求解车间作业调度问题的混合遗传算法

针对Job-Shop调度问题,将自适应遗传算法与改进的蚂蚁算法融合,提出了自适应遗传算法与蚂蚁算法混合的一种优化算法。首先利用自适应遗传算法产生初始信息素的分布,再运行改进的蚂蚁算法进行求解。该算法既发挥了自适应遗传算法和蚂蚁算法在寻优中的优势,又克服了各自的不足。实验结果表明,该算法在性能上明显优于遗传算法和蚂蚁算法,并且问题规模越大,优势越明显。     

An effective asexual genetic algorithm for solving the job shop scheduling problem

By using the notion of elite pool, this paper presents an effective asexual genetic algorithm for solving the job shop scheduling problem. Based on mutation operations, the algorithm selectively picks the solution with the highest quality from the pool and after its modification, it can replace the solution with the lowest quality with such a modified solution. The elite pool is initially filled with a number of non-delay schedules, and then, in each iteration, the best solution of the elite pool is removed and mutated in a biased fashion through running a limited tabu search procedure. A decision strategy which balances exploitation versus exploration determines (i) whether any intermediate solution along the run of tabu search should join the elite pool, and (ii) whether upon joining a new solution to the pool, the worst solution should leave the pool. The genetic algorithm procedure is repeated until either a time limit is reached or the elite pool becomes empty. The results of extensive computational experiments on the benchmark instances indicate that the success of the procedure significantly depends on the employed mechanism of updating the elite pool. In these experiments, the optimal value of the well-known 10 × 10 instance, ft10, is obtained in 0.06 s. Moreover, for larger problems, solutions with the precision of less than one percent from the best known solutions are achieved within several seconds.     

A new hybrid genetic algorithm for job shop scheduling problem

Job shop scheduling problem is a typical NP-hard problem. To solve the job shop scheduling problem more effectively, some genetic operators were designed in this paper. In order to increase the diversity of the population, a mixed selection operator based on the fitness value and the concentration value was given. To make full use of the characteristics of the problem itself, new crossover operator based on the machine and mutation operator based on the critical path were specifically designed. To find the critical path, a new algorithm to find the critical path from schedule was presented. Furthermore, a local search operator was designed, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed and its convergence was proved. The computer simulations were made on a set of benchmark problems and the results demonstrated the effectiveness of the proposed algorithm.     

Memetic algorithms for the job shop scheduling problem with operators

The job-shop scheduling problem with operators (JSO) is an extension of the classic job-shop problem in which an operation must be assisted by one of a limited set of human operators, so it models many real life situations. In this paper we tackle the JSO by means of memetic algorithms with the objective of minimizing the makespan. We define and analyze a neighborhood structure which is then exploited in local search and tabu search algorithms. These algorithms are combined with a conventional genetic algorithm to improve a fraction of the chromosomes in each generation. We also consider two different schedule builders for chromosome decoding. All these elements are combined to obtain memetic algorithms which are evaluated over an extensive set of instances. The results of the experimental study show that they reach high quality solutions in very short time, comparing favorably with the state-of-the-art methods.     

遗传算法求解柔性job shop 调度问题

在分析柔性job shop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．     

超启发式遗传算法求解模糊柔性作业车间调度

本文提出一种混合超启发式遗传算法(HHGA),用于求解一类采用三角模糊数表示工件加工时间的模糊柔性作业车间调度问题(FFJSP),优化目标为最小化最大模糊完工时间(即makespan).首先,详细分析现有三角模糊数排序准则性质,并充分考虑取大操作的近似误差和模糊度,设计一种更为准确的三角模糊数排序准则,可合理计算FFJSP和其他各类调度问题解的目标函数值.其次,为实现对FFJSP解空间不同区域的有效搜索, HHGA将求解过程分为两层,高层利用带自适应变异算子的遗传算法对6种特定操作(即6种有效邻域操作)的排列进行优化;低层将高层所得的每种排列作为一种启发式算法,用于对低层相应个体进行操作来执行紧凑的变邻域局部搜索并生成新个体,同时加入模拟退火机制来避免搜索陷入局部极小.最后,仿真实验和算法比较验证了所提排序准则和HHGA的有效性.     

