Fuzzy ART/RRR-RSS: a two-phase neural network algorithm for part-machine grouping in cellular manufacturing

In this paper an efficient methodology adopting Fuzzy ART neural network is presented to solve the comprehensive part-machine grouping (PMG) problem in cellular manufacturing (CM). Our Fuzzy ART/RRR-RSS (Fuzzy ART/ReaRRangement-ReaSSignment) algorithm can effectively handle the real-world manufacturing factors such as the operation sequences with multiple visits to the same machine, production volumes of parts, and multiple copies of machines. Our approach is based on the non-binary production data-based part-machine incidence matrix (PMIM) where the operation sequences with multiple visits to the same machine, production volumes of parts, and multiple identical machines are incorporated simultaneously. A new measure to evaluate the goodness of the non-binary block diagonal solution is proposed and compared with conventional performance measures. The comparison result shows that our performance measure has more powerful discriminating capability than conventional ones. The Fuzzy ART/RRR-RSS algorithm adopts two phase approach to find the proper block diagonal solution in which all the parts and machines are assigned to their most preferred part families and machine cells for minimisation of inter-cell part moves and maximisation of within-cell machine utilisation. Phase 1 (clustering phase) attempts to find part families and machines cells quickly with Fuzzy ART neural network algorithm which is implemented with an ancillary procedure to enhance the block diagonal solution by rearranging the order of input presentation. Phase 2 (reassignment phase) seeks to find the best proper block diagonal solution by reassigning exceptional parts and machines and duplicating multiple identical machines to cells with the purpose of minimising inter-cell part moves and maximising within-cell machine utilisation. To show the robustness and recoverability of the Fuzzy ART/RRR-RSS algorithm to large-size data sets, a modified procedure of replicated clustering which starts with the near-best solution and rigorous qualifications on the number of cells and duplicated machines has been developed. Experimental results from the modified replicated clustering show that the proposed Fuzzy ART/RRR-RSS algorithm has robustness and recoverability to large-size ill-structured data sets by producing highly independent block diagonal solution close to the near-best one.     

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 network approach to cell formation in cellular manufacturing

Formation of machine cells often results in some intercellular movements between the cells. These movements cause lack of segregation among the cells. This is in conflict with the main objective of group technology which aims at independently operating cells. This paper presents a non-heuristic network approach to form manufacturing cells with minimum intercellular interactions. The machine-part matrix containing machining times is represented as a network which is subsequently partitioned by using a modified Gomory-Hu algorithm to find a minimum intercellular interaction. The modified algorithm improves the computational efficiency.     

A possibilistic approach for designing hybrid cellular manufacturing systems

In this paper, a hybrid cellular manufacturing (HCM) system is presented in which the main sources of uncertainty, e.g. the demands of parts and unit costs are treated as fuzzy numbers in the form of possibilistic distributions. The basic concept of HCM is that high variation in demand might disturb cell efficiency, so forming cells with only those parts that have stable demand, will profit. Thus, to design stable and robust manufacturing cells, a two-phase method is proposed in which a fuzzy adaptive ranking method is first applied to identify those parts with low and non-repetitive demands (i.e. the special parts) which will then be assigned to a functional cell. Afterwards, an interactive possibilistic programming model is applied to cell formation of remaining regular parts while considering both part sequences and multiple routes. To show the capability and usefulness of the proposed method, an illustrative example is also provided. Finally, concluding remarks are reported.     

Joint cell loading and scheduling approach to cellular manufacturing systems

A hierarchical multi-objective heuristic algorithm and pricing mechanism are developed to first determine the cell loading decisions, and then lot sizes for each item and to obtain a sequence of items comprising the group technology families to be processed at each manufacturing cell that minimise the setup, inventory holding, overtime and tardiness costs simultaneously. The linkage between the different levels is achieved using the proposed pricing mechanism through a set of dual variables associated with the resource and inventory balance constraints, and the feasibility status feedback information is passed between the levels to ensure internally consistent decisions. The computational results indicate that the proposed algorithm is very efficient in finding a compromise solution for a set of randomly generated problems compared with a set of competing algorithms.     

