基于粒子群优化的开放式车间调度

开放式车间调度(OSP)是重要的调度问题,它在制造领域中的应用非常广泛。优化调度算法是调度理论的重要研究内容。基于人工智能的元启发式算法是解决该问题的常用方法。分析了一种新的元启发式算法——粒子群优化(PSO)在信息共享机制上的缺陷,提出新的基于群体智能的信息共享机制。在该信息共享机制的基础上, 设计新的基于PSO的元启发式调度算法——PSO-OSP。该算法利用问题的邻域知识指导局部搜索,可克服元启发式算法随机性引起的盲目搜索。该算法应用于开放式车间调度问题的标准测试实例。仿真结果显示,PSO-OSP算法在加快收敛速度的同时提高了开放式车间调度解的质量。     

Scheduling decisions in FMS using a heuristic approach

The paper deals with the multilevel scheduling decisions of a Flexible Manufacturing System (FMS) to generate realistic schedules for the efficient operation of the FMS. The primary concern of an Operations Management System (OMS) for a FMS is production scheduling, Material Handling System (MHS) scheduling, Automated Storage/Retrieval System (AS/RS) operation and control and tool management. Scheduling is a critical issue and determines how efficiently the production resources are utilised and how the selected parts are affected in the system. In this paper, the integrated scheduling of FMS, namely, the production scheduling conforming with the MHS scheduling, is addressed. An enumerative heuristic is used, namely Giffler and Thompson, which is an evolutionary combining a Genetic Algorithm (GA) and a stochastic neighborhood search technique using a Simulated Annealing (SA) algorithm is employed.A. Noorul Haq received his PhD in Manufacturing Management from the Indian Institute of Technology (IIT), New Delhi, India, a Master of Engineering degree from Madras University, and a Bachelor of Engineering degree from Annamalai University, India. He is currently an assistant professor in the Department of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His research interests include aggregate production planning, facility layout and scheduling and optimisation techniques.T. Karthikeyan received his Master's degree from Bharathidhasan University. Currently, he is a research scholar in the Department of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His research interests includes facility layout, FMS and simulation.M. Dinesh received his bachelor's degree in engineering from Bharathiar University, India and is currently working on his master's degee in engineering in the Department Of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His current research interests are in optimisation techniques, facility layout and scheduling.     

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling problem. The new algorithm is based on the principles of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. By reasonably combining these two different search algorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, is developed. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated by applying it to some benchmark job-shop scheduling problems and comparing results with other algorithms in literature. Comparing results indicate that PSO-based a     

An effective hybrid particle swarm optimization for no-wait flow shop scheduling

The no-wait flow shop scheduling that requires jobs to be processed without interruption between consecutive machines is a typical NP-hard combinatorial optimization problem, and represents an important area in production scheduling. This paper proposes an effective hybrid algorithm based on particle swarm optimization (PSO) for no-wait flow shop scheduling with the criterion to minimize the maximum completion time (makespan). In the algorithm, a novel encoding scheme based on random key representation is developed, and an efficient population initialization, an effective local search based on the Nawaz-Enscore-Ham (NEH) heuristic, as well as a local search based on simulated annealing (SA) with an adaptive meta-Lamarckian learning strategy are proposed and incorporated into PSO. Simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed hybrid algorithm.     

AGV schedule integrated with production in flexible manufacturing systems

Flexible manufacturing systems (FMS) comprise, automated machine tools, automated material handling, and automated storage and automated retrieval systems (AS/RS) as essential components. Effective sequencing and scheduling of the material handling systems (MHS) can have a major impact on the productivity of the manufacturing system. The material handling cannot be neglected while scheduling the production tasks. It is necessary to take into account the interaction between machines, material handling systems and computer. In this context, this paper attempts to link the operation of automated guided vehicles (AGV) with the production schedule and suggests a heuristic algorithm that employs vehicle dispatching rules (vdr) for conflict resolution. The vdrs considered in this paper are: shortest operation time (SPT), longest operation time (LPT), longest travel time (LTT) and shortest travel time (STT). The performance of the vdrs in the proposed heuristic is compared with makespan criteria. The results show that the STT provides the best solutions compared to other vdrs.     

A swarm optimization approach for flexible flow shop scheduling with multiprocessor tasks

In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow shop problem (FFSP). Flexible flow shops are thus generalization of simple flow shops. Flexible flow shop scheduling problems have a special structure combining some elements of both the flow shop and the parallel machine scheduling problems. FFSP can be stated as finding a schedule for a general task graph to execute on a multiprocessor system so that the schedule length can be minimized. FFSP is known to be NP-hard. In this study, we present a particle swarm optimization (PSO) algorithm to solve FFSP. PSO is an effective algorithm which gives quality solutions in a reasonable computational time and consists of less numbers parameters as compared to the other evolutionary metaheuristics. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast towards near-optimal solution and hence reduce computational efforts further. The performance of schedules is evaluated in terms of total completion time or makespan (C_max). The results are presented in terms of percentage deviation (PD) of the solution from the lower bound. The results are compared with different versions of genetic algorithm (GA) used for the purpose from open literature. The results indicate that the proposed PSO algorithm is quite effective in reducing makespan because average PD is observed as 2.961, whereas GA results in average percentage deviation of 3.559. Finally, influence of various PSO parameters on solution quality has been investigated.     

