Optimum machine selection in multistage manufacturing systems

Machine selection during the initial stages of facility planning is critical as most capital costs are incurred in the investment of new machines. This paper presents an approach for optimizing the number of machines acquired for batch processing in a multistage manufacturing system. Machines of the same type differ in their processing capabilities. Four cases of overlap in machine capabilities are examined. For each case, the problem is optimized with and without the time balance constraints between the stages. The output is the optimum number of machines of each type to be purchased for each stage, as well as the optimum time delay between the stages such that the system cost is minimized. The resulting multistage line is then simulated under specified conditions to observe its behaviour over time and to assess the viability of the developed model.     

Reconfiguration: a key to handle exceptions and performance deteriorations in manufacturing operations

During a manufacturing operation, exceptions may occur dynamically and unpredictably. Their occurrence may lead to the degradation of system performance or, in the worst case scenario may interrupt the production process by causing errors in the schedule plan. This paper classifies three families of exceptions: (1) out-of-order events such as machine breakdowns, (2) operational out-of-ordinary events such as rush orders and (3) deteriorations of manufacturing resource performance such as reductions of machines’ utilization. In all cases, in order to maintain an adequate level of system performance, it is necessary to detect exceptions, to diagnose them quickly and to recover them by taking corrective actions to avoid fault propagations. Decisions concerning how to deal with exceptions, i.e. which strategy to implement, depend on the manufacturing environment (dedicated line, flexible system, reconfigurable system or a mix of them) and the advantages arising from using a certain exception handling policy vary from one production system to another. The activity of reconfiguring manufacturing resources has been demonstrated to be a powerful operation strategy to handle machine breakdowns. This paper extends the concept of ‘reconfiguration for exception handling’ to other families of exceptions and proposes reconfiguration for their recovery. The reconfiguration process is handled by an agent-based control system that implements four negotiation processes among manufacturing resource agents.     

Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.     

Justification for the selection of manufacturing technologies: a fuzzy-decision-tree-based approach

In this paper, a developed model for the justification of alternative manufacturing technologies is presented. The approach, based on fuzzy decision trees, provides a methodology capable of identifying patterns within a technology case repository to support the evaluation of manufacturing systems. Experts are highly influential individuals in the decision process; they provide support and guidance when selecting investments. The experience-oriented task is founded on previous cases or an experts’ experience, and therefore difficult to express in a rational form. The concept is based on a number of characteristics of the case-based reasoning, rule induction and expert system theory. Structured around the fuzzy-decision-tree data-mining technique, the framework provides the ability of using regulated case information to act as structured experience for assisting in the decision process. Fuzzy induction extracts formal rules from a set of experience data, and the expert system philosophy computes the experience base of human expertise for problem-solving. A test case indicates the stability of the classification algorithm and verifies the applicability within the domain.     

A multi-agent architecture for multi-product supplier selection in consideration of the synergy between products

Supplier selection is an important task in purchasing. The performance of a supply chain depends on the selection of the right suppliers to provide the right materials or services at the right price and right time. Most of the research on supplier evaluation and selection assumes that products are procured independently. However, in reality, it is common for a purchasing company to procure a bundle of products together. In this regard, synergy could exist between products and hence affect the final choice of suppliers. It is therefore necessary for the purchasing company to incorporate the synergy between products in supplier selection. This paper presents a multi-agent system (MAS) architecture for multi-product supplier selection in consideration of the synergy between products. A three-phase supplier selection model is proposed to support multi-product procurement in consideration of the synergy between products. The three phases are, namely, product synergy determination phase, supplier pre-selection phase and negotiation-based final selection phase. The MAS is equipped with various types of autonomous function agents and different agent interaction protocols to effect the functioning of this three-phase multi-product supplier selection model.     

Joint decision-making of production and maintenance in mixed model assembly systems with delayed differentiation configurations

Mixed model assembly systems (MMASs) can simultaneously manufacture multiple product variants and are developed to satisfy customers’ increasing desire for products with a high variety. This paper investigates the joint decision-making of production and maintenance policies in MMASs with delayed differentiation configurations, where common operations are performed before differentiated processes. The problem is formulated as a Markov Decision Process (MDP) problem that minimises the average cost per unit time. Monte Carlo simulation is used to evaluate the system performance measures (e.g. volume mix ratio, product quality) under the optimal policy. Numerical examples are presented to illustrate the structure of the optimal policy and the impact of different factors on the system performance in an MMAS that produces two types of product variants. Techniques that can potentially solve the problem in large-sized MMASs are also discussed.     

