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Machine maintenance is essential in many industries. How to schedule maintenance becomes especially important when achieving high shop performance is desired. This paper aims to consider the maintenance scheduling problem in a company where different maintenance situations exist.

The company, which is famous in South East Asia, specializes in the manufacturing of textiles such as polypropylene and chemical chip. In this paper an algorithm is presented, for five specific situations, for the company to minimize the number of tardy jobs.

Owing to machine overload in 24‐hour production, machine breakdowns usually occur in the shop. There is an urgent need for the company to derive a procedure that will incorporate the maintenance into the schedule to reduce the machine breakdown. Computational results have showed that the proposed algorithm outperforms the current method with an average improvement of 32.5 percent, although the actual average improvement is somewhat lower partly because some jobs are unexpectedly cancelled or changed.

This proposed algorithm is appropriate not only for the studied company, but for those companies where maintenance has to be operated one or several times a month and the maintenance time is so significant that it cannot be ignored.

Provides an algorithm to aid in the maintenance scheduling problem.

This paper proposes a costing framework with the objective of providing reliable, relevant, and timely information about the actual costs and the cost efficiency of planned maintenance jobs. The proposed framework includes direct materials, direct labor, and support services costs. The traceability criterion is used for assigning direct materials and direct labor costs to planned maintenance jobs. However, support services costs are allocated to planned maintenance jobs using the concepts and techniques of activity‐based‐costing (ABC). The proposed framework provides a complete audit trail of planned maintenance costs. It also provides a functionality that could be added to enterprise resource planning (ERP) systems.

The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities.

The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs.

As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time.

Predictive maintenance (PM) activities carry out the operations on machines and tools before the breakdown takes place and it helps to prevent failures before they happen.

The purpose of this paper is to present the use of activity-based costing (ABC) approach as an alternative option to the traditional cost accounting system. The contribution of this study is to demonstrate, through a simple example, the application of that costing system in a service (maintenance) industry, i.e. the paper intended to develop a procedure for a cost model that help in calculating any maintenance job cost, to a reasonable degree of accuracy, based on the actual activities performed.

This research uses a simple example whereby hypothetical activities and cost data of maintaining an injector and a pump, of an internal combustion engine, are used, presented and analyzed based on the use of the developed procedure.

ABC system provides more accurate cost estimates rather than the traditional “order costing” methods that uses unit-level costs which are variable in relation to change in service volume. Traditional cost methods distort the costs by applying overhead uniformly over different jobs of varied complexities and activities scope. On the other hand, ABC is a useful means to distribute the overhead costs in proportion (fairly) to the actual activities performed in a specific job and, hence, enhance the rationality of decision making, i.e. will not distort the accounting information used for cost reduction, pricing, and evaluation matters. The results obtained from the analysis showed that allocating costs to the maintained injector decreased from $83.55 to $71.95 and, finally, to $67.57 when using the workshop-wide, two-stage and ABC overhead allocation methods, respectively; while that of the pump increased from $298.90 to $340.34 and, finally, to $359.48 when using the same three methods, in the same order, respectively. The result is quite fair when considering the complexity of the fuel pump, in terms of design and maintenance, when compared with the injector. Notice that using volume to allocate overhead costs results in over costing high-volume products, e.g. injectors (simple in terms of design and operation) and under costing low-volume products, e.g. pumps (more complex in terms of design and operation). The paper recommends to use ABC as a more accurate and fair method when charging maintenance job orders based on the analysis of costing two maintained items in the same premise while consuming different overhead resources.

This study attempts to analyze different methods to calculate a specific corrective maintenance job order. It strives to remedy the drawbacks of the traditional overhead costing of a job order when using principles related to the size of service, such as the direct labor cost/hours, as an allocation base. Consequently, the study proposed a new costing method, i.e. application of ABC. The traditional costing approach is considered by many firms as the best costing method. Nevertheless, it allocates overhead cost over job performed uniformly (equally) not differentiating between the complexity of the job and variety of the activities performed, e.g. using the same allocation base for “oil change” and “fuel pump adjustment” activities. So, ABC prevents cost distortions (unfairness) that could not be prevented by traditional cost accounting system. The author believes that the method presented in this paper will provide a useful management tool for costing maintenance jobs based on the appropriate selected activity drivers in maintenance workshops. The method could be applied for costing maintenance activities in maintenance of all industrial sectors.

