不确定需求下的设备动态布局方法

针对现有车间设备动态布局方法存在的不足,在考虑产品需求不确定性对布局性能稳定性影响的基础上,提出了一种结合模糊理论与改进遗传算法的不等面积设备动态布局方法。分析了产品需求不确定性及其随时间变化特性,引入了三角模糊数描述不确定产品需求;通过分析各生产阶段间的设备重组过程,将动态布局转化为数个静态布局,构建了基于柔性区域结构的不确定需求动态布局模型。结合三角模糊数运算及排序方法与自适应局部搜索机制提出了改进遗传算法,以物料搬运及设备重组费用总和为优化目标,解决不确定需求下的不等面积设备动态布局问题。通过算例测试和实例分析,验证了所提方法的有效性和实用性。     

基于遗传算法的动态连续型设备布局方法

在企业未来产品需求可预测的情况下,对具有多个计划期的车间内动态设备布局问题进行了研究.将动态设备布局中设备重置费用简化为设备移动成本与产能损失成本,结合静态设备布局问题连续模型以及动态设备布局问题离散模型的研究方法,构建了针对不等面积设备的动态多期布局问题的连续模型,并设计了改进型的遗传算法对模型进行了求解.此算法具有新颖的编码策略以及自适应选择交叉算子,具有良好的寻优能力.最后通过对一个3×8动态连续型设备布局问题的实例验证,证明了模型与方法的可行性.     

Managing a new multi-objective model for the dynamic facility layout problem

In today’s manufacturing environment, the facility layout needs to be adaptable to changes. This situation requires the solution of the dynamic layout problem. But in previous studies of dynamic facility layout optimization, the main objective is to minimize the sum of the re-arrangement and material handling costs. To be more realistic, each of these cost terms in objective function might be of different importance to decision makers. In this research, the objective function has been considered as two distinct functions. This formulation enables decision makers to apply their own views. On the other hand, in the proposed model the adjacency-based objective aims at maximizing adjacency scores between the facilities in a facility layout has also been used. The proposed multi-objective model was defined as a complex combinatorial optimization problem. It has also been the objective of the present study to evaluate some of the known methods that have been proposed to solve the multi-objective problem. The results for test problems showed that the population based metaheuristic methods are useful tools in solving proposed model.     

A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems

Facility layout problem is associated with the arrangement of facilities in a plant. It is a critical issue in the early stages of designing a manufacturing system because it affects the total manufacturing cost significantly. Dynamic and robust layouts are flexible enough to cope with fluctuations and uncertainties in product demands in volatile environment of flexible manufacturing systems. Since the facility layout is a hard combinatorial optimization problem, intelligent approaches are the most appropriate methods for solving the large size of this problem in reasonable computational time. In this paper, first of all, dynamic and robust layouts are surveyed. After a quick look of different mathematical models, including quadratic assignment, quadratic set covering, mixed integer programming, and graph theoretic models, the various solution methods especially intelligent approaches along with their advantages and disadvantages are surveyed. Finally, after review of hybrid algorithms, the conclusion of this paper is reported.     

Metaheuristics for fuzzy dynamic facility layout problem with unequal area constraints and closeness ratings

Dynamic facility layout problem deals with the problem of arranging and rearranging the layout plan of a system throughout several periods. In each period, the material handling costs are different from the past period due to the change in the market demand and product mix. In this paper, the uncertainty that exists in transportation values’ forecast is modeled by fuzzy theory. In this paper, departments have unequal areas. This means that in each period, departments can be placed only in certain places due to different spacing requirements. In addition, closeness rating matrix is considered in order to model the goodness of different layout plans with regard to qualitative factors according to decision maker. Accordingly, fuzzy dynamic facility layout problem with unequal areas and closeness rating matrix is considered that aims to minimize the uncertain material handling costs as well as the shifting costs, and maximize closeness rating with regard to space requirements of unequal area departments. A number of fuzzy algorithms are developed in order to deal with the problem. A number of ranking criteria from the literature are implemented in order to compare the performance of the developed algorithms.     

