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The Cell Formation Problem (CFP) is a widely studied issue that aims to group machines effectively based on criteria such as productivity, lower costs, and greater efficiency. In recent years, more characteristics were summarized relating to this problem. This paper provides a bibliographic examination of methodologies addressing the CFP in cellular manufacturing, focusing on novel approaches such as alternative routes and machine reliability. The articles were obtained from Scopus and Web of Science and filtered using the PRISMA methodology. Classification based on objective functions, constraints, and methodologies facilitated informative visualizations for analysis. Findings indicate a focus on capital utilization optimization, with cost reduction via intercellular moves minimization as the primary objective. Common constraints include limits on the number of machines per cell, restricting machines to a single cell and singular production routes per part. The genetic algorithm predominates as a non-exact solution approach, while the " ε -constraint" method is commonly used. This study offers insights into contemporary trends in solving the CFP with alternative routings and machine reliability, aiding researchers and professionals in the field to improve the quality of their investigations. [ABSTRACT FROM AUTHOR]

The Cell Formation Problem (CFP) is a widely studied issue that aims to group machines effectively based on criteria such as productivity, lower costs, and greater efficiency. In recent years, more characteristics were summarized relating to this problem. This paper provides a bibliographic examination of methodologies addressing the CFP in cellular manufacturing, focusing on novel approaches such as alternative routes and machine reliability. The articles were obtained from Scopus and Web of Science and filtered using the PRISMA methodology. Classification based on objective functions, constraints, and methodologies facilitated informative visualizations for analysis. Findings indicate a focus on capital utilization optimization, with cost reduction via intercellular moves minimization as the primary objective. Common constraints include limits on the number of machines per cell, restricting machines to a single cell and singular production routes per part. The genetic algorithm predominates as a non-exact solution approach, while the “ε-constraint” method is commonly used. This study offers insights into contemporary trends in solving the CFP with alternative routings and machine reliability, aiding researchers and professionals in the field to improve the quality of their investigations.

The Cell Formation Problem (CFP) involves the clustering of machines to enhance productivity and capitalize on various benefits. This study addresses a variant of the problem where alternative routes and machine reliability are included, which we call a Generalized Cell Formation Problem with Machine Reliability (GCFP-MR). This problem is known to be NP-Hard, and finding efficient solutions is of utmost importance. Metaheuristics have been recognized as effective optimization techniques due to their adaptability and ability to generate high-quality solutions in a short time. Since BWO was originally designed for continuous optimization problems, its adaptation involves binarization. Accordingly, our proposal focuses on adapting the Black Widow Optimization (BWO) metaheuristic to tackle GCFP-MR, leading to a new approach named Binary Black Widow Optimization (B-BWO). We compare our proposal in two ways. Firstly, it is benchmarked against a previous Clonal Selection Algorithm approach. Secondly, we evaluate B-BWO with various parameter configurations. The experimental results indicate that the best configuration of parameters includes a population size (P o p) set to 100, and the number of iterations (M a x i t e r) defined as 75. Procreating Rate (P R) is set at 0.8, Cannibalism Rate (C R) is set at 0.4, and the Mutation Rate (P M) is also set at 0.4. Significantly, the proposed B-BWO outperforms the state-of-the-art literature's best result, achieving a noteworthy improvement of 1.40%. This finding reveals the efficacy of B-BWO in solving GCFP-MR and its potential to produce superior solutions compared to alternative methods. [ABSTRACT FROM AUTHOR]

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods. [ABSTRACT FROM AUTHOR]

Mechanised plow and shearer systems are widely applied in underground mines all over the world. Both systems are used in the exploitation of hard coal deposited in the form of seams of various thickness. The selection of the appropriate complex depends on the mining and geological conditions and the thickness of the seam. However, with regard to thin and medium seams, these complexes are competitive solutions. Mines usually use either shearer or plow systems. Both have certain advantages and disadvantages resulting from their design and method of operation, which have been demonstrated and presented in many publications. However, in terms of their failure rate comparison, there are no relevant research and analysis results. Only selective studies of individual machines can be found. The article is concerned with the failure frequency of longwalls equipped with plow and shearer systems in the LW Bogdanka coal mine. The analysis covers a period of 13 months of the mine's operation, during which 2589 failures were recorded. All failures were taken into account, irrespective of their type or cause. The analysis was conducted for all longwalls exploited in this period, i.e., five plow and five shearer systems working in six different sections. In the analysed period, these longwalls worked for a total of 1484 days. It should be emphasised that all the complexes worked in one mine, thanks to which the data are comparable. The analysis is unique material regarding the failure rate of machines. Both solutions were analysed independently and subjected to a detailed comparison. A comprehensive analysis revealed that the failure rate of longwalls equipped with plow systems is noticeably higher than that of shearer ones. The main purpose of the article was to conduct a comparative analysis of the failure rate of machines in shearer and plow complexes operating in the same conditions. The analysis results contradict the previous opinion on the failure frequency of plow and shearer systems. The final conclusion has been very well-argued and is supported by hard data. The comparison of both techniques in terms of their failure rate is new knowledge and can be treated as an argument when choosing an appropriate longwall complex. [ABSTRACT FROM AUTHOR]

