Flow-line Manufacturing Research Articles

Fitness landscapes of buffer allocation problem for production lines with unreliable machines

We study the structural properties of the buffer allocation problem from the fitness landscape perspective. We consider manufacturing flow lines with series–parallel network structure. The machines are supposed to be unreliable, their time to failure and repair time are exponentially distributed. Tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with local search and genetic algorithms in order to evaluate the fitness landscape properties of previously published instances and their modifications. It turns out that the so-called ‘massif central’ or ‘big valley’ structure of the fitness landscape is present but only partially: The fitness of local optima is negatively correlated with the distance to the best found solution, yet the set of local optima cannot be encompassed by a ball of relatively small size with respect to the size of solution space. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The performance of genetic algorithms is discussed with respect to population clustering and the permanent usage of crossover is recommended.

On Local Optima Distribution in Buffer Allocation Problem for Production Line with Unreliable Machines

In this paper, we consider a buffer allocation problem in manufacturing flow lines with series-parallel network structure where nodes correspond to buffers of finite capacity, and arcs correspond to the machines. The machines are supposed to be unreliable, their time to failure and repair time are assumed to be exponentially distributed. Different machines may have different production rates and the production rates of all machines are assumed to be deterministic. The buffer allocation problem is to determine the capacities of all buffers with respect to a given optimality criterion, which is a function of the average production rate of the line, the buffer acquisition and installation cost and the inventory cost. In search for the optimum, the tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with the local search and genetic algorithms. It turns out that the “massif central” or “big valley” structure of the fitness landscape is present but only partially: The fitness of the local optima is negatively correlated with the distance to the best found solution, yet the set of local optima can not be encompassed by a ball of relatively small radius. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The symmetries of the fitness function are discussed and suggested as the possible cause of the local optima clustering. Finally the performance of genetic algorithms is bfiefly discussed with respect to solutions clustering.

Solution-processable electrode-material embedding in dynamically inscribed nanopatterns (SPEEDIN) for continuous fabrication of durable flexible devices

A facile and scalable lithography-free fabrication technique, named solution-processable electrode-material embedding in dynamically inscribed nanopatterns (SPEEDIN), is developed to produce highly durable electronics. SPEEDIN uniquely utilizes a single continuous flow-line manufacturing process comprised of dynamic nanoinscribing and metal nanoparticle solution coating with selective embedding. Nano- and/or micro-trenches are inscribed into arbitrary polymers, and then an Ag nanoparticle solution is dispersed, soft-baked, doctor-bladed, and hard-baked to embed Ag micro- and nanowire structures into the trenches. Compared to lithographically embossed metal structures, the embedded SPEEDIN architectures can achieve higher durability with comparable optical and electrical properties and are robust and power-efficient even under extreme stresses such as scratching and bending. As one tangible application of SPEEDIN, we demonstrate a flexible metal electrode that can operate at 5 V at temperatures up to 300 °C even under the influence of harsh external stimuli. SPEEDIN can be applied to the scalable fabrication of diverse flexible devices that are reliable for heavy-duty operation in harsh environments involving high temperatures, mechanical deformations, and chemical hazards.

Assessing manufacturing flow lines under uncertainties in processing time: An application based on max-plus equations, multicriteria decisions, and global sensitivity analysis

In this paper, a novel application on how uncertainties in a manufacturing flow line − MFL (e.g., times required to perform an action) could be analyzed and what the benefits are of such analysis. The approach proposed investigates three main goals: i) Uncertainty analysis, ii) Stochastic dominance, and iii) Sensitivity analysis. In particular, this paper extends the application of max-plus algebra to model MFL with different flow configurations and buffer capacities and provides the approximated probability density functions (PDFs) of selected performance indicators (e.g., the total idle time in the whole line, output rates, throughputs, among others). As a result, it is possible to quantify the variability of the selected output, compare different possible configurations among MFL, choose the best one, and identify critical variables and risk drivers (e.g., the processing times that affect the most a KPI − key performance indicator). The approach, illustrated by analyzing a case study of the literature, emphasizes the benefits for a decision-maker in charge of the design or managing of the manufacturing system.

