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.     

Agile manufacturing system control based on cell re-configuration

The control of an agile manufacturing system (AMS) is expected to be flexible, open, scalable and re-configurable so as to tackle the more complex and uncertain information flows. To meet these requirements, we propose agent-based control architecture for AMS, under which the functions of task planning, scheduling and dynamic control are integrated seamlessly. First of all, this paper introduces the concept of RMC (re-configurable manufacturing cell), based on which, we construct the control architecture for AMS in compliance with multi-agent system (MAS). The whole control process under the architecture comprises two hierarchies, i.e. the upper one for order planning and RMC forming and the lower one for task scheduling within each RMC. For the upper hierarchy, we establish a linear integer programming (LIP)-based mathematical model and a MAS-based dynamic process model, and present a two-step approach to order planning and RMC forming. For the lower hierarchy, we develop the scheduling model, a method based on the bidding mechanism from contract net, and describe the rescheduling mechanism in the control system. To illustrate the methodology proposed in the paper, a simulation study is thoroughly discussed. Our studies demonstrate that the RMC-based control architecture provides an AMS with an optimal, dynamic and flexible mechanism of responding to an unpredictable manufacturing environment, which is crucial to achieve agility for the whole manufacturing system.     

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.     

A comparison of focused cellular manufacturing to cellular manufacturing and job shop

The purpose of this study is to compare a focused cellular manufacturing environment with traditional cellular manufacturing, and job shop environments. We define focused cellular manufacturing as a configuration scheme that groups components by end-items and forms cells of machines to fabricate and assemble end-items. In addition, this research includes three levels of batch sizes and two levels of set-up times in its performance criteria which few researchers in this area have done. The results indicate that the focused cellular manufacturing scheme has a batching advantage. This advantage dominated the balanced machine utilization benefit of the job shop configuration scheme and out weighed the set-up time reduction advantage of the cellular manufacturing scheme for average end-item completion times and average work-in-process inventory levels. The cellular manufacturing and job shop schemes overcame the batch size advantage only when batch sizes were small or set-up times were large.     

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.     

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 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.     

A Petri net-based approach to incremental modelling of flow and resources in service-oriented manufacturing systems

In order to overcome the myopia problem, routing strategies must be based on formal representations of flow that automatically account for modifications in the values of parameters of interest and in the model itself. This work addresses this problem and discusses how to automatically incorporate resources (e.g. workstations/transportation devices/storage) in a Petri-net-derived model of flow that is modifiable at runtime to reflect and influence the routing in a manufacturing line. The modelling approach takes into consideration scalability needs and was experimentally validated. The applicability of the models is shown for PN-based dynamic scheduling.     

A simulation study of new multi-objective composite dispatching rules,CONWIP, and push lot release in semiconductor fabrication

This paper evaluates dispatching rules and order release policies in two wafer fabrication facilities (thereafter referred to as ‘fab’) representing ASIC (application specific integrated circuit) and low-mix high-volume production. Order release policies were fixed-interval (push) release, and constant work-in-process (CONWIP) (pull) policy. Following rigorous fab modelling and statistical analysis, new composite dispatching rules were found to be robust for average and variance of flow time, as well as due-date adherence measures, in both production modes.     

Changes in manufacturing facility-, network-, and strategy-types at the Michelin North America Company from 1950 to 2014

Large manufacturing firms operate networks of facilities which they design to achieve particular manufacturing strategies. Facilities, networks and strategies are of several distinct types. The facility-, network- and strategy-types used by a firm depend on the competitive environment in which a firm operates. This paper examines the facility-, network- and strategy-types used by the Michelin North America Company during the period from 1950 to 2014. The examination shows how three changes in the competitive environment (changes in tariffs and government industrial policy, the 1964 Canada – United States Automotive Trade Agreement, and the 1996 North American Free Trade Agreement) triggered significant changes in these types. The examination produces insights into categorisations of facility-, network- and strategy-types that are useful for understanding how large firms operate, how we can predict what changes large firms will make to their facilities, network and strategy and how stakeholders such as employees, suppliers and governments can manage the risks of working with large firms.     

Reliability analysis of a flexible manufacturing cell

In this study, mathematical models are developed to study and compare the operations of a fully reliable and an unreliable flexible manufacturing cell (FMC), each with a flexible machine, a loading/unloading robot, and a pallet handling system. The operation times, loading/unloading times, and material handling times by the pallet are assumed to be random. The operation of the reliable cell is compared to that of an unreliable cell with respect to utilization of the cell components, including the machine, robot, and pallet handling system. The unreliable cell is assumed to operate under random (machine and robot) failures with constant failure rates for the machine and the robot. The pallet handling system is assumed to be completely reliable.     

