The lean manufacturing approach is employed across several sectors to minimise waste and extend its ideas to institutional settings. The objective of this work is to provide instruction to aspiring engineers regarding the implementation and utilisation of lean tools. The machine shop of an engineering college utilised several tools, including 5S, standard work, and machine maintenance. The students were educated on the difficulties and achievements associated with the implementation of lean ideas across several tiers within the machine shop. The findings indicate that it is imperative to remove non-value-added operations, commonly referred to as Muda, in any manifestation inside the shop. A sequence of 5S audits was carried out to facilitate the enhancement of KAIZEN initiatives and uphold the shop's commitment to lean principles. The determination and assessment of the influence of lean tools on the Machine Shop were conducted through the utilisation of a questionnaire employing a five-point Likert scale. Following the implementation of lean principles, there has been a notable enhancement in several aspects. Specifically, there has been a 6.6% improvement in space utilisation, a substantial 95.12% increase in safety measures, a significant reduction of 83.3% in machine failure occurrences, and a noteworthy decrease of 80.2% in the time required for tool search.

Due to its proactive impact on the serviceability of components in a system, preventive maintenance plays an important role particularly in systems of geographically spread infrastructure such as utilities networks in commercial buildings. What makes such systems differ from the classical schemes is the routing and technicians' travel times. Besides, maintenance in commercial buildings is characterized by its short tasks’ durations and spatial distribution within and between different buildings, a class of problems that has not been suitably investigated. Although it is not trivial to assign particular duties solely to multi-skilled teams under limited time and capacity constraints, the problem becomes more challenging when travel routes, durations and service levels are considered during the execution of the daily maintenance tasks. To address this problem, we propose a Mixed Integer Linear Programming Model that considers the above settings. The model exact solution recommends collaborative choices that include the number of maintenance teams, the selected tasks, routes, tasks schedules, all detailed to days and teams. The model will reduce the cost of labor, replacement parts, penalties on service levels and travel time. The optimization model has been tested using different maintenance scenarios taken from a real maintenance provider in the UAE. Using CPLEX solver, the findings demonstrate an inspiring time utilization, schedules of minimal routing and high service levels using a minimum number of teams. Different travel speeds of diverse assortment of tasks, durations and cost settings have been tested for further sensitivity analysis.

This paper aims to develop a maintenance optimization model to maintain a high level of efficiency and reliability of the machinery. The methodological approach is based on preventive maintenance through the partial or total replacement of critical components. Although an intermediate intervention control, the focus is on a particular machine that has stopped several times, reducing its operational availability and resulting in a high cost of non-production. This study uses a Weibull model to analyze and optimize the correct maintenance process of the machinery considered. The failure data are then analyzed and scheduled. The final purpose is to standardize the operators' intervention procedures to reduce the time for the same interventions.

In this article, we present an acceptance sampling plan for machine replacement problem based on the backward dynamic programming model. Discount dynamic programming is used to solve a two-state machine replacement problem. We plan to design a model for maintenance by consid-ering the quality of the item produced. The purpose of the proposed model is to determine the optimal threshold policy for maintenance in a finite time horizon. We create a decision tree based on a sequential sampling including renew, repair and do nothing and wish to achieve an optimal threshold for making decisions including renew, repair and continue the production in order to minimize the expected cost. Results show that the optimal policy is sensitive to the data, for the probability of defective machines and parameters defined in the model. This can be clearly demonstrated by a sensitivity analysis technique.

The purpose of this paper is developing a method to choose an appropriate Computerized Maintenance Management System (CMMS) using Multiple Criteria Decision Making (MADM) for a dairy company. Among different methods, the grey group TOPSIS and fuzzy group VIKOR methods were selected. TOPSIS is a Technique for order of Preference by Similarity to Ideal Solution and VIKOR technique is a method based on compromise programming. The data in this article were gathered using the interviews with the company’s managers and elites in the field of maintenance. Then, out of five types of maintenance software, the best ones were selected using these two techniques and finally the results were compared with each other. In the selection process, 13 sub-criteria were introduced under 5 main criteria. These criteria were selected out of a huge number of criteria using the studies of others and by consulting with the company’s managers and experts. We have tried to make the choices in a way that the majority of aspects could be considered. This paper helps the maintenance managers in decision-making related to choosing the CMMS software in uncertainty environment. Using two different fuzzy and grey approaches and comparing the results can lead to appropriate selection and improve the confidence in decision-making. Maintenance planning issues are among the important issues in industrial production systems. Maintenance systems have basically made remarkable progress in recent years. The increase of companies’ competitive pressure on the one hand and the close relationship between maintenance activities and companies’ core activities, on the other hand, have encouraged companies to use the software in order to manage their maintenance activities.

