分布式环境的同顺序流水作业问题求解算法研究
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摘要
作为一类重要的组合优化问题,流水作业问题得到了计算机科学和运筹学界的广泛关注,在实际工业生产调度中发挥了重要的作用。然而随着工业化的进步和工业生产规模的扩大,在经济全球化影响之下现代工业生产模式有了根本改变——全球化的生产企业逐步淘汰了传统制造环境下的单一工厂生产模式,取而代之的是跨地域的多个工厂的联合生产方式。由于传统的流水作业问题是针对单个工厂进行建模的,不能有效地处理分布式的生产模式,于是分布式多工厂流水作业问题(DPFSP)被提出并逐渐成为研究热点。
虽然各类优化算法已经成功地用于求解传统的单个工厂流水作业问题,但是研究求解DPFSP的算法还处于起步阶段。本文围绕分布式生产环境中的流水作业问题展开,重点研究DPFSP问题的模型和求解算法。
理论基础方面,本文在研究同构工厂的分布式流水作业问题模型(DPFSP-I)的基础上,针对实际生产调度中多个工厂加工能力有所不同的情况,定义了异构工厂的同顺序流水作业问题(DPFSP-D)并分析其数学模型。通过分析最优解目标值与传统单个工厂流水作业问题最优解目标值之间的关系,本文确定了DPFSP-I最优解的上下界。此外,本文分析了DPFSP可行解的表示方法及解空间结构,讨论了最大完工时间的加速计算方法。
算法研究方面,以工厂最大完工时间为目标值,本文提出了多种DPFSP的求解算法：(1)求解DPFSP-I的构造启发式算法和变邻域下降搜索算法。原有的构造启发式和变邻域下降搜索算法在安排工件时,都是单个工件逐一考虑的,而本文采用多工件整体考虑的安排策略,提出一个基于工件组的指派规则。实验数据表明,结合该规则的构造启发式和变邻域下降搜索算法在求解效果方面有明显的提升。(2)求解DPFSP-I的遗传算法。在此之前尚没提出有效的智能优化算法求解该类问题。本文提出一个遗传算法,并根据可行解表示特点,重新定义了遗传算法中的操作,结合高效的局部搜索策略提升遗传算法的整体求解效率。在此基础上,本文还对所提出的遗传算法进行了改进,通过可行解中知识的提取改良种群质量,以达到提高求解质量的目的。通过大量的实验分析可知,本文的遗传算法更新了标准测试集中绝大部分的已知最优解。(3)求解DPFSP-I和DPFSP-D的禁忌搜索算法。为进一步提高优化速度,本文设计了针对工件子序列交换的禁忌策略并提出一个求解DPFSP-I的禁忌搜索算法,其特点是通过工厂间工件子序列的交换操作摆脱局部极小区域。重新设计的最大完工时间加速计算方法,将该禁忌搜索算法扩展为可以求解DPFSP-D的算法。实验表明,本文的禁忌搜索算法具有高效的优化能力。(4)基于多智能体的协同求解算法。文本提出一个针对DPFSP-D的多智能体的协同求解算法,并通过设计基于环式消息传递机制的交互协议达到协同求解保护私有数据的目的。
As an important combinational optimization problem, flowshop scheduling prob-lem and its algorithms have attracted much attention in computer science and OR com-munities, and those algorithms play an important role in industrial areas. However, many modern companies have changed their manufacturing environments, where tradi-tional single-factory environment is replaced by multi-factory environment and more factories are built to set up the environment. Traditional flowshop scheduling problem concerns single factory, and it cannot deal with distributed multi-factory environment, so the Distributed Permutation Flowshop Scheduling Problem (DPFSP) was presented and studying this problem becomes a new hotspot.
Though kinds of optimization algorithms have been studied to solve traditional flowshop scheduling problems with single factory, it is in the early stage of studies on algorithms for solving DPFSPs. This paper concentrates on the flowshop scheduling problems with multi-factories, studies the model of this problem and its scheduling al-gorithms.
