Machine Setup Time Research Articles

Setup Time Reduction for Toothpaste Tube Filling Machine: A Case Study of a Contract Toothpaste Manufacturing Company

This research aimed to find ways to reduce the idle time and improve the toothpaste tube filling machine setup procedure. The toothpaste manufacturing industry has many kinds of products nowadays, so the production process would need to frequently change its production plan, thus causing the number of machine setup times to increase. As such, the toothpaste tube filling machine would be unable to run simultaneously. According to the study, the average machine setup time on the filling machine was 157.1 minutes per time. This was more than the planned machine setup time of about 67.1 minutes, or 74.55 percent. The researchers presented the single-minute exchange of die and the eliminate, combine, reduce, and simplify approaches to reduce the average time of the machine setting by about 124.6 minutes per time, or 20.69 percent. The evaluation results of the process performance before and after the improvement by measuring the overall equipment efficiency from 41.66 percent to 58.61 percent increased by 16.95 percent.

Investigation on distributed scheduling with lot-streaming considering setup time based on NSGA-II in a furniture intelligent manufacturing

Distributed flexible flowshop scheduling is getting more important in the large-scale panel furniture industry. It is vital for a higher manufacturing efficiency and economic profit. The distributed scheduling problem with lot-streaming in a flexible flow shop environment is investigated in this work. Furthermore, the actual constraints of packaging collaborative and machine setup times are considered in the proposed approach. The average order waiting time for packaging and average order delay rate is used as objectives. Non-dominated sorting method is used to handle this bi-objective optimization problem. An improved encoding method was proposed to address the large-scale orders that need to be divided into sub-lots based on genetic algorithm. The proposed approach is firstly validated by benchmark with other multi-objectives evolutionary algorithms. The results found that the proposed approach had a good convergence and diversity. Besides, the influence of the proportion of large-scale orders priority level and sub-lot size was investigated in a panel furniture manufacturing scenario. The results can be concluded that the enterprise could obtain shorter order average waiting time and delay rate when the sub-lot sizes were set as two and the order priority level was allocated in the proportion of 1:2:3:4:5.

Penerapan Lean Manufacturing untuk Meminimasi Proses Dandori Mesin Molding Pada PT XYZ dengan Metode SMED

PT XYZ is a manufacturing company that produces various products, such as molds and several spare parts. The production process often experiences wasted time at the machine setup stage, known as dandori. To avoid production delays, companies need to increase their productivity. This research aims to propose the application of Lean Manufacturing methods, especially SMED, to reduce machine uptime by focusing on optimizing external activities in mold making. Through the application of the Single Minute Exchange of Dies (SMED) concept, fourteen internal activities were changed to eight internal activities, resulting in improvements with a reduction in machine setup time from 44 minutes to 22 minutes, or a reduction of 50%. The hope is that these results will increase the company's output significantly.

RESEARCH OF VARIABLE PARAMETERS OF NEEDLE THREAD TAKE-UP MECHANISMS AND DEVELOPMENT OF RECOMMENDATIONS FOR ADJUSTING MULTI-THREAD CHAIN STITCH SEWING MACHINES

The purpose of this research is to develop specific recommendations for optimizing the adjustment of the thread take-up mechanisms of typical chain stitch sewing machines based on the analysis of the thread take-up process. These recommendations are intended to help manufacturers and machine operators achieve maximum productivity and increased product quality, while taking into account the specific technological parameters of the operations performed on these machines. The research is aimed at increasing the efficiency of the sewing process, reducing machine setup time, and improving the overall result of the technological process in the sewing industry. The research uses an analytical method of determining and investigating functions of the actual thread take-up as the instantaneous sum of sections of the take-up contour when adjustable parameters of the mechanisms are changed, taking into account accepted assumptions. In the research, an analytical method was used to determine functions of the actual thread take-up as the instantaneous sum of sections of the take-up contour, taking into account the change in regulated parameters of the mechanisms. At the same time, assumptions were made that has simplified analysis and modelling of this process with sufficient accuracy of calculation results. The scientific novelty of this research lies in discovery of new relationships and establishment of regularities that determine influence of adjustable parameters on the nature and scope of the thread take-up function in chain stitch machines. Further re-search in this area can be aimed to the development of more efficient methods of adjustment and optimization of needle thread take-up mechanisms, as well as to the implementation of new technologies in pro-duction to improve productivity and quality of sewing operations. The obtained results became basis for the development of nomograms for determining the optimal values of adjustable parameters for needle thread take-up mechanisms of typical flat chain stitch machines, taking into account technological parameters of specific operations. These nomograms allow operators and manufacturers of sewing machines significantly simplify adjustment of these mechanisms when using operational equipment or when switching to other technological operations. The obtained results can be applied directly in production during adjustment or repairing of specified ma-chines to increase their productivity by reducing adjustment time and for improving the quality of the technological process.

