An integrated mathematical model for production scheduling and preventive maintenance planning

2020 ◽  
Vol 37 (6/7) ◽  
pp. 925-937
Author(s):  
Ahmet Kolus ◽  
Ahmed El-Khalifa ◽  
Umar M. Al-Turki ◽  
Salih Osman Duffuaa
Keyword(s):  
Mathematical Model ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Manufacturing Sector ◽  
Maintenance Planning ◽  
Due Dates ◽  
Planning And Scheduling ◽  
Content Type ◽  
Machine Availability ◽  
Simultaneous Scheduling

PurposeThe integration between production scheduling and maintenance planning is attracting the attention of planners in the manufacturing sector with the increase in global competitiveness. Researchers have developed various methodologies to optimize integrated decisions in planning and scheduling, including mathematical modeling under different conditions. This paper considers the simultaneous scheduling of production and maintenance activities with the objective of minimizing the expected total tardiness cost on a single machine (production line).Design/methodology/approachScheduling in these two types of activities, production and maintenance, are traditionally done independently, causing conflicts between the two functional areas. To eliminate or at least reduce conflicts, the scheduling of both activities can be done simultaneously with the objective of meeting due dates and maintaining maximum machine availability. In this paper, a mathematical model for an integrated problem is developed and demonstrated by an example.FindingsThe proposed integrated model shows a high potential for significant improvements in performance with respect to the cost of tardiness in delivery and machine availability. This is demonstrated by an example showing an average savings of approximately 40%.Originality/valueThis substantial saving is owed to the integration of two important decision-making processes in manufacturing systems. Although the integrated problem is complex and difficult to solve, the fact that it is savings driven makes the problem of interest to researchers and practitioners in manufacturing.

Improved Markov stable state simulation for maintenance planning

2019 ◽  
Vol 25 (2) ◽  
pp. 199-212
Author(s):  
Chibundo Princewill Nwadinobi ◽  
Bethrand Nduka Nwankwojike ◽  
Fidelis Ibiang Abam
Keyword(s):  
Manufacturing Systems ◽  
Stable State ◽  
Mechanistic Modeling ◽  
Maintenance Planning ◽  
Probability Models ◽  
Actual Performance ◽  
Content Type ◽  
Mean Time Between Failures ◽  
Relevant Variables ◽  
Mean Time

Purpose The purpose of this paper is to propose a software (Equipment State Simulator) used for predicting equipment performance parameters required for maintenance planning. Design/methodology/approach This maintenance software was developed from the derived stable state probability models using algebraic substitution and computation of total operational period, number of breakdowns, total downtime, mean time between failures and mean time to repair of equipment/component(s) at preventive maintenance and corrective maintenance states. The models were derived using mechanistic modeling technique such that all the relevant variables were accounted for. Findings Analysis of this software revealed that its predictions reckon with the actual performance of the test specimens by about 99 percent. Originality/value The research proposes a maintenance model and software for predicting state probabilities of manufacturing systems degradation. This program also predicts maintenance action(s) required by the equipment based on the predetermined alert levels.

scholarly journals Predicting maintenance work hours in maintenance planning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Waqas Khalid ◽  
Simon Holst Albrechtsen ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Industrial Applications ◽  
Research Literature ◽  
Work Hours ◽  
Machine Learning Algorithms ◽  
Maintenance Planning ◽  
Planning And Scheduling ◽  
Content Type ◽  
Work Hour ◽  
Maintenance Work

PurposeCurrent industry practices illustrate there is no standard method to estimate the number of hours worked on maintenance activities; instead, industry experts use experience to guess maintenance work hours. There is also a gap in the research literature on maintenance work hour estimation. This paper investigates the use of machine-learning algorithms to predict maintenance work hours and proposes a method that utilizes historical preventive maintenance order data to predict maintenance work hours.Design/methodology/approachThe paper uses the design research methodology utilizing a case study to validate the proposed method.FindingsThe case study analysis confirms that the proposed method is applicable and has the potential to significantly improve work hour prediction accuracy, especially for medium- and long-term work orders. Moreover, the study finds that this method is more accurate and more efficient than conducting estimations based on experience.Practical implicationsThe study has major implications for industrial applications. Maintenance-intensive industries such as oil and gas and chemical industries spend a huge portion of their operational expenditures (OPEX) on maintenance. This research will enable them to accurately predict work hour requirements that will help them to avoid unwanted downtime and costs and improve production planning and scheduling.Originality/valueThe proposed method provides new insights into maintenance theory and possesses a huge potential to improve the current maintenance planning practices in the industry.