Parallel variable neighborhood search for solving fuzzy multi-objective dynamic facility layout problem

In spite of the classic approaches of solution of dynamic facility layout problem, which only material handling and rearrangement costs are considered as objective function, these problems are the multi-objective problems. In this paper, a mixed integer linear programming formulation is presented for multi-objective dynamic facility layout problem concerning flexible bay structure. In addition, three current objectives in dynamic facility layout problems including minimizing material handling and rearrangement costs, maximizing adjacency rate, and minimizing shape ratio difference have been considered. Also, for solving this problem, two methods including the GAMS software and proposed parallel variable neighborhood search (PVNS) algorithm are used. So, it is worth mentioning that four test problems are solved by them, and the results show that the proposed PVNS algorithm is more efficient than the GAMS software.     

A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem

In this article, we consider the facility layout problem which combines the objective of minimization of the total material handling cost and the maximization of total closeness rating scores. Multi-objective optimization is the way to consider the two objectives at the same time. A simulated annealing (SA) algorithm is proposed to find the non-dominated solution (Pareto optimal) set approximately for the multi-objective facility layout problem we tackle. The Pareto optimal sets generated by the proposed algorithm was compared with the solutions of the previous algorithms for multi-objective facility layout problem. The results showed that the approximate Pareto optimal sets we have found include almost all the previously obtained results and many more approximate Pareto optimal solutions.     

An elitist strategy genetic algorithm for integrated layout design

While designing the layout of any manufacturing organizations, the primary objective is to decide an optimal arrangement of their departments (machines or cells) in a two-dimensional shop floor (facility) satisfying desired objectives, which is termed facility layout problem (FLP). In traditional layout design philosophy, the inter-cell layout and flow path layout design of material handling system (MHS) was carried out step by step in a sequential manner. This results in sub-optimal solutions for FLP. In this paper, an integrated approach is adopted to design the inter-cell layout and the flow path layout of MHS simultaneously. The quality of the final layout is evaluated by minimizing the weighted sum of two distance-based cost objectives namely, (1) total material handling cost. (2) Distance-weighted cost of total closeness rating score. Sequence-pair (SP) representation is used for layout encoding. The translation from SP to layout is efficiently done by longest common subsequence methodology. Due to NP-hard nature of the proposed problem, an elitist strategy genetic algorithm (ESGA) is developed and tested with three test problem instances available in the literature. It is found that the proposed ESGA algorithm is able to produce the best solutions consistently, twice faster than the standard GA for the test problem instances.     

An new approach for the automatic analysis and control of material handling systems: integrated layout flow analysis (ILFA)

In the majority of small and medium sized enterprises (SEMs), the direct costs of material handling cannot be clearly measured. There are several reasons for this, including the large number of product types, complexity of their production cycle, and continuous change in markets. Therefore, production managers require flexible tools to create a suitable material handling system model which explicitly and rapidly calculates the indices required as these are traditionally neglected or laboriously approximated, (i.e., time and cost in material flow inside the factory, storage area requirements, and MH utilization percentage). This paper proposes an integrated approach to analyzing and controlling material handling operations in an industrial manufacturing plant from a “full quantitative” point of view. The model presented unites quite different fields of research into a unique methodology. This material handling model rapidly and automatically provides production managers with extensive and significant information. As a result, integrated layout flow analysis interrelates systematic layout planning with operational research algorithms and visual interactive simulation, using a complete software platform to implement them. This integrated layout flow analysis approach focuses on determining the space requirement for manufacturing department buffers, the transportation system requirements, the performance indices, and the time and cost of material flows spent in the layout and in MH traffic jams.     

考虑布局熵的多态性车间布局稳健优化模型与算法

为满足大规模定制和产业集群下多品种、小批量的市场需求和订单动态波动的客户需求以及车间低成本、高稳健性的布局要求,设计了以单位面积布置成本、单位产品物流成本和布局熵为优化指标的多目标布局优化模型。提出了基于Pareto优化的聚类并行多目标遗传算法,引入模糊C-均值聚类算法以提高Pareto解集分布的多样性与均匀性,设计了多元胞差分进化重插入操作与基于“精英策略”的移民操作,增强了算法全局与局部搜索能力,有效避免了早熟现象。通过典型算例对比,验证了模型和算法的有效性;同时在企业布局实例应用中,获得了既能满足低成本又能将布局熵值控制在理想范围内的车间布局方案,表明模型具有良好的实用性。     