Unrelated parallel machines scheduling with dependent setup times in textile industry

The problem tackled in this paper is an unrelated parallel machines scheduling problem with machine and sequence dependent setup times, machine eligibility and different resource types constraints. This subject is inspired from a textile industry partner case. The different characteristics of this problem have never been combined as it is depicted in this paper. Two resolution methods are exposed. A new mathematical programming is proposed to have an exact resolution approach of this complex problem. An improved genetic algorithm (GA) with dedicated operators is also presented in order to solve the industrial case in a reasonable computational time.

Build-Up an Economical Tool for Machining Operations Cost Estimation

Currently, there is a lack of affordable and simple tools for the estimation of these costs, especially for machining operations. This is particularly true for manufacturing SMEs, in which the cost estimation of machined parts is usually performed based only on required material for part production, or involves a time-consuming, non-standardized technical analysis. Therefore, a cost estimation tool was developed, based on the calculated machining times and amount of required material, based on the final drawing of the requested workpiece. The tool was developed primarily for milling machines, considering milling, drilling, and boring/threading operations. Regarding the considered materials, these were primarily aluminum alloys. However, some polymer materials were also considered. The tool first estimates the required time for total part production and then calculates the total cost. The total production time is estimated based on the required machining operations, as well as drawing, programming, and machine setup time. A part complexity level was also introduced, based on the number of details and operations required for each workpiece, which will inflate the estimated times. The estimation tool was tested in a company setting, comparing the estimated operation time values with the real ones, for a wide variety of parts of differing complexity. An average error of 14% for machining operation times was registered, which is quite satisfactory, as this time is the most impactful in terms of machining cost. However, there are still some problems regarding the accuracy in estimating finishing operation times.

Evaluating the performance of a cellular manufacturing system proposal for the sewing department of a sportswear manufacturing company: A simulation approach

Cellular manufacturing systems (CMS) are a major application of group technology, that brings benefits related to the reduction of wastes, also known as ‘mudas’ in lean manufacturing philosophy, to organisations striving to compete on today’s markets. These benefits include the reduction of machine setup times, material handling and work-in-process inventory, among others. However, many organisations are reluctant to implement such decisions in their production systems, due to the uncertainty and economic and productive impact that these involve. For this reason, a simulation study is performed in this paper to evaluate the performance of a cellular manufacturing system approach for the sewing department of a sportswear manufacturing company. The simulation study was designed, and the results of the proposed model showed improvements of 51,46% in the average flow time, 1102,52% in the average throughput, and 50,65% in the average setup times of machines, compared to the current state of the case study.

A Case Study on Reducing Setup Time Using SMED on a Turning Line

Today, manufacturers must meet customer demands in a shorter time with the best quality and lowest cost to survive in competitive environment. One way to achieve this is to reduce production costs by eliminating waste activities in a production system. Machine adjustment processes are the activities that do not create value, and Single Minute Exchange of Dies (SMED), lean manufacturing tool, has been developed to reduce these activities. In this paper we have performed a case study in a bearing manufacturing company to reduce the machine setup time on turning line using the SMED. The emphasis is given to both transferring internal activities into external ones as many as possible and minimizing the internal ones. The main results attained from the study indicate that the machine setup times were reduced more than 45% for the turning line. Consequently, machine capacities can be significantly increased by the application of SMED method.

Hybrid meta-heuristics for the unrelated parallel machine scheduling problem with setup times

This study focuses on an unrelated parallel machine scheduling problem with machine and job sequence-dependent setup times (UPMST) aiming to minimise the makespan. Recently, adaptive large neighbourhood search with learning automata (LA-ALNS) has been employed to tackle the UPMST with excellent performance. However, its search efficiency is heavily affected by re-visiting the recent solutions, which is known as the short-term cycle. Moreover, it remains challenging to balance the computational cost and quality of the solutions. To address these challenges, we propose a hybrid meta-heuristic method based on LA-ALNS and tabu search (LA-ALNS-TS). In the proposed algorithm, LA-ALNS is used to guide the exploration and TS is used to alleviate the short-term cycle experienced during the search process. In addition, we design two local search operators to achieve a balanced trade-off between the search efficiency and quality of the solutions. A dynamic perturbation scheme is proposed to improve the solution and help the algorithm escape from local optima. Comprehensive experiments were conducted to assess the performance of the algorithm on two exhaustive benchmarks. Experimental results demonstrate the effectiveness and efficiency of the proposed hybrid meta-heuristic method.