Scheduling Optimization in Flowline Manufacturing Cell Considering Intercell Movement with Harmony Search Approach

Based on the non-permutation property of intercell scheduling in flowline manufacturing cells, a hybrid harmony search algorithm is proposed to solve the problem with the makespan criterion. On the basis of the basic harmony search algorithm, the three key elements of memory consideration, pitch adjustment and random selection are discretized and improved to adapt to the operation-based encoding. To compare the performance, different scale cases are generated in both the overall solution and the two-stage solution with the proposed algorithm, the hybrid particle swarm optimization algorithm and the hybrid genetic algorithm. The relative deviation is taken as the performance index. The compared results show that a better solution can be obtained with the proposed algorithm in both the overall solution and the two-stage solution, verifying the superior performance of the proposed algorithm.

Analysis of Throughput Parameters in A Flow Line Manufacturing System with Varying the Buffer Limits

Analysis of Throughput Parameters in A Flow Line Manufacturing System with Varying the Buffer Limits

A comparative study on design of static and dynamic cellular manufacturing system under deterministic production environment

In job shops and flow line manufacturing plants, the impact of Cellular Manufacturing System (CMS) with recent advancement in Group Technology (GT) and Automated Manufacturing System (AMS) has resulted in minimizing cost involved in machine maintenance and overhead, machine procurement, machine setup and intra/intercell material movement cost. Meanwhile, the industrialist addressed the problem in deciding the planning horizon to design CMS with respect to the change in customer demand pattern. The Classical CMS failed to capture the fickle in part demand and product mix. In the case of Dynamic CMS (DCMS), the fluctuation is considered by dividing the entire planning horizon into smaller periods. In this paper, a real time data of product mix and product demand were collected from an automobile components manufacturing industry located in SIPCOT Industrial Park, Sriperumbudur to visualize the potential benefits of incorporating DCMS. In this work, existing mathematical model has been used for designing DCMS and it is developed further to convert the model as static environment. Evaluation of the obtained CMS design was done by capturing the exact production parameters of the industry and it is found DCMS resulted in promising cell configuration with minimal overall objective value.

Scheduling flowline manufacturing cells with inter-cellular moves: non-permutation schedules and material flows in the cell scheduling problem

A major goal of the concept of cellular manufacturing is to form independent cells. However, the formation of entirely independent cells is rarely found in practice, where inter-cellular transports of jobs are often accepted to a certain level. The resulting scheduling task in flowline manufacturing cells is referred to as cell scheduling problem. Usually it is modelled and solved analogously to the group scheduling problem, which arises within a single cell with multiple part families. In this study, we point out several characteristics that specify the distinctiveness of cell scheduling. Furthermore, a new cell-based objective, namely total cell makespan, is introduced. As no appropriate benchmark instances are available so far, new test problems are developed, that integrate the derived characteristics. In an extensive computational study known constructive heuristics as well as simulated annealing algorithms to generate permutation and non-permutation schedules are tested. It is revealed that especially the type of material flows between cells has an immense impact on the algorithms' performance. The findings support a better problem understanding and point out the necessity of developing new heuristics especially for certain types of the cell scheduling problem.

Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies

We compare the optimal buffer allocation of a manufacturing flow line operating under three different production control policies: installation buffer (IB), echelon buffer (EB), and CONWIP (CW). IB is the conventional policy where each machine may store the parts that it produces only in its immediate downstream buffer if the next machine is occupied. EB is a more flexible policy where each machine may store the parts that it produces in any of its downstream buffers. CW is a special case of EB where the capacities of all buffers, except the last one, are zero. The optimization problem that we consider is to maximize the average gross profit (AGP) minus the average cost (AC), subject to a minimum average throughput constraint. AGP is defined as the average throughput of the line weighted by the gross marginal profit (selling price minus production cost per part), and AC is the sum of the average WIP plus total buffer capacity plus transfer rate of parts to remote buffers, weighted by the inventory holding cost rate, the cost of storage space, and the marginal cost of transferring parts to remote buffers, respectively. Numerical results show that the optimal EB policy generally outperforms the optimal IB and CW policies. They also show that as the production rates of the machines decrease, the relative advantage in performance of the EB policy over the other two policies increases. When the cost of transferring parts to remote buffers increases, the dominance of the EB policy over the IB policy decreases while the dominance of the EB policy over CW increases.

Improving Flow Lines by Unbalancing

The paper's aim is to show how the performance of a balanced manufacturing flow line can be improved on the critical WIP level by making it unbalanced to some extent. The explanation and a quantitative example are presented. There is a trade-off between system balance and stability, and it can be handled as an optimization problem. Data are gathered using a physical simulation system. The analysis is carried out with a discrete time simulation program which applies next-event time advance mechanism. The model has been implemented in AIMMS modelling language.