Background: The study focuses on simplified make-and-pack production in the sugar industry as a case study. The analyzed system is characterized by parallel packing lines, which share one resource with a sequenceindependent setup time. Additionally, the special characteristics that occur in many enterprises make scheduling difficult. The special characteristics of the system are the simultaneous occurrence of a variable input stream, scheduling of processes, and including the reliability of machines. Due to the variability of the input parameters, it is appropriate to consider the use of Digital Twin, which is a virtual representation of the real processes' performance. Therefore, this purpose of the paper is two-fold. First, an analysis of sequence determination of the stream-splitting machine was performed with taking into account the impact of logistics system reliability on system performance. Second, the concept of implementing Digital Twin in the analyzed production process is presented. Methods: The mathematical model for line efficiency was developed on the presented make-and-pack production presented in the selected sugar industry. Different sequences of stream-splitting machines were studied to examine the system's efficiency, availability, and utilization of packaging lines. Two scenarios were investigated with the use of computer simulation. Results: Computer simulation experiments were performed to investigate the sequencing and planning of packaging line problems. The results obtained for the case company indicated a significant dependence between the preferred packing sequence and the operational parameters. Conclusions: The simulations confirm the influence of internal and external factors on sugar line packaging processes. The main advantage of the developed simulation model is identifying the relationship between the size of the input stream and the system's availability level, as well as identifying the main constraints on the possibility of implementing the DT concept in the analyzed company. [ABSTRACT FROM AUTHOR]

The facility layout design and Cell Formation (CF) problems are the important sectors in designing a cellular manufacturing system (CMS). These problems are interrelated and simultaneous consideration of them is essential for a successful design of CMS. In this paper, a new non-linear mixed integer programming model is presented to solve the integrated cell formation and inter/intra cell layouts in continuous space. The proposed approach incorporated machine reliability with a stochastic time between failures. Some important factors such as stochastic process time, part demand, cell size, variable process routing, and both inter-cell and intra-cell layout are considered in proposed model. The objective is to minimize the total inter/intra cell transportation cost and total breakdown cost. The proposed model is then linearized to reduce computation time and an exact solver by using GAMS is proposed to tackle the computational complexity of the developed model. Results indicate the efficiency and the application of proposed model in the area of CMS conceptually

Cellular manufacturing systems (CMS) are essential for achieving efficient production processes, and their success relies on effective cell formation and layout design. This research paper presents a novel nonlinear mathematical programming model that employs the rectilinear distance notion to define the layout within a continuous space. The proposed model incorporates the stochastic nature of machine failures, considering the unreliability with the inclusion of a stochastic time between failures. With a bi-objective approach, the model aims to minimize inter and intra-cell movements of parts, as well as the associated costs related to exceptional elements (EEs), cell reconfigurations, and machine failures. To optimize the proposed model, several multi-objective meta-heuristic algorithms, including the multi-objective particle swarm optimization (MOPSO), multi-objective taboo search (MOTS), and non-dominated sorting genetic algorithm (NSGA-II), are introduced. The effectiveness of these algorithms is validated through numerical instances and a real case study. The results indicate that the MOPSO algorithm exhibits superior performance in optimizing various multi-objective criteria. The presented mathematical model and algorithms provide valuable tools for manufacturers to optimize their CMS, resulting in reduced costs, enhanced production efficiency, and increased competitiveness. By considering the simultaneous effects of time and cost associated with machine failures, this research offers practical insights and solutions for improving the performance of cellular manufacturing systems. [ABSTRACT FROM AUTHOR]

There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.