Process Design & Development Using Smed Concept- A Case Study

The growth of the industry and its production ultimately depends on its ability to respond systematically and continuously to market changes to increase product value. The process of adding value is necessary to obtain this advantage; therefore using a coherent production system and its tools becomes a key skill. Since the set time is the main reason for the production break time, a short set time is always desirable. The Single Minute Exchange of Dies (SMED) as proposed by Shingo is a tool that aims to reduce overtime setting, but does not work when used alone. SMED can be used effectively with the help of additional tools such as FMRS (Finish, Merge, Reduce and Simplify). This paper describes the process of planning and implementing SMED and other useful tools. It is based on a collaborative approach that allows for a gradual reduction in machine set-up time associated with an ongoing continuity program.

Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times

The aim of this paper is to present a model of energy efficient scheduling for series production systems during operation, including setup and shutdown activities. The flow shop system together with setup, shutdown times and energy consumption are considered. Production tasks enter the system with exponentially distributed interarrival times and are carried out according to the times assumed as predefined. Tasks arriving from one waiting queue are handled in the order set by the Multi Objective Immune Algorithm. Tasks are stored in a finite-capacity buffer if machines are busy, or setup activities are being performed. Whenever a production system is idle, machines are stopped according to shutdown times in order to save energy. A machine requires setup time before executing the first batch of jobs after the idle time. Scientists agree that turning off an idle machine is a common measure that is appropriate for all types of workshops, but usually requires more steps, such as setup and shutdown. Literature analysis shows that there is a research gap regarding multi-objective algorithms, as minimizing energy consumption is not the only factor affecting the total manufacturing cost—there are other factors, such as late delivery cost or early delivery cost with additional storage cost, which make the optimization of the total cost of the production process more complicated. Another goal is to develop previous scheduling algorithms and research framework for energy efficient scheduling. The impact of the input data on the production system performance and energy consumption for series production is investigated in serial, parallel or serial–parallel flows. Parallel flow of upcoming tasks achieves minimum values of makespan criterion. Serial and serial–parallel flows of arriving tasks ensure minimum cost of energy consumption. Parallel flow of arriving tasks ensures minimum values of the costs of tardiness or premature execution. Parallel flow or serial–parallel flow of incoming tasks allows one to implement schedules with tasks that are not delayed.

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

The Unrelated Parallel Machines Scheduling Problem with Machine and Job Dependent Setup Times, Availability Constraints, Time Windows and Maintenance Times

The Unrelated Parallel Machines Scheduling Problem with Machine and Job Dependent Setup Times, Availability Constraints, Time Windows and Maintenance Times

An iterated greedy algorithm for the parallel blocking flow shop scheduling problem and sequence-dependent setup times

This paper deals with the problem of scheduling jobs in a parallel flow shop configuration under the blocking constraint, in which the setup time of machines depends not only on the job to be processed but also on the previously processed one, i.e., there are sequence-dependent setup times. The performance analysis of several iterated greedy algorithms with different initial solution procedures and local searches lets us define an efficient algorithm to minimize the maximum job completion time. Moreover, the computational evaluation showed the efficiency of searching in different neighborhood structures and noted the significant influence of the initial solution. However, contrary to other scheduling problems, starting with a high quality solution does not guarantee better performance of the algorithm.

Multi-objective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time

The distributed reentrant permutation flow shop (DRPFS) is a combination of the reentrant flow shop problem and distributed scheduling. The DRPFS is a NP-hard problem that consists of two subproblems: (1) assigning a set of jobs to a set of available factories and (2) determining the operation sequence of jobs in each factory. This paper is the first study to consider the inclusion of sequence-dependent setup time in the DRPFS. The industrial applications of flow shop indicate that the machine setup time to process a job may depend on the previously processed jobs. Particularly, in DRPFS, the effect of sequence-dependent setup time is intensified due to its reentrant characteristic. An improved version of the multi-objective adaptive large neighborhood search (MOALNS) is proposed as a solution method for the sequence-dependent DRPFS with the aim to minimize the makespan, production cost, and tardiness. The proposed algorithm enhances the standard MOALNS by embedding an improved solution acceptance and non-dominated set updating criteria to assist the algorithm in finding the near-optimal Pareto front of the factory allocation and scheduling problems. To address the multiple objectives and the issue of non-uniform setup time, a new set of destroy and repair heuristics are developed. Further, the numerical experiments demonstrate the efficiency of IMOALNS in finding high-quality solutions in a relatively short time.

