A heuristic for designing cellular manufacturing facilities

In this paper, a heuristic for machine cell-part family identification that addresses some important design constraints—machine capacity, safety, technological requirements, upper limit on the size of a cell and number of cells—is presented. First, a model is used to determine the number of each type of machine necessary to process the parts. The heuristic then identifies a machine cell and corresponding part family. Next, it ascertains whether the design constraints listed above are satisfied, one by one. If not, it attempts to modify the original machine cell-part family combination by adding, removing or duplicating particular machines so that the design constraints are satisfied. Constraints of higher priority take precedence over those of lower priority. Similarly, other machine cell-part family combinations are identified. While generating a solution, the heuristic attempts to minimize the intercellular movement of parts as much as possible.     

A heuristic for formulating within-plant manufacturing focus

The concept of manufacturing focus suggests that each plant is assigned a unique set of manufacturing tasks derived from the firm's competitive strategy. Many empirical studies have verified the importance and benefits of manufacturing focus in enhancing a firm's competence in the market. Nonetheless there is no methodology offered for designing such a manufacturing system with considerations of resource requirements although the need has been recognized. In this study, we propose a methodology that can help managers arrive at a manufacturing focus design within a plant. Because of the large size and nonlinear nature of the problem, a heuristic solution is proposed. The heuristic translates the strategic concept of competitive priorities into management parameters, thereby allowing incorporation of market factors into manufacturing process design. Experimental results show that the proposed methodology offers managers the opportunity to generate and assess alternative focus designs which are otherwise unavailable. The use of the heuristic is straightforward in practical situations and should greatly facilitate the achievement of manufacturing focus with minimal resource requirements. Overall, this research provides an analytical framework for further research in focused manufacturing.     

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.     

Setting manufacturing strategy for a company's international manufacturing network

Manufacturing strategy is a plan for moving a company from where it is to where it wants to be. Determining the best manufacturing strategy is not easy because of the wide range of choices and constraints a company faces. Manufacturing strategy frameworks or models are helpful because they identify the objects that comprise manufacturing strategy and organise these objects into a structure that enables a company to understand and use the objects to develop strategy. This paper examines a company's international manufacturing network. It identifies and examines six manufacturing strategy objects (generic international strategies, manufacturing networks, network manufacturing outputs, network levers, network capability, and factory types), linkages between objects, and the manufacturing strategy framework that follows from these objects and linkages. Then the paper applies the framework to the manufacturing networks of three companies in the global steel industry: Arcelor (Luxembourg), Mittal (India), and Dofasco (Canada).     

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 review of synthesis methods for additive manufacturing

With the design freedoms afforded by additive manufacturing (AM) processes, an increasing interest in shape synthesis methods has led to a variety of advances in topology optimisation methods and associated synthesis technologies. In this paper, we identify research issues related to the application of AM to shape synthesis methods, review recent advances in topology optimisation, and outline a vision for future synthesis capabilities.     

A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.     

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.     

A manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation for aircraft structural parts

Manufacturing of aircraft structural parts has the characteristics of multiple varieties, complex structures and small batches, which make the manufacturing resource allocation highly difficult. This paper proposes a manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation, considering multiple manufacturing resources including process planners, machine tools and cutting tools, as well as manufacturing process schemes of aircraft structural parts. Knowledge in terms of experts’ experience and historical data is used for fuzzy comprehensive evaluation. A manufacturing resource allocation model is proposed based on the analysis of manufacturing processes of aircraft structural parts. The capability of planners, the complexity of structural parts, the reliability of machine tools, the reliability of cutting tools and the correlations between manufacturing resources and structural parts are evaluated using the fuzzy comprehensive evaluation method. Multiple manufacturing resources are allocated based on the fuzzy comprehensive evaluation results. A prototype system has been implemented and a case study is used to validate the proposed approach.     

A new methodological framework for design for additive manufacturing

Additive manufacturing (AM) offers numerous benefits for innovative design solutions. However, engineers are currently not supported in identifying and incorporating these potentials systematically in their design solutions. In this paper, previous Design for Additive Manufacturing (DfAM) approaches are first reviewed comprehensively and classified into distinct categories according to their main purpose and application. They are then analysed further by being related to conventional design methodologies like VDI 2221. Since previous DfAM approaches only provide selective assistance at single steps in the product development process, a new framework for DfAM is proposed. Existing methods and tools, both from DfAM and from general design methodologies, are integrated into the modular framework structure. A concept for using the framework is presented to provide design engineers with continuous support in all product development phases, thereby fostering the complete exploitation of AM potentials and the development of AM-conformal designs.     

