A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z     

Throughput centered prioritization of machines in transfer lines

In an environment of scarce resources and complex production systems, prioritizing is key to confront the challenge of managing physical assets. In the literature, there exist a number of techniques to prioritize maintenance decisions that consider safety, technical and business perspectives. However, the effect of risk mitigating elements—such as intermediate buffers in production lines—on prioritization has not yet been investigated in depth. In this line, the work proposes a user-friendly graphical technique called the system efficiency influence diagram (SEID). Asset managers may use SEID to identify machines that have a greater impact on the system throughput, and thus set prioritized maintenance policies and/or redesign of buffers capacities. The tool provides insight to the analyst as it decomposes the influence of a given machine on the system throughput as a product of two elements: (1) system influence efficiency factor and (2) machine unavailability factor. We illustrate its applicability using three case studies: a four-machine transfer line, a vehicle assembly line, and an open-pit mining conveyor system. The results confirm that the machines with greater unavailability factors are not necessarily the most important for the efficiency of the production line, as it is the case when no intermediate buffers exist. As a decision aid tool, SEID emphasizes the need to move from a maintenance vision focused on machine availability, to a systems engineering perspective.     

A cost‐based selective maintenance decision‐making method for machining line

Machine line is a type of manufacturing system in which machines are connected in series or in parallel. It is significant to ensure the reliability as well as to reduce the total cost of maintenance and failure losses in the maintenance programs of such systems. Cost‐based selective maintenance decision‐making, which is the best method for a selected group of machines in machine line is presented under limited maintenance durations. Fault losses and maintenance costs of a single machine under different maintenance actions i.e. minimal repair, preventive maintenance and overhaul on the fault rate of the machine are calculated. An algorithm combining the heuristic rules and tabu search is proposed to solve the presented selective maintenance model. Finally, a case study on the maintenance decision‐making problem of a connecting rod machining line in the automobile engine workshop is presented to illustrate the applicability of the proposed method. The end result shows that the fault losses can be further reduced by the optimization of maintenance interval and maintenance duration. Copyright © 2010 John Wiley & Sons, Ltd.     

The Graphical Analysis for Maintenance Management Method: A Quantitative Graphical Analysis to Support Maintenance Management Decision Making

This article proposes a logical support tool for maintenance management decision making. This tool is called the Graphical Analysis for Maintenance Management (GAMM), a method to visualize and analyze equipment dependability data in a graphical form. The method helps for a quick and clear analysis and interpretation of equipment maintenance (corrective and preventive) and operational stoppages. Then, opportunities can be identified to improve both operations and maintenance management (short–medium term) and potential investments (medium–long term). The method allows an easy visualization of parameters, such as the number of corrective actions between preventive maintenance, the accumulation of failures in short periods of time, and the duration of maintenance activities and sequence of stops of short duration. In addition, this tool allows identifying, a priori, anomalous behavior of equipment, whether derived from its own function, maintenance activities, misuse, or even equipment designs errors. In this method, we used a nonparametric estimator of the reliability function as a basis for the analysis. This estimator takes into account equipment historical data (total or partial) and can provide valuable insights to the analyst even with few available data. Copyright © 2012 John Wiley & Sons, Ltd.     

Joint optimization of maintenance,buffers and machines in manufacturing lines

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.     

Workload planning in small lot printed circuit board assembly

The problem of workload planning in small lot printed circuit board (PCB) assembly concerns the determination of the daily mix of production orders to be released into the production system. When switching from one production order (board type) to another, a considerable set-up time is incurred based on the number of component feeders to be replaced in the component magazine of the assembly machines. To support the order-mix decision faced by a major electronics manufacturer, two versions of a linear programming model are developed. The models differ primarily in their degree of aggregation and their computational effort. In order to reduce the aggregational error incurred, a fuzzy approach is developed to estimate the number of component set-ups at automatic SMD placement machines. Our numerical investigation reveals that sufficiently accurate solutions may be obtained from a highly aggregate fuzzy LP-model and this is achieved with considerably less computational effort than with a more detailed LP-model. We also demonstrate the potential suitability of the fuzzy LP-model for implementation within an interactive decision support system.     

