Bottleneck Machines Research Articles

Bottleneck alleviation and scheduling optimization of flexible manufacturing system based on information-energy flow model

The identification and alleviation of bottleneck machines in manufacturing systems are of paramount importance for optimizing production decision-making in enterprises. Extensive research has been conducted by numerous scholars on the identification of bottleneck machines; however, there is a relative scarcity of research, particularly in the context of flexible manufacturing systems, regarding how to effectively alleviate the identified bottlenecks. In this paper, a novel hyper-heuristic evolutionary scheduling algorithm with a bottleneck alleviation strategy based on the information-energy flow model (HESA-BA-IEF) is proposed for training dispatching rules and optimizing decision-making in flexible job shops with bottleneck machines. HESA-BA-IEF constructs an information-energy flow model for flexible job shops based on complex networks. Through this model, dynamic changes in machine processing status and energy consumption during the scheduling process can be effectively simulated, facilitating the accurate identification of bottleneck machines. Moreover, the algorithm adopts the genetic programming hyper-heuristics and introduces a scheduling mechanism considering the alleviation of bottleneck machines to optimize the makespan and total energy consumption, while simultaneously alleviating bottlenecks. Experiments show that HESA-BA-IEF generates dispatching rules that optimize scheduling and alleviate bottlenecks simultaneously. Furthermore, statistical analysis reveals a 7.71% reduction in bottleneck metrics of production line due to 30.40% scheduling rounds with bottleneck alleviation mechanism in effect.

Bottleneck identification and transfer prediction for automated production lines based on FNN

Abstract In a re-entrant production system, the throughput of the whole system depends on the capacity of the bottleneck machine. In this study, a new definition of bottleneck is proposed for a precision forging blade shop. The reinforcement learning algorithm is used to optimize the production scheduling to determine the most suitable scheduling scheme, which lays the foundation for bottleneck identification. Subsequently, the bottleneck identification index system was established according to the optimization objective, and the bottleneck identification problem was transformed into a multi-attribute decision-making problem. Finally, a fuzzy neural network is used for training, and the basic scheduling examples of each flow shop are utilized for bottleneck identification and prediction to verify their effectiveness.

Bounded scheduling two-component jobs simultaneously or hierarchically

This paper studies the multi-objective problem of scheduling n two-component jobs on a fabrication machine with bounded batch capacity in terms of the number of jobs. Each job consists of two components, one is common among all jobs (called standard component) and the other is unique to itself (called specific component). The machine processes all components so that it might become a capacitated bottleneck machine. Standard components have equal processing times. They are fabricated in batches sequentially on the machine, and a setup time is required to fabricate a batch. Each batch contains the standard components of at most b(b < n) jobs. Specific components are manufactured individually, therefore their associated setup times could be absorbed into their processing times. Standard components in a batch are not available until all these components are completed (meaning that the processing time of a batch is equal to the total processing time of the standard components in it), whereas a specific component is available on completion of its processing. A job is completed when both its two components are completed and available. For simultaneous optimization of makespan and maximum lateness, an O(n3)-time algorithm is obtained. Through a careful use of the heap data structure, the time bound can be improved to O(n2 log n). For hierarchical optimization of two maximum lateness objectives and makespan, an O(n5)-time algorithm is obtained, which can be improved to run in time O(n4).

Simulation-Based Study of the Resilience of Flexible Manufacturing Layouts Subject to Uncertain Demands of Product Variants

Due to market competition, manufacturers typically produce their products with different customized features, leading to the production of product variants (or a product family). Since the market trend can change swiftly, the demands of individual product variants can be difficult to predict. Two flexible manufacturing layouts are commonly considered: functional and cellular layouts. While the functional layout is more resilient to demand changes due to better resource pooling, the cellular layout can be more productive on some occasions due to better routing efficiency. In this context, the purpose of this paper is to quantify and study the criticality of product variants. The criticality score of a product variant can estimate and rank which product variants can sensitively cause bottlenecks in the functional and cellular layouts. The proposed criticality analysis starts with the estimation of bottleneck machines. Through the dependency information of machines and parts, we can estimate the criticality of product variants. The criticality analysis is demonstrated and examined through a simulation study with a study case involving the production of five furniture products with 16 unique parts using 11 machines. The simulation results show that the productions with more critical product variants tend to deteriorate the completion time of the cellular layout more severely. In practice, manufacturers can use the proposed criticality analysis to evaluate the criticality of product variants and support their facility layout decision. For example, if more demand for critical products is expected, the layout should support more resource pooling (e.g., functional layouts).

