考虑工件移动时间的柔性作业车间调度问题研究

分析生产车间的实际生产状况,建立了考虑工件移动时间的柔性作业车间调度问题模型,该模型考虑了以往柔性作业车间调度问题模型所没有考虑的工件在加工机器间的移动时间,使柔性作业车间调度问题更贴近实际生产,让调度理论更具现实性。通过对已有的改进遗传算法的遗传操作进行重构,设计出有效求解考虑工件移动时间的柔性作业车间调度问题的改进遗传算法。最后对实际案例进行求解,得到调度甘特图和析取图,通过对甘特图和析取图的分析验证了所建考虑工件移动时间的柔性作业车间调度问题模型的可行性和有效性。     

基于效率函数求解单件车间调度问题算法的完善

单件小批量生产形式下的单件车间（Job-shop)调度是生产计划中的一个重要问题，本文在文献[1]提出的求解Job-shop调度问题的动排算法及调解算法的基础上，做出了进一步的修改和完善，在调解算法中引入了交换与移动相结合的机制以提高调解效率，并在VBA For Projcet2000中实现了该算法，经分析及实验验证，利用这种算法求解Job-shop调度问题，可得到十分满意的结果。     

基于效率函数求解单件车间调度问题算法的软件实现和实例分析

单件小批量生产形式下的单件车间（Job-shop）调度是生产计划中的一个重要问题。西方在文献[1]提出的求解Job-shop调度问题的初排算法及调解算法的基础上，做出了进一步的修改和完善，在调解算法中引入了交换与移动相结合的机制以提高调解效率；在软件实现中引入了“虚工序”的概念，并在“VBA ForProject2000中实现了该算法。经分析及实验验证，利用这种算法求解Job-shop调度问题可得到十分满意的结果。     

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。     

柔性作业车间调度的多Agent协商策略

柔性作业车间调度问题是经典作业车间调度问题的扩展。为此,提出一种新的基于招投标的多Agent协商调度策略,并研究各Agent协商时的价格函数。系统主要由工件Agent和机器Agent组成,工件Agent通过招投标的方式,选择合适的机器完成加工任务,机器Agent按照市场机制通过自由竞争获得工件的加工权,根据基于规则的调度策略处理工件。用Java设计仿真实验程序,并通过实验验证所提价格协商函数的有效性。     

基于关键路径疫苗的免疫遗传算法求解JSP问题

车间作业调度是决定工件加工顺序以及分配相应设备的过程,合理的调度方案能提高设备利用率.针对单件车间调度问题,采用免疫遗传算法进行调度方案求解.基于关键路径的思想,提出一种新的疫苗提取和接种方法.疫苗提取时,取种群中最优个体的关键路径信息作为疫苗,接种时,依据该疫苗信息修改待接种个体各机器上的工件安排顺序,从而继承最优个体关键路径上的信息,提高个体适应度,加速算法寻优过程.通过对标准测试案例的求解,以及与其他算法的比较,结果表明关键路径疫苗技术是有效的.     

两机无等待流水车间调度问题的性质

针对两机无等待流水车间调度问题,提出目标函数最大完工时间最小化的快速算法,并给出算法的复杂度。分析两机无等待流水车间调度问题的排列排序性质,证明了两机无等待流水车间调度问题的可行解只存在于排列排序中,排列排序的最优解一定是两机无等待流水车间调度问题的最优解。最后研究了同时包含普通工件和无等待工件的两机流水车间调度问题的复杂性,为进一步研究两机无等待流水车间调度问题提供了理论依据。     

工件工序可并行的作业车间调度模型与求解

针对传统作业车间调度模型没有考虑工件工序存在并行性的不足,提出一种以最小化完工时间为目标的工件工序可并行作业车间调度模型,且在模型中考虑了工序加工设备柔性;设计了基于遗传算法的调度算法,其中染色体编码采用分段编码方式,并提出一种适用于工件工序存在并行性的染色体解码方法.实验结果表明,文中算法能够有效地解决工件工序可并行的作业车间调度问题.     

