Optimal integrated maintenance/production policy for randomly failing systems with variable failure rate

This article deals with the combined production and maintenance plans for a manufacturing system satisfying a random demand. We first establish an optimal production plan which minimises the average total inventory and production cost. Second, using this optimal production plan, and taking into account the deterioration of the machine according to its production rate, we derive an optimal maintenance schedule which minimises the maintenance cost. A numerical example illustrates the proposed approach, this analytical approach, based on a stochastic optimisation model and using the operational age concept, reveals the significant influence of the production rate on the deterioration of the manufacturing system and consequently on the integrated production/maintenance policy.     

A genetic algorithm approach for production scheduling with mould maintenance consideration

The traditional approach for maintenance scheduling concerns single-resource (machine) maintenance during production which may not be sufficient to improve production system reliability as a whole. Besides, in the literature many researchers schedule maintenance activities periodically with fixed maintenance duration. However, in a real manufacturing system maintenance activities can be executed earlier and the maintenance duration will become shorter since less time and effort are required. A practical example is that in a plastic production system, the proportion of machine-related downtime is even lower than mould-related downtime. The planned production operations are usually interrupted seriously because of the mismatch among the maintenance periods between injection machine and mould. In this connection, this paper proposes to jointly schedule production and maintenance tasks of multi-resources in order to improve production system reliability by reducing the mismatch among various processes. To integrate machine and mould maintenance tasks in production, this paper attempts to model the production scheduling with mould scheduling (PS-MS) problem with time-dependent deteriorating maintenance schemes. The objective of this paper is to propose a genetic algorithm approach to schedule maintenance tasks jointly with production jobs for the PS-MS problem, so as to minimise the makespan of production jobs.     

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.     

Single-machine scheduling with deteriorating effects and machine maintenance

In this paper, the single-machine scheduling problems with deteriorating effects and a machine maintenance are studied. In this circumstance, the deterioration rates of the jobs during the machining process are the same which reduces the production efficiency. The actual processing time of the job is a linearly increasing function of the starting time. In this process, the machine only performs a maintenance activity, and the maintenance time is a fixed value. After the maintenance work is completed, the machine will be restored to the initial state, and the deterioration of the job will be start again. The goal is to determine the optimal schedule in order to minimise the maximum completion time (i.e. the makespan) and the sum of job completion times. We prove that both problems are polynomial time solvable, and we also provide the corresponding algorithms.     

Maintenance strategy for tilting table of rolling mill based on reliability considerations

Reliability centred maintenance (RCM) is a new strategic framework for ensuring that any asset continues to perform, as its users want it to perform. RCM is a process used to determine the maintenance requirement of any physical asset in its operating context. RCM process entails asking seven questions about each of the selected assets. It makes use of two documents namely, RCM information worksheet and RCM decision worksheet. RCM decision diagram integrates all the decision processes into a single strategic framework. RCM concept developed by US commercial airlines industry has been successfully implemented by Military, Navy, Nuclear power plants, electric power generation and distribution undertakings and several other sectors. These projects have been carried out in the United Kingdom, The Republic of Ireland, the United States, Hong Kong, Australia, Spain and Singapore. The fact that people has enthusiastically received RCM at all levels and has enabled users to achieve some remarkable successes in all of these countries, suggests that it can be universally employed. Literature review indicates that RCM approach is not conventionally applied in process industries in India. Presently, predictive maintenance (PDM) approach along with conventional preventive maintenance is used in continuous/process industries. This approach if implemented in totality will increase the production cost to a large degree and make the production uneconomical. Similarly breakdown maintenance (BDM) approach cannot be applied in such industries as each breakdown involves huge costs. RCM approach is a compromise between PDM and BDM approach for optimising the cost and ensuring the availability of machine.The RCM approach has been applied to the tilting table system of rolling mill for the research work reported in this paper. In the present study, preventive maintenance tasks suggested for power transmission subsystem, guiding and transportation subsystem and hydraulic subsystem in tilting tables are 14 scheduled on-condition tasks, 10 scheduled on-restoration tasks, seven scheduled discard task. Whereas for 14 failure modes no scheduled maintenance has been proposed. Existing maintenance schedule for tilting tables indicates the maintenance action as and when required. Hence RCM based schedule specifies that additional preventive maintenance tasks need to be executed as compared to none initially. Cost incurred for this can be offset from the savings accrued from reduction in loss of production due to repetitive breakdowns. The methodology of RCM adopted in western industries cannot be applied as it is to Indian industries because of labour oriented nature, partially computerised information systems, non-availability of the information about cost of loss of production due to breakdown and age-reliability pattern of equipment, insufficient maintenance database. These problems can be overcome by development of sound MMIS, formulation of RCM review group and imparting suitable training to acquire the relevant skills in RCM. Thus RCM methodology can be applied to Indian industry for reduction of breakdowns as well as optimisation of preventive maintenance cost. This can further boost up the prospects of Indian industry to offer the products at globally competitive prices.     