A hybrid algorithm based on particle swarm optimization and simulated annealing for a periodic job shop scheduling problem

Generating schedules such that all operations are repeated every constant period of time is as important as generating schedules with minimum delays in all cases where a known discipline is desired or obligated by stakeholders. In this paper, a periodic job shop scheduling problem (PJSSP) based on the periodic event scheduling problem (PESP) is presented, which deviates from the cyclic scheduling. The PESP schedules a number of recurring events as such that each pair of event fulfills certain constraints during a given fixed time period. To solve such a hard PJSS problem, we propose a hybrid algorithm, namely PSO-SA, based on particle swarm optimization (PSO) and simulated annealing (SA) algorithms. To evaluate this proposed PSO-SA, we carry out some randomly constructed instances by which the related results are compared with the proposed SA and PSO algorithms as well as a branch-and-bound algorithm. In addition, we compare the results with a hybrid algorithm embedded with electromagnetic-like mechanism and SA. Moreover, three lower bounds (LBs) are studied, and the gap between the found LBs and the best found solutions are reported. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the PJSSP.     

A simultaneous planning of production and scheduling operations in flexible flow shops: case study of tile industry

The aim of this paper is to study a simultaneous lot-sizing and scheduling in multi-product, multi-period flexible flow shop environments. A new mixed integer programming (MIP) model is proposed to formulate the problem. The objective function includes the total cost of production, inventory, and external supply. In this study, in case of not meeting the demand of customers, this demand should be met by foreign suppliers in higher price. Due to the high computational complexity of the studied problem, a rolling horizon heuristic (RHH) and particle swarm optimization algorithm (PSO) are implemented to solve the problem. These algorithms find a feasible and near-optimal from production planning and scheduling. Additionally, Taguchi method is conducted to calibrate the parameters of the PSO algorithm and select the optimal levels of the influential factors. The computational results show that the algorithms are capable of achieving results with good quality in a reasonable time and PSO has better objective values in comparison with RHH. Also, the real case study for tile industry with real features is applied. Sensitivity analysis is used to evaluate the performance of the model.     

解决JOB SHOP问题的粒子群优化算法

设计了2种解决Job shop问题的粒子群算法,即实数编码的粒子群调度算法和工序编码的粒子群调度算法。工序编码的粒子群调度算法更符合Job shop问题的特点,优化性能相对高。但粒子群调度算法容易陷入局部最优。为了提高优化性能,将粒子群算法和模拟退火算法结合,得到了粒子群-模拟退火混合调度算法。仿真结果表明了算法的有效性。     

Job shop scheduling using fuzzy logic

In a flexible manufacturing system (FMS) scheduling problems become extremely complex, even for simple breakdowns, when dynamic uncertainties such as machine breakdowns and the uncertain arrival of jobs are taken into consideration. In the first stage of this study, a fuzzy logic-based algorithm for assigning priorities to part types that are to be machined is proposed. In the second stage, an operation-machine allocation and scheduling algorithm is presented. A criteria contribution equalizer is used in decision-making. The proposed algorithm can re-generate the schedule in case of a machine breakdown, and therefore can be used as an on-line controller. The system architecture and linguistic variables are presented and results showed that the proposed algorithm improves the system efficiency.     

An improved particle swarm optimization for the resource-constrained project scheduling problem

In this paper, an improved particle swarm optimization (PSO) algorithm is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The algorithm treats the solutions of RCPSP as particle swarms and employs a double justification skill and a move operator for the particles, in association with rank-priority-based representation, greedy random search, and serial scheduling scheme, to execute the intelligent updating process of the swarms to search for better solutions. The integration combines and overhauls the characteristics of both PSO and RCPSP, resulting in enhanced performance. The computational experiments are subsequently conducted to set the adequate parameters and compare the proposed algorithm with other approaches. The results suggest that the proposed PSO algorithm augments the performance by 9.26, 16.17, and 10.45 % for the J30, J60, and J120 instances against the best lower bound-based PSO currently available, respectively. Moreover, the proposed algorithms demonstrate obvious advantage over other proposals in exploring solutions for large-scale RCPSP problems such as the J60 and J120 instances.     

应用粒子群优化算法辨识Hammerstein模型

非线性系统的辨识一直是现代辨识领域中的一个主要课题。针对非线性系统中Hammerstein模型，文中提出了利用群集智能中的粒子群优化算法（PSO）对非线性模型进行辨识。讨论了PSO的基本算法与参数初值的设置与选择方法。通过仿真实验说明：与非线性最小二乘法相比PSO算法对于非线性辨识的有效性和鲁棒性。PSO算法是一种有效的解决优化问题的群集智能算法，它的突出特点是算法中需要选择的参数少，程序实现简单，并在种群数量、寻优速度等方面较其他进化算法具有一定的优势。尤其县存高噪信比情况下，也收到较满意的结果。     

Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling

This paper presents a hybrid evolutionary algorithm with marriage of genetic algorithm (GA) and extremal optimization (EO) for solving a class of production scheduling problems in manufacturing. The scheduling problem, which is derived from hot rolling production in steel industry, is characterized by two major requirements: (i) selecting a subset of orders from manufacturing orders to be processed; (ii) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties, earliness/tardiness (E/T) penalties, etc. A combinatorial optimization model is proposed to formulate it mathematically. For its NP-hard complexity, an effective hybrid evolutionary algorithm is developed to solve the scheduling problem through combining the population-based search capacity of GA and the fine-grained local search efficacy of EO. The experimental results with production scale data demonstrate that the proposed hybrid evolutionary algorithm can provide superior performances in scheduling quality and computation efficiency.     