Makespan optimization in a no-wait flowline manufacturing cell with sequence-dependent family setup times

The no-wait flowline manufacturing cell scheduling problem with sequence-dependent family setup times and the objective of minimizing makespan is NP-hard in the strong sense even in the two-machine case. This work proposes an efficient matheuristic for solving this problem to optimality in a reasonable computational time. To demonstrate the effectiveness and efficiency of the proposed matheuristic, computational experiments are performed using a well-known benchmark problem set. The computational results reveal that all test instances of the problem are solved to optimality using the proposed matheuristic, and that the computational times of the matheuristic are comparable with those of state-of-the-art meta-heuristics for finding approximate solutions. This work contributes significantly to the research into algorithms for solving this computationally intractable problem.

Decomposition methods for cost and tardiness reduction in aircraft manufacturing flow lines

A manufacturing system consisting of two subsystems is considered in low-volume manufacturing as found in the aerospace industry. The first subsystem consists of parallel mixed-model assembly lines whereas identical parallel stations form the second one. A finite buffer is between the subsystems. A combined objective function is used which accounts for total labor and inventory costs at the first subsystem and for the makespan and total weighted tardiness (TWT) at the second subsystem. The jobs have to be assigned to one of the parallel assembly lines. They then have to be sequenced at each single assembly line. Workers have to be assigned to each job and station. Assignment and sequencing decisions have to be made for the jobs at the second subsystem to calculate the makespan and the TWT value. A random-key genetic algorithm (RKGA) assigns jobs to the assembly lines and sequences them there. The RKGA is hybridized with heuristics and exact approaches to determine start and completion times of the jobs at the stations. A list scheduling approach makes the decisions associated with the second subsystem. Outsourcing is applied to deal with the finite buffer between the two subsystems. Results of computational experiments based on randomly generated problem instances demonstrate that the decomposition approaches perform well.

Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints

Configuration selection for reconfigurable manufacturing systems (RMS) is one of the key issue that needs to be addressed to transform RMS implementation from its nascent stage to a mature one. The problem is of vital importance because of the fact that the selection of machine configurations for operations to be performed on any job/part is based on multiple objectives, which often conflicts each other. In the present work, a framework for configuration selection for a manufacturing flow line (MFL) is proposed and demonstrated using non-dominated sorting genetic algorithm-II (NSGA-II). The proposed framework is explained with the help of a mathematical illustration. Results are presented in the form of non-dominated solutions obtained for machine configurations encompassing manufacturing of a product using a multi-tage reconfigurable serial product flow line (RSPFL). The outcome of this work would help in improving the performance of RMSs while considering multiple objectives as the selection criterions. The findings are finally discussed in detail in the light of previous works on the topic.

A dynamic multi-agent-based scheduling approach for SMEs

In modern manufacturing systems with computational complexities, decision-making with respect to dynamic rescheduling and reconfiguration in case of internal disturbances is an important issue. This paper introduces a multi-agent-based dynamic scheduling system for manufacturing flow lines (MFLs) using the Prometheus methodology (PM) considering the dynamic customer demands and internal disturbances. The PM is used for designing a decision-making system with the feature of simultaneous dynamic rescheduling. The developed system is implemented on a real MFL of a small- and medium-sized enterprise (unplasticized polyvinyl chloride (uPVC) door and window) where the dynamic customer demands and internal machine break downs are considered. The application has been completely modeled using a Prometheus design tool, which offers full support to the PM, and implemented in JACK agent-based systems. Each agent is autonomous and has an ability to cooperate and negotiate with other agents. The proposed decision-making system supports both static and dynamic scheduling. A simulation platform for testing the proposed multi-agent system (MAS) is developed, and two real scenarios are defined for evaluating the proposed system. The analysis takes into account the comparisons of the overall performances of the system models using the MAS scheduling and conventional scheduling approaches. The result of simulation indicates that the proposed MAS could increase the uptime productivity and the production rate of flexible flow-line manufacturing systems.

An Examination of Some Factory Physics Principles

In this paper, we examine some principles in managing manufacturing systems. These principles are concerned with the variability, the utilization, the rework, the lead time, and the constant work-in-process efficiency. While these principles are developed through analyzing some simpler disconnected flow line manufacturing systems, we examine whether they can have broad applications. For some of these principles, we provide sufficient conditions, while for others, we provide counterexamples. Our analysis suggests that we should be very cautious about these laws when applied to non-Markov and non-tandem systems.