Reliability-based maintenance and job scheduling for identical parallel machines

Quality has an important role in manufacturing, and on the other hand, machine condition has a significant effect on quality. Based on this fact, all manufacturers integrate the production scheduling with maintenance activities to keep the machines in perfect conditions. In this paper, we propose a mixed integer nonlinear model to optimise the quality cost, maintenance cost, earliness–tardiness cost and interruption cost simultaneously. We assume that if machines work in undesirable conditions, their quality is reduced, resulting in quality cost. On the other hand, if the machines are repaired to decrease the quality cost, maintenance cost and other cost such as earliness–tardiness cost and interruption cost are imposed to the manufacturer. Several numerical instances are implemented by the proposed model to show the model effectiveness to obtain the best maintenance and production scheduling with minimum quality cost.     

Single-machine group scheduling problem considering learning,forgetting effects and preventive maintenance

An integrated single-machine group scheduling model is proposed, which incorporates both learning and forgetting effects and preventive maintenance (PM) planning. The objective is to minimise the expected makespan by optimising job sequence and PM decisions. This model contains sequence-dependent set-up time, actual processing time, planned PM time and expected minimal repair time simultaneously. Based on the properties of group production, three learning functions under different circumstances are proposed to deduce the variable processing time of each part, considering the learning effect when consecutively producing identical or similar parts, together with the forgetting effect when transferring jobs interrupts the production process and makes retrogress in learning. Both run-based maintenance and minimal repair policies are specified to handle the uncertainty of machine breakdowns. The search algorithm for the model is developed, and the numerical example is studied. The computational results and sensitivity analysis show that this improved group scheduling model can well balance the machine resource requirements from different practical manufacturing-related activities.     

Multi-objective decision-making model based on CBM for an aircraft fleet with reliability constraint

Modern production management patterns, in which multi-unit (e.g. an aircraft fleet) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision-making. To schedule a good maintenance plan, not only does the individual aircraft maintenance have to be considered, but also the maintenance of the other aircraft in fleet have to be taken into account. Condition-based maintenance (CBM) is a maintenance scheme which recommends maintenance decisions according to equipment status collected by condition monitor over a period of time. Evaluating risk is necessary for scheduling appropriate maintenance, avoiding aircraft losses and maintaining the repairable components at a high-reliable state. In this paper, a novel two-models-fusion framework is proposed to predict the reliability of aircraft structures subjected to fatigue loads. Furthermore, we established a fleet maintenance decision-making model based on CBM for the maintenance of fatigue structures. The model concentrates on both minimising fleet maintenance cost and maximising fleet availability, overcoming the shortcomings of traditional fleet CBM research, which has simply focused on one or the other of these parameters. Finally, a case study regarding a fleet of 10 aircraft is conducted, and the results indicated that the proposed model efficiently generates outcomes that meet the schedule requirements.     

Scheduling preventive maintenance on a single CNC machine

In this study we attempt to deal with process planning, scheduling and preventive maintenance (PM) decisions, simultaneously. The objective is to minimize the total completion time of a set of jobs on a CNC machine. During the process planning, we decide on the processing times of the jobs which are controllable (i.e. they can be easily changed) on CNC machines. Using shorter processing times (higher production rates) would result in greater deterioration of the machine, and we would need to plan more frequent PM visits to the machine, during which it would not be available. Therefore, the selected processing times determine not only the completion times but also the PM visit times. We first provide optimality properties for the joint problem. We propose a new heuristic search algorithm to determine simultaneously the processing times of the jobs, their sequence and the PM schedule.     

Decision-making on multi-mould maintenance in production scheduling

The reliability of a critical tool like a mould on a machine affects the productivity seriously in many manufacturing firms. In fact, its breakdown frequency is even higher than machines. The decision-making on when mould maintenance should be started become a challenging issue. In the previous study, the mould maintenance plans were integrated with the traditional production schedules in a plastics production system. It was proven that considering machine and mould maintenance in production scheduling could improve the overall reliability and productivity of the production system. However, the previous model assumed that each job contained single operation. It is not workable in other manufacturing systems such as die stamping which may contain multiple operations with multiple moulds in each job. Thus, this study models a new problem for multi-mould production-maintenance scheduling. A genetic algorithm approach is applied to minimise the makespan of all jobs in 10 hypothetical problem sets. A joint scheduling (JS) approach is proposed to decide the start times of maintenance activities during scheduling. The numerical result shows that the JS approach has a good performance in the new problem and it is sensitive to the characteristic of the setup time defined.     

