Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems

This article presents a fuzzy goal programming-based approach for solving a multi-objective mathematical model of cell formation problem and production planning in a dynamic virtual cellular manufacturing system. In a dynamic environment, the product mix and part demand change over a planning horizon decomposed into several time periods. Thus, the cell formation done for one period may be no longer efficient for subsequent periods and hence reconfiguration of cells is required. Due to the variation of demand and necessity of reconfiguration of cells, the virtual cellular manufacturing (VCM) concept has been proposed by researchers to utilise the benefits of cellular manufacturing without reconfiguration charges. In a VCM system, machines, parts and workers are temporarily grouped for one period during which machines and workers of a group dedicatedly serve the parts of that group. The only difference of VCM with a real CM is that machines of the same group are not necessarily brought to a physical proximity in VCM. The virtual cells are created periodically depending on changes in demand volumes and mix, as new parts accumulate during a planning horizon. The major advantage of the proposed model is the consideration of demand and part mix variation over a multi-period planning horizon with worker flexibility. The aim is to minimise holding cost, backorder cost and exceptional elements in a cubic space of machine–part–worker incidence matrix. To illustrate the applicability of the proposed model, an example has been solved and computational results are presented.     

Cellular manufacturing systems design with routing flexibility,machine procurement,production planning and dynamic system reconfiguration

This paper investigates the problem of designing cellular manufacturing systems with multi-period production planning, dynamic system reconfiguration, operation sequence, duplicate machines, machine capacity and machine procurement. An important aspect of this problem is the introduction of routing flexibility in the system by the formation of alternate contingency process routings in addition to alternate main process routings for all part types. Contingency routings serve as backups so as to effectively address the reality of part process routing disruptions (in the main routings) owing to machine breakdowns and allow the cellular manufacturing system to operate in a continuous manner even in the event of such breakdowns. The paper also provides in-depth discussions on the trade-off between the increased flexibility obtained versus the additional cost to be incurred through the formation of contingency routings for all parts. Some sensitivity analysis is also performed on some of the model parameters. The problem is modelled and solved through a comprehensive mixed integer programming formulation. Computational results presented by solving some numerical examples show that the routing and process flexibilities can be incorporated within the cellular manufacturing system design without significant increase in the system cost.     

基于多功能机器与多能工的动态单元制造系统模型

为了成功地实施动态单元制造系统,同时考虑技术性问题（包括生产单元构建和设计、生产单元之间与生产单元内部的物料移动等）和人员问题（包括员工工资、员工雇佣和解雇等）,综合研究和分析了多功能机器和多操作技能员工的动态单元制造系统的生产单元构建、生产单元之间与生产单元内部物料移动、库存和延迟生产、员工分配和柔性生产路径,创新性地提出一个整合的混合整数规划模型。通过遗传算法对数值试验求解,结果验证了新模型的可行性和有效性。     

Dynamic cellular manufacturing system design considering alternative routing and part operation tradeoff using simulated annealing based genetic algorithm

In this paper, an integrated mathematical model of multi-period cell formation and part operation tradeoff in a dynamic cellular manufacturing system is proposed in consideration with multiple part process route. This paper puts emphasize on the production flexibility (production/subcontracting part operation) to satisfy the product demand requirement in different period segments of planning horizon considering production capacity shortage and/or sudden machine breakdown. The proposed model simultaneously generates machine cells and part families and selects the optimum process route instead of the user specifying predetermined routes. Conventional optimization method for the optimal cell formation problem requires substantial amount of time and memory space. Hence a simulated annealing based genetic algorithm is proposed to explore the solution regions efficiently and to expedite the solution search space. To evaluate the computability of the proposed algorithm, different problem scenarios are adopted from literature. The results approve the effectiveness of the proposed approach in designing the manufacturing cell and minimization of the overall cost, considering various manufacturing aspects such as production volume, multiple process route, production capacity, machine duplication, system reconfiguration, material handling and subcontracting part operation.     

Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing

This paper deals with the design and loading of Cellular Reconfigurable Manufacturing Systems in the presence of alternative routing and multiple time periods. These systems consist of multiple reconfigurable machining cells, each of which has Reconfigurable Machine Tools and Computer Numerical Control (CNC) machines. Each reconfigurable machine has a library of feasible auxiliary machine modules for achieving particular operational capabilities, while each CNC machine has an automatic tool changer and a tool magazine of a limited capacity. The proposed approach consists of two phases: the machine cell design phase which involves the grouping of machines into machine cells, and the cell loading phase that determines the routing mix and the tool and module allocation. In this paper, the cell design problem is modelled as an Integer Linear Programming formulation, considering the multiple process plans of each part type as if they were separate part types. Once the manufacturing cells are formed, a Mixed Integer Linear Programming model is developed for the cell loading problem, considering multi-period demands for the part types, and minimising transportation and holding costs while keeping the machine and cell utilisations in each period, and the system utilisation across periods, approximately balanced. An illustrative problem and experimental results are presented.     

A methodology for designing flexible cellular manufacturing systems

Cell formation in cellular manufacturing deals with the identification of machines that can be grouped to create manufacturing cells and the identification of part families to be processed within each cell. Dynamic and random variations in part demands can negatively impact cell performance by creating unstable machine utilizations. The purpose of this paper is to introduce and illustrate an interactive cell formation method that can be used to design 'flexible' cells. Flexibility in this context refers to routing flexibility (i.e., the ability for the cellular system to process parts within multiple cells) and demand flexibility (i.e., the ability of the cell system to respond quickly to changes in part demand and part mix). Through an experimental analysis using multiple data sets, we also validate the procedure and provide guidelines for parameter settings depending upon the type of flexibility of interest to the user. Finally, trade-offs and interdependences between alternative types of flexibility in the context of cellular systems are illustrated.     

Manufacturing cell design: an integer programming model employing genetic algorithms

The design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several problems from the literature.     

A robust cellular manufacturing system design for dynamic part population using a genetic algorithm

As changing conditions prevail in the manufacturing environment, the design of cellular manufacturing systems, which involves the formation of part families and machine cells, is difficult. This is due to the fact that the machines need to be relocated as per the requirements if adaptive designs are used. This paper presents a new approach (robust design) for forming part families and machine cells, which can handle all the changes in demands and product mixes without any relocations. This method suggests fixed machine cells for the dynamic nature of the production environment by considering a multi-period forecast of product mix and demand. A genetic algorithm based solution procedure is adopted to solve the problem. The results thus obtained were compared with the adaptive design proposed by Wicks and Reasor (1999 Wicks, EM and Reasor, RJ. 1999. Designing cellular manufacturing systems with dynamic part populations. IIE Trans., 31: 11–20. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). It is found that the robust design performs better than the adaptive design for the problems attempted.     

Design of distributed layouts

Distributed layouts are layouts where multiple copies of the same department type may exist and may be placed in non-adjoining locations. In this paper, we present a procedure for the design of distributed layouts in settings with multiple periods where product demand and product mix may vary from period to period and where a relayout may be undertaken at the beginning of each period. Our objective is to design layouts for each period that balance relayout costs between periods with material flow efficiency in each period. We present a multi-period model for jointly determining layout and flow allocation and offer exact and heuristic solution procedures. We use our solution procedures to examine the value of distributed layouts for varying assumptions about system parameters and to draw several managerial insights. In particular, we show that distributed layouts are most valuable when demand variability is high or product variety is low. We also show that department duplication (e.g., through the disaggregation of existing functional departments) exhibits strong diminishing returns, with most of the benefits of a fully distributed layout realized with relatively few duplicates of each department type.     