The facility layout design and Cell Formation (CF) problems are the important sectors in designing a cellular manufacturing system (CMS). These problems are interrelated and simultaneous consideration of them is essential for a successful design of CMS. In this paper, a new non-linear mixed integer programming model is presented to solve the integrated cell formation and inter/intra cell layouts in continuous space. The proposed approach incorporated machine reliability with a stochastic time between failures. Some important factors such as stochastic process time, part demand, cell size, variable process routing, and both inter-cell and intra-cell layout are considered in proposed model. The objective is to minimize the total inter/intra cell transportation cost and total breakdown cost. The proposed model is then linearized to reduce computation time and an exact solver by using GAMS is proposed to tackle the computational complexity of the developed model. Results indicate the efficiency and the application of proposed model in the area of CMS conceptually. [ABSTRACT FROM AUTHOR]

Forestry machines sold at auctions across the southern United States covering the years 2005 to 2019 (n = 640) were compiled into three equipment categories—rubber-tired feller bunchers, grapple skidders, and trailer-mounted loaders—with inflationary effects removed. The data were discounted by a reliability function derived from a two-parameter Weibull distribution, which defined the probability of future operability at machine hours h and a wrecking value based on each machine's specified worth as scrap. These adjusted salvage values were then transformed using the Box-Cox normal method. Age affected all fellers and certain manufacturers of skidders and loaders. A significant annual use effect was revealed for fellers and skidders. Interactions of age and use with horsepower revealed that machine size influenced buyers' feller and skidder purchasing decisions. Manufacturer intercept and slope shifters pointed to potential brand recognition by buyers. Perceived quality differences between manufacturers, however, were not revealed. Depreciation occurred at ever-increasing rates over machine lifetime. [ABSTRACT FROM AUTHOR]

The fundamental function of a cellular manufacturing system (CMS) is based on definition and recognition of a type of similarity among parts that should be produced in a planning period. Cell formation (CF) and cell layout design are two important steps in implementation of the CMS. This paper represents a new nonlinear mathematical programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the proposed model, machines are considered unreliable with a stochastic time between failures. The objective function calculates the costs of inter and intra-cell movements of parts and the cost due to the existence of exceptional elements (EEs), cell reconfigurations and machine breakdowns. Due to the problem complexity, the presented mathematical model is categorized in NP-hardness; thus, a genetic algorithm (GA) is used for solving this problem. Several crossover and mutation strategies are adjusted for GA and parameters are calibrated based on Taguchi experimental design method. The great efficiency of the proposed GA is then demonstrated via comparing with particle swarm optimization (PSO) and the optimum solution via GAMS considering several small/medium and large-sized problems.

In this paper, the influence of the technical condition of the crankshaft rotation frequency governor on the durability and reliability of operation of a diesel engine is studied. [ABSTRACT FROM AUTHOR]

This case study develops an innovative management and scheduling system for corrective maintenance of machines in a manufacturing facility. The study also involves a comparative evaluation of the proposed and the existing systems under a spectrum of operating conditions. A comprehensive simulation is used to evaluate system performances under a variety of settings which include reliability, service level, and cost consequences. The analysis is based on a full factorial experimental design. In summary, the developed self-regulating management system which involves dynamic work allocation and pre-emption is shown to yield higher machine availability and higher mechanic utilisation even with fewer mechanics. The study also finds that the new system is more streamlined, agile, and robust although it is subject to more-constrained machine reliability and mechanic service time environments. Further, a major reduction of current manpower can still achieve at least 95% machine availability, illustrating the cost effectiveness and efficacy of the developed system. This rule-based corrective maintenance system can be operated in uncertain environments on a real-time basis without additional reformatting costs and provides a competitive measure to deal with managerial issues such as low retention rate for skilled mechanics, highly uneven training levels and pay scales. The financial consequences and gains in strategic advantage with respect to the facility's operational structure are promising after implementation. Moreover, the system developed in this case study represents a meaningful starting point for a more vigorous theoretical research on the bucket brigade system to different functions in industrial and operations management. [ABSTRACT FROM AUTHOR]

