汽车维修服务站瓶颈工序的实时调度

在结合优化调度理论和约束理论的基础上,从最小化目标、机器环境、加工特征和约束几方面分析了汽车维修服务站瓶颈工序的实时调度问题的特征,建立了对应的数学模型。根据问题特性,设计了包含复合动态分派规则的启发式调度算法。以实例分析验证了算法的可行性,仿真结果展示了所用算法在优化目标函数值上的优越性和计算时间的可行性。     

基于公理设计的车间调度算法研究

车间调度问题是典型的NP难题,也是一种完全耦合的复杂系统.基于公理设计思想对车间调度系统进行了解耦设计,给出了相应的解耦思路及解耦矩阵,提出并实现了一种车间调度算法,并对算法的复杂性进行了分析.以实际车间生产调度作为研究对象,针对实际生产中零件紧急程度不一的情况,为待加工零件赋予不同的权值,并优先考虑调度加工工时较长的零件;采用以解耦设计为总目标,在满足约束条件的情况下,尽量优化压缩加工时间.对算法的复杂性进行了分析,该算法属于三次多项式复杂级,较优于一般的算法.通过2个实例计算和对比,验证了本算法的实用性和有效性.     

关键链上可被部分替代的受限资源动态调度

关键链上受限资源的优化调度,能有效提高系统运作效率,是关键链项目管理的重要研究方向之一.但现有研究主要针对资源能否更新,较少考虑资源的可替代性.针对此,研究了关键链上可被部分替代的受限资源调度问题,以达到提高资源利用率、降低成本等目的.采用α/β/γ三元组方法,将研究的问题描述为以最小化滞后时间和为目标,具有机器适用限制的并行异速机调度问题,并建立对应的数学模型;运用混合重调度策略和改进粒子群算法进行了算法设计;设计了仿真算例,并通过算法比较表明该方法在求解结果和问题求解规模上显著优于传统PSO、GA算法.     

钢管冷区生产调度的一种启发式算法

基于国内大型钢铁公司的实际操作,运用离散化时间的方法把钢管冷区生产调度抽象为可中断Job-Shop问题.同时考虑前置库存限制、生产尽可能连续、部分人工调度等约束条件,以最小化中断次数和尽可能满足人工调度为目标,建立相应的数学模型.再依据约束条件的主次性设计生产调度规则(赋值规则和生产规则),根据实际生产流程设计启发式算法.运用该算法对该问题的大规模算例进行求解,其结果在满足了约束条件情况下达到比较理想的中断次数.     

基于遗传粒子群混合算法的多生产线协调调度

为了求解多目标多生产线调度问题,结合PSO和GA算法的特点,提出了基于协同进化思想的多种群PSOGA混合优化算法(简称MC-HPSOGA).以最小化最大完工时间、最大化生产线利用率和最大化客户满意度为目标函数,建立了多生产线作业协调调度问题的多目标批量调度数学模型,并且设计最小批量动态分批策略,将MC-HPSOGA算法...     

微粒群优化算法在流水线干扰管理调度中的应用

微粒群优化算法(Particle Swarm Optimization,PSO)是起源于鸟群和鱼群群体运动行为的研究,是在蚁群算法提出之后的又一种新的进化计算技术,具有典型的群体智能特性。本文构建了干扰为工件到达的流水车间调度干扰管理模型,其经典目标函数为最大完工时间和干扰目标函数为干扰时间差相混合。本文运用微粒群优化算法求解流水线干扰管理调度问题,给出了计算实例并进行了详细分析,并对干扰管理问题和重调度问题进行了测试分析,得出了有参考意义的结果。     

工期不确定的模具车间柔性Flow-shop调度问题

由于模具制造属于非重复性单件订货生产,模具加工的任务工期具有较强的不确定性,导致生产调度混乱。为制定合理可行的生产调度方案,建立了任务工期离散概率模型,以最大完工时间的期望值最小为目标,建立不确定工期柔性Flow-shop调度模型;在遗传算法交叉、变异等操作中融入模拟退火操作,将遗传算法的全局搜索能力与模拟退火算法的良好局部搜索能力相结合,设计了不确定工期的柔性Flow-shop调度问题混合遗传模拟退火算法。利用混合遗传模拟退火算法对调度模型进行求解,通过仿真实验表明,该研究对于解决工期不确定的模具车间柔性Flow-shop调度问题是行之有效的。     

双重时间不确定性资源受限项目的预防调度研究北大核心CSSCI

以任务工期和发布时间分别描述项目内部与外部环境不确定性对项目实施的影响,研究了具有双重不确定性约束的项目调度问题。采用模糊数进行参数表示,首先对研究问题进行描述和分析;随后以最大化计划方案的鲁棒性为目标建立了预防调度数学模型;针对该问题的NPhard属性,设计了蚁群优化算法。最后,经算例验证和分析,得到如下结论:相较于传统方法所得到的计划方案,该研究所得到的预防调度方案在最糟糕环境下保证项目顺利完工的概率要高出8%,且完工时间更短。     

