Proactive preventive maintenance policy for buffered serial production systems based on energy saving opportunistic windows

Most forest harvesting operations are performed using two or more forestry machines in combination; in the typical forest production system in steep terrain in Japan, there are four production processes: felling, yarding, processing, and forwarding. It is essential to evaluate the overall productivity of such combined production systems, but the calculation method that indicates the productivity has not been fully established. In this study, we built simulation models of serial, parallel, and sequential production systems by using system dynamics to evaluate their productivity regarding the combined machine productivity (CMP) and combined labor productivity (CLP) indices. Comparing the three types of forest harvesting systems in Japan, the parallel production system had the highest CMP, while the serial production system had the highest CLP; the sequential production system lay between the serial and parallel production systems in terms of CMP and CLP. The overall productivity of Japanese forest production systems was lower than that of Central Europe, where processor tower yarders (PTYs) are used. Thus, the CLP of Japanese forest production systems was improved by 12.8 to 26.5% by incorporating the concept of a PTY, and it can be further improved by eliminating the forwarding process.

There have been many publications dealing with preventive maintenance policies for stochastically deteriorating systems. However, most of them study systems having one device subject to failure. Much of the limited research dealing with multi-device production systems has been conducted through simulation experiments. In this paper, the preventive maintenance problem for serial production systems is formulated as a mathematical model. This formulation permits management to analyse the impact of a preventive maintenance policy on a serial production system without resorting to simulation. Numerical examples are used to provide managerial implications for maintaining a serial production system. The results show that the operating characteristics of the stations are interrelated; therefore, it is important to examine the joint effects of a maintenance policy on the various stations of the production system simultaneously rather than study each station separately.

This paper considers a joint optimization problem of production and preventive maintenance for a serial production system subject to equipment degradations constrains The system processes different job types to meet different customer demands First a joint optimization problem domain of the system with equipment degradations is formally introduced A mathematic programming model is established with an optimal bi objective function of minimizing the system makespan and preventive maintenance cost Together with introducing an artificial bee colony theory a novel bi objective artificial bee colony algorithm is developed to pursue short production time and low preventive maintenance cost To guarantee the algorithm convergence performance a sorting rule of non dominated sorting genetic algorithm II NSGAII is introduced into the proposed algorithm Local Tabu search technology and probability criteria are involved Finally numerous experiments are conducted to evaluate the performance of the proposed algorithm In comparison with the well known NSGAII the proposed algorithm performs significantly better in terms of finding the spread compromise solutions

Linking maintenance strategies to performance

Energy efficiency improvement and timely preventive maintenance (PM) are critical in manufacturing industry due to the rising energy cost, environmental concerns, and increasing requirements on system reliability. By strategically turning appropriate machines in down state, the corresponding energy consumption can be reduced, and at the meantime, the necessary PM works can be carried out to increase PM completion rate and reduce potential extra expense on PM during nonproduction shifts. However, there is usually a tradeoff between time dedicated to production and time available for energy saving and PM. In this paper, a systematic method is developed to identify opportunity windows (OWs) during which certain machines can be shut down to save energy and PM tasks can be performed while maintaining a desired production throughput. The method is based on stochastic serial production lines and real-time production data. A profit function is formulated to illustrate the tradeoff between energy cost savings and potential throughput loss. The profit function is used to justify the cost savings by utilizing the proposed OWs during production operation.

