A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search

The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches.     

基于改进粒子群算法的无人机航迹规划

针对当前基本粒子群算法无人机航迹规划在后期收敛速度比较慢、效率不高、易陷入局部最优等问题,提出一种改进粒子群算法。首先,在迭代前期和后期分段设置惯性权值的调整,实现粒子惯性和寻优行为的平衡;其次,设置一个定值与相邻2次适应度函数最优值比较策略,防止陷入局部最优;最后,引入遗传算法的交叉、变异机制,得出更优的结果。并通过仿真验证了改进粒子群算法在三维空间航迹规划的有效性和可行性。结果表明,与其他航迹规划算法相比,新算法具有路径长度更短、耗时更少、路径更平滑等优点,加快了收敛速度,提高了航迹规划效率和稳定性。因此,改进算法的航迹规划可得到满足约束关系的最优航迹,对实现自主飞行有重要的参考价值。     

A multiple objective particle swarm optimization approach for inventory classification

This paper presents a particle swarm optimization approach for inventory classification problems where inventory items are classified based on a specific objective or multiple objectives, such as minimizing costs, maximizing inventory turnover ratios, and maximizing inventory correlation. In addition, this approach determines the best number of inventory classes and how items should be categorized for the desired objectives at the same time. Experiments are employed to determine the best combination of algorithm parameter values. Extensive numerical studies are conducted and results are compared to other known classification methods. The performance of the algorithm on a practical case is also presented.     

Cycles of production: from assembly lines to cells to assembly lines in the Volvo Cab Plant

This paper analyses changes in work organisation at the Volvo Cab Plant in Umeå, Sweden in order to identify those contingent elements that impact upon the choices made in selecting specific production technologies. It suggests that managerial decision making is not necessarily located within rational or business enhancement paradigms.     

基于PSO的信息熵数据融合非均匀分簇路由算法

针对无线传感器网络分簇算法中能量分布不均衡导致的"热区"和簇头负载过重问题,提出了一种基于PSO算法优化簇头选举的非均匀分簇算法。在候选簇头选举和竞争半径计算过程中综合考虑节点动态能量、节点密度和节点距基站距离,将网络进行非均匀分簇,并引入PSO算法进行最终簇头选举。根据节点能量、节点密度和距基站距离确定簇间单跳多跳结合的路由规则,选取代价函数小的节点作为下一跳节点。基于节点信息熵确定融合阈值,进行簇内数据融合剔除冗余数据。仿真结果表明,改进算法的数据传输量比EEUC算法和UCRA算法分别提高了20%和10%,提升了数据的融合效率,有效延长了网络生命周期,簇头能量消耗得到均衡,减少了网络能量消耗,网络的整体性能显著优于其他对比算法。     

Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems

This article addresses the particle swarm optimization (PSO) method. It is a recent proposed algorithm by Kennedy and Eberhart [1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (Perth, Australia), vol. IV, IEEE Service Center, Piscataway, NJ, pp. 1942–1948]. This optimization method is motivated by social behaviour of organisms such as bird flocking and fish schooling. PSO algorithm is not only a tool for optimization, but also a tool for representing socio-cognition of human and artificial agents, based on principles of social behaviour. Some scientists suggest that knowledge is optimized by social interaction and thinking is not only private but also interpersonal. PSO as an optimization tool, provides a population-based search procedure in which individuals called particles change their position (state) with time. In a PSO system, particles fly in a multidimensional search space. During flight, each particle adjusts its position according to its own experience, and according to the experience of neighbours, making use of the best position encountered by itself and its neighbours. In this paper, we propose firstly, an extension of the PSO system that integrates a new displacement of the particles (the balance between the intensification process and the diversification process) and we highlight a relation between the coefficients of update of each dimension velocity between the classical PSO algorithm and the extension. Secondly, we propose an adaptation of this extension of PSO algorithm to solve combinatorial optimization problem with precedence constraints in general and resource-constrained project scheduling problem in particular. The numerical experiments are done on the main continuous functions and on the resource-constrained project scheduling problem (RCPSP) instances provided by the psplib. The results obtained are encouraging and push us into accepting than both PSO algorithm and extensions proposed based on the new particles displacement are a promising direction for research.     

