes asymptotically optimal.Scope and purposeThe three-stage assembly flowshop scheduling problem models situations which arise frequently in manufacturing when various fabrication operations are performed concurrently and then collected and transported into an assembly area for a final assembly operation. The main criterion for this problem is the minimization of the maximum job completion time (makespan). The objective of this paper is to derive algorithms for minimizing the makespan. In doing so, we also demonstrate the reduction of assembly flowshop problems to their embedded serial flowshop problems.     

Impact of permutation enforcement when minimizing total weighted tardiness in dynamic flowshops with uncertain processing times

This paper is motivated by the problem of meeting due dates in a flowshop production environment with jobs with different weights and uncertain processing times. Enforcement of a permutation schedule to varying degrees for dynamic flowshops is investigated with the goal of minimizing total weighted tardiness (TWT). The approaches studied are categorized as follows: (1) pure permutation scheduling (2) shift-based scheduling (3) pure dispatching (which leads to non-permutation sequences). A simulation-based experimental study was carried out to study the performance of the above methods with respect to minimizing TWT when new jobs arrive to the flowshop at every shift change. Results indicate significant gains in performance are possible when the permutation requirement is relaxed and shift-based scheduling is allowed. Shift-based scheduling yields competitive results with respect to the pure dispatching approach, even though dispatching has the advantage of a full relaxation of the permutation requirement.     

Non-permutation flowshop scheduling with dual resources

Typically, in order to process jobs in a flowshop both machines and labor are required. However, in traditional scheduling problems, labor is assumed to be plentiful and only machine is considered to be a constraint. This assumption could be due to the lower cost of labor compared to machines or the complexity of dual-resource constrained problems. In this paper a mathematical model is developed to minimize the work-in-process inventory while maximizing the service level in a flowshop with dual resources. The model focuses on optimizing a non-permutation flowshop. There are different skill levels considered for labor and the setup times on machines are sequence-dependent. Jobs are allowed to skip one or more stages in the flowshop. Job release and machine availability times are considered to be dynamic. The problem is solved in two layers. The outer layer is a search algorithm to find the schedule of jobs on the machine (traditional flowshop scheduling problem) and the inner layer is a three-step heuristic to find a schedule of jobs on labor in accordance to the machine schedule. Three different search algorithms are developed to solve the proposed NP-hard problem. First algorithm can solve a permutation flowshop while the other two are developed to solve a non-permutation flowshop. The comparison between the optimal solution and the search algorithms in small examples shows a good performance of the algorithms with an average deviation of only 2.00%. An experimental design analyzes the effectiveness and efficiency of the algorithms statistically. The results show that non-permutation algorithms perform better than the permutation algorithm, although the former are less efficient. The effectiveness and efficiency in all three algorithms have an inverse relation. To the best of our knowledge, this research is the first of its kind to provide a comprehensive mathematical model for dual resource flowshop scheduling problem.