With the popularization of service-oriented manufacturing, the current operation & maintenance (O&M) has shifted from traditional in-house maintenance to proactive outsourcing maintenance. It is paramount for an original equipment manufacturer (OEM) to provide timely and cost-effective maintenance schemes to geographically distributed customer enterprises. Interestingly, transportation theories could be combined to facilitate multi-location O&M management. In this paper, a transportation-oriented cross-regional opportunistic maintenance (TCOM) policy is developed for solving O&M optimizations and planning real-time schemes for the multi-center service network. The optimization model of this TCOM policy addresses several inter-related decisions: (1) most suitable maintenance times for each leased machine, (2) cost-effective arrangements of technician teams to perform maintenance tasks, and (3) optimal service routes for required teams. We not only integrate maintenance grouping and technician routing, but also investigate the new issues arising from the collaborative sharing of technician teams belonging to different maintenance centers. Numerical examples show that this TCOM policy can achieve significant cost-saving in cross-regional maintenance grouping and multi-location routing optimization for OEMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical problem of computing an optimal maintenance policy for the multi-center service network of geographically distributed production systems to achieve a timely and cost-effective O&M management. We consider individual machine degradations, complex maintenance opportunities and network logistics optimization to establish a global maintenance scheme for the multi-center service network. The existing approaches are to schedule the preventive maintenance (PM) for a single machine or multi-unit leased system, which is unilateral and cannot be applied to multi-location production systems. Although the network opportunistic maintenance policy has been proposed in the literature for maintenance grouping and technician routing problem, it still does not consider the limited workforce resource and the collaborative sharing of technician teams from different maintenance centers. To fill this literature gap, in this paper, we develop the TCOM policy to determine the optimal maintenance timetable for leased machines, as well as the cost-effective service route of technician teams belonging to multiple maintenance centers, so as to conduct timely and cost-effective PM on multi-location production systems. Compared with the conventional maintenance policy, our adaptive policy greatly reduces the total outsourcing maintenance cost in long-term O&M services.

Two-stage maintenance of a production system with exponentially distributed On- and Off-periods

We investigate the problem of determining optimal planned leadtimes in serial production systems in which the actual procurement and processing times may be stochastic. The objective is to minimize the sum of inventory holding costs and job tardiness costs given a customer specified due-date. We present a general solution procedure for two stage serial systems, which for most cost structures and leadtime distributions is a single-pass algorithm. We also indicate how the procedure can be extended to N-stage systems. We present computational results which provide some insight into the characteristics of optimal safety time policies.

Optimum process mean and manufacturing quantity settings for serial production system under the quality loss and rectifying inspection plan

The field of "Building Condition Assessment on Maintenance Strategies" has seen rapid growth, reflecting the increasing importance of sustainable infrastructure management. However, challenges persist in consolidating existing research, identifying key trends, and enhancing understanding of global collaboration. This study aims to address these gaps through a comprehensive bibliometric analysis using Scopus Analyzer and VOSviewer software to map the research landscape. The problem centers on the lack of consolidated data and the fragmented knowledge on maintenance strategies essential for the longevity and efficiency of building infrastructure. Methodologically, Scopus Analyzer was utilized to gather and refine data, while VOSviewer facilitated visualization of keyword co-occurrences, research trends, and country collaborations within the field. The final dataset comprises 728 relevant documents spanning various disciplines, highlighting the interdisciplinary nature of this domain. The results indicate a consistent increase in publications from 2020 to 2023, as evidenced by the studied dataset, in relation to historical growth in the field since the 1980s, with a significant surge after 2015, indicating a shift towards data-driven and technology-enhanced approaches in building condition assessment. Key topics include sustainability, energy efficiency, and the integration of technologies such as Building Information Modelling (BIM) and artificial intelligence, which are increasingly central to modern maintenance strategies. The analysis also underscores the contributions of leading countries like the United States, Italy, and the United Kingdom, each showing high publication and citation impacts, along with strong collaborative networks. In conclusion, the study emphasizes a growing, globally dispersed research community focused on developing proactive and sustainable maintenance strategies, reflecting both technological advancements and the critical need for resilient infrastructure in the face of environmental and societal challenges. This is particularly beneficial to Technical and Vocational Education and Training (TVET) institutions that are aiming to digitalize and modernize their infrastructure management systems.

This study addresses the limitations of existing maintenance decision-making approaches that predominantly rely on single-objective strategies for leased production systems with complex series–parallel configurations. An integrated opportunity-based adaptive maintenance strategy is proposed, and a multi-objective optimization model incorporating multiple maintenance alternatives is developed. First, a proportional hazards model to characterize the degradation-dependent failure rates of key components is used to characterize equipment failure rates, which inform the selection of maintenance actions. Second, the effects of virtual age and maintenance strategies on the residual value of leased equipment are analyzed, leading to the formulation of a net residual value model from the lessor’s perspective. Simultaneously, a customer cost model is established by considering both product quality loss and downtime loss. Finally, the NSGA II algorithm is employed to solve the proposed multi-objective optimization model, yielding optimal preventive maintenance intervals, opportunistic maintenance thresholds, preventive maintenance thresholds, and the corresponding Pareto front. A case study illustrates the strategy’s superior flexibility and practical applicability, with its effectiveness further validated through comparative analysis against traditional maintenance strategies.