A participatory systems design methodology for changeable manufacturing systems

The ability to adapt to changes in products, processes and technologies is a key competitive factor. Changeable manufacturing paradigms have emerged to address this need, but the industrial implementation remains challenging. In this paper, a participatory design methodology for changeable manufacturing systems is proposed, including requirements specification, selection of appropriate manufacturing paradigm and suitable physical and logical enablers. The methodology supports companies in determining the potential for and mechanisms of transitioning towards changeable manufacturing systems, based on knowledge of products, production, technologies and facilities. The developed methodology is applicable to both new and existing manufacturing systems. It is demonstrated in two industrial cases which highlight its applicability and differences in the appropriate recommended manufacturing systems transition towards changeability as a result of differences in manufacturing characteristics, change requirements and enablers.     

A digital process for additive manufacturing of occlusal splints: a clinical pilot study

The aim of this study was to develop and evaluate a digital process for manufacturing of occlusal splints. An alginate impression was taken from the upper and lower jaws of a patient with temporomandibular disorder owing to cross bite and wear of the teeth, and then digitized using a table laser scanner. The scanned model was repaired using the 3Data Expert software, and a splint was designed with the Viscam RP software. A splint was manufactured from a biocompatible liquid photopolymer by stereolithography. The system employed in the process was SLA 350. The splint was worn nightly for six months. The patient adapted to the splint well and found it comfortable to use. The splint relieved tension in the patient''s bite muscles. No sign of tooth wear or significant splint wear was detected after six months of testing. Modern digital technology enables us to manufacture clinically functional occlusal splints, which might reduce costs, dental technician working time and chair-side time. Maximum-dimensional errors of approximately 1 mm were found at thin walls and sharp corners of the splint when compared with the digital model.     

A closed loop outsourcing decision model for developing effective manufacturing strategy

The case study deals with aligning production capacity for a US manufacturer of industrial thermal transfer bench-top printer, by exploring various insourcing and outsourcing options based on production, cost and delivery capabilities. A modelling framework is devised which aids in selecting an effective manufacturing strategy that considers key enablers and barriers to successful outsourcing. The proposed model is closed loop, which stresses regular re-evaluation of the existing outsourcing strategy. It models the total product cost for the product currently being manufactured in the US and compares the total costs if it were manufactured offshore in countries such as, Malaysia, China, Slovakia and Mexico. The analysis from the model shows moving manufacturing of the product to Mexico results in the largest business benefit of about $400 savings per unit compared to other countries studied. A closer look at China in the future could be considered as the economic, social and security risks may change enough to warrant re-evaluation of this option. To get a clearer picture of how Mexico rates against Malaysia in the existing state, these risks may be reviewed as an extra data point to finalise the decision to move manufacturing of industrial thermal transfer bench-top printers to Mexico.     

A reallocation-based heuristic to solve a machine loading problem with material handling constraint in a flexible manufacturing system

In this research, a comprehensive heuristic solution is evolved to include all the three segments of a machine loading problem of flexible manufacturing systems. These are part type sequence determination, operation allocation on machines and reallocation of part types. The machine loading problem has been formulated keeping in view two well-known objective functions, namely minimization of system unbalance and maximization of throughput. In addition to constraints related to machine time and tool slots availability, this research considers one more constraint related to material handling, i.e. number of AGVs available in the system. The part type sequence determination has been carried out by evaluating the contribution of part type to characteristics such as batch size, total processing time, and the AGV movement. Decisions pertaining to operation allocation are taken based on the enumeration of priority index. An iterative reallocation procedure has been devised to ensure minimum positive system unbalance and maximum throughput. A test problem is simulated to represent the real shop floor environment and the same has been solved using various steps of the proposed algorithm. Extensive computational experiments have been carried out to assess the performance of the proposed heuristic and validate its relevance to solve the real shop floor problems.     

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.     