Abstract The cell formation (CF) is one of the most important steps in the design of a cellular manufacturing system (CMS), which it includes machines’ grouping in cells and part grouping as separate families, so that the costs are minimized. The various aspects of the problem should be considered in a CF. The machine reliability and the tool assigned to them are the most important problems which have to be modeled correctly. Another important aspect in CMS is material handling costs that they consist of inter-cell and intra-cell movement costs. Moreover, setup and tool replacement costs can be effective in CF decision making. It is obvious that CF cannot be completed without considering the number of demand. With considering of all of the above aspects, an extended linear integer programming is represented for solving the cell formation problem (CFP) in this study. The objective is to minimize the sum of inter-cell movement, intra-cell movement, tool replacement, machine breakdown, and setup costs. In the other terms, for states that cost of movement is higher than tool-changing cost, although a part can have the inter- and/or intra-cell movements, the model tries to find a solution which part is allocated to one cell and with changing the tools, processes of that part is completed. In addition, to validate the model and show its efficiency and performance, several examples are solved by branch and bound (B&B) method.

For a long time, the machinery's reliability allocation methods have followed the electronics', and thus all of the interactions among moving components and parts in machines are neglected. In fact, the allocated results are not reasonable for mechanical products; even the allocation granularity is not appropriate to guide the reliability design. To overcome these problems, this paper proposes an allocation method based on meta-action. By using the function-movement-action method, we decompose the mechanical product into meta-actions. An action is used as the basic unit of reliability control, and all the parts contributing to the action are treated as a whole. The interactions of the parts which contribute to the same action are treated as the internal forces of meta-actions, and others are considered as the interactions between meta-actions by the consideration of common parts in different units. An illustrative example shows the proposed method makes the results more reasonable and more convenient to guide mechanical product designs. [ABSTRACT FROM AUTHOR]

Lean manufacturing is a systematic method to improve productivity and reduce costs for each industrial. One of the tools for lean is the cellular manufacturing that divides the production system into smaller subsystems. The objective is to reduce inventory, setup times, and material handling costs. The manufacturing cell formation problem presents the first issue faced in designing a cellular manufacturing system. In this paper, the presence of alternative process routings and machine reliability in addition to production volume, operation sequence and production time makes the problem more complex but also more realistic and appealing to real-world applications. Most authors solve this kind of problems by mathematical programming approaches that require large amounts of computational efforts. Therefore, a meta-heuristic algorithm, entitled Hybrid cuckoo search for the generalized cell formation problem, is considered in this paper. A set of benchmark problems with various scales is used to further evaluate the performance of the proposed method. Comparison of the results obtained with those obtained by LINGO and by the simulated annealing method reveals the better effectiveness and efficiency of our method. [ABSTRACT FROM AUTHOR]

In order to implement the cellular manufacturing system in practice, some essential factors should be taken into account. In this paper, a new mathematical model for cellular manufacturing system considering different production factors including alternative process routings and machine reliability with stochastic arrival and service times in a dynamic environment is proposed. Also because of the complexity of the given problem, a Benders’ decomposition approach is applied to solve the problem efficiently. In order to verify the performance of proposed approach, some numerical examples are generated randomly in hypothetical limits and solved by the proposed solution approach. The comparison of the implemented solution algorithm with the conventional mixed integer linear and mixed integer non linear models verifies the efficiency of Benders’ decomposition approach especially in terms of computational time.

The cell formation (CF) is one of the most important steps in the design of a cellular manufacturing system (CMS), which it includes machines’ grouping in cells and part grouping as separate families, so that the costs are minimized. The various aspects of the problem should be considered in a CF. The machine reliability and the tool assigned to them are the most important problems which have to be modeled correctly. Another important aspect in CMS is material handling costs that they consist of inter-cell and intra-cell movement costs. Moreover, setup and tool replacement costs can be effective in CF decision making. It is obvious that CF cannot be completed without considering the number of demand. With considering of all of the above aspects, an extended linear integer programming is represented for solving the cell formation problem (CFP) in this study. The objective is to minimize the sum of inter-cell movement, intra-cell movement, tool replacement, machine breakdown, and setup costs. In the other terms, for states that cost of movement is higher than tool-changing cost, although a part can have the inter- and/or intra-cell movements, the model tries to find a solution which part is allocated to one cell and with changing the tools, processes of that part is completed. In addition, to validate the model and show its efficiency and performance, several examples are solved by branch and bound (B&B) method. [ABSTRACT FROM AUTHOR]

The paper considers the problem of the assurance of the quality of machine repair in production plants. The primary quality repair index has been described analytically with the help of the arithmetic matrix in which the relationship between the production capacity and the structural and organizational factors of the quality of repair is taken into account. Ratios of input variables and the output relationship with the primary factors of the quality of machine repair were determined using matrix equations that reflect the relationship between design, process and organizational factors, and the basic quality ratios. It has been concluded that the quality of machine repair depends on proper diagnosis, the wear and tear of individual connections of elements, and the technological processes leading to the restoration of the damaged parts and reassembling the machine.