Impact of the use-based maintenance policy on the performance of cellular manufacturing systems

In this paper, a multi-objective integer programming approach is developed to investigate the impact of the use-based preventive maintenance (UPM) policy on the performance of the cellular manufacturing system (CMS). Under the UPM policy a maintenance schedule is established which provides for the performance of preventive maintenance (PM) only after a predetermined number of operating hours of machine use. This research indicates how PM and failure repair (FR) actions affect the effective availability of the machines and accordingly the machine and inter/intra-cell material handling costs under the UPM policy. The objective is to minimise the machine cost, inter- and intra-cell material handling and PM/FR costs. The proposed model is solved by an interactive fuzzy programming (IFP) approach to determine the best compromise solution from the decision maker point of view. IFP assumes that each objective function has a fuzzy goal and focuses on minimising the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. Compromise solutions are prioritised by two efficiency criteria, i.e. grouping efficiency and system availability. The performance of the proposed model is verified by a comprehensive numerical example.     

A new method for workshop real time scheduling

Workshop real time scheduling is one of the key factors in improving manufacturing system efficiency. This is especially true for workshops in which various products are processed simultaneously, and use multipurpose machines. Real time scheduling is appropriate to handle perturbations in the environment of the manufacturing process, a major issue at the shop floor level. The products to be processed have release times and due dates and the resources are multipurpose machines. A decision support system for real time scheduling is described. It is based on an original approach, aiming at searching for characteristics of a set of schedules compatible with the main manufacturing constraints to be satisfied. This set of schedules is obtained by defining sequences of groups of permutable operations for every resource. A method to find such a set is described. We emphasize the use of this group sequence as a decision support system. Significant states and events requiring real time decisions are identified and three main types of decisions are analysed. For each of them, the proposed decision support system is detailed and explained.     

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

To achieve a significant improvement in the overall performance of a flexible manufacturing system, the scheduling process must consider the interdependencies that exist between the machining and transport systems. However, most works have addressed the scheduling problem as two independent decision making problems, assuming sufficient capacity in the transport system. In this paper, we study the simultaneous scheduling (SS) problem of machines and automated guided vehicles using a timed coloured Petri net (TCPN) approach under two performance objectives; makespan and exit time of the last job. The modelling approach allows the evaluation of all the feasible vehicle assignments as opposed to the traditional dispatching rules and demonstrates the benefits of vehicle-controlled assignments over machine-controlled for certain production scenarios. In contrast with the hierarchical decomposition technique of existing approaches, TCPN is capable of describing the dynamics and evaluating the performance of the SS problem in a single model. Based on TCPN modelling, SS is performed using a hybrid heuristic search algorithm to find optimal or near-optimal schedules by searching through the reachability graph of the TCPN with heuristic functions. Large-sized instances are solved in relatively short computation times, which were a priori unsolvable with conventional search algorithms. The algorithm’s performance is evaluated on a benchmark of 82 test problems. Experimental results indicate that the proposed algorithm performs better than the conventional ones and compares favourably with other approaches.     

Simulation platform for anticipative plant-level maintenance decision support system

Global competition and increasing customer expectations are forcing automobile manufacturers to improve their operations. Maintenance, being one of the most critical components in many industries, has a direct impact on the improvement of the overall production performance. In this paper, we introduce an anticipative plant-level maintenance decision support system (APMDSS) that provides guidance on corrective and preventive maintenance priorities based on the equipment bottleneck ranks with the objective of improving daily plant throughput. APMDSS anticipates the plant dynamics (i.e. bottlenecks, hourly buffer levels and likelihood of machine breakdowns) for upcoming shifts using starting state information of the production shift (e.g. equipment maintenance history, operational status of machines, buffer levels and scheduled production model mix). We also evaluate the performance of APMDSS using real data from an automotive body shop experiencing routine throughput difficulties due to frequent machine breakdowns. The results are compared with other methods from the literature and found to be superior in many settings.     

Hybrid approach for remaining useful life prediction of ball bearings

Remaining useful life (RUL) prediction plays an important role in predictive maintenance systems to support decision‐makers for arranging maintenance tasks and related resources. We propose a hybrid approach that is combined an exponential weighted moving average (EWMA) control chart for anomaly detection and machine learning models such as support vector regression (SVR) and random forest regression (RFR) with differential evolution (DE) algorithm to predict the RULs of ball bearings. Here, DE algorithm is used to find the optimal hyperparameters of SVR model. The datasets of ball bearings from the Prognostics Data Repository of NASA are used to compare the prediction performance of different methods. The degradation behavior of training data from the anomaly time to the end of life is used to transfer learning for the testing data in the SVR and RFR models. The results indicate that the proposed methods outperform the other four existing methods in terms of score. Therefore, the proposed hybrid approach is a reliable tool for the RUL prediction of ball bearings.     