一种求解单件车间调度问题的单亲遗传算法

针对单件车间凋度问题,设计一种基于整数编码的单亲遗传算法。该算法既具有单亲遗传算法运算量小、不存在“早熟收敛”现象等优点,在编码中又体现了单件车问调度的“保序性”等工艺约束条件,增强了调度算法的整体性能。对不同规模的FT类问题的仿真结果表明,该算法的收敛速度随着问题规模的增大而加快,并在求解单件车问调度问题时能得到稳定的满意解。     

基于工件间隙的Blocking流程车间调度启发式算法

考虑Blocking流程车间调度的特殊性质,提出一种基于工件间隙以达到减少机器闲置和工件滞留时间的初始排序规则,结合插入搜索机制,构造解决Blocking流程车间的调度问题的启发式算法.通过大量的计算实验并与有效地解决该调度问题的NEH算法进行比较,结果表明本算法在解的质量上有改进.     

Optimal schedules for single facility with two job classes

A single facility scheduling problem with jobs divided into two mutually exclusive classes is considered when the setup time depends on the class of jobs immediately preceding the job being currently processed. The jobs in a given class need not be processed together. Based on a combinatorial analysis of the problem, an algorithm is developed to obtain an optimal schedule when the objective is to minimize mean flow time. The proposed algorithm is polynomially bounded in terms of the computational effort needed to solve the problem.     

Bounded single-machine parallel-batch scheduling with release dates and rejection

We consider the bounded single-machine parallel-batch scheduling problem with release dates and rejection. A job is either rejected, in which case a certain penalty has to be paid, or accepted and then processed on the machine. The objective is to minimize the sum of the makespan of the accepted jobs and the total penalty of the rejected jobs. When the jobs have identical release dates, we present a polynomial-time algorithm. When the jobs have a constant number of release dates, we give a pseudo-polynomial-time algorithm. For the general problem, we provide a 2-approximation algorithm and a polynomial-time approximation scheme.     

The one-machine sequencing problem with dependent jobs

The problem of minimizing the makespan on one machine with possible dependencies between jobs is considered. We enlarge the notion of precedence constraint by considering a minimal duration between the start times of each pair of dependent jobs. We propose an exact algorithm where this duration is taken into account. This algorithm is based on Carlier's algorithm [Carlier 82] for the One-Machine Sequencing (OMS) problem with independent jobs.     

Single-machine scheduling with deteriorating jobs and setup times to minimize the maximum tardiness

In many realistic production situations, a job processed later consumes more time than the same job when it is processed earlier. Production scheduling in such an environment is known as scheduling with deteriorating jobs. However, research on scheduling problems with deteriorating jobs has rarely considered explicit (separable) setup time (cost). In this paper, we consider a single-machine scheduling problem with deteriorating jobs and setup times to minimize the maximum tardiness. We provide a branch-and-bound algorithm to solve this problem. Computational experiments show that the algorithm can solve instances up to 1000 jobs in reasonable time.     

A water-flow algorithm for flexible flow shop scheduling with intermediate buffers

We investigate the flexible flow shop scheduling problem with limited or unlimited intermediate buffers. A common objective of the problem is to find a production schedule that minimizes the completion time of jobs. Other objectives that we also consider are minimizing the total weighted flow time of jobs and minimizing the total weighted tardiness time of jobs. We propose a water-flow algorithm to solve this scheduling problem. The algorithm is inspired by the hydrological cycle in meteorology and the erosion phenomenon in nature. In the algorithm, we combine the amount of precipitation and its falling force to form a flexible erosion capability. This helps the erosion process of the algorithm to focus on exploiting promising regions strongly. To initiate the algorithm, we use a constructive procedure to obtain a seed permutation. We also use an improvement procedure for constructing a complete schedule from a permutation that represents the sequence of jobs in the first stage of the scheduling problem. To evaluate the proposed algorithm, we use benchmark instances taken from the literature and randomly generated instances of the scheduling problem. The computational results demonstrate the efficacy of the algorithm. We have also obtained several improved solutions for the benchmark instances using the proposed algorithm. We further illustrate the algorithm’s capability for solving problems in practical applications by applying it to a maltose syrup production problem.     

