Using an evolutionary algorithm for catalog design

This paper describes an evolutionary algorithm that was developed for catalog design. This algorithm is based on genetic algorithms, but uses an object-oriented coding scheme to represent a design, and introduces unique crossover and mutation operators. To account for the dependence of system performance on both system configuration and component selection, the evolutionary algorithm allows for simultaneous alterations of configurations and components. This new approach allows the consideration of alternate configurations and allows the configurations to evolve to make the best use of the available components. Using this evolutionary algorithm, a piping system was designed in which cooling fluid was delivered to three machines on a manufacturing floor at specified pressures and flow rates. The algorithm was able to find good designs that satisfied the given design specifications.     

A cellular similarity coefficient algorithm for the design of manufacturing cells

The development and implementation of an integrated system for computer-aided process planning and cellular manufacturing design is described. A coding scheme is proposed for classifying parts according to the manufacturing processes required to produce them. The routeing data and the classification codes obtained from computer-aided process planning are then stored in a database. A heuristic algorithm has also been developed for designing manufacturing cells using the classification data stored in the process planning database. This algorithm is based on a new concept developed in this study called cellular similarity coefficient which considers the similarity between machine cells rather than individual machines, as in the case of other similarity coefficients.     

A multi-objective genetic algorithm approach to the design of cellular manufacturing systems

In this paper, a multi-objective integer programming model is constructed for the design of cellular manufacturing systems with independent cells. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. There are some features that make the proposed algorithm different from other algorithms used in the design of cellular manufacturing systems. These include: (1) a systematic uniform design-based technique, used to determine the search directions, and (2) searching the solution space in multiple directions instead of single direction. Four problems are selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.     

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.     

Capability-based distributed layout approach for virtual manufacturing cells

It is shown in the literature that in highly volatile manufacturing environments functional job shops and classical cellular manufacturing systems do not perform well. Classical cellular manufacturing systems are very sensitive to changing production requirements due to their limited flexibility. In order to adapt cellular manufacturing systems to volatile manufacturing environments, the virtual cellular manufacturing concept was proposed in the 1980s by the National Bureau of Standards in USA. This concept is similar to group technology where job families are processed in manufacturing cells. The main difference between a virtual cell and the classic cell is in the dynamic nature of the virtual manufacturing cell; whereas the physical location and identity of classic cell is fixed, the virtual cell is not fixed and will vary with changing production requirements. The virtual manufacturing cell concept allows the flexible reconfiguration of shop floors in response to changing requirements. In the literature, the formation and scheduling process of virtual cells are clearly explained and researched in detail. However, the layout issue is not addressed entirely. Virtual cells are generally formed over functionally divided job shops. Forming virtual cells over a functional layout may adversely affect the performance of a virtual cellular manufacturing system. There is a need to search for different layout strategies in order to enhance the performance. The distributed layout approach may be a better alternative for virtual cellular manufacturing applications. In this research paper, a novel capability-based approach is proposed for the design of distributed layouts. A simulated annealing based heuristic algorithm is developed from the distributed layout. The proposed approach is tested with a problem with real data. An example is also shown in order to give an idea about the superiority of a capability-based distributed layout over the functional layouts in forming virtual manufacturing cells.     

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.     

A PSO-based approach to cell formation problems with alternative process routings

Group technology (GT) has been extensively applied to cellular manufacturing system (CMS) design for decades due to many benefits such as decreased number of part movements among cells and increased machine utilisation in cells. This paper considers cell formation problems with alternative process routings and proposes a discrete particle swarm optimisation (PSO) approach to minimise the number of exceptional parts outside machine cells. The approach contains two main steps: machine partition and part-routing assignment. Through inheritance and random search, the proposed algorithm can effectively partition machines into different cells with consideration of multiple part process routings. The computational results are compared with those obtained by using simulated annealing (SA)-based and tabu search (TS)-based algorithms. Experimental results demonstrate that the proposed algorithm can find equal or fewer exceptional elements than existing algorithms for most of the test problems selected from the literature. Moreover, the proposed algorithm is further tailed to incorporate various production factors in order to extend its applicability. Four sample cases are tested and the results suggest that the algorithm is capable of solving more practical cell formation problems.     

Converting functional manufacturing systems into focused machine cells— a bicriterion approach

The main purpose of adopting cellular manufacturing (CM) is to achieve a preferred compromise between flow-line efficiency and job-shop flexibility. This paper presents a bicriterion approach to seek such a preferred design compromise in converting functional manufacturing systems into focused CM systems. The problem is formulated as a bicriterion nonlinear-integer programming model. The number of part types accommodated into the focused cells is employed as a measure of system flexibility and the average system similarity level is used as a measure of system efficiency. A heuristic algorithm, consisting of seeding, grouping, and inserting modules, is then proposed to solve the model. Finally, an example problem is included to illustrate the application of the model and solution procedure.     