Scheduling in a cellular manufacturing system: a simulated annealing approach

The problem of scheduling in a cellular manufacturing system is considered with the objective of minimizing the sum of completion times (or total flow time) of jobs. A correct formulation of recursive equation for the flowline-based cellular manufacturing system is first proposed. Subsequently a heuristic is developed to obtain a sequence that minimizes total flow time in a flowline cell. The proposed heuristic makes use of the simulated annealing technique and is developed in two stages. A good initial heuristic seed sequence obtained in the first stage is improved upon by a proposed new variant of the simulated annealing technique wherein three different perturbation schemes have been experimented with. One of the perturbation schemes is newly proposed in this paper and is called the Adjacent interchange scheme. The proposed simulated annealing algorithm has been compared with the existing heuristics for minimizing flow time and has shown consistently good and superior solutions.     

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.     

A transiently chaotic neural network approach to the design of cellular manufacturing

The design of Cellular Manufacturing Systems (CMS) has attained the significant attention of academicians and practitioners over the last three decades. Minimizing intercellular movements while maximizing utilization of machines are the main objectives of interest in designing CMS and are considered in present research. In this paper, the drawbacks of former neural networks-based approaches to cell formation are discussed. The standard version of cell formation problem is formulated and a 'Transiently Chaotic Neural Network' (TCNN) with supplementary procedures is introduced as a powerful rival. A simplified network is constructed. After developing the related equations the approach is tested using the proposed algorithm with 18 problems selected from literature. The results are compared with various other approaches including ART1, Extended-ART1, Ortho-Synapse Hopfield Neural Network (OSHN), etc. The main advantages of our proposed method are: (1) the ability to avoid the local optima trap, (2) the ability to solve problems of different sizes with the same set of values for parameters, and (3) the less computation time. The results also indicate considerable improvement in grouping efficiency through the proposed approach.     

A within-cell utilization based heuristic for designing cellular manufacturing systems

The initial stage in the facilities design of cellular manufacturing systems involves the identification of part families and machine groups and forming cells possessing specific manufacturing capabilities. A new heuristic for the part family/machine group formation (PF/MGF) problem is presented in this paper. The distinguishing feature of this heuristic is its consideration of several practical criteria such as within-cell machine utilization, work load fractions, maximum number of machines that are assigned to a cell, and the percentage of operations of parts completed within a single cell. Computational results, based on several examples from the literature, show that this heuristic performs well with respect to more than one criteria. The heuristic also clarifies the source of various arguments in the literature concerning the ‘goodness’ of the solutions obtained by other researchers. An application of the heuristic to a large sample of industrial data involving 45 workcentres composed of 64 machines, and 305 parts is given, and the usefulness of the heuristic for trading-off several objectives is illustrated.     

An expert system based approach to manufacturing cell design

The design of cellular systems is a complex, multi-criteria and multi-step process which can have significant implications for the entire organization. Most research in this area focuses on the formation of pan families and associated machine groups, one step in the cell design process. Numerous quantitative techniques have been developed to address this part-family/machine group formation problem. Existing approaches include math programming, algorithms for matrix diagonalization, the application of network modelling and the use of similarity coefficients. These mathematically-oriented techniques can handle a relatively limited set of quantitative objectives and. in addition, require many simplifying assumptions. For this reason, the solutions generated by these techniques are of limited usefulness in actual cell design. This paper proposes an expert system approach to cell system design. The starting point for the expert system is the initial solution generated by traditional mathematical techniques. Based on a flexible set of user-driven quantitative and qualitative factors, the expert system evaluates these preliminary solutions for feasibility and quality. If the solutions are not satisfactory (infeasible or of low quality), the system suggests modifications.     