A mathematical model and a heuristic approach for design of the hybrid manufacturing systems to facilitate one-piece flow

One-piece flow is a design rule that entails production in manufacturing cells on a ‘make one, check one, and move-on one’ basis (Black, J.T., 2007. Design rules for implementing Toyota Production System. International Journal of Production Research, 45 (16), 3639–3664), which reduces manufacturing lead time significantly. This paper proposes a sequential methodology comprised of a mathematical model and a heuristic approach (HA) for the design of a hybrid cellular manufacturing system (HMS), to facilitate one-piece flow practice. The mathematical model is employed in the cases of small- and medium-sized problems, and it attempts to minimise the total number of exceptional operations, while considering machine capacities and alternative machines. The machine-part matrix achieved by the mathematical model is input into the flow line design stage of the HA, where backflow within the cells is eliminated. However, for industrial problems, the proposed HA is utilised. After the formation of the cells by clustering, the HA attempts to eliminate exceptional operations of a given cellular configuration together with a functional structure by employing alternative machines, based on the decision rules developed. Later, unidirectional flow within the cells is achieved and the capacity and budget constraints are satisfied. A medium-sized problem is solved by using both of the approaches, namely, the model integrated with the flow-line design stage of the HA and the complete HA. The results are discussed and the limitations are explained.     

A model for co-evolution in manufacturing based on biological analogy

Manufacturing systems continue to adapt in order to survive the changing and challenging markets and global competition. Product and manufacturing design and capabilities are configured to allow the needed adaptation through innovative design, improved system paradigms, intelligent design and optimisation models, and product grouping to increase efficiency. In this research, it is hypothesised that the evolution and co-evolution of products and the machines used to manufacture them is akin to that observed in the adaptation of biological species. The symbiosis between products and manufacturing capabilities is studied using real examples, and a new model that establishes the symbiotic relationship between their evolution paths and observed co-evolution trends based on available historical information is proposed. Dual cladograms are used to track their evolution and detect useful potential development and plausible future evolution trends. When a state of co-evolution equilibrium is reached, a stimulus for more abrupt changes would be needed to cause further evolution on both sides. The co-evolution model has been applied to an example based on analysing the history of machine tools development and data from a major machine tools manufacturer. The evolution and co-evolution hypotheses of machined parts and machine tools were charted up to the currently observed state of equilibrium in this application field. This innovative model of co-evolution in manufacturing can help improve the utility of manufacturing resources and prolong the life of manufacturing systems beyond a single product generation and its variants.     

Requirement estimation for indirect workforce allocation in semiconductor manufacturing

This work, based on a real case, presents a model to estimate indirect workforce requirements of semiconductor fabrication facilities (fabs) so that the workforce can be fairly allocated. There is a concern in a real setting to fairly allocate the overall corporate workforce among the fabs, particularly when they compete in performance, and properly determining the actual requirement is the most critical in the decision process. The actual requirements of the workforce, especially the indirect workforce, for fabs may be indeterminate due to the lack of a well-defined workforce-output relationship. This paper presents a non-parametric frontier approach for estimating the indirect workforce, and the estimate is based on the best past performance adjusted to reflect the expected productivity growth. An empirical study was conducted in a leading foundry in Taiwan that has a number of 8-inch fabs. The proposed (re)allocation approach can provide an explicit decision support mechanism to balance the workloads in light of various production environments to enable an equitable basis for performance evaluation to foster constructive competition among the fabs.     

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 cellular manufacturing system with quasi-dynamic dual resource using multi-objective GA

A new concept is presented in this paper of quasi-dynamic cell formation for the design of a cellular manufacturing system, based on analysing the fact that static and dynamic cell formation could not reflect the real situation of a modern cellular manufacturing system. Further, workforce resources are integrated into quasi-dynamic cell formation and thus a quasi-dynamic dual-resource cell-formation problem is proposed. For solving this problem, this paper first establishes a non-linear mixed integer programming model, where inter-cell and intra-cell material cost, machine relocation cost, worker operation time, loss in batch quality and worker salary are to be minimised. Then, a multi-objective GA is developed to solve this model. Finally, a real life case study is conducted to validate the proposed model and algorithm. The actual operation results show that the case enterprise significantly decreases its material handling cost and workforce number and obviously increases its product quality after carrying out the obtained scheme.