Production reliability in flexible manufacturing systems

A typical flexible manufacturing system, Westland Helicopters' sheet metal detail manufacturing complex, has been analysed for reliability. The techniques of fault tree analysis and event tree analysis are presented and their applicability to this study investigated. Event tree analysis has been found to be a more effective method for analysing manufacturing systems. The failure states of the system have been identified from the construction of an event tree which considers random hardware faults that influence production. Failure rate data have been used to quantify the critical production failure states in terms of machine failures. Estimates are made of the system's MTTF and percentage availability using typical MTTR figures. The probability that a selected production route fails to complete the manufacture of a set of parts is also evaluated. A dependency of systems reliability on the production demand has been discovered, and a possible method for modelling and assessing the reliability of systems capable of producing several products is proposed.     

An adaptive heuristic cross-entropy algorithm for optimal design of water distribution systems

The optimal design problem of a water distribution system is to find the water distribution system component characteristics (e.g. pipe diameters, pump heads and maximum power, reservoir storage volumes, etc.) which minimize the system's capital and operational costs such that the system hydraulic laws are maintained (i.e. Kirchhoff's first and second laws), and constraints on quantities and pressures at the consumer nodes are fulfilled. In this study, an adaptive stochastic algorithm for water distribution systems optimal design based on the heuristic cross-entropy method for combinatorial optimization is presented. The algorithm is demonstrated using two well-known benchmark examples from the water distribution systems research literature for single loading gravitational systems, and an example of multiple loadings, pumping, and storage. The results show the cross-entropy dominance over previously published methods.     

Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL)

The dynamic nature of today’s manufacturing industry, which is caused by the intense global competition and constant technological advancements, requires systems that are highly adaptive and responsive to demand fluctuations. Reconfigurable manufacturing systems (RMS) enable such responsiveness through their main characteristics. This paper addresses the problem of RMS configuration design, where the demand of a single product varies throughout its production life cycle, and the system configuration must change accordingly to satisfy the required demand with minimum cost. A two-phased method is developed to handle the primary system configuration design and the necessary system reconfigurations according to demand rate changes. This method takes advantage of Reconfigurable Machine Tools in RMS. In fact, by adding/removing modules to/from a specific modular reconfigurable machine, its production capability could be increased, with lower cost. A novel mixed integer linear programming formulation is presented in the second phase of the method to optimise the process of selecting the best possible transformation for the existing machine configurations. Two different cases are designed and solved by implementing the established method. The results of these cases in terms of capital cost, capacity expansion cost, unused capacity and number of transformations, are compared with two hypothetical scenarios. Analyses of the obtained results indicate the efficiency of the proposed approach and offer a promising outlook for further research.     

Supplier selection for outsourcing from the perspective of protecting crucial product knowledge

This paper considers the supplier selection problem in terms of the characteristics of the products/processes to be outsourced. Emphasis is placed on the possible risk that may arise in sharing or transferring crucial product/process knowledge to suppliers during outsourcing. Two indices are employed to assess the suitability of a supplier for outsourcing a product/process. The first measures the supplier's capability for technological innovation in the products for which they offer outsourcing services, and includes investment in R&D projects and the ratio of R&D personnel to the total work force. The second index measures the supplier's practices in protecting its clients’ intellectual property rights, and includes corporate image, track record in protecting clients’ intellectual property rights, and compliance with internal and external requirements. These two indices are then used to classify suppliers into four major groups, which are in turn assigned to classes of components/processes depending on their strategic importance and vulnerability – a portfolio approach. The proposed portfolio approach has been validated via a substantial empirical study involving data for 401 parts, 216 suppliers, and 36 manufacturing companies operating in China.     

Development of operational complexity measure for selection of optimal layout design alternative

Characterising existing approaches to operational complexity, it can be stated that for those metrics are characterful different factors used as variables such as product structure, machine composition, number of technological functions performed by machine, and others. Moreover, the complexity metrics using the information content as a basis can be divided into two groups: those which define complexity as an absolute entropy quantity, and metrics defining complexity as the relative entropy magnitude. Our view on the operational complexity is based on an assumption that process complexity value obtained as a sum of the partial complexities is not so important than a balanced operational complexity value expressing relative quantity to equilibrium levels. Accordingly, this paper introduces the novel operational complexity measure that initially identifies operational complexities of individual machines based on the number of parts, machines and operations. In the subsequent steps, these sub-measures are used to define summary complexity measure involving two balanced operational complexity characteristics. The novel measure can be effectively used to find the most suitable layout design alternative. For the purpose to prove its effectiveness, on two practical cases where tested its practicability by comparing it to the complexity indicator expressing the sum of the partial complexities.     