考虑急件到达的单机鲁棒调度方法

实际生产过程中经常会有急件到达。由于急件的优先级最高,其到达容易扰乱初始调度,使实际调度性能恶化,影响调度目标的实现。针对以总拖期为目标且带有释放时间的单机调度问题,研究了在有急件到达情况下的鲁棒调度方法,以降低急件对实际调度性能的影响。鉴于该调度问题是NP hard问题,根据工件释放时间和交货期的关系构造“金字塔”结构,获得该调度问题的占优性质。根据这些占优性质和急件到达特点,研究急件到达情景下的占优规则,据此求解急件到达情景下的占优调度集合,作为鲁棒调度的备选调度方案集合。提出了应对急件到达的鲁棒调度算法。给出仿真算例验证了算法的有效性,算例表明本文给出的鲁棒调度方法能有效避免急件到达造成实际调度性能的恶化。      

光伏电池片生产车间智能物流调度研究

为实现光伏电池片生产车间物流的智能化改造,提出以最大化瓶颈工序机台产能的方式来最大化生产车间产能,并设计相关数学模型和智能物流调度算法。首先,通过对光伏电池片生产车间的问题描述与分析,建立以瓶颈工序机台产能最大化（瓶颈工序机台总停机时间最短）为目标的数学模型。然后,设计了嵌入模型约束规则的车间智能物流调度算法,包括物料调度算法、AGV选择与路径规划、AGV碰撞避免策略设计等,并提出了另一种物料调度算法作为对照方案。最后,通过仿真实验与分析,证明了所设计模型及智能物流调度算法的高效性及适用性,给予车间管理人员相应的管理启示。     

Studying the effect of different combinations of timetabling with sequencing algorithms to solve the no-wait job shop scheduling problem

This paper considers the no-wait job shop (NWJS) problem with makespan minimisation criteria. It is well known that this problem is strongly NP-hard. Most of the previous studies decompose the problem into a timetabling sub-problem and a sequencing sub-problem. Each study proposes a different sequencing and timetabling algorithm to solve the problem. In this research, this important question is aimed to be answered: is the timetabling or the sequencing algorithm more important to the effectiveness of the developed algorithm? In order to find the answer, three different sequencing algorithms are developed; a tabu search (TS), a hybrid of tabu search with variable neighbourhood search (TSVNS), and a hybrid of tabu search with particle swarm optimisation (TSPSO). Afterwards, the sequencing algorithms are combined with four different timetabling methods. All the approaches are applied to a large number of test problems available in the literature. Statistical analysis reveals that although some of the sequencing and timetabling algorithms are more complicated than the others, they are not necessarily superior to simpler algorithms. In fact, some of the simpler algorithms prove to be more effective than complicated and time-consuming methods.     

A novel timetabling algorithm for a furnace process for semiconductor fabrication with constrained waiting and frequency-based setups

This study aims to solve the scheduling problem arising from oxide–nitride–oxide (ONO) stacked film fabrication in semiconductor manufacturing. This problem is characterized by waiting time constraints, frequency-based setups, and capacity preoccupation. To the best of our knowledge, none of the existing studies has addressed constrained waiting time and frequency-based setups at the same time. To fill this gap, this study develops a genetic algorithm for batch sequencing combined with a novel timetabling algorithm. For validation, we conducted several experiments based on empirical data. As a benchmark for small-sized problem instances, a mixed-integer linear programming model was used. The results show that the proposed algorithm optimally solves most cases of the ONO scheduling problem in real settings and significantly outperforms dispatching rule-based heuristics.     

A method combining rules with genetic algorithm for minimizing makespan on a batch processing machine with preventive maintenance

This paper considers the problem of minimising makespan on a single batch processing machine with flexible periodic preventive maintenance. This problem combines two sub-problems, scheduling on a batch processing machine with jobs’ release dates considered and arranging the preventive maintenance activities on a batch processing machine. The preventive maintenance activities are flexible but the maximum continuous working time of the machine, which is allowed, is determined. A mathematical model for integrating flexible periodic preventive maintenance into batch processing machine problem is proposed, in which the grouping of jobs with incompatible job families, the starting time of batches and the preventive maintenance activities are optimised simultaneously. A method combining rules with the genetic algorithm is proposed to solve this model, in which a batching rule is proposed to group jobs with incompatible job families into batches and a modified genetic algorithm is proposed to schedule batches and arrange preventive maintenance activities. The computational results indicate the method is effective under practical problem sizes. In addition, the influences of jobs’ parameters on the performance of the method are analyzed, such as the number of jobs, the number of job families, jobs’ processing time and jobs’ release time.     