In the aspect of theory,this paper studies Distributed Permutation Flowshop Scheduling Problem with Identical factories (DPFSP-I). Then with the consideration that processing times of jobs in two factories differs from each other, this paper defines a new problem called Distributed Permutation Flowshop Scheduling Problem with Dis-tinct factories (DPFSP-D) and its mathematic model. Furthermore, it discusses lower and upper bounds of solutions by analyzing relationship between makespan of DPFSP-I and traditional flowshop scheduling problems. Additionally, solution representation and solution space are studied in this paper, as well as acceleration methods to compute makespan.
In the aspect of algorithm, this paper proposes several algorithms for DPFSPs to minimize makespan, including:(1) Constructive heuristics and variable neighbourhood descent algorithms for DPFSP-I. Different from the existing methods that insert one job each time, it considers several jobs at each time and proposes a job dispatching rule based on this strategy. The results of experiments show that constructive heuristics and variable neighbourhood descent algorithms can benefit from the proposed dispatching rule and thus improve their performance greatly.(2) Genetic algorithms for DPFSP-I. There is no effective intelligent optimization algorithm proposed for this problem, so this paper presents a genetic algorithm, as well as its operators according to the repre-sentation of scheduling solutions, then an efficient local search method is also proposed and is combined into the genetic algorithm. In addition, in order to obtain better solu-tions, this paper improves the genetic algorithm by introducing a new knowledge-based infection operator, through which the solution quality is improved. Intensive experi-ments have performed and the results update most best-known solutions of instances from DPFSP benchmark.(3) Tabu search algorithms for DPFSP-I and DPFSP-D. This paper proposes a tabu search algorithm for DPFSP-I to improve efficiency, where a job exchange rule that operates on job sequences and a tabu strategy are introduced. This strategy uses the exchange rule to escape local optima. Besides, this paper designs a new acceleration method to compute makespan of DPFSP-D and then proposed a tabu search algorithm for DPFSP-D. The computational results indicate the effectiveness of those algorithms.(4) A cooperative algorithm based on multi-agent systems. This paper proposes a cooperation algorithm for DPFSP-D and studies a ring-based mes-sage-passing strategy to ensure security of local information.
虽然各类优化算法已经成功地用于求解传统的单个工厂流水作业问题,但是研究求解DPFSP的算法还处于起步阶段。本文围绕分布式生产环境中的流水作业问题展开,重点研究DPFSP问题的模型和求解算法。
理论基础方面,本文在研究同构工厂的分布式流水作业问题模型(DPFSP-I)的基础上,针对实际生产调度中多个工厂加工能力有所不同的情况,定义了异构工厂的同顺序流水作业问题(DPFSP-D)并分析其数学模型。通过分析最优解目标值与传统单个工厂流水作业问题最优解目标值之间的关系,本文确定了DPFSP-I最优解的上下界。此外,本文分析了DPFSP可行解的表示方法及解空间结构,讨论了最大完工时间的加速计算方法。
算法研究方面,以工厂最大完工时间为目标值,本文提出了多种DPFSP的求解算法：(1)求解DPFSP-I的构造启发式算法和变邻域下降搜索算法。原有的构造启发式和变邻域下降搜索算法在安排工件时,都是单个工件逐一考虑的,而本文采用多工件整体考虑的安排策略,提出一个基于工件组的指派规则。实验数据表明,结合该规则的构造启发式和变邻域下降搜索算法在求解效果方面有明显的提升。(2)求解DPFSP-I的遗传算法。在此之前尚没提出有效的智能优化算法求解该类问题。本文提出一个遗传算法,并根据可行解表示特点,重新定义了遗传算法中的操作,结合高效的局部搜索策略提升遗传算法的整体求解效率。在此基础上,本文还对所提出的遗传算法进行了改进,通过可行解中知识的提取改良种群质量,以达到提高求解质量的目的。通过大量的实验分析可知,本文的遗传算法更新了标准测试集中绝大部分的已知最优解。(3)求解DPFSP-I和DPFSP-D的禁忌搜索算法。为进一步提高优化速度,本文设计了针对工件子序列交换的禁忌策略并提出一个求解DPFSP-I的禁忌搜索算法,其特点是通过工厂间工件子序列的交换操作摆脱局部极小区域。重新设计的最大完工时间加速计算方法,将该禁忌搜索算法扩展为可以求解DPFSP-D的算法。实验表明,本文的禁忌搜索算法具有高效的优化能力。(4)基于多智能体的协同求解算法。文本提出一个针对DPFSP-D的多智能体的协同求解算法,并通过设计基于环式消息传递机制的交互协议达到协同求解保护私有数据的目的。