A two-step scheduling and rescheduling framework for integrated production and usage-based maintenance planning under TOU electricity tariffs: A case study of the tile industry

The primary motivation of this study is to provide an easily implementable and cost-effective solution for reducing energy expenses, improving slurry quality, mitigating system instability due to random breakdowns, and minimizing maintenance frequencies in the ball mills of the tile industry. To achieve this goal, a two-step framework is proposed which can also be applied to parallel machines in make-to-order environments. The first step integrates the usage-based maintenance and production scheduling under time-of-use (TOU) electricity prices and involves a mixed integer linear programming (MILP) model with buffer time insertion to handle unpredictable machine unavailability. The second step suggests a combination of model-based and manual reactive policies for rescheduling. Compared to its non-energy-aware counterpart, the proposed model shows a 22% improvement in energy costs and a 4% increase in energy savings. Additionally, the usage-based maintenance strategy reduces maintenance frequencies by more than 50% compared to the time-based one. By integrating TOU-based production scheduling with usage-based maintenance scheduling, we offer three advantages for energy-intensive industries: 1) increased scheduling flexibility to shift production to low-tariff periods, 2) the ability to schedule maintenance operations during idle time resulting from production shifts, and 3) reduced maintenance frequencies for machines with extended idle times through a usage-based maintenance strategy. The proposed approach performs best when machines exhibit significant differences in energy consumption rates and have high total idle times. However, implementing it for bottleneck machines does not lead to notable improvements in energy-related indicators.

An implementation of lean technology to improve the lead time of syrup batching process and reduce hot fill line downtime

The research would present an effective performance of lean tools implementing in an Lemon Tea processing line of a Drink Industry as a case study. An improved strategy to reduce the lead time of syrup batching process and then reduce downtime on hot fill line would be considered as well as used. The DMAIC (Define - Measure - Analyze - Improve - Control) methodology would be followed. As the results, the lead time of syrup batching process was reduced from around 140 minutes to (after implementation of lean technology) 98 minutes, and downtime on hot fill line was also dropped significantly by approximately 50 percent. Besides, the production flow was optimized by minimizing several non value added activities and time such as waiting time at the bottleneck points, machine breakdown, queuing time, and other unnecessary waiting time. Eventually, this case study would be useful in developing a generic approach to design lean environment in management system of the studied drink industry organization.

Reduction of Machine Breakdown in the Brewery Manufacturing Company, A Case Study of Brewery “A” in the Eastern Nigeria

In this study, the machine break down pattern in Brewery “A” in Eastern Nigeria situated in Onitsha Anambra state has been considered. In the first step of this study, structured questionnaires were produced and administered to target audience for primary data collection. The brewery has limited newsletters, annual reports and bulletins that were used in addition for the reviews. The machines used in the packaging department and its breakdown data over a period of twelve months were gathered and from this, the frequency of breakdown of the machines were established and the data analyzed. The analysis was done by evaluating bottleneck machine breakdowns for different work stations in the production plant, and the effects of breakdown on system output. As a result of the study, critical rates of machine breakdown, which have affected system output, were determined. The frequency of machine breakdown, downtime and maintenance failure problems were also identified. Data collected were analyzed and model for optimization developed. Root cause analysis tools–fishbone and “5why” were used to eliminate the frequent occurring machine breakdown. Mean Time to Failure (MTBF), Availability, and Mean Time to Repair (MTTR) values for the various production lines were evaluated before and after root cause analysis. The result shows that a percentage increase of 13.66%, 10.62% and decrease of 46.42% respectively were obtained which indicated improved Factory efficiency, machine availability and compliancy to plan. Also, Exponential distribution model was used to calculate the failure rate and reliability of the machines using the reliability measures result obtained after root cause analysis technique was applied to each failure. A reliability of 99.7% was obtained.

