An effective genetic algorithm for the flexible job-shop scheduling problem

In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.     

求解作业车间调度问题的微粒群遗传退火算法

标准微粒群算法（PSO）通常被用于求解连续优化的问题,很少被用于离散问题的优化求解,如作业车间调度问题（JSP）。因此,针对PSO算法易早熟、收敛慢等缺点提出一种求解作业车间调度问题（JSP）的混合微粒群算法。算法将微粒群算法、遗传算法（GA）、模拟退火（SA）算法相结合,既增强了算法的局部搜索能力,降低了算法对参数的依赖,同时改善了PSO算法和GA算法易早熟的缺点。对经典JSP问题的仿真实验表明：与标准微粒群算法相比,该算法不仅能有效避免算法中的早熟问题,并且算法的全局收敛性得到了显著提高。     

An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem

An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job-shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA’s convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.     

柔性作业车间调度中的组合遗传优化研究

针对柔性作业车间调度问题,提出一种组合遗传算法。该算法在种群初始化、选择、交叉、变异各阶段,组合使用各种不同的策略。针对机器编码部分的交叉,提出一种基于工件的机器交叉算子,用以改进机器分配部分随机交叉引起的对父代优秀基因继承不足的缺陷。通过对典型算例的计算以及与其他文献的研究成果比较,证明该算法的优良性能。     

模拟退火下布谷鸟算法求解车间作业调度问题

针对车间作业调度问题（JSP）,在标准布谷鸟算法的莱维飞行中加入自适应机制,寻优过程中引入二值交叉算子保持改进算法的种群多样性,最后在模拟退火框架下增强改进算法跳出局部最优的能力。通过标准算例对所提的改进算法进行实验仿真,结果证明了改进算法的正确性和有效性。     

遗传算法与人工免疫算法对车间调度问题求解

针对求解job-shop调度问题中存在的易出现局部最优、效率低下的问题,提出了一种新算法。该算法 采用了一种评价种群过早收敛标准的方法,引进了新的加快遗传算法进化速度的交叉算子,最后设计了人工免 疫算法中疫苗的提取和接种方法,即基于加工机器的基因片断抽取疫苗方法和最后完工机器个体的接种方法。 通过实验证明该算法能够有效地解决易出现局部最优、效率低下等问题。     

一种求解车间调度问题的混沌遗传规划方法

作业车间调度问题是制造业的一个经典NP-hard组合优化难题。提出一种基于混沌遗传规划的调度算法,利用遗传规划进行染色体的结构设计,采用混沌序列改善初始种群质量,利用混沌扰动来维持进化群体的多样性,并自适应调整个体权重,使算法具有优良的综合求解性能。实验表明,算法对典型的标准调度测试问题具有较强的全局搜索能力,甘特图表明其获得的最优解优于当前已知的最优解历史记录,对比结果表明了该方法的有效性。     

A multi-modal immune algorithm for the job-shop scheduling problem

This paper describes the application of an artificial immune system to a scheduling application. A novel approach multi-modal immune algorithm is proposed for finding optimal solutions to job-shop scheduling problems emulating the features of a biological immune system. Inter-relationships within the proposed algorithm resemble antibody molecule structure, antibody-antigen relationships in terms of specificity, clonal proliferation, germinal center, and the memory characteristics of adaptive immune responses. Gene fragment recombination and several antibody diversification schemes including somatic recombination, somatic mutation, gene conversion, gene reversion, gene drift, and nucleotide addition were incorporated into the algorithm in order to improve the balance between exploitation and exploration. In addition, niche antibody was employed to discover multi-modal solutions. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The results indicate the effectiveness and flexibility of the immune algorithm.     

