Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms

In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.     

Multi-criteria sequence-dependent job shop scheduling using genetic algorithms

Real world job shops have to contend with jobs due on different days, material ready times that vary, reentrant workflows and sequence-dependent setup times. The problem is even more complex because businesses often judge solution goodness according to multiple competing criteria. Producing an optimal solution would be time consuming to the point of rendering the result meaningless. Commonly used heuristics such as shortest processing time (SPT) and earliest due date (EDD) can be used to calculate a feasible schedule quickly, but usually do not produce schedules that are close to optimal in these job shop environments. We demonstrate that genetic algorithms (GA) can be used to produce solutions in times comparable to common heuristics but closer to optimal. Changing criteria or their relative weights does not affect the running time, nor does it require programming changes. Therefore, a GA can be easily applied and modified for a variety of production optimization criteria in a job shop environment that includes sequence-dependent setup times.     

遗传算法求解柔性job shop 调度问题

在分析柔性job shop调度问题特点的基础上，提出一种新的求解该问题的遗传算法，即利用编码方法表示各工序的优先调度顺序及工序的加工机器，由此产生可行的调度方案，使得问题的约束条件在染色体中得以体现．所设计的遗传算子不仅能避免非法调度解的出现，保证后代的多样性，而且可使算法具有记忆功能．仿真结果证明了该算法的有效性．     

A comparison of dispatching rules and genetic algorithms for job shop schedules of standard hydraulic cylinders

Hydraulic cylinders perform straight-line reciprocating movements, and they have been used widely in various forms in many different industries. In this paper, we select a sample of the various types of standard hydraulic cylinders. Each cylinder’s near-optimal processing time and the processing order of the cylinder’s parts are investigated using two different techniques. First, we study typical procedures, known as ‘Dispatching Rules’, which would be used in a job shop to resolve scheduling problems. Second, we investigate another kind of technique, called ‘Genetic Algorithms’. The goal of this paper, we show that efficient scheduling solutions are calculated by using dispatching rules and genetic algorithms for manufacturing standard hydraulic cylinders, and we propose that a way to use dispatching rules in association with genetic algorithms should be considered for resolving job shop scheduling problems.     

Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling

Fuzzy flexible job shop scheduling problem (FfJSP) is the combination of fuzzy scheduling and flexible scheduling in job shop environment, which is seldom investigated for its high complexity. We developed an effective co-evolutionary genetic algorithm (CGA) for the minimization of fuzzy makespan. In CGA, the chromosome of a novel representation consists of ordered operation list and machine assignment string, a new crossover operator and a modified tournament selection are proposed, and the population of job sequencing and the population of machine assignment independently evolve and cooperate for converging to the best solutions of the problem. CGA is finally applied and compared with other algorithms. Computational results show that CGA outperforms those algorithms compared.     

Using genetic algorithms in process planning for job shop machining

This paper presents a novel computer-aided process planning model for machined parts to be made in a job shop manufacturing environment. The approach deals with process planning problems in a concurrent manner in generating the entire solution space by considering the multiple decision-making activities, i.e., operation selection, machine selection, setup selection, cutting tool selection, and operations sequencing, simultaneously. Genetic algorithms (GAs) were selected due to their flexible representation scheme. The developed GA is able to achieve a near-optimal process plan through specially designed crossover and mutation operators. Flexible criteria are provided for plan evaluation. This technique was implemented and its performance is illustrated in a case study. A space search method is used for comparison     

求解柔性作业车间调度的GATOC混合方法

柔性作业车间调度问题是经典作业车间调度问题的扩展,它允许工序在可选加工机器集中任意一台上加工,加工时间随加工机器不同而不同。针对柔性作业车间调度问题的特点,提出一种基于约束理论的局部搜索方法,对关键路径上的机器的负荷率进行比较,寻找瓶颈机器,以保证各机器之间的负荷平衡。为了克服传统遗传算法早熟和收敛慢的缺点,设计多种变异操作,增加种群多样性。为了更好保留每代中的优良解,设计了基于海明距离的精英解保留策略。运用提出的算法求解基准测试问题,验证了算法的可行性和有效性。     