This study aims to develop comprehensive maintenance strategies tailored to enhance the dependability, performance, and lifespan of critical assets within industrial and organizational settings. By integrating proactive, preventive, predictive, and corrective maintenance tactics, our strategy seeks to minimize downtime, reduce costs, and optimize asset performance. Drawing from extensive case studies across various industrial sectors, our research utilizes robust data analysis to inform strategy development.We employ mathematical cost models and simulations using the Monte Carlo Method in MATLAB to evaluate the performance and robustness of different maintenance strategies, including time-based and condition-based approaches. Our findings demonstrate that a holistic maintenance approach significantly improves operational efficiency and asset longevity. Specifically, our analysis reveals that integrated maintenance strategies lead to reduced downtime, lower maintenance costs, and enhanced asset reliability.Policy implications of our research suggest that organizations should adopt integrated maintenance strategies to enhance asset reliability and performance, ultimately achieving sustained operational excellence. By emphasizing the importance of proactive maintenance measures alongside traditional reactive approaches, organizations can effectively manage their critical assets, leading to improved operational outcomes and long-term success.–Integration of proactive, preventive, predictive, and corrective maintenance tactics–Evaluation of performance and robustness through mathematical cost models–Application of the Monte Carlo Method in MATLAB for comparative analysis

This study explains about a serial smart production system where a single-type of product is produced. This system uses an unequally sized batch policy in subsequent stages. The setup cost is not always deterministic, it can be controllable and reduced by increasing the capital investment cost, and that the production rates in the system may vary within given limits across batches of shipments. Furthermore, as imperfect items are produced in long-run system, to clean the imperfectness autonomation policy is adopted for inspection, which make the process smarter. The shipment lot sizes of the deliveries are unequal and variable. In long-run production system, defective items are produced in “out-of-control” state. In this model, the defect rate is random with a uniform distribution which is clean from the system by autonomation. In addition, in the remanufacturing process, it is assuming that all defective products are repaired, and no defective products are scrapped. The main theme of developing this model is to determine the number of shipments and the optimal production lot size to adjust the production rates and decrease the total system cost under a reduced setup cost by considering the discrete investment and make a serial smart production system. A solution procedure along with an advanced algorithm was proposed for solving the model. Numerical examples with some graphical representations are provided to validate the model.

The nodes in wireless sensor networks (WSNs) are responsible for communicating data which is primarily of three types viz. video, audio and text. In literature, a large number of energy aware and shortest path based route selection approaches have been proposed to enhance the routing efficiency in WSNs. Most of these route selection approaches are designed by assuming fixed type and size of data packets and results in the same path selection for all types of data. This path selection, does not consider data size and data priority, results in high contention losses, topology failures and non-uniformity in energy depletion of nodes. In this paper, a route selection approach which is based on the preservation of network connectivity for improving overall network lifetime has been proposed for applications where sensor nodes are required to transmit different types of data. The proposed approach preserve highly connected edges at the initial rounds of data communication in an energy efficient manner, such that network connectivity would be maintained even at later rounds of data communications. The proposed route selection approach, VDR, (Variable Data rate Routing) has been simulated with both proactive and reactive route maintenance strategies. The simulation results show that the proposed algorithm with both proactive and reactive route maintenance strategies result in better network lifetime, energy consumption, overall network connectivity, packet delivery ratio as compared to the existing State-of-the-Art energy efficient route selection approaches with proactive and reactive route maintenance strategies. The results also show that the proposed algorithm is scalable and performs better than the compared algorithms both at low node density and high node density.

The problems of M-machine, J-product, N-time point preemptive scheduling in parallel and serial production systems are the focus of this paper. The objective is to minimize the sum of the costs related to inventory level and production rate along a planning horizon. Although the problem is NP-hard, the application of the maximum principle reduces it into a well-tractable type of the two-point boundary value problem. As a result, algorithms of O(NMJk(N-L)+1) and O(N(MJ)k(N-L)+1) time complexities are developed for parallel and serial production systems, respectively, where L is the time point when the demand starts and k is the ratio of backlog cost MN over the inventory cost. This compares favorably with the time complexity O((J+1MN) of a naive enumeration algorithm.