A new optimal control policy for a well-known S3PR (systems of simple sequential processes with resources)

There are many studies reported in the literature comparing the effectiveness of new control policies by testing them against a well-known S³PR (systems of simple sequential processes with resources) model. We propose a new approach that recovers the system from empty-siphon states to former live states. It therefore attains the same number of states as the original uncontrolled model by adding monitors (and control arcs) similar to the prevention approach. There is no need to perform a reachability analysis. INA (integrated net analyser) analysis indicates that the resulting controlled model is live and reaches all 26,750 states (in the uncontrolled model), more than the maximally permissive 21,581 states. Only seven monitors are employed, fewer than in most other approaches. This arises from the fact that no new problematic siphons are generated due to the added monitors. We discuss the disadvantages (a variant of the scheme to overcome the disadvantages is also discussed) and physical meaning of the policy. We further propose a lossless approach by colouring some arcs. This not only avoids material loss, but also tackles the livelock problem.     

Spreadsheet modeling of optimal maintenance schedule for components in wear-out phase

This paper addresses a method for determining the optimum maintenance schedule for components in the wear-out phase. The interval between maintenance for the components is optimized by minimizing the total cost. This consists of maintenance cost, operational cost, downtime cost and penalty cost. A decision to replace a component must also be taken when a component cannot attain the minimum reliability and availability index requirement. Premium solver platform, a spreadsheet-modeling tool, is utilized to model the optimization problem. Constraints, which are the considerations to be fulfilled, become the director of this process. A minimum and a maximum value are set on each constraint so as to give the working area of the optimization process. The optimization process investigates n-equally spaced maintenance at an interval of Tr. The increase in operational and maintenance costs due to the deterioration of the components is taken into account. This paper also performs a case study and sensitivity analysis on a liquid ring primer of a ship's bilge system.     

Optimizing the product-based availability of a buffered industrial process

Many industrial processes for discrete consumable products consist of a series (or set) of sequential process operations (or subsystems) which are de-coupled by means of in-process storage buffers. Each subsystem of such a process contains one or more parallel coupled or uncoupled operating lanes. We describe the use of a discrete-event simulation model for determining the availability of such a process. We likewise define and use a genetic algorithm to determine process designs and operating rules that have high availability. A 65-variable example, consisting of four operating subsystems with at most four lanes per subsystem, is used to illustrate the method. The results for this and similar real-world applications indicate that, by applying this methodology, it is possible to design buffered industrial processes having high availability.     

Characterization and selection of optimal parameters to achieve the best tribological performance of the electrodeposited Cr nanocomposite coating

In this study, Nano particles were co-deposited with chromium from a hexavalent chromium bath by the conventional electrodeposition onto steel substrate as a cathode. The main goal of this work is to improve the wear and corrosion resistance, microhardness, coefficient of friction and select the best coating condition to satisfy these parameters using combined Analytic Hierarchy Process (AHP) – Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The dependence of the mentioned parameters was investigated in relation to the Al₂O₃, TiO₂, SiO₂ concentration in bath and particle size and it was found that the best tribological behavior improves by decreasing the particle size and increasing the particles concentration in the bath up to 10 g/l. AHP–TOPSIS method led to choose the Cr–Al₂O₃ nanocomposite coating achieved at 10 g/l Al₂O₃ content with mean particle size of 10 nm as the preferred alternative which is in good accordance with empirical findings.     

On the optimal scheduling of periodic tests and maintenance for reliable redundant components

Periodically, some m of the n redundant components of a dependable system may have to be taken out of service for inspection, testing or preventive maintenance. The system is then constrained to operate with lower (n−m) redundancy and thus with less reliability during these periods. However, more frequent periodic inspections decrease the probability that a component fail undetected in the time interval between successive inspections. An optimal time schedule of periodic preventive operations arises from these two conflicting factors, balancing the loss of redundancy during inspections against the reliability benefits of more frequent inspections.Considering no other factor than this decreased redundancy at inspection time, this paper demonstrates the existence of an optimal interval between inspections, which maximizes the mean time between system failures. By suitable transformations and variable identifications, an analytic closed form expression of the optimum is obtained for the general (m, n) case. The optimum is shown to be unique within the ranges of parameter values valid in practice; its expression is easy to evaluate and shown to be useful to analyze and understand the influence of these parameters.Inspections are assumed to be perfect, i.e. they cause no component failure by themselves and leave no failure undetected. In this sense, the optimum determines a lowest bound for the system failure rate that can be achieved by a system of n-redundant components, m of which require for inspection or maintenance recurrent periods of unavailability of length t.The model and its general closed form solution are believed to be new [2] and [5]. Previous work [1], [4] and [10] had computed optimal values for an estimation of a time average of system unavailability, but by numerical procedures only and with different numerical approximations, other objectives and model assumptions (one component only inspected at a time), and taking into account failures caused by testing itself, repair and demands (see in particular [6], [7] and [9]).System properties and practical implications are derived from the closed form analytical expression. Possible extensions of the model are discussed. The model has been applied to the scheduling of the periodic tests of nuclear reactor protection systems.     