双种群混合遗传算法求解具有预防性维护的分布式柔性作业车间调度问题

在实际生产过程中,生产调度和设备维护相互影响,因此两者应该统筹优化.为研究具有预防性维护的分布式柔性作业车间调度问题,以最小化最大完工时间为目标,提出一种双种群混合遗传算法.结合问题特性,设计三维编码以及对应的机器解码方案,采用不同的策略初始化种群以均衡一部分工厂负载,为双种群设计不同的交叉变异算子提高算法的多样性,并利用交换精英解的方法实现两个种群的协作优化,同时针对关键工厂和预防性维护操作设计相应的局部搜索.最后对比现有算法,在同构和异构工厂的算例上进行实验,使用正交试验法优化算法参数设置.实验结果验证了局部搜索以及种群协作的有效性和双种群混合遗传算法求解具有预防性维护的分布式柔性作业车间调度问题的优越性.     

改进人工蜂群算法求解模糊柔性作业车间调度问题

模糊柔性作业车间调度问题(FFJSP)是柔性作业车间调度问题(FJSP)的拓展,具有很强的现实意义.针对FFJSP,本文提出了一种基于领域搜索的改进人工蜂群算法.该算法以最小化最大模糊完工时间为目标.首先,为了提高初始种群的多样性,引入混沌理论来初始化种群.其次,为了提高算法的局部搜索能力,采用4种邻域结构对蜜源进行邻域搜索.为了进一步优化蜜源和加快种群的收敛速度,采用了一种新颖的交叉操作.并且在解码的过程中采用左移策略,从而很好地利用机器的空闲时间.最后,选取了3组通用数据集来测试算法的性能,并与代表性算法进行比较.结果表明,对于大部分实例,本文所提出的的算法的结果要优于与之对比的算法.     

烟花算法求解JSP问题的研究

烟花算法是一种新型智能优化算法,该算法模拟烟花在空中爆炸产生火花这一过程。烟花算法的求解过程包含两种机制：产生爆炸火花,从而实现算法的局部和全局寻优过程;产生高斯变异火花,从而增加种群的多样性以便将优良个体遗传到下一代。通过设计四个参数实验,分析了主要参数对算法求解能力的影响,找出求解作业车间调度问题的较优参数。最后通过对作业车间调度的标准问题进行仿真对比实验,证明了烟花算法求解作业车间调度问题的有效性和稳定性。     

An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.     

改进生物地理学算法求解柔性作业调度问题

针对柔性作业车间调度问题的特点,提出了一种基于改进生物地理学优化算法的求解方案。该方案采用基于工序和基于机器相结合的编码机制,在初始种群中引入启发式算法生成的优良个体,并在标准生物地理学算法基础上对迁移和变异操作进行了改进,采用符合该调度问题的迁移率模型和自适应变异机制,克服了传统算法易于早熟或收敛慢的缺点。通过仿真验证了该算法的可行性和有效性。     

免疫克隆选择算法求解柔性生产调度问题

为减少计算复杂度,将具有解决复杂组合优化问题的免疫克隆选择算法应用于求解柔性生产调度问题.首先设计一种有效的抗原和抗体的数据结构,用抗原表示待调度的生产计划,抗体表示高效的柔性生产调度结果;然后着重设计了用于产生高效的柔性生产调度结果的克隆免疫算子;最后运用该模型对一个实际生产系统进行仿真调度决策,实验评估结果验证了算法的正确性和有效性.     

A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem

This paper considers the job-shop problem with release dates and due dates, with the objective of minimizing the total weighted tardiness. A genetic algorithm is combined with an iterated local search that uses a longest path approach on a disjunctive graph model. A design of experiments approach is employed to calibrate the parameters and operators of the algorithm. Previous studies on genetic algorithms for the job-shop problem point out that these algorithms are highly depended on the way the chromosomes are decoded. In this paper, we show that the efficiency of genetic algorithms does no longer depend on the schedule builder when an iterated local search is used. Computational experiments carried out on instances of the literature show the efficiency of the proposed algorithm.     