基于改进粒子群的分布式通信干扰资源分配

为了解决分布式通信干扰场景下面临的资源分配效率低、干扰效益无保障等问题,结合通信干扰资源分配数学模型,设计了一种改进的粒子群算法。首先设计了分布式通信干扰场景并构建了通信干扰资源分配模型,以最大化干扰效益作为目标函数;其次采用自适应惯性因子和学习因子,并引入遗传变异策略和精英保留策略,提出一种改进的粒子群算法,最后对不同场景规模的通信干扰资源分配进行仿真实验。结果表明,相比小生境遗传算法、粒子群算法、遗传算法,改进的粒子群算法在不同场景规模下,均能获得更优的干扰效益,性能方面具备整体干扰效益更高、算法收敛速度更快、算法收敛误差更小等优势。所设计的改进粒子群算法可应用在分布式通信干扰场景中,为指挥决策提供参考。     

基于CPSO的基础设施模糊投资组合优化模型

针对基础设施效益模糊、难以度量的特点,结合模糊集理论,建立了模糊投资组合优化模型,改进粒子群算法,加入混沌思想,使用混沌粒子群算法(CPSO)求解基础设施的模糊投资组合优化模型。以4个城市投资公司的数据为样本,验证该方法的科学性与有效性。研究结果表明:模糊投资组合优化模型可较好地表征基础设施的模糊效益,提高基础设施投资决策的科学性;混沌寻优思想改进的粒子群算法可求得模糊投资组合优化模型的全局最优解,增强算法的鲁棒性。     

Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables

This article presents an artificial intelligence-based solution to the problem of product line optimization. More specifically, we apply a new hybrid particle swarm optimization (PSO) approach to design an optimal industrial product line. PSO is a biologically-inspired optimization framework derived from natural intelligence that exploits simple analogues of collective behavior found in nature, such as bird flocking and fish schooling. All existing product line optimization algorithms in the literature have been so far applied to consumer markets and product attributes that range across some discrete values. Our hybrid PSO algorithm searches for an optimal product line in a large design space which consists of both discrete and continuous design variables. The incorporation of a mutation operator to the standard PSO algorithm significantly improves its performance and enables our mechanism to outperform the state of the art Genetic Algorithm in a simulated study with artificial datasets pertaining to industrial cranes. The proposed approach deals with the problem of handling variables that can take any value from a continuous range and utilizes design variables associated with both product attributes and value-added services. The application of the proposed artificial intelligence framework yields important implications for strategic customer relationship and production management in business-to-business markets.     

Job rotation in assembly lines employing disabled workers

In this paper we consider the programming of job rotation in the assembly line worker assignment and balancing problem. The motivation for this study comes from the designing of assembly lines in sheltered work centers for the disabled, where workers have different task execution times. In this context, the well-known training aspects associated with job rotation are particularly desired. We propose a metric along with a mixed integer linear model and a heuristic decomposition method to solve this new job rotation problem. Computational results show the efficacy of the proposed heuristics.     

Search for good policies in a single-warehouse, multi-retailer system by particle swarm optimisation

An optimal control of sufficiently realistic single-warehouse, multi-retailer systems (SWMR-systems) requires the simulation optimisation approach. After a brief discussion of modelling and simulation aspects for such systems, we present a simulator providing a sufficiently large set of options to customize it to realistic problem situations. The paper then focuses on the applicability of optimisation algorithms and introduces the methods particle swarm optimisation and threshold accepting. We show that both approaches yield good optimisation results for several examples of a 5-retailer system investigated according to their reordering strategy and transportation resources.     

硫酸生产酸洗废酸生产硫酸铝工艺优化研究

为节约资源、减少污染物的排放,将硫酸生产过程中产生的废酸作为硫酸铝生产的原料,以解决废酸的处理与硫酸铝生产的成本调控问题。得到较佳工艺条件:矿渣洗涤次数为4次,矿粉过量5%,硫酸的质量分数为55%,通过在矿粉中加入一定比例的氢氧化铝调整反应温度,采用设备防腐改进措施。在该工艺条件下,生产1t硫酸铝节省自产工业硫酸51kg,每年可节省硫酸约1 021.39t,实现了硫酸生产酸洗废酸减排和降低硫酸铝生产成本的双重效果。     

Hybrid heuristics for planning job rotation schedules in assembly lines with heterogeneous workers

We investigate the problem of balancing assembly lines with heterogeneous workers while considering job rotation schedules. This problem typically occurs in assembly lines in sheltered work centers for disabled. We propose a hybrid algorithm that uses a Mixed Integer Programming (MIP) to select appropriate schedules from a pool of heuristically constructed solutions. A local search based on MIP neighborhoods is used as a post-optimization method. Our results show that this approach is fast, flexible and accurate when compared with current available methods.     

Advantages of assembly lines in Sheltered Work Centres for Disabled. A case study

Current practices for the treatment of the physically and/or mentally handicapped prescribe meaningful job activity as a means towards both a more fulfilling life and societal integration. In many countries, these practices have facilitated the development of many Sheltered Work Centres for Disabled.In the case study presented, a reengineering process is done starting from individual workplaces where only certain workers were capable of assembling the entire product, and finishing with an assembly line implementation. It is revealed how the traditional division of work in single tasks, typical in assembly lines, becomes a perfect tool for making certain worker disabilities invisible, providing new jobs for disabled people; always taking into account certain special constraints that are analysed.     