Weak-restriction bi-objective optimization algorithm for scheduling with rejection on non-identical batch processing machines

We investigate the problem of scheduling a set of jobs with arbitrary sizes and unequal weights on a set of parallel batch machines with non-identical capacities. The objective is to minimize the makespan of the accepted jobs and the total rejection penalty of the rejected jobs, simultaneously. To address the studied problem, a Pareto-based ant colony optimization algorithm with the first job selection probability (FPACO) is proposed. A weak-restriction selection strategy is proposed to obtain the desirability of candidate jobs. Two objective-oriented heuristic information and pheromone matrices are designed, respectively, to record the experience in different search dimensions. Moreover, a local optimization algorithm is incorporated to improve the solution quality. Finally, the proposed algorithm is compared with four existing algorithms through extensive simulation experiments. The experimental results indicate that the proposed algorithm outperforms all of the compared algorithms within a reasonable time.     

Minimizing the weighted sum of squared tardiness on a single machine

This paper considers a problem in which there is a set of jobs to be sequenced on a single machine. Each job has a weight and the objective is to sequence the jobs to minimize total weighted squared tardiness. A branch-and-bound algorithm is developed for optimally solving the problem. Several dominance conditions are presented for possible inclusion in the branch-and-bound algorithm. The dominance conditions are included in the branch-and-bound algorithm, which is tested on randomly generated problems of various numbers of jobs, due date tightness and due date ranges. The results show that the dominance conditions dramatically improve the efficiency of the branch-and-bound algorithm.     

Two-agent singe-machine scheduling with release times to minimize the total weighted completion time

In many management situations multiple agents pursuing different objectives compete on the usage of common processing resources. In this paper we study a two-agent single-machine scheduling problem with release times where the objective is to minimize the total weighted completion time of the jobs of one agent with the constraint that the maximum lateness of the jobs of the other agent does not exceed a given limit. We propose a branch-and-bound algorithm to solve the problem, and a primary and a secondary simulated annealing algorithm to find near-optimal solutions. We conduct computational experiments to test the effectiveness of the algorithms. The computational results show that the branch-and-bound algorithm can solve most of the problem instances with up to 24 jobs in a reasonable amount of time and the primary simulated annealing algorithm performs well with an average percentage error of less than 0.5% for all the tested cases.     

Parallel machine earliness/tardiness scheduling problem under the effects of position based learning and linear/nonlinear deterioration

This paper considers a parallel machine earliness/tardiness (ET) scheduling problem with different penalties under the effects of position based learning and linear and nonlinear deterioration. The problem has common due-date for all jobs, and effects of learning and deterioration are considered simultaneously. By the effects of learning we mean that the job processing time decreases along the sequence of partly similar jobs, and by the effects of deterioration we mean slowing performance or time increases along the sequence of jobs. This study shows that optimal solution for ET scheduling problem under effects of learning and deterioration is V-shape schedule under certain agreeable conditions. Furthermore, we design a mathematical model for the problem under study and algorithm and lower bound procedure to solve larger test problems. The algorithm can solve problems of 1000 jobs and four machines within 3 s on average. The performance of the algorithm is evaluated using results of the mathematical model.     

Minimizing the number of late jobs on a single machine under due date uncertainty

We study the problem of minimizing the number of late jobs on a single machine where job processing times are known precisely and due dates are uncertain. The uncertainty is captured through a set of scenarios. In this environment, an appropriate criterion to select a schedule is to find one with the best worst-case performance, which minimizes the maximum number of late jobs over all scenarios. For a variable number of scenarios and two distinct due dates over all scenarios, the problem is proved NP-hard in the strong sense and non-approximable in pseudo-polynomial time with approximation ratio less than 2. It is polynomially solvable if the number s of scenarios and the number v of distinct due dates over all scenarios are given constants. An O(nlog?n) time s-approximation algorithm is suggested for the general case, where n is the number of jobs, and a polynomial 3-approximation algorithm is suggested for the case of unit-time jobs and a constant number of scenarios. Furthermore, an O(n ^s+v?2/(v?1) ^v?2) time dynamic programming algorithm is presented for the case of unit-time jobs. The problem with unit-time jobs and the number of late jobs not exceeding a given constant value is solvable in polynomial time by an enumeration algorithm. The obtained results are related to a min-max assignment problem, an exact assignment problem and a multi-agent scheduling problem.