The use of traditional costing method has proven to be distorted by applying overhead uniformly over different jobs of varied complexities and activities scope. In this paper the authors strive to present an effective costing alternative that outperforms the traditional ones with regard to overhead allocation. The paper aims to find reliable and fair maintenance costing method, i.e. to find out the relationships between maintenance activities and cost drivers. Although, ABC is widely used in manufacturing industry, no application or current research has presented an applicable thorough worked-out example, with the exception, to the author’s knowledge, of one in the aeronautical industry, to implement ABC method in maintenance industry. The importance of using this method comes from the fact that it provides, relatively, accurate and fair maintenance bills that provide customer satisfaction and firm good image. Hence, the paper is relevant in this respect and intended to contribute to the practice of maintenance management.

In many manufacturing systems, machines are subject to preventive maintenance. This paper aims to schedule the operations of jobs and preventive maintenance tasks in such a way that the completion time of jobs and preventive maintenance tasks is minimized.

An heuristic approach based on artificial immune algorithm is proposed for solving the multiple‐route job shop‐scheduling problem subject to fixed periodic and age‐dependent preventive maintenance tasks. Under fixed periodic assumption, the time between two consecutive preventive maintenance tasks is assumed constant. Under age‐dependent assumption, a preventive maintenance task is triggered if the machine operates for a certain amount of time. The goal is to schedule the jobs and preventive maintenance task subject to makespan minimization.

In addition to presenting mathematical formulation for the multiple‐route job shop‐scheduling problem, this paper proposes a novel approach by which one can tackle the complexity that is raised in scheduling and sequencing the jobs and the preventive maintenance simultaneously and obtain the required schedule in reasonable time.

Integrating preventive maintenance tasks into the scheduling procedure is vital in many manufacturing systems. Using the proposed approach, one can obtain a schedule that defines the production route through which each part is processed, the time each operation must be started, and when preventive maintenance must be carried out on each machine. This, in turn, results in overall manufacturing cost reduction.

Using the approach proposed in this paper, good solutions, if not optimal, can be obtained for scheduling jobs and preventive maintenance task in one of the most complicated job shop configurations, namely, multiple‐route job shop. Thus, the approach can dominate all other simpler configurations.

The purpose of this study is to determine simultaneously the period of preventive maintenance and the sequence of each job for two parallel machines problem so that the makespan is minimized. With proper planning of preventive maintenance, a shop can avoid lack of flexibility between maintenance planning and production scheduling, and thus concentrate on production efficiency.

This study addresses a scheduling problem, wherein each machine has to be shut down for maintenance during a maintenance interval [C, T] arranged in advance. The start time and end time of the preventive maintenance are the decision variables. A maintenance action is attended by a server who managers only one machine at a time. Three cases are studied in this paper: the unequal lengths of unavailable periods on both machines, the equal lengths of unavailable periods on both machines, and no waiting time is allowed between the two unavailable periods. Each case is solved optimally by an analytical algorithm developed in the study.

Although having exponential time complexities, all the proposed algorithms are quite efficient in solving large‐sized problems. Computational results show that it is able to generate the optimal solution for large sizes (up to 10,000 jobs) in a few minutes of computation time.

Most of the papers that dealt with preventive maintenance assumed that the maintenance period is known and specified. However, the flexible preventive maintenance problem, in which the start time and end time of the preventive maintenance are the decision variables, often occurs in the computer center, NC‐machine and IC‐testing machine for job scheduling and repairs arrangement.

The paper presents an efficient approach for each of the three cases of flexible preventive maintenance, which is relatively unexplored in the literature.

The purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.

A literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.

The study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.

The findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

Productivity, based on estimated and actual hours, of most maintenance workers is only 30 to 50 per cent. Given the significance of maintenance to manufacturing competitiveness, it is surprising how little research is being carried out. Scheduling is a crucial component of maintenance management and is a focus of research. Identifies the areas of concern in maintenance scheduling and surveys representative work from the academic and practitioner literature. Specific points of practice and theory which need further investigation are pinpointed.

In today's industry, a machine breakdown is common for a machine running a long period of time without maintenance. To avoid a sudden breakdown, periodic maintenance is usually performed in the production system. This paper aims to find a set of efficient schedules that considers both jobs and maintenance simultaneously.

This paper addresses a real‐life scheduling problem in a plastic company. An algorithm based on the variable range technique is developed to solve the problem by providing a small set of efficient schedules.

Once maintenance is performed, the job being processed must be stopped. This will result in some jobs being late or tardy and a relatively larger flow time is generated. Therefore, how to minimize these two criteria in the production system becomes an important issue in the company. Computational results show that problems with larger maintenance intervals and smaller maintaining time can produce a smaller number of efficient schedules.

It is seen that scheduling maintenance will result in some jobs being tardy and a larger flow time is generated. A decision maker can easily select a preferred schedule from the small set of efficient schedules. The proposed algorithm is appropriate not only for the studied company but also for those companies where periodic maintenance is required.

Presents an algorithm to find a small set of efficient schedules.