An Effective Hybrid Multiobjective Flexible Job Shop Scheduling Problem Based on Improved Genetic Algorithm

Multiobjective Flexible Job Shop Scheduling Problem (MO-FJSP) is a scheduling problem used in manufacturing sectors to use energy efficiently and thriftily. The scheduling problem aims to increase productivity and reduce energy consumption via a mathematical model. With this paper, an effective genetic algorithm is proposed for MO-FJSP based on maximum completion time, total machine load, and bottleneck machine load. The solution method utilizes a hybrid multiobjective genetic algorithm. A combination of global selection and fast selection is used for initialization and obtaining a uniformly distributed initial population. The cross-variance operator is adaptively improved to enhance the searching in the population. Following that an elite retention mechanism is designed to address the possible limitations of the elite strategy in maintaining population diversity. As a result, an improved harmonic search algorithm is introduced to improve the quality of individuals in the elite pool. The proposed hybrid method is implemented in MATLAB R2018a. Tests were conducted using the benchmark Kacem test set, the BR data data set, and with the actual production cases. The algorithm succeeded in achieving 13 nondominated solutions in the initial 20 runs. Moreover, the method obtains the optimal value criterion for the solution accuracy factor. As a whole, results of the evaluation testify that the proposed method can be used to solve the MO-FJSP with high accuracy and fast convergence. The method also provides feasible and effective scheduling solutions for the decision-makers in actual production. Based on the promising results obtained, it is deduced that the method has a wide applicability range particularly in manufacturing sector.

Performance analysis and optimisation of stochastic flow lines with limited material supply

We consider stochastic flow lines with limited buffer sizes and limited material supply. In these systems, the configuration of the flow line parameters and the configuration of the material supply determine the system output. Shortages of material supply can limit the performance of the production system. We use flexible (mixed-integer) linear programming approaches to evaluate and optimise the performance of long stochastic flow lines with limited material supply in discrete and continuous time. The approaches are used to quantify the impact of material shortages on the system output. Further, they are applied to determine the minimum material levels that are required to prevent material shortages of a given flow line configuration. The results of the numerical study reveal insights on the approximation accuracy of the linear programs as well as on the dependence of optimal material levels on flow line characteristics such as the presence of bottleneck machines and the system variability. The contribution of this paper consists of both, integrated models for stochastic flow lines with limited material supply and new insights on the optimal material supply of stochastic flow lines.

A hybrid multi-objective algorithm to solve a cellular manufacturing scheduling problem with human resource allocation

A cellular manufacturing system (CMS) is a suitable system for the economic manufacture of part families. Scheduling the manufacturing cells plays an effective role in successful implementation of the manufacturing system. Due to the fact that in the CMS, bottleneck machine and human resources are two important factors, which so far have not been studied simultaneously in a mathematical model, there should be a model to consider them. Therefore, this research develops a bi-objective model for CMS in a three-dimensional space of machine-part and human resources. The main objective is to minimize the maximum completion time of all tasks in the system and reduce the number of intercellular translocation based on bottleneck machines’ motion and human resources. Due to the NP-hardness of the studied problem, applying the conventional solution methods is very time-consuming, and is impossible in large dimensions. Therefore, the use of metaheuristic methods will be useful. The accuracy of the proposed model is investigated using LINGO by solving a small example. Then, to solve the problem in larger dimensions, a hybrid Multi-Objective Tabu Search-Genetic Algorithm (MO-TS-GA) is designed and numerical results are reported for several examples.

Work in Progress Level Prediction with Long Short-Term Memory Recurrent Neural Network

Since the reliability of production plans drops largely within several days after plan creation, production control faces huge challenges, when trying to foresee the work in progress (WIP) level at bottleneck machines and trying to react appropriately. Whereas several researchers applied artificial intelligence to predict lead times or transition times to improve the planning reliability, only small efforts have been taken on time series prediction in the field of production control, especially on the topic WIP prediction. In this paper univarate times series approaches are used for predicting the work in progress for a bottleneck machine for one and more step ahead. Long short-term memory recurrent neural networks, LSMT models show higher accuracy than classical methods. For more step ahead forecasting four different approaches are investigated. Systematical model tuning and comparison of various error measures are presented for a real industrial use case from the steal manufacturing industry.

Workload control with shifting bottlenecks: norms optimisation through design of experiments

Workload control is a production planning and control system designed to overcome the trade-off between high throughput and short and stable lead time. Specifically, work-in-process is continuously monitored, and new jobs are not admitted in the shop floor until work-in-process drops below predefined threshold values or norms. To exploit performance, norms should be fine-tuned to minimise queues, without generating starvation at the bottleneck machines. The optimisation process is straightforward for a perfectly balanced system, but much harder in case of shifting bottlenecks. The paper focuses on this issue and presents an innovative procedure, based on the response surfaces method, which allows one to optimise the norms in a precise way, keeping unaltered the maximal or desired throughput of the manufacturing system. A comprehensive simulation analysis demonstrated the quality of the proposed approach and showed the importance of using different norms to boost the overall performance of the manufacturing system.