Agent-based dynamic part family formation for cellular manufacturing applications

One of the main critique on cellular manufacturing and its algorithms is their inability to handle dynamics events, especially dynamic changes in part spectrum. Unfortunately, there are not many efforts in the literature to overcome this problem. Agent oriented computing provides a marvellous opportunity to handle dynamic problems and to provide effective solutions, if carefully and intelligently implemented. In this paper, we have proposed a novel agent-based clustering algorithm for part family formation in cellular manufacturing by considering dynamic demand changes. However, it is not easy to directly compare the performance of the proposed algorithm with the literature results as there is no benchmark for dynamic cell formation problems. We attempt to compare the performance of the present algorithm on static test problems by dynamically introducing parts in these data-sets to our algorithm. Many results have been presented on these static data-sets by utilising several heuristics, meta-heuristics and optimisation-based algorithms. Although the proposed algorithm is not an optimisation-based algorithm and its operation is directed to handle dynamic changes in the problem domain through negotiation, we have shown that it has ability to provide very good results which are comparable to the best known solutions.     

A framework for the design of cellular manufacturing systems

A two-stage procedure for the design of a cellular manufacturing system is proposed. The first stage forms the part families. The use of clustering techniques with a new proximity measure is advocated for this stage. The proximity measure uses the manufacturing operations and the operations' sequences. The second stage forms the machine cells. An integer programming model is proposed for this stage. The solution of this model will specify the type and the number of machines in each cell and the assignment of the part families to the cells. The relevance of this approach in the design of flexible manufacturing systems is discussed.     

An interactive tool for designing manufacturing cells for an assembly job-shop

Cellular manufacturing is often implemented to reduce work in progress, materials handling, set-ups and storage space, as well as to improve quality and worker satisfaction. Wemmerlov and Johnson (1997) have pointed out that cellular configurations do not automatically deliver these advantages. An interactive tool is presented to design manufacturing cells for an assembly shop. The method is based on an analysis of operation sequences and durations and it allows the design of hybrid layouts. We show that a cellular configuration is not always desirable and discuss the conditions where this is so.     

A set partitioning based heuristic procedure for incremental cell formation with routing flexibility

One of the important issues regarding the implementation of cellular manufacturing relates to deciding whether to convert an existing job shop into a cellular manufacturing system comprehensively in a single go, or to convert in stages incrementally wherein the cells are formed one after the other taking the advantage of experiences of implementation. In this paper, a heuristic method based on iterative set partitioning is proposed for incremental cell formation where part operations can be processed on alternative machines. The objective is to minimize cycle time for a given number of workstations. The proposed method is numerically compared with the existing branch and bound technique and another heuristic algorithm based on multistage programming. It is found that the proposed method requires significantly less computational efforts to yield the optimal solution.     

Design of dedicated,shared and remainder cells in a probabilistic demand environment

In this paper, a new layered cellular manufacturing system is proposed to form dedicated, shared and remainder cells to deal with the probabilistic demand, and later its performance is compared with the classical cellular manufacturing system. In the layered cellular design, each family may need more than one cell to cover capacity requirements. The proposed approach for layered cellular design involves five stages: (1) product clustering, (2) identifying number of cells and demand coverage probabilities, (3) determining cell types using the proposed heuristic procedure, (4) performing simulation to determine operating conditions and (5) statistical analysis to pick the best design configuration among layered cellular designs. Simulation and statistical analysis are performed to help identify the best design within and among both layered cellular design and classical cellular design. It was observed that as the number of part families increased, the number of machines needed to process the parts decreased first. Then the number of machines started to increase once again as the number of part families continued to increase. Another observation was that the average flow time and total WIP were not always the lowest when additional machines were used by the system. The last and the most important observation was that the layered cellular system provided much better results than the classical cellular system when high demand fluctuation was observed.     

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.     