A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems

The dynamic cellular facility layout problem (DCFLP) is a well-known NP-hard problem. It has been estimated that the efficient design of DCFLP reduces the manufacturing cost of products by maintaining the minimum material flow among all machines in all cells, as the material flow contributes around 10–30% of the total product cost. However, being NP hard, solving the DCFLP optimally is very difficult in reasonable time. Therefore, this article proposes a novel similarity score-based two-phase heuristic approach to solve the DCFLP optimally considering multiple products in multiple times to be manufactured in the manufacturing layout. In the first phase of the proposed heuristic, a machine–cell cluster is created based on similarity scores between machines. This is provided as an input to the second phase to minimize inter/intracell material handling costs and rearrangement costs over the entire planning period. The solution methodology of the proposed approach is demonstrated. To show the efficiency of the two-phase heuristic approach, 21 instances are generated and solved using the optimization software package LINGO. The results show that the proposed approach can optimally solve the DCFLP in reasonable time.     

Monitoring manufacturing quality for multiple Li-BPIC processes based on capability index C pmk

The multi-process performance analysis chart (MPPAC) based on process capability indices has been developed to analyse the manufacturing performance for multiple processes, which conveys critical information regarding the departure of the process mean from the target value, process variability, capability levels, which provides a guideline of directions for capability improvement. Existing MPPAC researches have plotted the sample estimates of the process indices on the chart. Conclusions were then made on whether processes meet the capability requirement and directions need to be taken for further quality improvement. Such an approach is highly unreliable since the sample point estimate is a random variable with no assessment of the sampling errors. Further, existing MPPAC researches only considered one single sample. Current quality control practice is to estimate process capability using multiple groups of control chart samples rather than one single sample. In this paper, we propose the C _pmk MPPAC combining the accuracy index C _a to access the performance of multiple manufacturing processes. Distributions of the estimated C _pmk and C _a are derived based on multiple control chart samples, and accurate lower confidence bounds are calculated. The lower confidence bounds of the estimated C _pmk and C _a are then employed to the MPPAC to provide reliable capability grouping for those multiple processes. A real-world example is presented to illustrate the applicability of the proposed MPPAC.     

A fuzzy approach to multi-objective routing problem with applications to process planning in manufacturing systems

In this paper a method is developed for the selection of an efficient path in a fuzzy multi-objective network. The application of the methodology developed is illustrated by a process plan selection problem in a manufacturing environment.     

An approach based on fuzzy sets for manufacturing system design

The aim of this work is to provide Small and Medium Enterprises (SMEs) with adapted design methods for manufacturing systems. In a total quality strategy, the design activity consists of searching for the means able to satisfy all users' needs from company customers to floor plant workers. That includes usual costs, delays and quality targets but also employees' involvement. Due to the variety of data sources and to the continuous changes of goals, the information is imperfect. The main concerns are uncertainty, inaccuracy and vagueness of both objectives and solutions. The fuzzy set theory provides a quite convenient framework to represent all those imperfections using one mathematical objet. Fuzzy logic is used to aggregate specifications and to deploy solutions. Possibility theory finally allows a graded evaluation of expected performances. The obtained result is graded, providing the engineer with some degrees of freedom for the realization of the manufacturing system. The valuation of the design quality is an undeniable progress towards the continuous improvement of the whole system performance. Finally, such a criterion may also be used for an empirical search for a convenient solution, as for the automatic computation of an optimal result.     

Implications of form postponement to manufacturing: a case study

Postponement is an approach that helps to deliver more responsive supply chains. Form postponement involves the delay of final manufacturing until a customer order is received and is commonly regarded as an approach to mass customization. However, while much is written in the literature about the benefits and strategic impact of form postponement, little is still known about its application. This paper addresses how form postponement was applied in terms of its operational implications within a manufacturing facility through a retrospective study that tracked implementation of form postponement in a manufacturer of specialist high-voltage cabling equipment, called here ‘ElectriCo’. The operational measures for products selected for form postponement were compared with those for products that continued to be made under make-to-order and make-to-stock regimes. Propositions were tested that addressed the operational implications of form postponement within the manufacturing facility, the impact of form postponement on selected performance metrics, and the demand profile for which form postponement was the preferred regime. Multiple data collection methods were used whereby documentary, archival and database evidence were used to measure operational characteristics across a broad front. The findings show that form postponement improved responsiveness but did not necessarily improve delivery reliability. The extent to which the order lead time can be reduced is dependent not only on the responsiveness of the postponed process, but also crucially on the responsiveness of the planning system. It was found that the problem of adapting the manufacturing planning and production scheduling systems at ElectriCo had been underestimated. This and other problems subsequently undermined the form postponement application and led to its eventual abandonment. The authors are undertaking further research of form postponement applications in different production contexts to provide further evidence for its operational implications.     