求解作业车间调度问题的禁忌分布估计算法

为优化作业车间调度问题的解,提出一个禁忌和分布估计的混合算法。分布估计算法是一种新的进化模式,通过概率优化模型在连续空间进行求解;通过对已获得的群体进行选择操作生成优势群体,提出的分布估计算法使用单变量边缘分布算法构建概率模型,估计离散空间中的联合概率分布,从概率向量采样生成新群体;采用基于工件编号的编码和解码机制保证解的可行性。为提高局部搜索能力,算法基于禁忌搜索算法设计新的双重移动组合、块禁忌和选择策略,在搜索陷入局部最优时利用遗传算法的变异算子生成新解;算法通过混合分布估计算法和禁忌搜索算法的优点,兼具全局搜索与局部搜索能力,提高了搜索的效率和性能。通过与现有算法在典型实例上的实验结果比较,表明该算法在求解作业车间调度问题上具有可行性和有效性。     

求解作业车间调度问题的混合帝国主义竞争算法

针对最小化最大完工时间的作业车间调度问题（JSP）,提出一种结合帝国主义竞争算法（ICA）和禁忌搜索（TS）算法的混合算法。混合算法以帝国主义竞争算法为基础,在同化操作中融入遗传算法中的杂交算子和变异算子,使算法全局搜索能力更强。为了克服帝国主义竞争算法局部搜索能力弱的缺点,引入禁忌搜索算法进一步优化同化操作后的后代。禁忌搜索算法采用混合邻域结构和新型选择策略,使得算法能够更有效地搜索邻域解。混合算法兼具全局搜索能力和局部搜索能力,通过对13个经典的Benchmark调度问题进行仿真测试,并与近年4种新型混合算法进行对比分析,实验结果表明了所提算法求解Job Shop调度问题的有效性和稳定性。     

A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem

The genetic algorithm with search area adaptation (GSA) has a capacity for adapting to the structure of solution space and controlling the tradeoff balance between global and local searches, even if we do not adjust the parameters of the genetic algorithm (GA), such as crossover and/or mutation rates. But, GSA needs the crossover operator that has ability for characteristic inheritance ratio control. In this paper, we propose the modified genetic algorithm with search area adaptation (mGSA) for solving the Job-shop scheduling problem (JSP). Unlike GSA, our proposed method does not need such a crossover operator. To show the effectiveness of the proposed method, we conduct numerical experiments by using two benchmark problems. It is shown that this method has better performance than existing GAs.     

A heuristic algorithm for the distributed and flexible job-shop scheduling problem

The distributed manufacturing takes place in a multi-factory environment including several factories, which may be geographically distributed in different locations, or in a multi-cell environment including several independent manufacturing cells located in the same plant. Each factory/cell is capable of manufacturing a variety of product types. An important issue in dealing with the production in this decentralized manner is the scheduling of manufacturing operations of products (jobs) in the distributed manufacturing system. In this paper, we study the distributed and flexible job-shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. A fast heuristic algorithm based on a constructive procedure is developed to obtain good quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and promising for practical problems.     

An adaptive hybrid genetic algorithm for the three-matching problem

This paper presents a hybrid genetic algorithm (GA) with an adaptive application of genetic operators for solving the 3-matching problem (3MP), an NP-complete graph problem. In the 3MP, we search for the partition of a point set into minimal total cost triplets, where the cost of a triplet is the Euclidean length of the minimal spanning tree of the three points. The problem is a special case of grouping and facility location problems. One common problem with GA applied to hard combinatorial optimization, like the 3MP, is to incorporate problem-dependent local search operators into the GA efficiently in order to find high-quality solutions. Small instances of the problem can be solved exactly, but for large problems, we use local optimization. We introduce several general heuristic crossover and local hill-climbing operators, and apply adaptation to choose among them. Our GA combines these operators to form an effective problem solver. It is hybridized as it incorporates local search heuristics, and it is adaptive as the individual recombination/improvement operators are fired according to their online performance. Test results show that this approach gives approximately the same or even slightly better results than our previous, fine tuned GA without adaptation. It is better than a grouping GA for the partitioning considered. The adaptive combination of operators eliminates a large set of parameters, making the method more robust, and it presents a convenient way to build a hybrid problem solver