Integrating theory of constraints and particle swarm optimization in order planning and scheduling for machine tool production

Order planning and scheduling has become a significant challenge in machine tool enterprises, who want to meet various demands of different customers and make full use of existing resources in enterprises simultaneously. Based on the Theory of Constraints, a three-stage order planning and scheduling solution is proposed to optimize the whole system performance with bottleneck resources' capability as the constraints. After the identification of bottleneck resources, multicriteria priority sequencing is made with order per-contribution rate, order delivery urgency, and customer importance as the evaluation criteria, and the evaluation result deduced from the ideal point function can decide the production mode of all orders and products. Then, a PSO-based multiobjective optimization model is set up with minimizing bottleneck machines' makespan and minimizing total products' tardiness as the two objectives. Finally, the proposed solution is applied in one machine tool enterprise by integrating into Baosight MES (Manufacturing Execution System) system. In addition, some comparisons are carried out to evaluate the proposed PSO optimization method. The comparison with actual report shows that PSO can satisfy enterprise's needs better than before; the comparisons with genetic algorithm and ant colony optimization algorithms indicate that PSO is more effective than the others because of its faster convergence rate.     

Sequencing and scheduling of job and tool in a flexible manufacturing cell

Flexible manufacturing cells (FMCs) are now common place in many manufacturing companies, due to their numerous advantages such as the production of a wide range of part types with short lead times, low work-in-progress, economical production of small batches and high resource utilization. Part and tool flows, two major dynamic entities, are the key factors and their management plays an important role in the operation of a FMC. The theme of this paper is to a generate joint operation - tool schedule in a FMC consisting of several machines and a common tool magazine (CTM). To achieve this aim, the jobs and tools must be jointly sequenced and scheduled in a tool constrained environment. Two heuristic algorithms, priority dispatching rules algorithm (PDRA) and simulated annealing algorithm (SAA) are proposed to derive optimal solutions. PDRA, are the most frequently applied heuristics for solving job shop/combinatorial scheduling problems in practice because of their ease of implementation and their low complexity, when compared with excel algorithms. SAA that belong to search categories, which are emerging along with the high computational capability of computers, can be used for FMS scheduling problems. Both adopt the Giffler & Thompson procedure for active feasible schedule generation. The performance of these two algorithms is compared with makespan and computational time. The analysis reveals that the SAA based heuristic provides an optimal or near optimal solution with reasonable computational time.     

PSO算法在FMS刀具可复用调度中的应用与仿真

刀具调度在现代FMS生产中越来越频繁,在前期刀具可复用策略研究的基础上,将粒子群算法(PSO)嵌入可复用策略的具体刀具调度中。算法设计上采用了四维变量编码法、整数圆整解码法、以及浮动目标函数迭代法,将刀具复杂的多指标十进制迭代过程灵活的加以实现。对该PSO算法以实例进行了仿真,结果证实了刀具的利用率有所提高、系统加工完成时间缩短,从而证明了带PSO的可复用调度策略应用于刀具调度带来的经济性。     

Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm

Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. In this paper, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered here is a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs are properly scheduled, then the idle time of the machining center can be minimized; as such, their utilization can be maximized. Minimizing the penalty cost for not meeting the delivery date is also considered in this work. Two contradictory objectives are to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results are compared to those obtained by conventional genetic algorithm.     

Research on job-shop scheduling optimization method with limited resources

Job-shop scheduling is an important subject in the fields of production management and combinatorial optimization. It is also an urgent problem to be solved in actual production. It is usually difficult to achieve the optimal solution with classical methods, due to a high computational complexity (NP-Hard). According to the nature of job-shop scheduling, a solution based on a particle swarm optimiser (PSO) is presented in this paper. In addition to establishing a job-shop scheduling model based on PSO, we have researched the coding and optimized operation of PSO. We have also considered more suitable methods of coding and operation for job-shop scheduling as well as the target function and calculation of the proper figure. The software system of job-shop scheduling is developed according to the PSO algorithm. Test simulations illustrate that the PSO algorithm is a suitable and effective approach for solving the job-shop scheduling problem.     

Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm

Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. In this paper, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered here is a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs are properly scheduled, then the idle time of the machining center can be minimized; as such, their utilization can be maximized. Minimizing the penalty cost for not meeting the delivery date is also considered in this work. Two contradictory objectives are to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results are compared to those obtained by conventional genetic algorithm.