Dynamic cellular manufacturing systems design��a comprehensive model

This paper addresses the dynamic cell formation problem (DCF). In dynamic environment, the product demand and mix changes in each period of a multiperiod planning horizon. It causes need of reconfiguration of cells to respond to the product demand and mix change in each period. This paper proposes a mixed-integer nonlinear programming model to design the dynamic cellular manufacturing systems (DCMSs) under dynamic environment. The proposed model, to the best of the author??s knowledge, is the most comprehensive model to date with more integrated approach to the DCMSs. The proposed DCMS model integrates concurrently the important manufacturing attributes in existing models in a single model such as machine breakdown effect in terms of machine repair cost effect and production time loss cost effect to incorporate reliability modeling; production planning in terms of part inventory holding, part internal production cost, and part outsourcing; process batch size; transfer batch size for intracell travel; transfer batch size for intercell travel; lot splitting; alternative process plan, and routing and sequence of operation; multiple copies of identical copies; machine capacity, cutting tooling requirements, work load balancing, and machine in different cells constraint; machine in same cell constraint; and machine procurements and multiple period dynamic cell reconfiguration. Further, the objective of the proposed model is to minimize the sum of various costs such as intracell movement costs; intercell movement costs and machine procurement costs; setup cost; cutting tool consumption costs; machine operation costs; production planning-related costs such as internal part production cost, part holding costs, and subcontracting costs; system reconfiguration costs; and machine breakdown repair cost, production time loss cost due to machine breakdown, machine maintenance overheads, etc. ,in an integrated manner. Nonlinear terms of objective functions are transformed into linear terms to make mixed-integer linear programming model. The proposed model has been demonstrated with several problems, and results have been presented accordingly.     

A new model of dynamic cell formation by a neural approach

This paper proposes a nonlinear integer model of cell formation under dynamic conditions. The cell formation (CF) problem is a portion of a cellular manufacturing strategy (CMS), in which the parts and machines are clustered with the aim of minimizing the material handling cost. In most previous research the cell formation problem has always been under static conditions in which cells are formed for a single-period planning horizon where product mix and demand are constant. In contrast, in dynamic conditions, a multi-period planning horizon is considered, where the product mix and demand in each period is different. This occurs in seasonally or monthly production. As a result, the best cell design for one period may not be efficient for subsequent periods. To verify the presented model, different problems have been solved and results are reported. Where the cell formation problem belongs to NP class, the use of a novel approach is necessary. In this research, we apply a neural approach based on mean filed theory for solving the proposed model. In this approach, the network weights are updated by an interaction procedure. The proposed model is solved by LINGO software and an optimum solution is obtained. Comparison of optimum and neural approach solutions shows the efficiency of the presented neural network approach.     

基于随机秘钥蝙蝠算法的多行设施布局设计

针对行内纵向通道数量和位置具有不确定性的多行设施布局问题,建立了以最小化物流强度、最小化搬运设备空载运行强度及最大化相互关系为设计目标的布局优化模型,并针对所建立的模型提出了一种基于映射规则的随机秘钥蝙蝠算法。在蝙蝠算法中引入随机秘钥编码思想,定义了基于随机秘钥编码的蝙蝠位置向多行设施布局组合解的映射规则与映射步骤,使算法在不进行离散化处理的前提下,可以在连续空间上执行,并在组合空间上映射出码长不同的布局方案。最后,以一个多行设施布局问题为实例,采用所提出的随机秘钥蝙蝠算法进行求解,证明了算法的优越性。     

Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm

Finding positions of departments on the plant floor for multiple periods while minimizing the sum of material handling and rearrangement costs is the base consideration of dynamic facility layout problems (DFLP). In this paper, a new kind of DFLP using flexible bay structure is studied. In a plant layout, based on flexible bay structure, departments are assigned to parallel bays in a plant floor. Departments could be free oriented and may have unequal areas as well as a mixed integer programming formulated to find optimal solutions. Due to complexity, only small-size problems could be solved in logical time while using exact methods. Therefore, a genetic algorithm (GA) was proposed to solve this optimization problem. This method was tested on some test problems of the DFLP literature. The results show the effectiveness of the proposed algorithm.     

基于车间动态布局的产品模糊聚类研究

结合原始设备制造(OEM)企业多产品生产的特点,提出了新的动态布局实现方法.主要是对车间的多产品进行聚类,以产品类为变量,随着产品类生产的不同进行布局切换.产品模糊聚类过程中,首先采用F-统计量方法确定产品最佳聚类数量;其次考虑到车间重布局对车间总成本的影响,以车间总成本最小化为优化目标构建产品最佳聚类数量数学模型;最后,以某车间为实例验证了布局方法的有效性.结果表明该方法不仅可以降低物流费用、缩短产品生产周期,而且丰富了动态布局理论,能给相关研究提供参考.     

