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.     

Bi-criteria flexible job-shop scheduling with sequence-dependent setup times—Variable neighborhood search approach

In scheduling problems, taking the sequence-dependent setup times into account is one of the important issues that have recently been considered by researchers in the production scheduling field. In this paper, we consider flexible job-shop scheduling problem (FJSP) with sequence-dependent setup times to minimize makespan and mean tardiness. The FJSP consists of two sub-problems from which the first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on all machines. To solve this problem, a variable neighborhood search (VNS) algorithm based on integrated approach is proposed. In the presented optimization method, the external loop controlled the stop condition of algorithm and the internal loop executed the search process. To search the solution space, the internal loop used two main search engines, i.e. shake and local search procedures. In addition, neighborhood structures related to the sequencing problem and the assignment problem were employed to generate neighboring solutions. To evaluate the performance of the proposed algorithm, 20 test problems in different sizes are randomly generated. Consequently, computational results and comparisons validate the quality of the proposed approach.     

An effective heuristic for flexible job-shop scheduling problem with maintenance activities

Most production scheduling problems, including the standard flexible job-shop scheduling problem (FJSP), assume that machines are continuously available. However, in most realistic situations, machines may become unavailable during certain periods due to preventive maintenance (PM). In this paper, a flexible job-shop scheduling problem with machine availability constraints is considered. Each machine is subject to preventive maintenance during the planning period and the starting times of maintenance activities are either flexible in a time window or fixed beforehand. Moreover, two cases of maintenance resource constraint are considered: sufficient maintenance resource available or only one maintenance resource available. To deal with this variant FJSP problem with maintenance activities, a filtered beam search (FBS) based heuristic algorithm is proposed. With a modified branching scheme, the machine availability constraint and maintenance resource constraint can be easily incorporated into the proposed algorithm. Simulation experiments are conducted on some representative problems. The results demonstrate that the proposed filtered beam search based heuristic algorithm is a viable and effective approach for the FJSP with maintenance activities.     

多目标FJSP的一维编码粒子群优化求解方法

以单件小批量生产方式为主的柔性车间调度中,快速得到满足低生产成本、高生产效率,避免瓶颈发生的调度方案,是调度优化算法的设计目标。就此建立了以制造期、机床总负荷和单机最大负荷为综合目标的柔性车间调度问题（Flexible Job-shop Scheduling Problems,FJSP）优化模型;设计了一种以概率值为分量的一维粒子群优化算法,通过概率区间划分将连续粒子分量离散化,结合完工时间最早启发式规则,实现工序的排序与加工机床的选取。通过不同规模算例的比较,分析结果表明该方法在求解较大规模问题时具有一定的优势。     

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.     

Simulation modeling and analysis of tool sharing and part scheduling decisions in single-stage multimachine flexible manufacturing systems

This paper focuses on a simulation-based study of tool sharing problem in single-stage multimachine Flexible Manufacturing Systems. Three different scenarios are considered for investigation. A simulation model has been developed for each of these scenarios. A number of scheduling rules are incorporated in the simulation models for the decisions such as tool request selection and part launching in the context of tool sharing environment. The performance measures evaluated are mean tardiness, conditional mean tardiness and mean flow time. Based on the analysis of the simulation results, the best possible scheduling rule combinations for part launching and tool request selection have been identified for the three scenarios.     

生物地理学算法求解柔性车间作业调度问题

针对加工设备和操作工人双资源约束的柔性作业车间调度问题,建立以生产时间和生产成本为目标函数的柔性作业车间调度模型,提出基于模糊Pareto支配的生物地理学算法,采用模糊Pareto支配的方法计算解之间的支配关系并对Pareto解集排序,进行全局最优值的更新,并采用余弦迁移模型来改善生物地理学算法的收敛速度。将该方法应用于某模具车间的柔性作业车间调度中,仿真结果验证了该方法的可行性和有效性。     

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。     

An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems

This paper proposes an effective hybrid tabu search algorithm (HTSA) to solve the flexible job-shop scheduling problem. Three minimization objectives – the maximum completion time (makespan), the total workload of machines and the workload of the critical machine are considered simultaneously. In this study, a tabu search (TS) algorithm with an effective neighborhood structure combining two adaptive rules is developed, which constructs improved local search in the machine assignment module. Then, a well-designed left-shift decoding function is defined to transform a solution to an active schedule. In addition, a variable neighborhood search (VNS) algorithm integrating three insert and swap neighborhood structures based on public critical block theory is presented to perform local search in the operation scheduling component. The proposed HTSA is tested on sets of the well-known benchmark instances. The statistical analysis of performance comparisons shows that the proposed HTSA is superior to four existing algorithms including the AL + CGA algorithm by Kacem, Hammadi, and Borne (2002b), the PSO + SA algorithm by Xia and Wu (2005), the PSO + TS algorithm by Zhang, Shao, Li, and Gao (2009), and the Xing’s algorithm by Xing, Chen, and Yang (2009a) in terms of both solution quality and efficiency.     