An AIS-based hybrid algorithm for static job shop scheduling problem

A static job shop scheduling problem (JSSP) is a class of JSSP which is a combinatorial optimization problem with the assumption of no disruptions and previously known knowledge about the jobs and machines. A new hybrid algorithm based on artificial immune systems (AIS) and particle swarm optimization (PSO) theory is proposed for this problem with the objective of makespan minimization. AIS is a metaheuristics inspired by the human immune system. Its two theories, namely, clonal selection and immune network theory, are integrated with PSO in this research. The clonal selection theory builds up the framework of the algorithm which consists of selection, cloning, hypermutation, memory cells extraction and receptor editing processes. Immune network theory increases the diversity of antibody set which represents the solution repertoire. To improve the antibody hypermutation process to accelerate the search procedure, a modified version of PSO is inserted. This proposed algorithm is tested on 25 benchmark problems of different sizes. The results demonstrate the effectiveness of the PSO algorithm and the specific memory cells extraction process which is one of the key features of AIS theory. By comparing with other popular approaches reported in existing literatures, this algorithm shows great competitiveness and potential, especially for small size problems in terms of computation time.     

An efficient memetic algorithm for solving the job shop scheduling problem

The job shop scheduling problem (JSP) is well known as one of the most complicated combinatorial optimization problems, and it is a NP-hard problem. Memetic algorithm (MA) which combines the global search and local search is a hybrid evolutionary algorithm. In this paper, an efficient MA with a novel local search is proposed to solve the JSP. Within the local search, a systematic change of the neighborhood is carried out to avoid trapping into local optimal. And two neighborhood structures are designed by exchanging and inserting based on the critical path. The objective of minimizing makespan is considered while satisfying a number of hard constraints. The computational results obtained in experiments demonstrate that the efficiency of the proposed MA is significantly superior to the other reported approaches in the literature.     

A parallel memetic algorithm with explicit management of diversity for the job shop scheduling problem

Applied Intelligence - The job shop scheduling problem (JSSP) is a very popular NP-hard optimization problem that involves assigning jobs to resources. Recent advances in the field of memetic...     

求解Job Shop问题的改进遗传算法

通过对JobShop问题的研究,基于工件的编码方式提出了一种新的活动化解码算法.该算法主动寻找后续工件提前加工,不仅节省了存储空间,而且提高了解码效率.并对遗传算子进行了改进,采用一种基于模式交叉的交叉算子,防止非法解的产生,同时将变异算子与局部搜索结合起来.最后采用典型算例对改进算法进行仿真试验,结果表明,改进的算法提高了效率,具有一定的实用价值.     

改进的萤火虫算法求解阻塞流水线调度问题

为了提高阻塞流水线调度问题的求解性能,提出了一种改进的萤火虫算法来求解阻塞流水线调度问题.首先,提出一种离散机制把个体的实数编码形式转换成离散的作业序列,从而使算法能够应用于离散问题求解;其次,设计一种双重初始化方法,并将NEH启发式方法应用到初始化中来,使算法有一个较优的初始化环境,提高初始种群的解的质量;此外,重新设计了算法中个体的移动方式来增大搜索域;最后,以一定概率对种群中的个体进行局部搜索,加强算法的局部搜索性能.通过对Taillard数据集中部分实例进行求解,实验结果验证了新算法的有效性.     

相关工件车间调度问题的拓扑算法

针对加工装配型离散制造企业实际生产的特点,提出了一类用于表示工序之间偏序关系的相关工件车间调度问题。为了利用已有的求解表示工序之间的线序关系的传统车间调度算法求解相关工件车间调度问题,设计了一种拓扑算法,该算法能够将工序之间的偏序关系转化为线序关系,将相关工件车间调度问题转化为传统的车间调度问题,通过实证研究,结果表明了拓扑算法是可行和高效的。     

An improved intelligent water drops algorithm for solving multi-objective job shop scheduling

Multi-objective job shop scheduling (MOJSS) problems can be found in various application areas. The efficient solution of MOJSS problems has received continuous attention. In this research, a new meta-heuristic algorithm, namely the Intelligent Water Drops (IWD) algorithm is customized for solving the MOJSS problem. The optimization objective of MOJSS in this research is to find the best compromising solutions (Pareto non-dominance set) considering multiple criteria, namely makespan, tardiness and mean flow time of the schedules. MOJSS-IWD, which is a modified version of the original IWD algorithm, is proposed to solve the MOJSS problem. A scoring function which gives each schedule a score based on its multiple criteria values is embedded into the MOJSS-IWD’s local search process. Experimental evaluation shows that the customized IWD algorithm can identify the Pareto non-dominance schedules efficiently.     