An elitist strategy genetic algorithm using simulated annealing algorithm as local search for facility layout design

A well-planned arrangement of manufacturing departments on a two-dimensional planar region considerably increases the efficiency of its production systems, which is termed facility layout problem (FLP). Conventional layout design approach often designs intercell layout (determining exact location of each department on shop floor area) and flow path layout design of material handling system (MHS) step by step in a sequential manner. This results in suboptimal solutions for FLP. In this paper, an integrated approach is adopted to design the intercell layout and the flow path layout of MHS simultaneously. The quality of the final layout is evaluated by minimizing total material handling cost. Sequence pair (SP) representation is used for layout encoding. The translation from SP to layout is efficiently made by longest common subsequence (LCS) methodology. An elitist strategy genetic algorithm using simulated annealing (E-GASAA) as a local search mechanism is developed and tested with four test problem instances available in the literature. Elitist strategy is incorporated to enhance convergence characteristic of the proposed algorithm. It is found that the proposed E-GASAA is able to produce best solutions consistently for the test problem instance of different sizes within acceptable computational effort. In addition to that, we tried to reduce the computational load with the help of adopted LCS computation methodology and achieved a good improvement.     

基于NSGA-II的多目标设备动态布局方法

对多个计划期内需求可预测的车间动态设备布局问题进行了研究.针对这一多目标、多约束的问题,以物流搬运和重布局费用之和、非物流关系以及面积利用率作为优化目标,将动态布局问题转化为重布局过程和多个子计划期的静态布局问题,构建了针对不等面积设备的动态多期布局问题的连续型多目标优化模型.采用带精英策略的非支配遗传算法(Non-dominated Sorting Genetic Algorithm II,NSGA-II)进行求解,克服了传统加权法求解多目标问题时加权系数难以确定和无法保证多目标同时优化的缺点,求解得到Pareto解集,供决策者根据企业实际情况优中选优.通过实例验证了本方法的有效性.     

Capacities-based supply chain network design considering demand uncertainty using two-stage stochastic programming

The design of a supply chain (SC) aims to minimize cost so the product can reach the customer at the cheapest cost with flexible demand. The demand of a product is variable with time and environment. Most of the researchers have considered investment cost, processing cost, and transportation cost as variable costs to minimize the cost while considering a constant demand. In actual practice, the demands are flexible. In this paper, a two-stage stochastic programming model has been proposed for a capacities-based network design of a supply chain for flexible demands while considering inventory carrying cost and missed opportunity cost in addition to the above-mentioned costs. It will enhance the logistic planning and seek the location network optimally. Furthermore, in the first stage, decision variables represent different nodes (facility locations of echelons) of the supply chain, with the assumption that they will be considered at the design stage before uncertain parameters are unveiled. On the other hand, decision variables related to the amount of products to be produced and stored in the nodes of the SC, the flows of materials among the entities of the network, and shortfalls and excess at the customer centers are considered as second-stage variables. The methodology has been illustrated by solving an example. It was found that the proposed model yields more feasible and advantageous results.     

基于质量屋的产品级再使用维护设计

利用质量屋的方法，给出了产品级再使用维护设计的过程，提出了维护度的概念，建立了一个维护设计规划模型。模型的目标是维护度最大化，约束条件包括总维护成本约束范围和用户对某些产品特性改进的最低要求，以此方法对水泵再使用进行了维护设计，确定出每个产品特性的改进率及相关成本，案例表明该模型能帮助维护人员，在不确定的成本约束条件下有效地规划出较优的设计方案。     

Java-based computer-aided process planning

This investigation addresses distributed environments for computer-aided process planning (CAPP) over a network. The Java 2 Enterprise Edition (J2EE) standard using Java is applied to implement cross-platform and distributed computing architecture, thus reducing the system loading during manufacturing process planning. The product-oriented standard, standard exchange of product (STEP), model data is applied to define a robust data model that associates product, shape, and feature definitions and provides the mechanisms for data exchange, sharing, and integration with other engineering systems, such as computer-aided design (CAD) and computer-aided manufacturing (CAM). STEP AP224 is engaged to define the machining features of machined products in the proposed CAPP system. The life cycle of a product from design to sale, and especially the period of manufacturing, can be greatly shortened; the cost of manufacturing can be reduced and the quality of the product can be improved using the proposed CAPP system.