Multi-objective maintenance planning under preventive maintenance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abbas Al-Refaie ◽  
Hiba Almowas
Keyword(s):  
Mathematical Model ◽  
Single Machine ◽  
Preventive Maintenance ◽  
Probability Distributions ◽  
Time To Failure ◽  
Maintenance Planning ◽  
Maintenance Policy ◽  
Content Type ◽  
Series Configuration ◽  
Preventive Policy

PurposeThis research developed and examined a mathematical model for concurrent corrective and preventive maintenance policy of a system of series configuration.Design/methodology/approachA mathematical model was developed to maximize availability, and maximal net revenues, and minimal cost. Different probability distributions for time to failure and time to repair were considered. The model was then implemented on a real case study, which was studied under corrective maintenance policy and concurrent corrective and preventive policy.FindingsA comparison between results at current policy (90 days) and optimal period of corrective and preventive policy was conducted. It was found that availability, profit was increased from 94.4% and $20.091 – 96.5% and $24.803, respectively. Further, the cost was reduced from $1104.8 to $797.22.Research limitations/implicationsThe proposed optimization model can be adopted in planning maintenance activities for a single machine as well as for a system of series configuration machines under various probability distributions.Practical implicationsThe proposed model can significantly enhance performance of the production as well as maintenance systems. In addition, the developed model may support maintenance engineering in effective management of maintenance resources and the performance of its activities.Originality/valueThis research considers a mathematical model with multi-objective functions and distinct probability distributions for time-to-failure for a system of series machines. Moreover, appropriate approximation solution was deployed to find integral of some functions. Finally, it provides maintenance planning for a single machine or a series of machines.

Models for maintenance planning and scheduling – a citation-based literature review and content analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Syed Asif Raza ◽  
Abdul Hameed
Keyword(s):  
Content Analysis ◽  
Literature Review ◽  
Citation Analysis ◽  
Citation Network ◽  
Maintenance Planning ◽  
Bibliometric Data ◽  
Planning And Scheduling ◽  
Content Type ◽  
Network Analyses ◽  
Research Studies

PurposeThe findings of this study have lightened the focal research areas in maintenance planning and scheduling. These also served as effective guidelines for future studies in this area. This research, therefore, contributes in fulfilling the gap by carrying out an SLR of contemporary research studies in the area of models for maintenance planning and scheduling. At present, SLR rooted in BA has not been carried focusing on a survey over models for maintenance planning and scheduling. SLR uses advanced scientific methodologies from BA tools to unveil thematic structures.Design/methodology/approachWe have systematically reviewed over 1,021 peer-reviewed journal articles. Advanced contemporary tools from Bibliometric Analysis (BA) are used to perform a Systematic Literature Review (SLR). First, exploratory analysis is presented, highlighting the influential authors, sources and region amongst other key indices. Second, the large bibliographical data is visualized using co-citation network analyses, and research clusters (themes) are identified. The co-citation network is extended into a dynamic co-citation network and unveils the evolution of the research clusters. Last, cluster-based content analysis and historiographical analysis is carried out to predict the prospect of future research studies.FindingsBA tools first outlined an exploratory analysis that noted influential authors, production countries, top-cited papers and frequent keywords. Later, the bibliometric data of over 1,021 documents is visualized using co-citation network analyses. Later, a dynamic co-citation analysis identified the evolution of research clusters over time. A historiographical direct citation analysis also unveils potential research directions. We have clearly observed that there are two main streams of maintenance planning and scheduling applications. The first has focused on joint maintenance and operations on machines. The second focused on integrated production and maintenance models in an echelon setting for unrealizable production facilities.Originality/valueThere are many literature review-based research studies that have contributed to maintenance scheduling research surveys. However, most studies have adopted traditional approaches, which often fall short in handling large bibliometric data and therefore suffer from selection biases from the authors. As a result, in this area, the existing reviews could be non-comprehensive. This study bridges the research gap by conducting an SLR of maintenance models, which to the best of our knowledge, has not been carried out before this study.

scholarly journals A Mathematical Model for Multiworkshop IPPS Problem in Batch Production

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Li Ba ◽  
Yan Li ◽  
Mingshun Yang ◽  
Xueliang Wu ◽  
Yong Liu ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Process Planning ◽  
Production Scheduling ◽  
Complex Problem ◽  
Integrated Process ◽  
Batch Production ◽  
Swarm Optimization ◽  
Planning And Scheduling ◽  
Factors Affecting

Integrated Process Planning and Scheduling (IPPS) problem is an important issue in production scheduling. Actually, there exit many factors affecting scheduling results. Many types of workpieces are commonly manufactured in batch production. Moreover, due to differences among process methods, all processes of a workpiece may not be performed in the same workshop or even in the same factory. For making IPPS problem more in line with practical manufacturing, this paper addresses an IPPS problem with batches and limited vehicles (BV-IPPS). An equal batch splitting strategy is adopted. A model for BV-IPPS problem is established. Makespan is the objective to be minimized. For solving the complex problem, a particle swarm optimization (PSO) with a multilayer encoding structure is proposed. Each module of the algorithm is designed. Finally, case studies have been conducted to validate the model and algorithm.