Three-machine open shop with a bottleneck machine revisited

The paper considers the three-machine open shop scheduling problem to minimize the makespan. In the model, each job consists of two operations, one of which is to be processed on the bottleneck machine, which is the same for all jobs. A new linear-time algorithm to find an optimal non-preemptive schedule is developed. The suggested algorithm considerably simplifies the only previously known method as it straightforwardly exploits the structure of the problem and its key components to yield an optimal solution.

Consideration of processing time dissimilarity in batch-cyclic scheduling of flowshop cells

Flowshop scheduling methods that appear in the literature assume that products are processed in either batch mode or cyclic mode. In batch mode, a product starts after another product is completely processed. On the other hand, in cyclic mode, products are run in mixed mode with unit transfer. In this paper, a new scheduling method, the batch-cyclic scheduling method, is proposed to schedule the products. In the proposed method, multiple sets of products (Cyclic Sets) are selected first and then products in each cyclic set are run in mixed mode. Overall, the general solution approach consists of three phases: (1) family formation based on processing similarity; (2) subfamily formulation based on a processing time dissimilarity (proposed mathematical model); (3) product scheduling using the batch-cyclic method (proposed mathematical model). As in the cyclic method, batch-cyclic scheduling is feasible when setup times are negligible. Due to the lack of work in this newly proposed batch-cyclic method, it is compared with the batch method. Additionally, the performance of the three-phase approach is compared with the two-phase approach using 27 scenarios. The results show that the proposed three-phase approach minimises the number of machines and lowers makespan when the bottleneck machine shifts.

Incorporating shifting bottleneck identification in assembly line balancing problem using an artificial immune system approach

The manufacturing industry has evolved in the past few years due to the competitive global economy where the performance of its assembly line operations is primarily dependent upon optimum resource utilization. The assembly line operations are balanced among the available resources to obtain an equal amount of workload to achieve optimum resource utilization, called the assembly line balancing (ALB) problem. Various approaches have been proposed to solve the ALB problem, which is broadly categorized as exact, heuristic, and meta-heuristic approaches. Although solving the ALB problem is crucial, a bottleneck may still occur over the next operation stages. By using problem-specific information (bottleneck identification), it is expected to improve the solution quality of the ALB problem. As such, the contribution of this study is the computational method, namely as the swarm of immune cells with bottleneck identification (SIC+) approach, where both the ALB and bottleneck identification problems are addressed. In addition to the flexible problem representation, the SIC+ approach is equipped with a discrete bottleneck simulator to simulate the bottleneck scenario and bottleneck-specific operators to redistribute the machine workload of the identified bottleneck machine. The approach was tested on 24 benchmark data sets of the ALB problem, and the impact of incorporating bottleneck identification was illustrated. The experimental results show that the proposed SIC+ approach has achieved a total of 66.12% optimal solution over all instances of the benchmark data sets and has been compared with approaches from the literature where high-quality solutions were statistically justified.

A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem

In this work we propose an efficient branch-and-bound (B&B) algorithm for the permutation flow-shop problem (PFSP) with makespan objective. We present a new node decomposition scheme that combines dynamic branching and lower bound refinement strategies in a computationally efficient way. To alleviate the computational burden of the two-machine bound used in the refinement stage, we propose an online learning-inspired mechanism to predict promising couples of bottleneck machines. The algorithm offers multiple choices for branching and bounding operators and can explore the search tree either sequentially or in parallel on multi-core CPUs. In order to empirically determine the most efficient combination of these components, a series of computational experiments with 600 benchmark instances is performed. A main insight is that the problem size, as well as interactions between branching and bounding operators substantially modify the trade-off between the computational requirements of a lower bound and the achieved tree size reduction. Moreover, we demonstrate that parallel tree search is a key ingredient for the resolution of large problem instances, as strong super-linear speedups can be observed. An overall evaluation using two well-known benchmarks indicates that the proposed approach is superior to previously published B&B algorithms. For the first benchmark we report the exact resolution – within less than 20 minutes – of two instances defined by 500 jobs and 20 machines that remained open for more than 25 years, and for the second a total of 89 improved best-known upper bounds, including proofs of optimality for 74 of them.