Just in time (JIT) philosophy has grown to a new high level since its evolution and has successfully been implemented in manufacturing. As it is universally accepted that manufacturing and maintenance cannot be managed as a separate function, the aim of the present study is to evaluate various elements of JIT to implement in maintenance sector which have high degree of importance in Global context. It is because now maintenance also plays an important role in optimization of business processes. Maintenance operations are much like manufacturing operations where both employ processes that add value to the basic input used to create the end products. JIT focuses on the processes, not the product. It can, therefore, be applied to any process in manufacturing or maintenance operations. For this, literature related to JIT usage and performance is reviewed. Thirty-eight elements are analyzed from sixty-five research papers of global context. With aid of theoretical analysis and brain storming with maintenance specialists of JIT elements who have implemented it for manufacturing industries, eighteen elements are selected to check the implementation of JIT philosophy in maintenance sector. Relative importance of such elements are also highlighted in the article.

This paper presents an empirical investigation to rank different factors influencing on maintenance strategies on Iranian oil terminals’ company. The study determines four main factors, production quality, reliability, cost and safety. Using fuzzy analytical process, the study determines various factors associated with each main factor and ranks them by performing pair-wise comparisons. The results indicate that reliability ranks first (0.255), followed by production quality (0.252), cost (0.25) and safety (0.244). In terms of reliability, the best utilization of resources is number one priority followed by increase access to maintenance tools, reduction in production interruption are among the most important issues. In terms of production quality, reduction in system failure as well as reworks is the most important factors followed by customer satisfaction and defects. In terms of cost items, ease of access to accessories and consulting are important factors followed by necessary software, hardware and training programs. Finally, in terms of safety factors, external, internal and employee services are the most important issues, which are needed to be considered.

Building Maintenance plays an important role throughout the building lifecycle from devis-ing conceptual plans to the end. Due to the high cost of building maintenance and the direct impact of maintenance effectiveness on the quality of life of building occupants, special attention must be devoted. One of the most important issues in this field is building maintenance contractor selection. This issue becomes even more critical in public buildings, such as hospitals, offices, and military centers. The purpose of this study is to present a method that can be used to select the contractor in such a way that the response robustness is high and the employed method is the most accurate one among other similar methods. To do this, the contractors are ranked by 7 multi-criteria decision-making methods. Then, the Spearman correlation coefficients are obtained for each pair of methods. When there is a significant difference between the outcomes of the methods, the output of each method is compared with the output of the Artificial Neural Network (ANN) model. The method with the least difference with the neural network output is taken as the superior method. After selecting the best method, a robustness analysis is performed on it to verify the stability of the answer. The proposed model is implemented on a real case study. Statistical analysis shows that the implementation of this method has increased the satisfaction of the residents.

It is necessary for companies and industries to select the most appropriate maintenance strategy to increase the reliability and safety level with reasonable cost. The primary objective of this paper is to assess different maintenance strategies and to select the best and the most appropriate alternatives for Saipa vehicle industry in Tehran, Iran. For this purpose, we simultaneously consider numerous conflicting objectives and constraints. In this study to counter with this conflicting and to consider the dependency among the qualitative and quantitative criteria and sub-criteria, an integration of Analytic Network Process (ANP) and fuzzy set theory are considered. Therefore, factors playing important role in selecting the best maintenance strategy are determined by reviewing the research literature and interviewing with the experts by Delphi technique. Considering the relations among different factors, a network with 4 criteria and 28 sub-criteria are proposed. In the next step, ANP technique is applied for ranking effective factors in evolution of appropriate maintenance strategy. Results reveal that the best maintenance strategy for fixture body of pride (setter) is corrective maintenance.

This paper deals with a single-machine scheduling problem with maintenance activities. Our purpose is to provide a near optimal solution using metaheuristics approach. In this problem, there are n jobs and m machines (m?n), each job must be assigned to one and only one machine, where the processing time of job (j) is (p_j). Furthermore there are M_G groups where each group has a fix periodic interval T and for each group, the maximum number of jobs processed in the machines available time interval (T) is K, (M_G=m/K). For finding the near optimal solution, we consider optimizing total cost scheduling problem. This problem has two types of costs, group cost and gap cost. In this study, first, proposed problem is formulated in a mathematical model. Next, a heuristic genetic algorithm is used to obtain the proposed problem and on example is presented to verify the efficiency of the algorithm.