Flexible job shop scheduling using hybrid differential evolution algorithms

This paper proposes hybrid differential evolution (HDE) algorithms for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize the makespan. Firstly, a novel conversion mechanism is developed to make the differential evolution (DE) algorithm that works on the continuous domain adaptive to explore the problem space of the discrete FJSP. Secondly, a local search algorithm based on the critical path is embedded in the DE framework to balance the exploration and exploitation by enhancing the local searching ability. In addition, in the local search phase, the speed-up method to find an acceptable schedule within the neighborhood structure is presented to improve the efficiency of whole algorithms. Extensive computational results and comparisons show that the proposed algorithms are very competitive with the state of the art, some new best known solutions for well known benchmark instances have even been found.     

Genetic algorithms and job shop scheduling

We describe applications of Genetic Algorithms (GAs) to the Job Shop Scheduling (JSS) problem. More specifically, the task of generating inputs to the GA process for schedule optimization is addressed.

We believe GAs can be employed as an additional tool in the Computer Integrated Manufacturing (CIM) cycle. Our technique employs an extension to the Group Technology (GT) method for generating manufacturing process plans. It positions the GA scheduling process to receive outputs from both the automated process planning function and the order entry function. The GA scheduling process then passes its results to the factory floor in terms of optimal schedules.

An introduction to the GA process is discussed first. Then, an elementary n-task, one processor (machine) problem is provided to demonstrate the GA methodology in the JSS problem arena. The technique is then demonstrated on an n-task, two processor problem, and finally, the technique is generalized to the n-tasks on m-processors (serial) case.     

Finding multiple solutions in job shop scheduling by niching genetic algorithms

The interest in multimodal optimization methods is increasing in the last years. The objective is to find multiple solutions that allow the expert to choose the solution that better adapts to the actual conditions. Niching methods extend genetic algorithms to domains that require the identification of multiple solutions. There are different niching genetic algorithms: sharing, clearing, crowding and sequential, etc. The aim of this study is to study the applicability and the behavior of several niching genetic algorithms in solving job shop scheduling problems, by establishing a criterion in the use of different methods according to the needs of the expert. We will experiment with different instances of this problem, analyzing the behavior of the algorithms from the efficacy and diversity points of view.     

Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop

Much of the research on operations scheduling problems has ignored dynamic events in real-world environments where there are complex constraints and a variety of unexpected disruptions. Besides, while most scheduling problems which have been discussed in the literature assume that machines are incessantly available, in most real life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to a scheduled preventive maintenance where the periods of unavailability are determined in advance (deterministic unavailability). This paper describes how we can integrate simulation into genetic algorithm to the dynamic scheduling of a flexible job shop with machines that suffer stochastic breakdowns. The objectives are the minimization of two criteria, expected makespan and expected mean tardiness. An overview of the flexible job shops and scheduling under the stochastic unavailability of machines are presented. Subsequently, the details of integrating simulation into genetic algorithm are described and implemented. Consequently, problems of various sizes are used to test the performance of the proposed algorithm. The results obtained reveal that the relative performance of the algorithm for both abovementioned objectives can be affected by changing the levels of the breakdown parameters.     

超启发式遗传算法求解模糊柔性作业车间调度

本文提出一种混合超启发式遗传算法(HHGA),用于求解一类采用三角模糊数表示工件加工时间的模糊柔性作业车间调度问题(FFJSP),优化目标为最小化最大模糊完工时间(即makespan).首先,详细分析现有三角模糊数排序准则性质,并充分考虑取大操作的近似误差和模糊度,设计一种更为准确的三角模糊数排序准则,可合理计算FFJSP和其他各类调度问题解的目标函数值.其次,为实现对FFJSP解空间不同区域的有效搜索, HHGA将求解过程分为两层,高层利用带自适应变异算子的遗传算法对6种特定操作(即6种有效邻域操作)的排列进行优化;低层将高层所得的每种排列作为一种启发式算法,用于对低层相应个体进行操作来执行紧凑的变邻域局部搜索并生成新个体,同时加入模拟退火机制来避免搜索陷入局部极小.最后,仿真实验和算法比较验证了所提排序准则和HHGA的有效性.     

Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm

Most flexible job shop scheduling models assume that the machines are available all of the time. However, in most realistic situations, machines may be unavailable due to maintenances, pre-schedules and so on. In this paper, we study the flexible job shop scheduling problem with availability constraints. The availability constraints are non-fixed in that the completion time of the maintenance tasks is not fixed and has to be determined during the scheduling procedure. We then propose a hybrid genetic algorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints (fJSP-nfa). The genetic algorithm uses an innovative representation method and applies genetic operations in phenotype space in order to enhance the inheritability. We also define two kinds of neighbourhood for the problem based on the concept of critical path. A local search procedure is then integrated under the framework of the genetic algorithm. Representative flexible job shop scheduling benchmark problems and fJSP-nfa problems are solved in order to test the effectiveness and efficiency of the suggested methodology. Received: June 2005 /Accepted: December 2005     

Machine allocation algorithms for job shop manufacturing

In this paper we present algorithms for the solution of two server (machine) allocation problems that occur in manufacturing networks. The manufacturing network is modelled as an open network of queues with general interarrival time and service time distributions. The queueing network is analyzed by using the parametric decomposition method: a two-moment approximation scheme. The server allocation problems are solved by means of a marginal analysis scheme. Numerical results on two manufacturing networks are presented.     

A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems

This paper addresses the flexible job shop scheduling problem (fJSP) with three objectives: min makespan, min maximal machine workload and min total workload. We developed a hybrid genetic algorithm (GA) for the problem. The GA uses two vectors to represent solutions. Advanced crossover and mutation operators are used to adapt to the special chromosome structure and the characteristics of the problem. In order to strengthen the search ability, individuals of GA are first improved by a variable neighborhood descent (VND), which involves two local search procedures: local search of moving one operation and local search of moving two operations. Moving an operation is to delete the operation, find an assignable time interval for it, and allocate it in the assignable interval. We developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time. The local optima of moving one operation are further improved by moving two operations simultaneously. An extensive computational study on 181 benchmark problems shows the performance of our approach.     

带有跨工序约束的柔性job shop调度问题*

飞机制造企业的金属加工车间是一种小批量、多品种生产,其生产指挥是一种带有跨工序约束的柔性job shop调度问题。针对这个NP-hard问题,提出一种三阶段启发式方法,通过依次完成瓶颈工作中心的判定、设备分配和任务排序,使这一问题的复杂度得以逐步降低,从而可以在多项式时间内得到有效的调度方案。实际运行表明,依据该启发式方法产生的调度方案,其关键路径的等待时间占总完工时间的比例不足1.5%,取得了满意的效果。     

Branch-and-bound and PSO algorithms for no-wait job shop scheduling

This paper deals with the no-wait job shop scheduling problem resolution. The problem is to find a schedule to minimize the makespan (\(C_{max}\)), that is, the total completeness time of all jobs. The no-wait constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. For this, we have proposed two different resolution methods, the first is an exact method based on the branch-and-bound algorithm, in which we have defined a new technique of branching. The second is a particular swarm optimization (PSO) algorithm, extended from the discrete version of PSO. In the proposed algorithm, we have defined the particle and the velocity structures, and an efficient approach is developed to move a particle to the new position. Moreover, we have adapted the timetabling procedure to find a good solution while respecting the no-wait constraint. Using the PSO method, we have reached good results compared to those in the literature.     

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。     

Dynamic scheduling of manufacturing job shops using genetic algorithms

Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date.A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.