Failure prediction in electrical connector assembly: a case in automotive assembly process

2020 ◽  
Vol 40 (6) ◽  
pp. 881-893
Author(s):  
Armagan Altinisik ◽  
Utku Yildirim
Keyword(s):  
Mathematical Model ◽  
Manufacturing Systems ◽  
Model Development ◽  
Assembly Process ◽  
Critical Factors ◽  
Content Type ◽  
New Model ◽  
Electrical Connection ◽  
Electrical Connector ◽  
Process Complexity

Purpose Electrical defects cover an important part of assembly defects and strongly affect the vehicle system performance. Almost 40% of assembly defects are classified as human errors and electrical connection failures represent a significant part of them. Humans still remain a cost-effective solution for the flexible manufacturing systems with increasing product complexity. So, understanding human behaviors is still a challenging task. The purpose of this study is to define, prioritize and validate the critical factors for the complexity of electrical connector plugin process. Design/methodology/approach The critical variables were defined by the expert team members. The required number of measurements and variables were revised resulting preliminary analysis of binary logistic regression. After the revision of measurement plan, the list of critical input variables and the mathematical model were defined. The model has been validated by the fitted values of the residuals (FITS analysis). Findings To the best of the authors’ knowledge, this is one of the limited studies, which defines the critical factors for electrical connection process complexity. Female connector harness length, connector width/height/length differences, operator sense of correct connector matching and ergonomy were defined as the factors with the highest impact on the failure occurrence. The obtained regression equation strongly correlates the failure probability. Practical implications The obtained mathematical model can be used in new model development processes both for the product and assembly process design (ergonomy, accessibility and lay-out). Originality/value The obtained risk factors demonstrated a strong correlation with assembly process complexity and failure rates. The output of this study would be used as an important guide for process (assembly line ergonomy, accessibility and lay-out) and product design in new model development and assembly ramp-up phases.

A Review of Multi Agent based Production Scheduling in Manufacturing System

2020 ◽  
Vol 14 ◽  
Author(s):  
Om Ji Shukla ◽  
Gunjan Soni ◽  
Rajesh Kumar ◽  
Rashpal S. Ahluwalia
Keyword(s):  
Production Scheduling ◽  
Global Economy ◽  
Manufacturing Systems ◽  
Manufacturing Sector ◽  
Local Economy ◽  
Future Research ◽  
Scheduling Problems ◽  
Key Issues ◽  
Multi Agent ◽  
Traditional Approaches

Backgrounds: The manufacturing sector has seen dynamic changes during the last few years, namely the move from product-oriented local economy to customer-driven global economy. In this environment, manufacturing systems have been required to deliver highly flexible, demand-driven and customized products. Hence, multi agent system (MAS) technology can play an important role in making highly responsive production scheduling systems in order to meet dynamic and uncertain changes in demand. Methods: This paper offers a review of MAS for production scheduling problems in manufacturing systems. The objective of the paper is twofold. First, it describes traditional and MAS based approaches for different production scheduling problems and presents advantages of MAS over traditional approaches. Second, it aims to review different MAS platforms and evaluate some key issues involved in MAS based production scheduling. Results: A variety of different MAS applications in production scheduling is reviewed in four categories of key issues: agent encapsulation, agent organization, agent coordination & negotiation and agent learning. Conclusion: Finally, this review presents a conceptual framework to implement MAS in production scheduling and also highlights the future research opportunities as well as challenges.

Exploring organizational contexts on lean manufacturing and supply chain relationship

2019 ◽  
Vol 31 (2) ◽  
pp. 236-259 ◽  
Author(s):  
Assadej Vanichchinchai
Keyword(s):  
Supply Chain ◽  
Firm Size ◽  
Lean Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing Sector ◽  
Organizational Characteristics ◽  
Measurement Instruments ◽  
Content Type ◽  
Organizational Contexts ◽  
Supply Chain Relationships

Purpose The purpose of this paper is to explore the levels of lean manufacturing (LM) and supply chain relationships (SCR) in the manufacturing sector in Thailand, and analyze the differences across organizational characteristics (i.e. firm size, nationality of firms, manufacturing system, product brand, export level, nationality of customers, nationality of suppliers and existence of supply chain management (SCM) departments) on LM and SCR. Design/methodology/approach The measurement instruments of LM and SCR were developed and validated by experts, pilot test and various statistical techniques. Descriptive statistics were applied to investigate the levels of LM and SCR in the sample firms. Independent samples t-test and ANOVA were employed to examine the differences across organizational characteristics on overall LM and SCR, and their individual sub-construct. Findings It was found that the measurement instruments of LM and SCR were reliable and valid. Manufacturers in Thailand emphasized internal LM at the operational level much more extensively than external SCR. Overall, for LM, this study revealed significant differences across firm size, nationality of firms, manufacturing systems, export levels, nationality of customers and existence of SCM departments. For SCR, there were significant differences across export level, nationality of suppliers and existence of SCM departments. Differences across contexts of individual sub-construct of LM and SCR were analyzed and discussed. Originality/value This study is one of the first to present insights into the existence of LM and SCR and into the differences across organizational contexts on LM, SCR and their sub-constructs in the manufacturing sector in Thailand. The methodologies and findings are applicable to other developing countries.

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