A production model and maintenance planning model for the process industry

In this paper a model is developed to simultaneously plan preventive maintenance and production in a process industry environment, where maintenance planning is extremely important. The model schedules production jobs and preventive maintenance jobs, while minimizing costs associated with production, backorders, corrective maintenance and preventive maintenance. The formulation of the model is flexible, so that it can be adapted to several production situations. The performance of the model is discussed and alternate solution procedures are suggested.Production Models;Maintenance;production

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

In a mass customisation manufacturing system, the production schedule is tailored to the customer's specifications. However, the production system must be accompanied by an effective maintenance program to ensure that the production lines operate as intended. The purpose of this study is to optimise planned preventive maintenance across multiple production lines. An optimised Weibull distribution is proposed to model the machine's Mean Time Between Failures (MTBF), and the total expected maintenance cost is calculated using this distribution, taking into account the probability of the machines remaining operational and failing. Because the optimised Weibull distribution is a continuous distribution, in order to simulate the continuous time domain, it will be divided into several sub-systems and optimised using Bayesian optimisation during simulation. The maintenance scheduling is carried out by considering available time capacity after production scheduling was arranged. The study's findings indicate that the proposed method successfully optimised the planned maintenance schedule without interfering production activity with total cost for the proposed maintenance planning as low as IDR 50.017,75/maintenance unit time

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

In a mass customisation manufacturing system, the production schedule is tailored to the customer's specifications. However, the production system must be accompanied by an effective maintenance program to ensure that the production lines operate as intended. The purpose of this study is to optimise planned preventive maintenance across multiple production lines. An optimised Weibull distribution is proposed to model the machine's Mean Time Between Failures (MTBF), and the total expected maintenance cost is calculated using this distribution, taking into account the probability of the machines remaining operational and failing. Because the optimised Weibull distribution is a continuous distribution, in order to simulate the continuous time domain, it will be divided into several sub-systems and optimised using Bayesian optimisation during simulation. The maintenance scheduling is carried out by considering available time capacity after production scheduling was arranged. The study's findings indicate that the proposed method successfully optimised the planned maintenance schedule without interfering production activity with total cost for the proposed maintenance planning as low as IDR 50.017,75/maintenance unit time

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

A Simulation Based Approach for Determining Maintenance Strategies

Manufacturing organizations are continuously in the mode of identifying and implementing mechanisms to achieve a competitive edge. To this point manufacturers have recognized the critical role of equipment in the productivity of manufacturing operations. With the current trend of manufacturers attempting to lean out their production processes, primary and auxiliary equipment have become even more important to manufacturers as measured by productivity, quality, delivery, and cost metrics. As a result of the focus on lean manufacturing, maintenance management has found a new vigor and purpose to increase equipment capacity and capability. However, the most proactive maintenance strategy is not always the most effective utilization of resources. It is typical for manufacturers to integrate both reactive and proactive maintenance to define a cost effective maintenance strategy. A simulation-based approach is presented that allows an end user to develop such a maintenance strategy

Cost-conscious manufacturing – Models and methods for analyzing present and future performance from a cost perspective

Manufacturing is an industry in which the effects of globalization are obvious. Manufacturing costs are a key factor in this respect and affect, for example, decisions about offshoring, i.e., moving production abroad. If Sweden and other Western countries are to maintain large manufacturing sectors, they must be competitive, making cost one of the most critical parameters. The work presented here seeks to develop tools for cost-conscious manufacturing. These tools should provide insight into how well a manufacturing system is performing and support the analysis and prioritization of manufacturing development activities. To achieve this objective, two research questions were formulated. The first research question (RQ1) concerns how a general cost model should be designed to take into consideration the most important process-near parameters influencing manufacturing system performance. A cost model developed in accordance with this research question includes critical parameters affecting performance, such as cycle time, setup time, and performance loss parameters. The model is centered on the processing steps involved in processing a part. The losses occurring in the processing steps are important in the model, so the links between structured, detailed monitoring of the loss causes and their impacts on costs are emphasized. Modified versions of the model to analyze volume flexibility and downtime variability are also presented. The second research question (RQ2) concerns how such a cost model can be used in practice, i.e., the requirements and conditions for industrial use. Implementation in an automotive company indicated that the model was applicable in this context and that interesting insights into manufacturing costs could be gained from using the model. A study of the industrial conditions for applying the cost model identified software products for collecting manufacturing loss data that support the level of detail needed for model input, but found that manufacturing companies do not necessarily collect such detailed data. A demonstration program developed based on the databases available in a collaborating company indicated how the cost model could be used practically in a company. The somewhat deficient detail in the collected loss data, found in the above study, led to an inquiry in another company into the pros and cons of collecting highly detailed performance loss data. The results identified more advantages than disadvantages with collecting more detailed data: the operators responsible for data collection did not perceive any particular difficulties with the increased detail and the production manager believed that the increased detail led to better knowledge of performance losses