Deploying NSBA algorithm for bi-objective manufacturing cells considering percentage utilisation of machines

Percentage utilisation of machines is considered as an important production factor for manufacturing cell formation problem (CFP) in cellular manufacturing (CM). This recently developed concept correctly emphasises ratio data in context of CM. In this paper, an utilisation-based bi-objective mathematical model is developed, which minimises the total machine utilisation induced by bottleneck machines and number of voids. Thereafter, a new data-generating algorithm is introduced. The abovementioned bi-objective CFP is solved using a non-dominated sorting bat algorithm (NSBA), which is compared with published multi-objective bat algorithm (MOBA) successfully. Statistical tests are conducted and data consistency is confirmed on obtained results. The computational experiments depict that the Pareto solutions of NSBA are 35.7% improved. The contribution of this research is threefold. First, an accurate bi-objective mathematical expression is developed for utilisation-based CFPs. Second, a novel data-generating algorithm is stated. Third, NSBA technique is successfully tested.

Deploying NSBA algorithm for bi-objective manufacturing cells considering percentage utilisation of machines

Percentage utilisation of machines is considered as an important production factor for manufacturing cell formation problem (CFP) in cellular manufacturing (CM). This recently developed concept correctly emphasises ratio data in context of CM. In this paper, an utilisation-based bi-objective mathematical model is developed, which minimises the total machine utilisation induced by bottleneck machines and number of voids. Thereafter, a new data-generating algorithm is introduced. The abovementioned bi-objective CFP is solved using a non-dominated sorting bat algorithm (NSBA), which is compared with published multi-objective bat algorithm (MOBA) successfully. Statistical tests are conducted and data consistency is confirmed on obtained results. The computational experiments depict that the Pareto solutions of NSBA are 35.7% improved. The contribution of this research is threefold. First, an accurate bi-objective mathematical expression is developed for utilisation-based CFPs. Second, a novel data-generating algorithm is stated. Third, NSBA technique is successfully tested.

A prognostic algorithm to prescribe improvement measures on throughput bottlenecks

Throughput bottleneck analysis is important in prioritising production and maintenance measures in a production system. Due to system dynamics, bottlenecks shift between different production resources and across production runs. Therefore, it is important to predict where the bottlenecks will shift to and understand the root causes of predicted bottlenecks. Previous research efforts on bottlenecks are limited to only predicting the shifting location of throughput bottlenecks; they do not give any insights into root causes. Therefore, the aim of this paper is to propose a data-driven prognostic algorithm (using the active-period bottleneck analysis theory) to forecast the durations of individual active states of bottleneck machines from machine event-log data from the manufacturing execution system (MES). Forecasting the duration of active states helps explain the root causes of bottlenecks and enables the prescription of specific measures for them. It thus forms a machine-states-based prescriptive approach to bottleneck management. Data from real-world production systems is used to demonstrate the effectiveness of the proposed algorithm. The practical implications of these results are that shop-floor production and maintenance teams can be forewarned, before a production run, about bottleneck locations, root causes (in terms of machine states) and any prescribed measures, thus forming a prescriptive approach. This approach will enhance the understanding of bottleneck behaviour in production systems and allow data-driven decision making to manage bottlenecks proactively.

Hybrid algorithm based on improved extended shifting bottleneck procedure and GA for assembly job shop scheduling problem

With the makespan as the optimisation goal, we propose a hybrid solving method that combines improved extended shifting bottleneck procedure (i-ESB) and genetic algorithm (GA) for the assembly job shop scheduling problem (AJSSP). Hybrid genetic algorithm (HGA) uses a GA based on operation constraint chain coding to achieve global search and a local search based on an i-ESB. In the design of i-ESB, an extended disjunctive graph model (EDG) corresponding to AJSSP is presented. The calculation method of the operation head and tail length based on EDG is studied, as well as the searching method of key operations. The Schrage algorithm with disturbance is used to solve the single-machine scheduling subproblem. The selection criterion for bottleneck machines is increased. A greedy bottleneck machine re-optimisation process is designed. The effectiveness and superiority of the proposed algorithm are verified by testing and analysing the relevant examples in the literature.