Machine Changeover Research Articles

Combinatorial optimization methods for yarn dyeing planning

AbstractManaging yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.

A Model to Reduce Machine Changeover Time and Improve Production Efficiency in an Automotive Manufacturing Organisation

One of the key postulates of the modern automotive industry is the increase in production efficiency while minimizing costs. In the opinion of experts from the automotive industry, meeting this condition may be the first stage on the way to preventing waste generation and implementing a circular economy model. The article presents a case study of issues related to the lean manufacturing methodology in terms of the impact of shortening the changeover time of the assembly line on the overall production efficiency. The presented considerations focus on the optimization of the production process using the SMED (Single Minute Exchange of Die) technique of a selected spare part. From the point of view of the Lean Manufacturing concept, the main goal of the SMED technique is to increase the flexibility of responding to changing customer needs by shortening the changeover times and faster responses to changing orders. The article describes the stages of implementing the SMED method and its impact on the increase in the OEE (Overall Equipment Efficiency) index, which allows for the percentage recognition of the degree of machine park utilization, which is one of the key factors for assessing energy efficiency. In addition, the benefits that have been achieved by using this method in terms of time and economy have been presented. The theoretical aspects related to the method used were supplemented with its practical implementation in order to improve the changeovers in a manufacturing company in the automotive industry. Based on the obtained test results, an analysis of the effectiveness of the measures taken to reduce the changeover time was carried out. The use of the SMED methodology contributed to a significant reduction in changeover time—by as much as 291.4 s. The burden on operators was significantly reduced—the total time and number of operations performed by them (both internal and external) was reduced. Operator paths have also been shortened using simple procedures such as changing the layout of the lines and modifying the changeover tool trolleys and tool locking system at the stations. The presented research may be helpful in answering the question whether the implementation of the SMED idea may be the key to effective resource management and, at a later stage, to the implementation of the circular economy model. In addition, the research results can find their practical application among both manufacturers of spare parts and the vehicles themselves, considering introducing process changes on their production lines in order to increase production efficiency and implementing the idea of industrial sustainability.

OPTIMISING THE SEALING STATION IN PRODUCTION PROCESS OF TUBES FOR CATALYTIC CONVERTERS INVOLVING SMED METHOD – A CASE STUDY

The article presents the stages of the development of the lean management concept. The authors presented the SMED methodology. They made the analysis of the sealing station in the production process of tubes for catalytic converters in relation to the real model. Optimisation the sealing station in production process of tubes for catalytic converters has been done in this article. For this purpose was used one of the tools of Lean Manufacturing, SMED. Diagnosis of machine changeover operations was carried out and also measurement of the times necessary for doing these operations. Using spaghetti diagram, the path which is covered by the operator during machine changeover, was shown. Delegation of specific tasks carried out previously by the operator to production workers and implementation of organisational and technical solution has had the effect of relieving of the operator and shortening of time necessary for machine changeover and increasing productivity at the sealing station.

Mathematical model of steel consumption minimization considering the two-stage billets cutting

Purpose. To achieve a decrease in energy and resource costs in the multi-stage production of rolled products within a given plan through the development of appropriate mathware. Methodology. The multi-stage problem mathware for producing rolling steel products is developed on the basis of system approach applying fundamental principles of the optimization and operation research theory. Realization of the developed mathematical model allows discovering such a strategy of using steel during the whole manufacturing process, which minimizes not only the steel waste at the moment of its casting in a mold, but also the offcuts in the process of cutting the obtained ingots into the billets. Findings. A mathematical task model is built to minimize the amount of steel for producing a certain order of size of one cast volume only. The developed model specifies the possibility to pre-evaluate the billet optimal size, based on the necessary cutting along the final product length, appropriate for the certain billet form of section, and ingot weight limits. Originality. A mathematical model is provided for the optimal metal distribution process when implementing the plan of manufacturing rolling products. The model, in contrast to the existing ones, shifts the emphasis on forming the optimal ingot weight, which has a pre-calculated optimal cutting plan. Practical value. The use of the developed mathematical optimization model (minimizing the amount of steel for producing a certain order) as part of an automated decision support system for management of rolling production will reduce the number of cutting machine changeover and minimize resources use and stock balance.