Collaborative Maintenance in flow-line Manufacturing Environments: An Industry 4.0 Approach

In a manufacturing shop-floor, context-aware intelligent service systems, along with mobile solutions can be used for the provision of information services to shop-floor personnel according to their situation. This paper presents an Industry 4.0 system that provides decision support for line operators and maintenance personnel, in an industrial production scenario, where maintenance needs immediate response. A multi-layered, Service Oriented Architecture (SOA) based system, which integrates several sub-systems, such as sensor data fusion, context modelling and contextual data information provision has been developed. The solution includes a number of context-aware apps that support the collaborating users to address maintenance issues. A case study was conducted, providing insight into the suitability of the proposed solution and bringing to light further aspects that will improve the proposed context-aware system.

A constructive algorithm and a simulated annealing approach for solving flowshop problems with missing operations

In many practical cases of flowshop environments and especially in flowline manufacturing cells, some or all jobs may not require processing on all machines. Hence, this paper focuses on the flowshop scheduling problem with missing operations. A modification of the constructive NPS-set heuristic is proposed, which generates non-permutation schedules effectively. Furthermore, a two-phase simulated annealing (SA) method is presented that specifically considers missing operations in its procedure. The modified NPS-set heuristic and the two-phase SA are tested and statistically evaluated by an extensive computational study for total flow time criteria. The results show that the modified NPS-set heuristic as well as the specific consideration of missing operations can enhance the algorithms’ performance significantly.

Minimising makespan in flowshop group scheduling with sequence-dependent family set-up times using inserted idle times

This paper focuses on the permutation flowshop group scheduling problem to minimise makespan, which is typically found in flowline manufacturing cells. In contrast to classical flowshop scheduling, it is characterised by a scheduling task at two levels: on the one hand, jobs within part families and on the other hand, a number of part families have to be sequenced. Integrating sequence-dependent set-up times for every changeover of families, this problem can represent practical cases. By modelling each family as a job with time lags, some specific problem characteristics of the group scheduling problem are pointed out. It is shown that generating job sequences by minimising the sum of inserted machine idle times instead of makespan on the first level of scheduling and the use of the schedule heads on the second level can lead to significant improvements for some test problems. These findings are used for the improvement of existing constructive heuristic algorithms, whose effectiveness is assessed for several test instances with sequence-dependent family set-up times.

Generating max-plus equations for efficient analysis of manufacturing flow lines

An elegant mathematical tool, max-plus algebra, can be used to model manufacturing flow lines in the form of linear state-space-like equations for application in manufacturing systems analysis and control. A method for quick and efficient generation of the max-plus equations for manufacturing flow lines of any size or structure has been developed. The generated equations are used to model flow lines with finite buffers between stations as well as parallel identical stations.A flow line is initially assumed to have infinite buffers for all stations and no parallel identical stations. An adjacency matrix is then used to encode the layout of the flow line and determine the relationships between different stations in the line. The max-plus equations for the line are then generated for the simplified line while capturing the line dynamics in two matrices that are function of the processing times of the different stations in the line. Additional terms are used to model finite buffers and the parallel identical stations.The developed manufacturing systems modelling method using max-plus algebra is intuitive and easy to understand and code in software. It is a decision support tool which facilitates quick analysis of different configurations of manufacturing flow lines and assessment of several what-if scenarios. A case study is presented where the max-plus equations are generated for three possible assembly line configurations for a back flushing control valve. The effect of buffer sizes and changes in the assembly time of one of the stations on the total line idle time is analyzed using the generated model.

Max-plus Modeling of Manufacturing Flow Lines

Max-plus algebra can be used to model manufacturing flow lines using linear state-space-like equations which can be used in analysis and control. This paper presents a method for easy and quick generation of the max-plus equations for manufacturing flow lines of any size or structure. The generated equations can model flow lines with infinite as well as finite buffer sizes.A flow line to be modeled is initially assumed to have infinite buffers for all stations. The line model equations are then generated as a combination of serial and merging stations after identifying the different stages using an adjacency matrix for the flow line. In the generated equations, the dynamics of the system are captured in two matrices that are function of the processing times of the different stations in the line. After generating these equations, extra terms are added to account for the finite buffers where for each buffer size, a matrix is added multiplied by the vector of system parameters delayed by the buffer size plus one.The method is intuitive and easy to understand and code in software and thus can facilitate quick analysis of different configurations of manufacturing flow lines and assessing what if scenarios. This can also allow quick on-line reconfiguration of controllers for frequently reconfiguring flow lines.