As an important combinational optimization problem, flowshop scheduling prob-lem and its algorithms have attracted much attention in computer science and OR com-munities, and those algorithms play an important role in industrial areas. However, many modern companies have changed their manufacturing environments, where tradi-tional single-factory environment is replaced by multi-factory environment and more factories are built to set up the environment. Traditional flowshop scheduling problem concerns single factory, and it cannot deal with distributed multi-factory environment, so the Distributed Permutation Flowshop Scheduling Problem (DPFSP) was presented and studying this problem becomes a new hotspot.
Though kinds of optimization algorithms have been studied to solve traditional flowshop scheduling problems with single factory, it is in the early stage of studies on algorithms for solving DPFSPs. This paper concentrates on the flowshop scheduling problems with multi-factories, studies the model of this problem and its scheduling al-gorithms.
In the aspect of theory,this paper studies Distributed Permutation Flowshop Scheduling Problem with Identical factories (DPFSP-I). Then with the consideration that processing times of jobs in two factories differs from each other, this paper defines a new problem called Distributed Permutation Flowshop Scheduling Problem with Dis-tinct factories (DPFSP-D) and its mathematic model. Furthermore, it discusses lower and upper bounds of solutions by analyzing relationship between makespan of DPFSP-I and traditional flowshop scheduling problems. Additionally, solution representation and solution space are studied in this paper, as well as acceleration methods to compute makespan.
In the aspect of algorithm, this paper proposes several algorithms for DPFSPs to minimize makespan, including:(1) Constructive heuristics and variable neighbourhood descent algorithms for DPFSP-I. Different from the existing methods that insert one job each time, it considers several jobs at each time and proposes a job dispatching rule based on this strategy. The results of experiments show that constructive heuristics and variable neighbourhood descent algorithms can benefit from the proposed dispatching rule and thus improve their performance greatly.(2) Genetic algorithms for DPFSP-I. There is no effective intelligent optimization algorithm proposed for this problem, so this paper presents a genetic algorithm, as well as its operators according to the repre-sentation of scheduling solutions, then an efficient local search method is also proposed and is combined into the genetic algorithm. In addition, in order to obtain better solu-tions, this paper improves the genetic algorithm by introducing a new knowledge-based infection operator, through which the solution quality is improved. Intensive experi-ments have performed and the results update most best-known solutions of instances from DPFSP benchmark.(3) Tabu search algorithms for DPFSP-I and DPFSP-D. This paper proposes a tabu search algorithm for DPFSP-I to improve efficiency, where a job exchange rule that operates on job sequences and a tabu strategy are introduced. This strategy uses the exchange rule to escape local optima. Besides, this paper designs a new acceleration method to compute makespan of DPFSP-D and then proposed a tabu search algorithm for DPFSP-D. The computational results indicate the effectiveness of those algorithms.(4) A cooperative algorithm based on multi-agent systems. This paper proposes a cooperation algorithm for DPFSP-D and studies a ring-based mes-sage-passing strategy to ensure security of local information.
引文
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