In the article problems related to human labor and factors affecting the increasing use of industrial robots are discussed. Since human factors affect the production processes stability, robots are preferred to apply. The application of robots is characterized by higher performance and reliability comparing to human labor. The problem is how to determine the real difference in work efficiency between human operator and robot. The aim of the study is to develop a method that allows clearly definition of productivity growth associated with the replacement of human labor by industrial robots. Another aim of the paper is how to model robotized and manual operated workstation in a computer simulation software. Analysis of the productivity and reliability of the hydraulic press workstation operated by the human operator or an industrial robot, are presented. Simulation models have been developed taking into account the availability and reliability of the machine, operator and robot. We apply OEE (Overall Equipment Effectiveness) indicator to present how availability and reliability parameters influence over performance of the workstation, in the longer time. Simplified financial analysis is presented considering different labor costs in EU countries. [ABSTRACT FROM AUTHOR]

The cell formation is the first step in the design of Cellular Manufacturing systems. It consists of grouping parts with similar processing needs into cells and identifying the set of machines needed to process these parts. The aim is to minimize the material handling costs and maximize the use of the machines. In this paper, the machine reliability and the alternative process routings are taken into account to form the production cells. The presence of these factors in addition to the production volume, operation sequence and production time makes the problem more realistic but also more complex. Most authors solve this kind of problems by mathematical programming approaches that require large amounts of computational efforts. Therefore, a modified version of the Clonal Selection Algorithm is introduced and a local search mechanism is adopted in this paper. The obtained results are compared with those of the Branch and Bound (B&B) method using LINGO software. The comparison reveals the effectiveness and the efficiency of the proposed method in terms of both solution quality and computation time required. [ABSTRACT FROM AUTHOR]

The problem of production flow in manufacturing systems is analyzed. The machines can be operated by workers or by robots, since breakdowns and human factors destabilize the production processes that robots are preferred to perform. The problem is how to determine the real difference in work efficiency between humans and robots. We present an analysis of the production efficiency and reliability of the press shop lines operated by human operators or industrial robots. This is a problem from the field of Operations Research for which the Discrete Event Simulation (DES) method has been used. Three models have been developed, including the manufacturing line before and after robotization, taking into account stochastic parameters of availability and reliability of the machines, operators, and robots. We apply the OEE (Overall Equipment Effectiveness) indicator to present how the availability, reliability, and quality parameters influence the performance of the workstations, especially in the short run and in the long run. In addition, the stability of the simulation model was analyzed. This approach enables a better representation of real manufacturing processes. [ABSTRACT FROM AUTHOR]

In this study, a robust optimization approach is developed for a new integrated mixed-integer linear programming (MILP) model to solve a dynamic cellular manufacturing system (DCMS) with unreliable machines and a production planning problem simultaneously. This model is incorporated with dynamic cell formation, inter-cell layout, machine reliability, operator assignment, alternative process routings and production planning concepts. To cope with the parts processing time uncertainty, a robust optimization approach immunized against even worst-case is adopted. In fact, this approach enables the system’s planner to assess different levels of uncertainty and conservation throughout planning horizon. This study minimizes the costs of machine breakdown and relocation, operator training and hiring, inter-intra cell part trip, and shortage and inventory. To verify the performance of the presented model and proposed approach, some numerical examples are solved in hypothetical limits using the CPLEX solver. The experimental results demonstrate the validity of the presented model and the performance of the developed approach in finding an optimal solution. Finally, the conclusion is presented. [ABSTRACT FROM AUTHOR]

The problem of production flow in manufacturing systems is analyzed. The machines can be operated by workers or by robots, since breakdowns and human factors destabilize the production processes that robots are preferred to perform. The problem is how to determine the real difference in work efficiency between humans and robots. We present an analysis of the production efficiency and reliability of the press shop lines operated by human operators or industrial robots. This is a problem from the field of Operations Research for which the Discrete Event Simulation (DES) method has been used. Three models have been developed, including the manufacturing line before and after robotization, taking into account stochastic parameters of availability and reliability of the machines, operators, and robots. We apply the OEE (Overall Equipment Effectiveness) indicator to present how the availability, reliability, and quality parameters influence the performance of the workstations, especially in the short run and in the long run. In addition, the stability of the simulation model was analyzed. This approach enables a better representation of real manufacturing processes.