Mathematical Modeling for a Flexible Manufacturing Scheduling Problem in an Intelligent Transportation System

This paper presents a new mathematical model for a production system through a scheduling problem considering a material handling system as an intelligent transportation system by automated guided vehicles (AGVs). The traditional systems cannot respond to the changes and customer’s demands and for this reason, a flexible production system is used. Therefore, for this purpose, automated transportation systems are used for more flexibility in production. Thus, several AGVs are considered to perform various jobs among different machines and warehouses. In this production system, there are possibilities of failure and breakdown of AGVs and machines simultaneously. A modified rate is considered for determining the maintenance duration time as a percentage of the setup time when the maintenance time is dependent on the total setup time of machines and the total transfer jobs time of AGVs. Hence, we consider the probability of breakdown of AGVs and machines simultaneously and show the effect of these problems. The objective function is to minimize the maximum completion time (i.e., makespan or Cmax), the tardiness penalty, and the total transportation cost bearing in mind that the impact of new constraints with mathematical innovation on how failure and repair time are affected by the entire production scheduling. The proposed model belongs to mixed-integer linear programming (MILP). Finally, several small-sized problems are generated and solved by the CEPLEX solver of GAMS software to show the efficiency of the proposed model.

Estimation of machine setup and changeover times by survival analysis

The losses associated with changeovers are becoming more significant in manufacturing due to the high variance of products and requirements for just-in-time production. The study is based on the single minute exchange of die (SMED) philosophy, which aims to reduce changeover times. We introduced a method for the analysis of these losses based on models that estimate the product- and operator-dependent changeover times using survival analysis. The root causes of the losses are identified by significance tests of the utilized Cox regression models. The resulting models can be used to design a performance management system that considers the stochastic nature of the work of the operators. An anonymized manufacturing example related to the setup of crimping and wire cutting machines demonstrates the applicability of the method.

Bi-objective parallel machine scheduling with additional resources during setups

We present a bi-objective parallel machine scheduling problem with machine and job sequence dependent setup times, with the additional consideration of resources needed during setups. The availability of such resources is limited. This models many practical situations where setup times imply, for example, cleaning and/or the reconfiguration of productive equipment. These setups are performed by personnel, who are of course limited in number. The objectives considered are the minimization of the makespan and the minimization of the number of resources. Fewer available resources reduce production costs but inevitably increase the makespan. On the contrary, a greater number of resources increase costs but allow for more setups to be done in parallel and a reduced makespan. An algorithm based on iterated greedy approaches is proposed to search for the Pareto front of the problem. This algorithm is compared with state-of-the art methods adapted to the problem. Computational experiments, supported by statistical analyses, indicate that the proposed approach outperforms all other tested procedures.

Two approaches to handle the dynamism in a scheduling problem with sequence-dependent setup times

In this work we address the minimization of the makespan in a scheduling problem where the machine setup times are sequence-dependent. The jobs, which arrive throughout the production process, has a release time that is unknown in advance. Considering both, dynamic environments and sequence-dependent setup times, make the problem more realistic, but also more challenging from an algorithmic and modeling point of view. To deal with the addressed problem, we implement the continuous and periodic rescheduling approaches, providing them with the same re-optimization methods. To implement the re-optimization methods, we design two insertion procedures for adding the new released jobs in the processing sequence, as well as improvement procedures, based on Iterated Greedy strategies, to reduce the sequence makespan. In the improvement phase of the Iterated Greedy strategies we implement three improvement methods that combine four local searches. The developed algorithms are assessed based on the quality of the solutions they find and the CPU time they consume to reach these solutions. We use instances from the literature and larger instances generated in this work. The three algorithm versions showed quality solutions when compared with optimal solutions for the static problem and with solutions of the Perfect Information Model for the dynamic problem. Additionally, they showed a good performance for both the continuous and periodic approach. When these two approaches are compared, results indicate that the continuous approach would be the most appropriate when the proportion of dynamic jobs is low, while when the proportion is high, it would seem more advisable to use the periodic approach, appropriately selecting the frequency of re-optimization processes.

Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud

Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases the cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus environmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and efficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In this paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed acyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by sorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform each task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the right time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor determined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of virtual machines. Finally, a test dataset is developed to evaluate the proposed BHS algorithm compared to the multiheuristic resource allocation algorithm (MHRA). The results show that the proposed algorithm facilitates 19.71% more energy storage than the MHRA algorithm. Furthermore, the makespan is reduced by 56.12% in heterogeneous environments.