Efficient algorithm for cell formation with sequence data,machine replications and alternative process routings

Cell formation is an important problem in the design of a cellular manufacturing system. Despite a large number of papers on cell formation being published, only a handful incorporate operation sequence in intercell move calculations and consider alternative process routings, cell size, production volume and allocating units of identical machines into different cells. Modelling the above factors makes the cell formation problem complex but more realistic. The paper develops a model and solution methodology for a problem of cell formation to minimize the sum of costs of intercell moves, machine investment and machine operating costs considering all the factors mentioned above. An algorithm comprised of simulated annealing and local search heuristics has been developed to solve the model. A limited comparison of the proposed algorithm with an optimal solution generated by complete enumeration of small problems indicates that the algorithm produces a solution of excellent quality. Large problems with 100 parts and 50 machine types are efficiently solved using the algorithm.     

Non-Cooperative Game of Coordinated Scheduling of Parallel Machine Production and Transportation in Shared Manufacturing

Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry, the coordinated scheduling problem of parallel machine production and transportation is investigated. The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance. A non-cooperative game model is established, considering the competition and self-interest behavior of jobs from different customers for machine resources. The job from different customers is mapped to the players in the game model, the corresponding optional processing machine and location are mapped to the strategy set, and the makespan of the job is mapped to the payoff. Then the solution of the scheduling model is transformed into the Nash equilibrium of the non-cooperative game model. A Nash equilibrium solution algorithm based on the genetic algorithm (NE-GA) is designed, and the effective solution of approximate Nash equilibrium for the game model is realized. The fitness function, single-point crossover operator, and mutation operator are derived from the non-cooperative game model’s characteristics and the definition of Nash equilibrium. Rules are also designed to avoid the generation of invalid offspring chromosomes. The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes. Compared with other algorithms such as heuristic algorithms (FCFS, SPT, and LPT), the simulated annealing algorithm (SA), and the particle swarm optimization algorithm (PSO), experimental results show that the proposed NE-GA algorithm has obvious performance advantages.     

基于约束和邻域的炼钢连铸重调度算法

针对机器故障扰动,研究了炼钢连铸重调度问题及其求解算法。通过将机器故障映射为资源约束,建立了基于动态约束满足的炼钢连铸重调度模型,模型以最大化连浇量与调度方案相似度为目标。针对问题的模型及其特点,采用约束满足和邻域搜索相结合的混合算法对其进行了求解。仿真实验表明本文提出的模型和算法是有效的。     

Parallel machine scheduling with step-deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm

This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup–Hermann–Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX^® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.     

Design of high-performing hybrid meta-heuristics for unrelated parallel machine scheduling with machine eligibility and precedence constraints

This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.     

A re-entrant hybrid flow shop scheduling problem with machine eligibility constraints

A production scheduling problem originating from a real rotor workshop is addressed in the paper. Given its specific characteristics, the problem is formulated as a re-entrant hybrid flow shop scheduling problem with machine eligibility constraints. A mixed integer linear programming model of the problem is provided and solved by the Cplex solver. In order to solve larger sized problems, a discrete differential evolution (DDE) algorithm with a modified crossover operator is proposed. More importantly, a new decoder addressing the machine eligibility constraints is developed and embedded to the algorithm. To validate the performance of the proposed DDE algorithm, various test problems are examined. The efficiency of the proposed algorithm is compared with two other algorithms modified from the existing ones in the literatures. A one-way ANOVA analysis and a sensitivity analysis are applied to intensify the superiority of the new decoder. Tightness of due dates and different levels of scarcity of machines subject to machine eligibility restrictions are discussed in the sensitivity analysis. The results indicate the pre-eminence of the new decoder and the proposed DDE algorithm.     

Jobshop lot streaming with routing flexibility,sequence-dependent setups,machine release dates and lag time

Lot streaming is a technique of splitting production lots into smaller sublots in a multi-stage manufacturing system so that operations of a given lot can overlap. This technique can reduce the manufacturing makespan and is an effective tool in time-based manufacturing. Research on lot streaming models and solution procedures for flexible jobshops has been limited. The flexible jobshop scheduling problem is an extension of the classical jobshop scheduling problem by allowing an operation to be assigned to one of a set of eligible machines during scheduling. In this paper we develop a lot streaming model for a flexible jobshop environment. The model considers several pragmatic issues such as sequence-dependent setup times, the attached or detached nature of the setups, the machine release date and the lag time. In order to solve the developed model efficiently, an island-model parallel genetic algorithm is proposed. Numerical examples are presented to demonstrate the features of the proposed model and compare the computational performance of the parallel genetic algorithm with the sequential algorithm. The results are very encouraging.