Combined Anomalies Prediction Using the Bayesian Theory

The industry world uses machines and plants that are increasingly powerful and complex. The requirements of high safety, the reduction of the exploitation costs, and the control of the equipment availability give to maintenance a dominating role. The industrialists attach a great importance to the conditional maintenance of the revolving machines that use primarily the vibrations of their rotors. The temperature measurements in the stages can bring additional information to vibrations. In conditional preventive maintenance, the diagnosis of failures of the industrial systems, if it is carried out with effectiveness, represents one of the means to gain points of productivity. It consists in observing the symptoms of a failure and then identifying the cause using a logical reasoning founded on observations, that is, to dismount a deterministic mechanism between the cause and its effect. This research presents and discusses the decision making that is practically exerted with each stage in the procedure of industrial diagnosis and tool of assistance to the decision making. The approach used is Bayesian theory to reveal a defect masked by another in the low frequency (combined defect). The turbo compressor as an object of research, vibratory analyses, and thermography are the techniques used in this work. Copyright © 2011 John Wiley & Sons, Ltd.     

Reconfiguration of assembly lines under the influence of high product variety in the automotive industry–a decision support system

In this paper we consider the mixed model assembly line reconfiguration problem in the context of auto production which is characterised by a make-to-order production process and a huge product variety. Starting from a given line balancing solution the goal is to minimise production costs in the short term for a largely known production program by reassigning and shifting tasks between workstations. We present a mathematical optimisation model that aims at minimising the costs incurred by overload situations, regular workers and reconfiguration measures. Due to the model's complexity, lack of data and acceptance issues it is hardly possible to fully automate the solution process in an industrial environment. Therefore, we present a decision support approach that consists of visualisation components, new numerical indicators and an integrated heuristic optimisation procedure to semi-automate the reconfiguration process. In particular, reconfiguration costs can be taken into account and no complete precedence graph is required. Finally, we show on the basis of two industrial case studies that our approach can be successfully applied in a practical environment where it was capable of drastically reducing the occurrence of overload situations.     

Lockout/tagout and optimal production control policies in failure-prone non-homogenous transfer lines with passive redundancy

This paper considers a production problem for a transfer line subject to random failures and repairs, and differs from other studies on transfer lines. It considers a manufacturing system consisting of three machines (two machines with passive redundancy, and one in series with the previous ones) producing one part type. The control problem is subject to non-negative constraints on work-in-process (WIP). The decision variables are the production rates of two main machines and a standby machine, and influence the WIP levels, the inventory levels and the system's capacity, which is assumed to be described by a finite-state Markov chain. The objective of this paper is to minimise WIP and finished goods inventory costs; it also aims to respect the essential space–time during intervention on machine down, in order to minimise the possibility of the circumvention of protection devices or of the retraction of lockout/tagout procedures through a passive redundancy system. This paper therefore verifies the effect of passive redundancy on optimal stock levels. Given that an analytical or even a numerical solution of the problem is very difficult to find, and that we want to have a more realistic model for industries, we present a combined approach, which is presented based on a combination of analytical formalism, simulation modelling, design of experiments, and response surface methodology to optimise a transfer line with passive redundancy, producing one part type. The usefulness of the proposed approach is illustrated through a numerical example and a sensitivity analysis.     

Modelling maintenance effects on manufacturing equipment performance: results from simulation analysis

Equipment maintenance and system reliability are important factors affecting the organisation’s ability to provide quality and timely services to customer. While maintenance remains an important function to manufacturing, it is only recently that attempts have been made to quantify its impact on equipment performance. In this research, an approach of linking maintenance with equipment performance is developed using simulation modelling. The modelling approach involves defining probabilistic models and assumptions affecting system performance, such as: the probabilistic model for the initial failure rate/intensity of the equipment; the probabilistic model for the system deterioration and corresponding effect; the probabilistic model for the random times of corrective maintenance (CM) and preventive maintenance (PM) that takes into the account the types of maintenance plans/policies and the potential dependency between CM and PM times; and the probabilistic model for the random effects of CM and PM on the reliability of the equipment. Using a continuous manufacturing equipment, the model is used to analyse the impact of deterioration, failures and maintenance (policies, timing and efficiency) on equipment performance. It is shown that modelling the effect maintenance provides a basis of evaluating maintenance efforts with the potential application in performance evaluation and decision support while investigating opportunities for manufacturing equipment performance improvement.     