Maintenance,shutdown and production scheduling in semiconductor robotic cells

Our approach is the first to study simultaneous scheduling of preventive maintenance, shutdowns and production for robotic cells in semiconductor manufacturing. It hereby exploits the frequent periods of overcapacity in semiconductor manufacturing to reduce wear and tear. In contrast to existing approaches, our scheduling approach is able to deal with different preventive-maintenance types. We borrow the Resource Task Network representation from the process-industry domain to represent our problem and facilitate its formulation as a mathematical model. In addition, we develop efficiency-improving constraints based on the characteristics of the preventive-maintenance activities. In numerical tests based on industry data, we show that the model generates high-quality schedules even without applying the inequalities, although the optimality gap is reduced only when including inequalities. We furthermore assess the trade-off between shutdowns and batch lead times. We compare our model’s schedule quality to (i) the simple industry practice of shutting down chambers permanently to reduce wear and tear and (ii) an approach that schedules maintenance and production sequentially. The numerical tests yield the following managerial insights. First, integrating maintenance and production scheduling has substantial advantages. Second, the practice of shutting equipment down permanently diminishes scheduling flexibility and solution quality. Third, shutdowns scheduling must also consider the impact on batch waiting times.     

Bi-objective optimisation for integrated scheduling of single machine with setup times and preventive maintenance planning

This paper deals with an integrated bi-objective optimisation problem for production scheduling and preventive maintenance in a single-machine context with sequence-dependent setup times. To model its increasing failure rate, the time to failure of the machine is subject to Weibull distribution. The two objectives are to minimise the total expected completion time of jobs and to minimise the maximum of expected times of failure of the machine at the same time. During the setup times, preventive maintenance activities are supposed to be performed simultaneously. Due to the assumption of non-preemptive job processing, three resolution policies are adapted to deal with the conflicts arising between job processing and maintenance activities. Two decisions are to be taken at the same time: find the permutation of jobs and determine when to perform the preventive maintenance. To solve this integrated problem, two well-known evolutionary genetic algorithms are compared to find an approximation of the Pareto-optimal front, in terms of standard multi-objective metrics. The results of extensive computational experiments show the promising performance of the adapted algorithms.     

A full history proportional hazards model for preventive maintenance scheduling

This paper is concerned with the development of a realistic preventive maintenance (PM) scheduling model. A heuristic approach for implementing the semi-parametric proportional-hazards model (PHM) to schedule the next preventive maintenance interval on the basis of the equipment's full condition history is introduced. This heuristic can be used with repairable systems and does not require the unrealistic assumption of renewal during repair, or even during PM. Two PHMs are fitted, for the life of equipment following corrective work and the life of equipment following PM, using appropriate explanatory variables. These models are then used within a simulation framework to schedule the next preventive maintenance interval. Optimal PM schedules are estimated using two different criteria, namely maximizing availability over a single PM interval and over a fixed horizon. History data from a set of four pumps operating in a continuous process industry is also used to demonstrate the proposed approach. The results indicate a higher availability for the recommended schedule than the availability resulting from applying the optimal PM intervals as suggested by using the conventional stationary models. © 1997 John Wiley & Sons, Ltd.     

Forecasting and maintenance problem under subcontracting constraint with transportation delay

In this paper, a forecasting production/maintenance optimization problem has been proposed with a random demand and single machine M₁ on a finite horizon. The function rate of the machine M₁ is depending on the production rate for each period of the forecasting horizon. In order to satisfy the customer, a subcontracting assures the rest of the production through machine M₂ with transportation delay. An analytic formulation of the problem has been proposed using a sequential computation of the optimal production plan for which an optimal preventive maintenance policy has been calculated based on minimal repair. Firstly, we find, the optimal production plans of principal and subcontracting machines, which minimises the total production and inventory cost for the cases without and with returned products under service level and subcontracting transportation delay. Secondly, we determine a joint effective maintenance policy with the optimal production plan, which integrates the various constraints for the production rates, the transportation delay and the returned production deadline. Numerical results are presented to highlight the application of the developed approach and sensitivity analysis shows the robustness of the model.     

Investigating the value of integrated operations planning: A case-based approach from automotive industry

During the last decade, many researchers have focused on joint consideration of various operations planning aspects like production scheduling, maintenance scheduling, inventory control, etc. Such joint considerations are becoming increasingly important from the point of view of current advancement in intelligent manufacturing, also known as Industry 4.0. Under the concept of Industry 4.0, advanced data analytics aim to remove human intervention in decision-making. Thus, the managerial level coordination of decisions taken independently by various departments will be out of trend. Therefore, developing an approach that optimises various operations planning decisions simultaneously is essential. Available literature on such joint considerations is more of the exploratory in nature and is limited to simplistic production environments. This necessitates the investigations of value of integrated operations planning for wide range of manufacturing scenarios. Present paper adopts a case-oriented approach to investigate the value of integrated operations planning. First, an integrated approach for simultaneously determining job sequencing, batch-sizing, inventory levels and preventive maintenance schedule is developed. The approach is tested in a complex production environment of an automotive plant and substantial economic improvement was realised. Second, a comprehensive evaluation is performed to study the robustness and implications of proposed approach for various production scenarios. Results of such pervasive performance investigations confirm the value of proposed approach over conventional approaches.     