This paper presents a dynamic multi-objective mixed integer mathematical model for cell formation problem with probabilistic demand and machine reliability analysis, where the total system costs, machine underutilization cost, and maximum system failure rate over the planning time periods are to be minimized simultaneously. The total system cost objective function calculates machine operating, internal part production, intercellular material handling, and subcontracting costs. Since in this type of problems, objectives are in conflict with each other, so finding an ideal solution (a solution that satisfies all objectives simultaneously) is not possible. Therefore, this study uses the augmented ε-constraint method (to solve small size problems) and a nondominated sorting genetic algorithm (NSGAII) (to solve large size problems) to find the Pareto optimal frontier that decision makers can select her/his preferred solution. Numerical examples will be solved to demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

One of the most important steps in designing a cellular manufacturing system is cell formation which includes grouping the machines in cells and the parts as part families, so that the costs are minimized. Several aspects of the problem should be taken into account in cell formation; more specifically, machines and their reliability are among the most important issues that should be modelled correctly. Another important facet of a cellular manufacturing system is material handling cost consisting of inter-cellular and intra-cellular movement costs. In addition, setup cost may play a significant role in decision-making in many real world problems of cell formation. Obviously, cell formation cannot be completed without considering the demands for parts. Considering all these aspects of the problem in this research, a generalized model for solving cell formation problem is proposed. Exact methods, e.g., B&B, are cumbersome in solving such complex models for large-size problems. Therefore, in this paper, a modified version of simulated annealing algorithm is designed and numerical examples are provided to show that the proposed method is efficient and effective. [ABSTRACT FROM AUTHOR]

Cell formation (CF) is the most well-known problem in designing cellular manufacturing systems. In the generalised version of the CF problem, each part has a number of alternative process plans. Two issues should be addressed in the generalised cell formation (GCF) problem: selecting the best process plan for each part and assigning machines to the cells. In addition to the minimisation of movement costs, the selection of the most reliable process plans is an important task for the GCF problem. Although the expected value (EV) method is a straightforward procedure to model stochastic situations, this method has some serious drawbacks. Therefore, we use chance-constrained programming (CCP) to model this problem. To demonstrate the advantages of the proposed model, the model is compared with the traditional GCF model and the EV model through some numerical examples. The results reveal that the proposed model is superior to the other alternatives. [ABSTRACT FROM AUTHOR]

In the area of e-Maintenance the analysis of condition data plays an important role in order to determine the health condition of machines. The results of such an analysis are used to decide whether maintenance actions for a machine have to be scheduled or not. Predictive Maintenance is the next step as it offers the possibility to prognosticate the remaining time until a maintenance action of a machine has to be scheduled. Unfortunately, current solutions are only suitable for very specific use cases like reliability predictions based on vibration monitoring. Furthermore, they do not consider the fact that a machine may deteriorate non-uniformly, depending on external impacts (e.g., the work piece material in a milling machine, the changing fruit acid concentration in a bottling plant). These two problems are addressed in this paper. Therefore, concepts for a generic determination of reliability indicators and the handling of aging variability are presented. [ABSTRACT FROM AUTHOR]

Cell formation is the first step in the design of cellular manufacturing systems. The main objective of this step is to cluster machines and parts into machine cells and part families respectively so that the minimum of intercellular part movements will be achieved. In this paper, we will focus on the configuration of machine cells considering production volumes and process sequences of parts. Also, we will study alternative process routings for part types and machine reliability considerations. A pure linear integer program will be developed to formulate this situation. The results of solving numerical examples show that the reliability consideration has significant impacts on the final block diagonal form of machine-part matrixes. [ABSTRACT FROM AUTHOR]

The paper presents some aspects of analysing the reliability of machines when the recognized states of a machine form the base for conclusions about the further effectiveness of its functioning. In the first part the problem of states and classes of states is introduced, as well as the classic approach to the problem of machine reliability. The next part of the paper shows briefly the methodology of identifying the states of functioning machines by means of diagnostic methods. Special attention is paid to the domain of time in diagnostic investigations. The idea of representing the changes of states by time series of diagnostic symptoms has been presented and illustrated by some examples. In the conclusions, some proposals for further research are discussed. [ABSTRACT FROM AUTHOR]