An impact of manufacturing flexibility and technological dimensions of manufacturing strategy on improving supply chain responsiveness: Business environment perspective

The main objective of this research is to investigate the impact of manufacturing flexibility and technological dimensions of manufacturing strategy on responsiveness in the supply chain. Based on the theoretical background of dynamic capability, this study also examines the role of the business environment on the relationship between manufacturing flexibility and supply chain responsiveness. 144 structured surveys were collected and the partial least squares of structural equation modelling approach were utilised for data analysis. The result establishes relationships among various dimensions of manufacturing flexibility. Although the technological dimensions in manufacturing strategy of such advanced manufacturing technology (AMT) and e-procurement do not have any direct impact on new product and market flexibility, they increase supply chain responsiveness, which helps to react quickly against supply chain disruptions. More importantly, the business environment has a moderating effect on the relationship between market flexibility and supply chain responsiveness.     

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.     

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.     

A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems

Instead of using expensive multiprocessor supercomputers, parallel computing can be implemented on a cluster of inexpensive personal computers. Commercial accesses to high performance parallel computing are also available on the pay-per-use basis. However, literature on the use of parallel computing in production research is limited. In this paper, we present a dynamic cell formation problem in manufacturing systems solved by a parallel genetic algorithm approach. This method improves our previous work on the use of sequential genetic algorithm (GA). Six parallel GAs for the dynamic cell formation problem were developed and tested. The parallel GAs are all based on the island model using migration of individuals but are different in their connection topologies. The performance of the parallel GA approach was evaluated against a sequential GA as well as the off-shelf optimization software. The results are very encouraging. The considered dynamic manufacturing cell formation problem incorporates several design factors. They include dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing, production cost and other practical constraints.     

Cumulative manufacturing capabilities: an extended model and new empirical evidence

There has been intense debate in the manufacturing strategy literature on the way in which firms work on different manufacturing capabilities, with two opposing approaches considered – the trade-off model and the sand cone model. Analysis of these models has essentially been based on study of the links amongst four classic manufacturing capabilities (quality, delivery, flexibility, and cost efficiency) and has obviated the need to consider environmental protection as an important manufacturing capability. This study analyses the theoretical arguments and the prior empirical evidence on the two models, and proposes and tests an extended sand cone model which includes the environmental protection objective alongside the four traditional ones. The research uses structural equation modelling and data from a sample of 274 manufacturers to contribute additional empirical evidence on the existence of cumulative effects amongst manufacturing capabilities. It is observed that the predominant strategic model in these firms is one of multiple, non-incompatible capabilities with cumulative effects according to the following sequence: quality, delivery, flexibility, environmental protection, and cost efficiency.     

Two-step approach to robust deadlock control in automated manufacturing systems with multiple resource failures

ABSTRACT

In an automated manufacturing system (AMS), resource failures are inevitable, which renders the existing deadlock control policies for AMSs without considering resource failures ineffective. For the AMSs with multiple unreliable resources, in this paper, a method is developed for robust deadlock resolution using the framework of Petri nets (PNs). The considered AMSs are modeled with PN models called system of simple sequential process with resources (S³PR). An unreliable S³PR (U-S³PR) is obtained by adding recovery subnets that model the resource failures and recovery procedures of the places where resource failures may happen. Based on the model, a two-step approach is developed to design a robust controller. At Step 1, we use a siphon-based deadlock control method to analyze the behavior with resource failures and propose an incomplete robust deadlock controller for a U-S³PR. At Step 2, a reachability-graph-analysis-based method is utilized to consummate the robust deadlock controller. Then, a robust liveness-enforcing supervisor is derived to make an unreliable S³PR live even if multiple types of resources break down. Finally, the proposed method is illustrated by using an example.     

A multi-model approach to determine early manufacturing parameters for small-data-set prediction

Constructing an accurate prediction model from a small training data set is an important but difficult task in the field of forecasting. This is because when the data size is small, the incomplete data may mean that the model produced cannot sufficiently represent the true data structure or cause the model training to be overfitted. To address this issue, this paper presents an approach that combines multiple prediction models to extract data information in multiple facets. In the multi-model approach, a compromise weight method is proposed to determine the relative reliability of each of the prediction model. The methods used include multiple regression, artificial neural network, and support vector machines for regression. A thin-film transistor liquid crystal display manufacturing case study is used to illustrate the details of this research. The empirical results not only show that the proposed multi-model can reduce the manufacturing variation and increase the production yield, but also can propose a robust and reliable parameter interval to the online engineers in the early manufacturing stage.     

A new approach to solving stochastic programming problems with recourse

A new numerical approach to the solution of two-stage stochastic linear programming problems is described and evaluated. The approach avoids the solution of the first-stage problem and uses the underlying deterministic problem to generate a sequence of values of the first-stage variables which lead to successive improvements of the objective function towards the optimal policy. The model is evaluated using an example in which randomness is described by two correlated factors. The dynamics of these factors are described by stochastic processes simulated using lattice techniques. In this way, discrete distributions of the random parameters are assembled. The solutions obtained with the new iterative procedure are compared with solutions obtained with a deterministic equivalent linear programming problem. It is concluded that they are almost identical. However, the computational effort required for the new approach is negligible compared with that needed for the deterministic equivalent problem.