A genetic programming based fuzzy regression approach to modelling manufacturing processes

Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model. To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods.     

A novel cellular automata based approach to storm sewer design

Optimal storm sewer design aims at minimizing capital investment on infrastructure whilst ensuring good system performance under specified design criteria. An innovative sewer design approach based on cellular automata (CA) principles is introduced in this paper. Cellular automata have been applied as computational simulation devices in various scientific fields. However, some recent research has indicated that CA can also be a viable and efficient optimization engine. This engine is heuristic and largely relies on the key properties of CA: locality, homogeneity, and parallelism. In the proposed approach, the CA-based optimizer is combined with a sewer hydraulic simulator, the EPA Storm Water Management Model (SWMM). At each optimization step, according to a set of transition rules, the optimizer updates all decision variables simultaneously based on the hydraulic situation within each neighbourhood. Two sewer networks (one small artificial network and one large real network) have been tested in this study. The CA optimizer demonstrated its ability to obtain near-optimal solutions in a remarkably small number of computational steps in a comparison of its performance with that of a genetic algorithm.     

Multiple objective facility layout: a heuristic to generate efficient alternatives

Most multiple-objective approaches to solving the facility layout problem generate different layouts by varying the weights of the objectives using heuristic methods. It is possible, however, to generate the same layout using different weights. It is also possible that many efficient layouts will not be generated because it is not clear which weights would generate them. Furthermore, there may be some efficient layouts that cannot be generated regardless of what weights are used. In this paper we propose a heuristic algorithm for generating efficient layouts through pairwise interchange of departments. We show that if the decision maker's preferences for the selection of the best layout cannot be expressed as a linear utility function, it is essential to use other techniques such as the one developed in this paper, rather than weighting techniques, to generate efficient layouts. An example and some experiments are discussed.     

A simulation based continuous improvement approach for manufacturing based field repair service contracting

This paper develops and tests a novel extension to traditional supplier selection practice, with a particular focus on the concluding stages of a manufacturing-based field service. Action-based research was used to design and develop a discrete event simulation decision support for a large multinational manufacturing organisation with a significant after-sales service supply chain. The framework has been designed to identify and validate the value attributable to collaborative supplier contracting with built-in costed performance improvement targets. Use of the framework in the case organisation was found to produce greater cost savings over traditional practice, facilitating extended supply chain contracts. The results provide evidence of the high level of savings achievable while also improving customer delivery through targeted service improvements over the contracts life cycle. This framework advances beyond the prevalent practice of cost-focused short-term adversarial supply contracting and is innovative in terms of its continuous improvement simulation based framework design.     

Implications of form postponement to manufacturing: a cross case comparison

While much is written in the literature about the benefits and strategic impact of form postponement (FPp), little is still known about its application. We address ‘how’ FPp is applied in terms of its operational and logistics implications within manufacturing facilities. This paper is a retrospective study of the application of FPp in three diverse manufacturing environments: a manufacturer of specialist high voltage cabling equipment we call Electrico; a manufacturer of industrial electric motors we call Motorco; and a manufacturer of control systems and components mainly for automated telling machines we call Controlco. Our findings show that FPp improved responsiveness of manufacturing in all cases, but that none of the applications of FPp was ideal from a theoretical perspective. The production planning system must be both responsive and flexible to support the application. For highly customised products, the customer order decoupling point (CODP) must be located sufficiently upstream in the manufacturing process to avoid removal of components and time-consuming modifications. We present conclusions from all three studies, including an inventory management decision framework for FPp and a framework for the application of FPp which encompasses a number of practical considerations.