基于多区域采样策略的混合粒子群优化求解多目标柔性作业车间调度问题

柔性作业车间调度问题(FJSP)是一类应用广泛的组合优化问题.针对多目标FJSP求解过程复杂、算法易陷入局部最优的问题,提出了一种基于多区域采样策略的混合粒子群优化算法(HPSO-MRS),以同时优化最大完工时间和总机器延迟时间这两个目标.多区域采样策略能够区分粒子所在Pareto前沿面的位置,根据不同区域进行采样重组...     

Integrated scheduling of resource-constrained flexible manufacturing systems using constraint programming

This contribution presents a novel approach to address the scheduling of resource-constrained flexible manufacturing systems (FMSs). It deals with several critical features that are present in many FMS environments in an integrated way. The proposal consists in a constraint programming (CP) formulation that simultaneously takes into account the following sub-problems: (i) machine loading, (ii) manufacturing activities scheduling, (iii) part routing, (iv) machine buffer scheduling, (v) tool planning and allocation, and (vi) AGV scheduling, considering both the loaded and the empty movements of the device. Before introducing the model, this work points out the problems that might appear when all these issues are not concurrently taken into account. Then, the FMS scheduling model is presented and later assessed through several case-studies. The proposed CP approach has been tested by resorting to problems that consider dissimilar number of parts, operations per part, and tool copies, as well as different AGV speeds. The various examples demonstrate the importance of having an integrated formulation and show the important errors that can occur when critical issues such as AGV empty movements are neglected.     

A fast estimation of distribution algorithm for dynamic fuzzy flexible job-shop scheduling problem

Due to the complicated circumstances in workshop, most of the conventional scheduling algorithms fail to meet the requirements of instantaneity, complexity, and dynamicity in job-shop scheduling problems. Compared with the static algorithms, dynamic scheduling algorithms can better fulfill the requirements in real situations. Considering that both flexibility and fuzzy processing time are common in reality, this paper focuses on the dynamic flexible job-shop scheduling problem with fuzzy processing time (DfFJSP). By adopting a series of transforming procedures, the original DfFJSP is simplified as a traditional static fuzzy flexible job-shop problem, which is more suitable to take advantage of the existing algorithms. In this paper, estimation of distribution algorithm (EDA) is brought into address the post-transforming problem. An improved EDA is developed through making use of several elements omitted in original EDA, including the historical-optimal solution and the standardized solution vectors. The improved algorithm is named as fast estimation of distribution algorithm (fEDA) since it performs better in convergence speed and computation precision, compared with the original EDA. To sum up, the ingenious transformation and the effective fEDA algorithm provide an efficient and practical way to tackle the dynamic flexible fuzzy job-shop scheduling problem.     

柔性车间调度问题的协作混合帝国算法

针对柔性车间调度问题（FJSP）的非确定性多项式特性,提出一种新的改进算法——协作混合帝国算法,用于寻找最小化最大完工时间的调度。首先,根据标准帝国竞争算法（ICA）的流程特性,设计了自适应参数的改进,可提高算法的收敛速度;然后,引入帝国和殖民地双改革变异,并针对工序排序和选择机器的不同阶段提出多变异改革策略,可提高算法的局部搜索效率;最后,创建大陆间国家交流合作机制,促进优秀国家对外信息交流,可提高算法全局搜索能力。通过对多个柔性车间调度实例进行仿真,结果表明,所提出算法在求解质量和稳定性上均优于多种群体智能进化算法,更适合解决该类调度问题。     