U-shaped production lines: A review of theory and practice

Most writers describe the U-shaped production line as the special type of cellular manufacturing used in just-in-time (JIT) production systems. JIT is defined to be an umbrella term for a number of techniques whose purpose is to improve product quality and cost by eliminating all waste in the production system. The U-line arranges machines around a U-shaped line in the order in which production operations are performed. Operators work inside the U-line. One operator supervises both the entrance and the exit of the line. Machine-work is separated from operator-work so that machines work independently as much as possible. Standard operation charts specify exactly how all work is done. U-lines may be simple or complex. U-lines are rebalanced periodically when production requirements change. The U-line satisfies the flow manufacturing principle. This requires operators to be multi-skilled to operate several different machines or processes. It also requires operators to work standing up and walking. When setup times are negligible, U-lines are operated as mixed-model lines where each station is able to produce any product in any cycle. When setup times are larger, multiple U-lines are formed and dedicated to different products. 114 US and Japanese U-lines are examined in this study. The average U-line has 10.2 machines and 3.4 operators. About one-quarter of all U-lines are manned by one operator and so run in chase mode. The reported benefits are impressive. Productivity improved by an average of 76%. WIP dropped by 86%. Leadtime shrunk by 75%. Defective rates dropped by 83%.     

A method for reducing inter-departure time variability in serial production lines

The inter-departure time variability is an important measure in production lines. Higher variability means added work-in-process and less predictability in output. It can be a primary obstacle towards achieving on-time delivery. The effects of line parameters (e.g., line length or buffer capacity) on inter-departure time variability have been studied in recent years but no method has been proposed for its reduction. In this paper, such a strategy is proposed and studied via simulation. Results indicate that significant reductions (of more than 20%) in inter-departure time variability can be achieved for as little as 0.5% increase in the mean inter-departure time or without any increase at all, for a majority of the line parameter values experimented. This was found to be the case for symmetrical (uniform) processing time distributions as well as for asymmetrical skewed (exponential) distributions. Similar results have also been obtained in the application of the proposed strategy for the case when one station has a higher variance than the others. Therefore, in situations where output predictability is more of a problem than capacity, this strategy constitutes an effective alternative.     

A comparison of product costing practices in discrete-part and assembly manufacturing and continuous production process manufacturing

Research into product costing practice has not studied whether there are differences in product costing practice between different types of manufacturing. This paper compares the product costing practices of operating units in discrete-part and assembly manufacturing, and continuous production process manufacturing. The results show that there are few differences in product costing practices between these two manufacturing methods. Similar proportions of operating units in these two manufacturing environments use similar methods to treat overheads and have similar experiences of activity-based costing. The only area of difference is in the use of overhead rates where significantly more units in discrete-part and assembly manufacturing use a direct labour hour rate and significantly more units in continuous production process manufacturing use units produced and production time-based rates.     

A production rate control policy for stochastic repair and remanufacturing systems

We consider the control of a manufacturing system responding to planned demand at the end of the expected life of each individual piece of equipment and unplanned demand triggered by a major equipment failure. The difficulty of controlling this type of production system resides in the variable nature of the remanufacturing process. In practice, remanufacturing operations for planned demand can be executed at different rates, referring to different component replacement and repair strategies. We formulate this problem as a multi-level control problem and propose a suboptimal control policy. The proposed control policy is described by inventory thresholds triggering the use of different execution modes. Determination of the control policy parameters is based on parameter optimization of analytical cost expressions. A numerical example based on a real case is presented. Our analysis demonstrates that the use of the proposed control approach can lead to a significant reduction in the total average cost, as compared to current practices.     

A dynamic model for the optimization of decoupling point and production planning in a supply chain

In this paper, we propose a dynamic model to simultaneously determine the optimal position of the decoupling point and production-inventory plan in a supply chain such that the total cost of the deviation from the target production rate and the target inventory level is minimized. Using the optimal control theory, we derive the closed form of the optimal solution when the production smoothing policy and the zero-inventory policy are applied. The result indicates that under the production smoothing policy, the overestimation of demand rate during the pre-decoupling stage guarantees the existence of the optimal decoupling point; meanwhile the optimal decoupling point exists under zero-inventory policy when the demand rate is underestimated. Also we perform mathematical analysis on the behavior of the optimal production rate and the inventory level and the effect of problem parameters such as the length of the product life cycle and the forecast error on the performance.     

Brainstorming: An examination of idea production rate and level of speculation in real managerial situations

The usefulness of brainstorming (Osborn type) as a management technique has been examined i n a series of exercises over the period 1972- 1974. In a considerable proportion of the exercises the material produced was interpreted as arising from low levels of speculation within the groups. It is argued that the procedures of brainstorming will not automatically lead to highly speculative material, and that managers using brainstorming as a means of bringing about suspension of judgment may not achieve that result. In real situations the intrusion of longer term belief systems may be influencing the group in the brainstorming process. Nevertheless, the principle of separating the idea generation and evaluation stages within managerial problem-solving exercises can be constructively adapted into regular management practice.