MACHINE LEARNING IN SMED

The paper discusses Single Minute Exchange of Die (SMED) and machine learning methods, such as neural networks and a decision tree. SMED is one of lean production methods for reducing waste in the manufacturing process, which helps to reorganize a conversion of the manufacturing process from current to the next product. SMED needs set-up activity analyses, which include activity classification, working time measurement and work improvement. The analyses presented in the article are focused on selecting the time measurement method useful from the SMED perspective. Time measurement methods and their comparison are presented in the paper. Machine learning methods are used to suggest the method of time measurement which should be applied in a particular case of workstation reorganization. A training set is developed and an example of classification is presented. Time and motion study is one of important methods of estimating machine changeover time. In the field of time study, researchers present the obtained results by using (linear) multi-linear regression models (MLR), and (non-linear) multi-layer perceptrons (MLP). The presented approach is particularly important for the enterprises which offer make-to-order products. Development of the SMED method can influence manufacturing cost reduction of customized products. In variety oriented manufacturing, SMED supports flexibility and adaptability of the manufacturing system.

Methodology to improve machine changeover performance on food industry based on SMED

This article discusses how to apply Single Minute Exchange of Die (SMED) methodology in the food industry, bearing in mind the peculiarities of this sector, as it is comprised within the mass market industry (cleaning and cosmetics products, textile, etc.). Regarding innovation, this research delves into the application of SMED to the field of machine changeovers (changes in format, products, tools, etc.), while also analysing the possible in order to be minimised. These losses are focused on irregular machine changeovers, that is, lack of standard work methodology, maintenance defects, lack of spare parts, lack of operator specialisation, etc. The implemented methodology contains a proposal to measure the results of the SMED technique using the mean time between failures (MTBF) and mean time to repair such failure (MTTR) along with indicators such as global efficiency (GE) and overall equipment effectiveness (OEE). MTBF and MTTR help evaluate and improve the implementation of SMED-based methodology. But we can also assess how long it takes us to act in the event of contingency, such as a line failure or repair, and reduce it accordingly.

Scheduling and lot sizing problems for variable range of products using GA-based method

Article proposes a method based on genetic algorithm to determine the lot size and production scheduling for variable ranges of products-couplings. The simulation computer checks the established deadlines for the various orders and strives to minimize the total cycle for production times and machine changeover times. The authors present the assumptions of the model and its results in the generation of an optimal production sequence for the analyzed couplings. The paper also comprise the analysis if it is reasonable to combine orders or share their according to individual customer orders.

A multi-objective optimisation model to integrating flexible process planning and scheduling based on hybrid multi-objective simulated annealing

Process planning and production scheduling play important roles in manufacturing systems. In this paper we present a mixed integer linear programming (MILP) scheduling model, that is to say a slot-based multi-objective multi-product, that readily accounts for sequence-dependent preparation times (transition and set up times or machine changeover time). The proposed scheduling model becomes computationally expensive to solve for long time horizons. The aim is to find a set of high-quality trade-off solutions. This is a combinatorial optimisation problem with substantially large solution space, suggesting that it is highly difficult to find the best solutions with the exact search method. To account for this, the hybrid multi-objective simulated annealing algorithm (MOHSA) is proposed by fully utilising the capability of the exploration search and fast convergence. Two numerical experiments have been performed to demonstrate the effectiveness and robustness of the proposed algorithm.