Using Utility Functions for Aggregate Scheduling in Health Maintenance Organizations (HMO'S)

The multiple attributes and characteristics of the services provided by a health maintenance organization (HMO) are reduced to a common unit of measurement by means of utility functions. This reduction allows the development of a micro-economic aggregate planning model that can be used both for the short and the long term to make optimizing decisions on system inputs and resource allocations. The model is designed to deal with the common revenue- and budget-constrained situations and is directed to maximize the level of service provided by the HMO, according to the management's perceptions of the system characteristics. An empirical short term application of the model is presented and compared against actual management decisions. Data needs and implementation requirements are discussed, along with the managerial implications of this approach.     

A sustainability-oriented multi-dimensional value assessment model for product-service development

In the product-service system (PSS), products are carriers of functions and media of services. Currently, value creation in PSS is directly related to the capabilities of the product-service solutions provided to serve customer demands and supplier business interest over time. There is a need for an evaluation approach that can identify in what manner and to what extent products and services should best be bundled in the development of a company’s value proposition. Furthermore, with an increasing focus on the sustainability profile of business operations, it is necessary to incorporate environmental impact measures with technical performance and cost measures to present a more comprehensive value assessment of product-service development. In this paper, a sustainability-oriented value assessment model is proposed as an attempt for product-service development decision support based on life cycle thinking. In this proposed approach, the fitness for extended utilisation indicators, the net present value (NPV) approach and life cycle assessment (LCA), are applied as the measures of life cycle performance, life cycle cost, and life cycle environmental impact, which are used for value assessment. A case study on a solar heating system will be presented to show how the assessment model can be used to support the evaluation and selection of different product-service development alternatives.     

Joint optimization of preventive maintenance and inventory control in a production line using simulation

This paper proposes an integrated method for preventive maintenance and inventory control of a production line, composed of n machines (n?≥?1) without intermediate buffers. The machines are subject to failures and an age-dependent preventive maintenance policy is used. Approximate analytical results are proposed for the one machine case. Simulation software is used to model and simulate the behaviour of the production line of n machines under various maintenance and inventory control strategies. A methodology combining the simulation and genetic algorithms is proposed jointly to optimize maintenance and inventory control policies. Results are compared with the analytical solutions.     

A knowledge-based system for solving multi-objective assembly line balancing problems

This paper presents design-and-development details of a knowledge-based system that solve multi-objective assembly line balancing problems to obtain an optimal assignment of a set of assembly tasks to a sequence of workstations. Assembly line balancing problems arise in high-volume production systems with a significant regularity. The formulation and solutions currently employed by managers and practitioners usually aims at optimizing one objective (e.g., number of work stations or cycle time), thus ignoring the multi-dimensional nature of the overall objectives of the manager. Furthermore, in practice ALBPs are ill-defined and ill-structured, making it difficult to formulate and solve them by mere mathematical approaches. The knowledge-based system multi-objective assembly line balancing approach, presented in this paper, addresses these needs. This paper presents a knowledge-base multi-objective approach to assembly line balancing problems. It demonstrates how such a system can be constructed and how a variety of assembly line balancing methods can be used in a uniform structure to support the decision maker (DM) to formulate, validate the formulation, generate alternatives, and choose the best alternative. Its capabilities include: (1) Elimination of inconsistencies in the problem structure. (2) The use of multi-objective formulation of the problem, (3) A well-designed user-interface, (4) Pursuance of the overall objectives of the manager via a new mechanism, (5) Development of several efficient alternatives, consistent with the user-specified constraint structure, providing the decision maker with a larger number of choices, and, (6) An approach for ranking and prioritizing alternatives consistent with the decision-maker's preferences.     

Machining conditions-based preventive maintenance

In this study we propose an operating conditions-based preventive maintenance (PM) approach for computer numerical control (CNC) turning machines. A CNC machine wears according to how much it is used and the conditions under which it is used. Higher power or production rates result in more wear and higher failure rates. This relationship between the operating conditions and maintenance requirements is usually overlooked in the literature. On CNC turning machines we can control the machining conditions such as cutting speed and feed rate, which in turn affect the PM requirements of the CNC machine. We provide a new model to link the PM decisions to the machining conditions selection decisions, so that these two decision-making problems can be solved together by considering their impact on each other. We establish that our proposed geometric programming model captures the related cost terms along with the technological restrictions of CNC machines. The proposed preventive maintenance index function can be used to provide an intelligent CNC machine degradation assessment.