基于蚁群粒子群算法求解多目标柔性调度问题

通过分析多目标柔性作业车间调度问题中各目标的相互关系,提出一种主、从递阶结构的蚁群粒子群求解算法。算法中,主级为蚁群算法,在选择工件加工路径过程中实现设备总负荷和关键设备负荷最小化的目标;从级为粒子群算法,在主级工艺路径约束下的设备排产中实现工件流通时间最小化的目标。然后,以设备负荷和工序加工时间为启发式信息设计蚂蚁在工序可用设备间转移概率;基于粒子向量优先权值的大小关系设计解码方法实现设备上的工序排产。最后,通过仿真和比较实验,验证了该算法的有效性。     

A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem

In this paper, an effective bi-population based estimation of distribution algorithm (BEDA) is proposed to solve the flexible job-shop scheduling problem (FJSP) with the criterion to minimize the maximum completion time (makespan). The BEDA stresses the balance between global exploration and local exploitation. In the framework of estimation of distribution algorithm, two sub-populations are used to adjust the machine assignment and operation sequence respectively with a splitting criterion and a combination criterion. At the initialization stage, multiple strategies are utilized in a combination way to generate the initial solutions. At the global exploration phase, a probability model is built with the superior population to generate the new individuals and a mechanism is proposed to update the probability model. At the local exploitation phase, different operators are well designed for the two sub-populations to generate neighbor individuals and a local search strategy based on critical path is proposed to enhance the exploitation ability. In addition, the influence of parameters is investigated based on Taguchi method of design of experiment, and a suitable parameter setting is determined. Finally, numerical simulation based on some widely used benchmark instances is carried out. The comparisons between BEDA and some existing algorithms as well as the single-population based EDA demonstrate the effectiveness of the proposed BEDA in solving the FJSP.     

重启进化提高较大搜索规模时柔性作业解质量

种群多样性下降导致的早熟收敛限制了进化算法的求解质量与搜索效率。为应对收敛,提高较大搜索规模时的求解质量,引入随机算法中重启策略。种群收敛时,利用算法前期搜索结果（优势元素）和新产生的随机元素重新构造新种群继续进化。提高柔性作业车间调度问题解质量对实际工业生产有重要的现实意义。将重构思想应用于协同进化算法求解复杂柔性作业调度问题并跟踪种群进化状态。仿真实验结果表明,改进算法在进化过程中维持了较好的种群多样性,大幅提高了算法求解复杂柔性作业调度的搜索性能,并可以简单通过扩大搜索规模提高作业调度解质量。     

教与同伴学习粒子群算法求解多目标柔性作业车间调度问题

针对多目标柔性作业车间调度问题,提出了带Pareto非支配解集的教与同伴学习粒子群算法。首先,以工件的最大完工时间、最大机器负荷和所有机器总负荷为优化目标建立了多目标柔性作业车间调度模型。然后,该算法结合多目标Pareto方法和教与同伴学习粒子群算法,采用快速非支配排序算法产生初始Pareto非支配解集,用提取Pareto支配层程序更新Pareto非支配解集,同时采用混合分派规则产生初始种群,采用开口向上抛物线递减的惯性权重选择策略提高算法的收敛速度。最后,对3个Benchmark算例进行仿真实验。理论分析和仿真表明,与带向导性局部搜索的多目标进化算法(MOEA-GLS)和带局部搜索的控制遗传算法(AL-CGA)相比,对于相同的测试实例,该算法能产生更多更好的Pareto非支配解;在计算时间方面,该算法要小于带向导性局部搜索的多目标进化算法。实验结果表明该算法可以有效解决多目标柔性作业车间调度问题。     

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。     

改进蝙蝠算法柔性作业车间调度问题研究

针对柔性作业车间调度问题,提出了一种改进的离散蝙蝠算法。该算法采用双层编码序列方式,利用均衡机器负载分配策略和插入式解码方案初始化种群,同时设计了离散蝙蝠算法的速度、位置更新的相关算子和操作,引入了平衡调整因子改善算法搜索能力。通过案例测试并与其他算法比较,验证了改进的离散蝙蝠算法可以有效地求解柔性作业车间调度问题,并具有较高的精确度。     

Flexible job-shop scheduling with parallel variable neighborhood search algorithm

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. This paper proposes a parallel variable neighborhood search (PVNS) algorithm that solves the FJSP to minimize makespan time. Parallelization in this algorithm is based on the application of multiple independent searches increasing the exploration in the search space. The proposed PVNS uses various neighborhood structures which carry the responsibility of making changes in assignment and sequencing of operations for generating neighboring solutions. The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the FJSP.