Layout design in fractal organizations

Today's complex, unpredictable and unstable marketplace requires flexible manufacturing systems capable of cost-effective high variety–low volume production in frequently changing product demand and mix. In fractal organizations, system flexibility and responsiveness are achieved by allocating all manufacturing resources into multifunctional cells that are capable of processing a wide variety of products. In this paper, various fractal cell configuration methods for different system design objectives and constraints are proposed. These parameters determine the level of interaction between the cells, the distribution of different product types among the cells and the similarity of cell capabilities. A tabu-search-based method is proposed to optimize the product distribution to the cells and the arrangement of machines and cells on the shop floor. This optimization is performed for different fractal cell configuration methods and cell quantities. The quality of the resulting shop floor layouts is measured in terms of resource requirements and material movements. The results indicate that in fractal layouts, a trade-off is required between machine quantities and material travelling distance. It was generally possible to reduce travelling distances by increasing the degree of optimization on machine layout and product distribution for a specific product demand and mix.     

Facility layout design in a changing environment

Increasing global competition, rapid changes in technology and the necessity to respond quickly to a cost and quality conscious customer have changed the dynamics of facilities planning. Today's manufacturing facility needs to be responsive to the frequent changes in product mix and demand while minimizing material handling and machine relocation costs. In this paper, we present a framework for the design of a dynamic facility which can respond effectively to the changes in product design, mix and volume in a continuously evolving work environment. A genetic algorithm-based heuristic is used for solving the design problem and two test cases are presented to illustrate the use of the methodology.     

Selection of an optimal subset of sizes

For a company that produces a product in a range of sizes, it is sometimes possible to meet demand for a smaller size by substituting a larger size. In this paper, we give an integer linear programming model that addresses this issue. Various characteristics of the optimal policy are given. These properties are exploited when the formulation is extended to the multi-period stochastic demand case. This application was motivated by our experience with a company that manufactures a range of multiple-piece blind fasteners where savings in setup cost can be made using long-grip fasteners to meet the demand for short-grip fasteners.     

A simulation analysis of factors influencing the flexibility of cellular manufacturing

The performance of cellular manufacturing (CM) systems in a variable demand and flexible workforce environment has been examined using simulation modelling. Discrepancies between academicians and practitioners’ findings with respect to flexibility and uneven machine utilization in CM systems are discussed. The views of two parties were incorporated in simulation models to rectify the existing discrepancies. While the results of this study confirm the previous findings of academicians regarding the deterioration of the performance of CM in a variable product mix situation, it appears that those results may be significantly influenced by considering a flexible workforce. The simulation results show that the practice of using flexible crossed-trained operators can improve the flexibility of CM in dealing with an unstable demand and can reduce load imbalance inherent in machine dedication in manufacturing cells.     

Formation of independent flow-line cells based on operation requirements and machine capabilities

In this paper we study the generalized grouping problem of cellular manufacturing. We propose an operation-sequence-based method for forming flow-line manufacturing cells. Process planning in the form of selection of the machine for each operation is included in the problem formulation. Input requirements include the set of operation requirements for each part type, and operation capabilities for all available machine types. The objective is to find the minimum-cost set of flow-line cells that is capable of producing the desired part mix. A similarity coefficient based on the longest common operation subsequence between part types is defined and used to group parts into independent, flow-line families. An algorithm is developed for finding a composite operation supersequence for each family. Given machine options for each operation in this sequence, the optimal machine sequence and capacity for each cell is then found by solving a shortest path problem on an augmented graph. The method is shown to be efficient and computational results are reported.     

Designing integrated cellular manufacturing systems with tactical decisions

In this paper, we aim to design cellular manufacturing systems that optimize the performance of a manufacturing system subject to the optimization aspects of production planning. Consequently, the demand for each part – one of the production planning features – plays a vital role in determining the part families and the formation of machine cells in each period. In our study, holding and backorder costs follow a probabilistic structure, and they are described by a set of stochastic scenarios. In this model, five objective functions are employed: one of them minimizes the expected total holding and backorder costs in order to evaluate the risk in the model. The aim of this model is to select and optimize the assignment of parts and machines to different cells as well as the number of each produced part in each period. A new heuristic algorithm based on the optimization method is established to yield the best solution for this complicated mathematical formulation. Further, the performance of the proposed algorithm is verified using certain test problems in which the obtained results are compared with those obtained using the branch-and-bound algorithm and heuristic procedures.     