Single-machine scheduling with joint deterioration and learning effects under group technology and group availability assumptions

The goal of group technology in manufacturing process is to identify a set of products with similar processing requirements and minimize machine changeover and setup. Although different models of joint deterioration and learning effects have been extensively studied in various areas, it has rarely been studied in the context of group scheduling. In this article, we consider single-machine scheduling problems involving deterioration and learning effects simultaneously under the group technology and the group availability assumptions. We assume that the group setup time is a function of position-dependent learning while the actual processing time of a job is a function of joint time-dependent deterioration and position-dependent learning. We further assume that after the setup, the machine deterioration will reset first and then starts anew. We prove that the makespan and the total completion time minimization problems can be optimally solved in polynomial time algorithms with agreeable conditions.

Special Topic Forum on Using Archival and Secondary Data Sources in Supply Chain Management Research

<i>Special Topic Forum</i> on Using Archival and Secondary Data Sources in Supply Chain Management Research

Residual Control Charts for Improving Changeover Productivity

Although statistical control charts have been popularly used in monitoring process quality, the concepts of statistical control are essentially suited to the improvement of productivity in virtually any area of business. This article presents a new statistical control chart, the residual control chart, which was employed to improve machine changeover productivity in a textile manufacturing company. To control productivity of various manned operations, the authors identified variation due to the performance of technicians while controlling for the effect of other factors. Instead of making numerous charts for individual categories, the proposed control chart reduces the number of charts, while still blocking out the effect of extraneous factors. The residual chart may be used to help managers balance workloads, plan training programs, and/or assist in conducting performance appraisals.

The economic justification of machine changeover time reduction in a manufacturing cell

In this paper, an analysis model is formulated to resolve the economic justification of machine changeover time reduction in a manufacturing cell. This model examines the effects of changeover time reduction for each machine and minimizes the total relative cost in the manufacturing cell while demand is variable. Adding the concept of a product's life cycle, we develop a mathematical model to deal with variable demand in the planning horizon. The budget constraint is also added to meet the conditions of different invested amounts. Finally, an example, which is derived from different set-up time reduction projects, and simulated production environment is used to demonstrate the details of the model. The optimum investment amount on changeover reduction operation for each machine can be found via the proposed model and the maximum benefits are then obtained.

Scheduling sequence-dependent jobs on identical parallel machines to minimize completion time criteria

Abstract We consider the problem of scheduling sequence-dependent jobs on identical parallel machines. Each job has to be processed without interruption on one of the machines. A machine changeover time must be respected between job processing on each machine. This changeover time is dependent on the job sequence. Our objective is to minimize the maximum completion time of the jobs or the mean completion time of the jobs. This problem is a vehicle routeing problem and is NP hard. We propose an assignment algorithm to solve it. Our heuristic is an extension of the Hungarian method to the multiple use of resources and has been developed during past years to solve routeing problems. This paper models the scheduling problem, presents the solution tool and shows the heuristic result quality for 24 problem sizes.

Scheduling Problem with Changeover Costs in Industrial Applications

This paper presents an application study of two algorithms to solve the One Machine Scheduling Problem with Changeover Cost, in a glass bottle product industry. The One Machine Changeover Cost problem is modelled upon the Traveling Salesman Problem. The first algorithm is based on Littles algorithm which uses the branch and bound approach. The second algorithm is based on Lins algorithm which uses the 2-opt heuristic. The second algorithm is also applied for the parallel machine scheduling problem, with changeover cost, and provides a reassignment of jobs to machines.

Textile Production Systems: a Succession of Non-identical Parallel Processor Shops

The different problems of sequencing production operations in parallel processor shops of a textile company are considered in this paper. The complexity of these problems depends on: (1) the machine changeover times which occur when the changeovers cannot be realized in parallel during processing times, and (2) the operation due dates which have to be at best respected when the workshops do not constitute stocks. The objective is to minimize mean completion time or mean tardiness. This paper models these different scheduling problems. It presents some tools to solve these problems, and it explains the choice of algorithms issued from the graph theory and employed in industrial studies. Some results are shown.