Configuration of cellular manufacturing systems using association rule induction

A cell-formation approach based on association rule induction is developed to find the effective configurations for cellular manufacturing systems. To gain the benefits of flexibility and efficiency, the manufacturing system is decomposed into several manageable subsystems by categorizing similar parts into part families and disparate machines into cells. It is advantageous to find the important associations among machines such that the occurrence of some machines in a machine cell will cause the occurrence of other machines in the same cell. Relationships among machines can be found from the process database by inducting association rules. By applying association rules to cell-formation problems, certain sets of machines (machine groups) that frequently process some parts together can be inducted. A data-mining technique referred to as association rule induction is used herein to find the association rules among machines from the process database. Seventeen data sets of various size and complexity were used to evaluate the effectiveness of the proposed cell-formation algorithm based on association rule induction. The performance of the proposed approach is compared with several existing techniques. From the computational results, the proposed approach shows its ability to find quality solutions.     

A HIERARCHICAL DIVISIVE CLUSTERING METHOD FOR MACHINE-COMPONENT GROUPING PROBLEMS

This paper proposes a new approach based on graph theory for the machine-component grouping problem in a cellular manufacturing system. The objective is to form components into part families such that the degree of interrelations is high among components within the same part family and low between components of different part families.

Since finding an optimal solution through total enumeration is prohibitive in terms of time and efforts even for problems with a moderate number of components, a heuristic algorithm is proposed. The algorithm is hierarchical and divisive in nature and illustrated with numerical examples.     

Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA

To facilitate the configuration selection of reconfigurable manufacturing systems (RMS) at the beginning of every demand period, it needs to generate K (predefined number) best configurations as candidates. This paper presents a GA-based approach for optimising multi-part flow-line (MPFL) configurations of RMS for a part family. The parameters of the MPFL configuration comprise the number of workstations, the number of paralleling machines and machine type as well as assigned operation setups (OSs) for each workstation. Input requirements include an operation precedence graph for each part, relationships between operations and OSs as well as machine options for each OS. The objective is to minimise the capital cost of MPFL configurations. A 0-1 nonlinear programming model is developed to handle sharing machine utilisation over consecutive OSs for each part which is ignored in the existing approach. Then a novel GA-based approach is proposed to identify K economical solutions within a refined solution space comprising the optimal configurations associated with all feasible OS assignments. A case study shows that the best solution found by GA is better than the optimum obtained by the existing approach. The solution comparisons between the proposed GA and a particle swarm optimisation algorithm further illustrate the effectiveness and efficiency of the proposed GA approach.     

A robust cell formation approach for varying product demands

In cellular manufacturing environments, manufacturing cells are generally formed based on deterministic product demands. In this paper, we consider a system configuration problem with product demands expressed in a number of probabilistic scenarios. An optimization model integrating cell formation and part allocation is developed to generate a robust system configuration to minimize machine cost and expected inter-cell material handling cost. A two-stage Tabu search based heuristic algorithm is developed to find the optimal or near optimal solutions to the NP-hard problem. Numerical examples show that this model leads to an appropriate compromise between system configuration costs and expected material handling costs to meet the varying product demands. These example problems also show that the proposed algorithm is effective and computationally efficient for small or medium size problems.     

A new approach to the cell formation problem with alternative processing routes and operation sequence

Cellular manufacturing (CM) is an important application of group technology in manufacturing systems. One of the crucial steps in the design of CM is the identification of part families and manufacturing cells. This problem is referred to as cell formation problem (CFP) in the literature. In this article, a solution approach is proposed for CFP, which considers many parameters such as machine requirement, sequence of operations, alternative processing routes, processing time, production volume, budget limitation, cost of machines, etc. Due to the NP-hardness of CFP, it cannot be efficiently solved for medium- to large-sized problems. Thus, a genetic algorithm (GA) is proposed to solve the formulated model. Comparison of the results obtained from the proposed GA to the globally optimum solutions obtained by Lingo Software and those reported in the literature reveals the effectiveness and efficiency of the proposed approach.     

Multiple routeings and capacity considerations in group technology applications

This paper addresses the problem of manufacturing cell formation, given multiple part routeings, and multiple functionally similar workcentres. Cellular manufacturing is intended to facilitate production, and thus should be based on projected production requirements. The originality of the approach lies in considering both the manufacturing system as well as projected production, and distributing the demand among alternate routeings in order to obtain a better manufacturing cell design. The suggested choice of part routeings favours the decomposition of the manufacturing system into manufacturing cells in a way that minimizes part traffic, along with satisfying the part demand and workcentre capacity constraints. We show that the problem can be formulated as a linear programming type problem which simultaneously addresses two problems: (i) routeing selection, and (ii) cell formation. The common objective is to minimize the inter-cell traffic in the system. The proposed algorithm iteratively solves two problems. The first problem is formulated as a linear-programming problem, while the latter is approached by an existing heuristic bottom-up aggregation procedure, known as Inter-Cell Traffic Minimization Method (ICTMM), enhanced appropriately.