A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem

In this paper, a novel competitive co-evolutionary quantum genetic algorithm (CCQGA) is proposed for a stochastic job shop scheduling problem (SJSSP) with the objective to minimize the expected value of makespan. Three new strategies named as competitive hunter, cooperative surviving and the big fish eating small fish are developed in population growth process. Based on improved co-evolution idea of multi-population and concepts of quantum theory, this algorithm could not only adjust population size dynamically to increase the diversity of genes and avoid premature convergence, but also accelerate the convergence speed with Q-bit representation and quantum rotation gate. FT benchmark-based problems where the processing times are subjected to independent normal distributions are solved effectively by CCQGA. The experiment results achieved by CCQGA are compared with quantum-inspired genetic algorithm (QGA) and standard genetic algorithm (GA), which shows that CCQGA has better feasibility and effectiveness.     

柔性作业调度的串并行模型对比与求解

作业处理中的柔性使得作业调度更为灵活,作业中操作的执行顺序满足拓扑排序是作业调度的前提。是否允许没有优先关系的操作在不同的机器上同时执行是区分串行和并行调度的条件。文中以共生进化算法求解一个复杂的作业调度模型为例,给出了算法实现串行调度和并行调度的具体区别,并给出了串行和并行调度的结果。结果表明,并行相对于串行对算法效率的提高与柔性大小相关,与作业的规模成反比。     

Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling

Fuzzy flexible job shop scheduling problem (FfJSP) is the combination of fuzzy scheduling and flexible scheduling in job shop environment, which is seldom investigated for its high complexity. We developed an effective co-evolutionary genetic algorithm (CGA) for the minimization of fuzzy makespan. In CGA, the chromosome of a novel representation consists of ordered operation list and machine assignment string, a new crossover operator and a modified tournament selection are proposed, and the population of job sequencing and the population of machine assignment independently evolve and cooperate for converging to the best solutions of the problem. CGA is finally applied and compared with other algorithms. Computational results show that CGA outperforms those algorithms compared.     

The hybrid heuristic genetic algorithm for job shop scheduling

Scheduling for the job shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization methods owing to the high computational complexity (NP-hard). Genetic algorithms (GA) have been proved to be effective for a variety of situations, including scheduling and sequencing. Unfortunately, its efficiency is not satisfactory. In order to make GA more efficient and practical, the knowledge relevant to the problem to be solved is helpful. In this paper, a kind of hybrid heuristic GA is proposed for problem n/m/G/C_max, where the scheduling rules, such as shortest processing time (SPT) and MWKR, are integrated into the process of genetic evolution. In addition, the neighborhood search technique (NST) is adopted as an auxiliary procedure to improve the solution performance. The new algorithm is proved to be effective and efficient by comparing it with some popular methods, i.e. the heuristic of neighborhood search, simulated annealing (SA), and traditional GA.     

A hybrid genetic algorithm for the job shop scheduling problems

The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. We design a scheduling method based on Single Genetic Algorithm (SGA) and Parallel Genetic Algorithm (PGA). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, the initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling methods based on genetic algorithm are tested on five standard benchmark JSSP. The results are compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement in solution quality. The superior results indicate the successful incorporation of a method to generate initial population into the genetic operators.     

基于多小组协同学习教学算法的车间作业调度问题

为求解车间作业调度问题(JSSP),提出一种新颖的多小组协同学习的教学算法,实现小组间学习的协同及基于学习能力的深度和广度搜索策略.针对JSSP问题因其复杂度较高容易导致算法陷入局部最优的不足,引入学习小组协同学习,通过组内学习和组内交流,使学习过程跳出当前的局限.为了兼顾局部和全局搜索能力,引入基于学习能力的深度和广度搜索策略,小组内学生按照学习能力强弱进行学习,较优的学生进行深度的学习,较差的学生进行广度的学习.最后,对OR-Library中的标准仿真实例进行实验,结果表明,所提出的教学算法在JSSP问题上的收敛精度和搜索能力较其他算法均得到了有效的提高.