A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data

Many times, reliability studies rely on false premises such as independent and identically distributed time between failures assumption (renewal process). This can lead to erroneous model selection for the time to failure of a particular component or system, which can in turn lead to wrong conclusions and decisions. A strong statistical focus, a lack of a systematic approach and sometimes inadequate theoretical background seem to have made it difficult for maintenance analysts to adopt the necessary stage of data testing before the selection of a suitable model. In this paper, a framework for model selection to represent the failure process for a component or system is presented, based on a review of available trend tests. The paper focuses only on single-time-variable models and is primarily directed to analysts responsible for reliability analyses in an industrial maintenance environment. The model selection framework is directed towards the discrimination between the use of statistical distributions to represent the time to failure (“renewal approach”); and the use of stochastic point processes (“repairable systems approach”), when there may be the presence of system ageing or reliability growth. An illustrative example based on failure data from a fleet of backhoes is included.     

A hierarchical model for optimal supplier selection in multiple sourcing contexts

This paper addresses a crucial objective of the strategic purchasing function in supply chains, i.e. optimal supplier selection. We present a hierarchical extension of the data envelopment analysis (DEA), the most widespread method for supplier rating in the literature, for application in a multiple sourcing strategy context. The proposed hierarchical technique is based on three levels. First, a modified DEA approach is used to evaluate the efficiency of each supplier according to some criteria proposed by the buyer. Second, the well known technique for order preference by similarities to ideal solution (TOPSIS) is applied to rank the maximally efficient suppliers given by the previous step. Third and finally, a linear programming problem is stated and solved to find the quantities to order from each maximally efficient supplier in the multiple sourcing context. The presented approach is able to straightforwardly discern between efficient and inefficient partners, avoid the confusion between efficient and effective suppliers and split the supply in a multiple sourcing context. The hierarchical model is applied to the supply of a C class component to show its robustness and effectiveness, while comparing it with the DEA and TOPSIS approaches.     

A decision support system for selection of net-shape primary manufacturing processes

Developments in the capabilities of the manufacturing processes increased the number of processes that can produce a part within the requirements determined by its design and market research. The increased number of processes and unfamiliarity of manufacturing engineers to many new manufacturing processes forces the researchers to develop systematic process selection tools instead of depending on the accumulated human expertise only. In this paper, a net-shape primary manufacturing process selection decision support system (DSS), which is named PROSEL (PROcess SELection), is developed. The developed selection programme eliminates the unsuitable processes step by step by checking a part’s material, annual production quantity, specified shape, thickness and presents the most economical process as the most appropriate net-shape primary process after a final cost analysis. The developed DSS is written in Visual Studio and tested with a great deal of real-life examples. It can be concluded from the tests that the programme provides the same or better primary manufacturing process selection decisions than the practical usage, and it is a very useful support tool for net-shape primary process selections.     

Application of fuzzy analytic network process for agile concept selection in a manufacturing organisation

Twenty-first century manufacturing organisations are capable of satisfying the varied requirements of modern customers called agile manufacturing (AM). AM enables an organisation to produce a variety of products within a short period of time in a cost-effective manner. Few researchers have brought out models for enabling organisations to improve their agility level. Total agile design system (TADS) is a model that is analogous to the technology integrated agile product development cycle. Concept selection is an important phase of TADS which is a multi-criteria decision-making (MCDM) problem. Selection of the best concept from agile perspective gains vital importance. Analytical network process (ANP) is a holistic approach that considers interactions and dependencies of various attributes and alternatives in a hierarchical framework used in MCDM problems. The concepts of fuzzy logic have been integrated with ANP in order to overcome the vagueness and uncertainty associated with opinions of the decision makers. Fuzzy ANP has been used to enable the selection of best concept. The case study has been carried out in an Indian traditional manufacturing organisation. The results of the validation indicate that fuzzy ANP is an effective approach for selecting best concept thereby improving agility of product development process.