A variable neighbourhood search for integrated production and preventive maintenance planning in multi-state systems

This paper presents a variable neighbourhood search (VNS) to the integrated production and maintenance planning problem in multi-state systems. VNS is one of the most recent meta-heuristics used for problem solving in which a systematic change of neighbourhood within a local search is carried out. In the studied problem, production and maintenance decisions are co-ordinated, so that the total expected cost is minimised. We are given a set of products that must be produced in lots on a multi-state production system during a specified finite planning horizon. Planned preventive maintenance and unplanned corrective maintenance can be performed on each component of the multi-state system. The maintenance policy suggests cyclical preventive replacements of components, and a minimal repair on failed components. The objective is to determine an integrated lot-sizing and preventive maintenance strategy of the system that will minimise the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs and production costs, while satisfying the demand for all products over the entire horizon. We model the production system as a multi-state system with binary-state components. The formulated problem can be solved by comparing the results of several multi-product capacitated lot-sizing problems. The proposed VNS deals with the preventive maintenance selection task. Results on test instances show that the VNS method provides a competitive solution quality at economically computational expense in comparison with genetic algorithms.     

Rescheduling strategies for integrating rush orders with preventive maintenance in a two-machine flow shop

Preventive maintenance and rush orders are related. Although preventive maintenance is essential for maximising equipment reliability, it can substantially slow the manufacturing process. Rush order rescheduling involves similar conflicts. Scheduling maintains the robustness of the production schedule, but rush orders require rescheduling. Although preventive maintenance and rush orders are essential manufacturing processes, research on the integration of these functions is insufficient. Unlike recent work that analyses preventive maintenance or rush orders as separate functions, this study proposes an integrated model that analyses both preventive maintenance and rush orders in a two-machine flow shop. The model is then evaluated using two different rescheduling methods. Non-parametric analysis of the models revealed that these two rescheduling methods differ significantly under integrated maintenance and rush order situations.     

Development of joint maintenance and production strategies in a subcontracting environment

This article, inspired by an industrial problem, develops efficient maintenance and just-in-time production policies in a subcontracting environment according to two orientations. The first invokes subcontracting with the objective of satisfying a constant customer demand knowing that our production system, composed of a machine M ₁, cannot satisfy the totality of demand. Subcontracting is represented by a machine M ₂ which has a constant failure rate, while three maintenance policies for M ₁ are tested and evaluated. The second orientation takes the perspective of our production system as a supplier which is obliged to allocate part of its production capacity to subcontracting so as to satisfy a constant demand. We consider a production system made up of two machines, both of which produce a single type of product, are subject to breakdowns and can carry out subcontracting tasks. The objective of this part of the article is to prove the efficiency of the so-called integrated maintenance policy, which combines production and maintenance decisions in a subcontracting environment.     

A method combining rules with genetic algorithm for minimizing makespan on a batch processing machine with preventive maintenance

This paper considers the problem of minimising makespan on a single batch processing machine with flexible periodic preventive maintenance. This problem combines two sub-problems, scheduling on a batch processing machine with jobs’ release dates considered and arranging the preventive maintenance activities on a batch processing machine. The preventive maintenance activities are flexible but the maximum continuous working time of the machine, which is allowed, is determined. A mathematical model for integrating flexible periodic preventive maintenance into batch processing machine problem is proposed, in which the grouping of jobs with incompatible job families, the starting time of batches and the preventive maintenance activities are optimised simultaneously. A method combining rules with the genetic algorithm is proposed to solve this model, in which a batching rule is proposed to group jobs with incompatible job families into batches and a modified genetic algorithm is proposed to schedule batches and arrange preventive maintenance activities. The computational results indicate the method is effective under practical problem sizes. In addition, the influences of jobs’ parameters on the performance of the method are analyzed, such as the number of jobs, the number of job families, jobs’ processing time and jobs’ release time.     

An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems that have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real-life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). This paper deals with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. In this research, a computational method based on a clonal selection principle and an affinity maturation mechanism of the immune response is used. This paper describes how we can incorporate simulation into an immune algorithm for the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdowns. The results obtained are analysed using a Taguchi experimental design.