A mathematical programming model for reconfiguration of flexible manufacturing cells

In flexible manufacturing cells, changes in demand size, product mix, part variety, existing routings and set-up/operation times may require reconfiguration of the cells. In this study, an approach is developed to decide when and how such a reconfiguration should be carried out for existing cells. This study considers reconfiguration in terms of changing part routings, adding a new machine type in a cell, duplicating an existing machine type, removing an existing machine from a cell and transferring a machine to another cell. The study also shows the total number of tools of each type on each machine located in each cell after reconfiguration. To make the optimal reconfiguration decisions, a mathematical programming model to minimize the total reconfiguration cost is developed. The developed model considers the lower and upper bounds on machine utilizations and the time limits on machine cycle times to decide when to reconfigure the system.     

Unidirectional AGV guidepath network design: A heuristic algorithm

In an AGV system, the design of the guidepath network is one of the most important factors that determine the system effectiveness. Many alternative guidepath design schema have been introduced in the literature. However, the methods generally address the static production environment where the product mix or machine routings are assumed to be stable over time. In today's dynamic production systems with small lot sizes and short product cycles, the assumption of an unchanging product mix over an extended period is unrealistic in some situations. Considerable system inefficiency can be introduced when a network designed under the assumption of a stable product mix is used when indeed the product mix has changed. To avoid this kind of hidden source of inefficiency, what is needed is a tool that can recognize when a previously designed network is inappropriate for a new product mix and then uses the new product routing information to reconfigure a new network appropriate for the new production condition. A heuristic algorithm for the design of AGV guidepath in an environment with changing product mix is proposed here. The algorithm not only makes it possible to adapt the system network as the product mix changes, but also produces new designs at reasonably short computational time.     

Formation of machine cells and part families: A modified p-median model and a comparative study

This paper is concerned with machine-cell and part-family formation for the design of cellular manufacturing systems. The paper has two distinguished purposes. The first purpose of the paper is to introduce a modified p-median model, an integer linear programming formulation, for the machine-cell or partfamily formation problem. Compared with the original p-median model, the modified model has additional desirable features. The modified model allows the control of the size of machine cells or part families by introducing an upper bound on the maximal number of machines per cell or maximal number of parts per family. The modification also results in a substantial reduction of the number of constraints in the model formulation. The second purpose of this paper is to present various computational results in a comparative study for machine-cell and part-family formation based on the original and modified p-median models using three different definitions of similarity coefficients. Guidelines are given to choose design parameters, definition of similarity coefficients, and appropriate solution sequence of machine-cell and part-family formation.     

Effective two-phase p-median approach for the balanced cell formation in the design of cellular manufacturing system

In this paper, we present an effective two-phase p-median approach for the balanced cell formation (CF) in the design of cellular manufacturing system. In phase 1, the p-median mathematical model of machine CF, which adopts a linear integer programming formulation, is developed. Our formulation uses a new similarity coefficient based on the generalised nonbinary part-machine incidence matrix (PMIM) which incorporates realistic manufacturing aspects such as setup time, processing time, operation sequences and lot size of parts and duplicate machine types. In phase 2, a systematic part assignment procedure based on the new classification scheme of part types is established in pursuit of balancing the workload among machine cells. New efficiency measures for evaluating the quality of the binary and nonbinary PMIM-based block diagonal solutions are proposed to judge the degree of cell load imbalance. Computational experiments with moderately intermediate-sized data-sets selected from the literature show effectiveness of our two-phase p-median approach for the balanced CF.