Product Line Design Research Articles

Optimal product line design using Tabu Search

Product design constitutes a critical process for a firm, which if not implemented effectively it may even question its viability. The optimal product line design is an NP-hard problem, where a company aims at designing a set of products that will optimize a specific objective. Whilst Tabu Search (TS) has effectively solved a large number of combinatorial optimization problems, it has not yet been evaluated in product design. In this paper we design and implement a TS algorithm, which is applied to both artificial and actual consumer-related data preferences for specific products. The algorithm’s performance is evaluated against previous approaches like Genetic Algorithm and Simulated Annealing. The results indicate that the proposed approach outperforms nine tested heuristics in terms of accuracy and efficiency. It also constitutes a more robust technique, and can be effectively generalized to larger problem sizes, which include higher number of products, attributes, or levels. Finally, a novel variant of TS capable of reducing execution time called Tabu Search Class Move, is introduced.

Clonal selection algorithms for optimal product line design: A comparative study

Product design constitutes a critical process for a firm to stay competitive. Whilst the biologically inspired Clonal Selection Algorithms (CSA) have been applied to efficiently solve several combinatorial optimization problems, they have not yet been tested for optimal product lines. By adopting a previous comparative analysis with real and simulated conjoint data, we adapt and compare in this context 23 CSA variants. Our comparison demonstrates the efficiency of specific cloning, selection and somatic hypermutation operators against other optimization algorithms, such as Simulated Annealing and Genetic Algorithm. To further investigate the robustness of each method to combinatorial size, we extend the previous paradigm to larger product lines and different optimization objectives. The consequent performance variation elucidates how each operator shifts the search focus of CSAs. Collectively, our study demonstrates the importance of a fine balance between global and local search in such combinatorial problems, and the ability of CSAs to achieve it.

Optimizing the application of plasma functionalised water (PFW) for microbial safety in fresh-cut endive processing

The microbiological profiles and responses of native microflora of endive were investigated using a model process line, to establish where a defined PFW should be optimally applied to retain or improve produce microbiological quality. The PFW processes were compared with tap water and ClO2. The antimicrobial efficacy of PFW was quantified by determining the reduction in microbial load, the microbial viability and vitality. Depending on the stage of application of PFW, up to 5 log10-cycles reduction was achieved, accompanied by a reduction of metabolic activity, but not necessarily with a decrease in metabolic vitality. Multiple application (3-step-PFW-application) was more effective than single application (1-step-PFW-application) and PFW showed stronger antimicrobial effect in pre-cleaned endive. High concentrations of nitrite (315 mg l−1) and nitrate (472 mg l−1) in PFW were the main factors for the antimicrobial efficacy of PFW against bacteria. Furthermore, H2O2 and an acidic pH supported the mechanism of action against the endive microflora. These results identify the pathway to scale up successful industrial application of PFW targeting microbiological quality and safety of fresh leafy products. Industrial relevanceThe safety, quality and shelf life of freshly cut vegetables, e.g. lettuce, are strongly influenced by the microbial load. In addition, the hygienic design of production line, and a good handling/ production practice are indispensable. This study shows that the application of PFW, as a promising non-thermal sanitation technology, enables the inactivation of native microbial contamination on fresh-cut endive depending on the process stage of application. It further describes the impact of PFW on the metabolic activity and metabolic vitality of the lettuce-associated microflora. For higher acceptance, the mechanism of action of PFW was assumed based on previous chemical analyses and compared to the industrial standard of ClO2. The results contribute to the understanding and product-specificity of PFW-induced effects on safety, quality and shelf life of fresh cut lettuce and could be a basis for a possible industrial implementation and complement of common technologies.

Layout design of a mixed-flow production line based on processing energy consumption and buffer configuration

Layout design of a mixed-flow production line based on processing energy consumption and buffer configuration

A finite state method in improvement and design of lean Bernoulli serial production lines

Production systems have a significant impact on national and global economies. It is, therefore, of particular interest to develop, master, and apply new methodologies improving the performance of the production systems. A systematic approach to the improvability and design for lean production in the case of the serial Bernoulli production lines is presented in this research using the finite state method and application of the Markovian modeling approach. A new concept of the bottleneck identification procedure is presented based on the analytical relationship between line properties and performance measures. Also, a new design methodology combining features of the finite state method and genetic evolution algorithm is developed to enable the efficient lean design of the serial Bernoulli production lines. The developed procedures are applied in the case of an automotive paint shop system including bottleneck identification concerning the production rate, the work-in-process, and the probabilities of blockage and starvation. Finally, the lean automotive paint shop system is obtained in an efficient and designer-friendly way.

Estimating net ideal cycle time for body-in-white production lines

In the automotive industry, a Body in White (BIW) refers to the first step, the basic structure, in the production of a vehicle. Once a BIW production line has been built, the (maximum) capacity is fixed and throughput is therefore limited by the equipment specified during the design phase. The main metric used to inform the production line design is the Net Ideal Cycle Time (NICT). Unfortunately, the state of practice to estimate the NICT is a basic heuristic that does not account for production variation. In this paper, we challenge the current estimation approach by proposing an alternative that assumes actual production to follow a Weibull distribution. The proposed model is derived and estimated from empirical data. The results suggest that BIW production lines have traditionally been designed with too low a capacity, resulting in planned throughput rarely being achieved. On the other hand, increasing the design capacity implies a higher initial investment. In this paper it is demonstrated that the higher investment required is offset by reduced losses, resulting in more reliable planning and returns.

A bibliometric analysis of the product line design problem

In the product design problem, firms aim to find suitable configurations of product attributes with the objective of increasing their participation in the marketplace. This problem belongs to the field of quantitative marketing and is considered a NP-Hard problem, due to its wide search space for an optimal solution. Among the related literature, there are different methodologies to address this problem, gaining ground those that apply metaheuristics, with an emphasis in Genetic Algorithms. The main aim of this work is to present an overview of the most significant contributions in this area using a bibliometric analysis approach. The paper uses Scopus database and Web of Science Core Collection, in order to obtain leading and the most influential articles, conferences papers, journals, authors, institutions and countries. The results highlight Kwong, C.K. as the most productive author while Nagamachi M. is the most influential author. Furthermore, China is the leading country in this research field. The use of Genetic Algorithms in the solutions of the Product Design Problem is a growing area of study with important development of methodologies and approaches.
 JEL Codes: C00, C02
 Received: 07/10/2020. Accepted: 20/02/2021. Published: 01/06/2021.

Digital Twin-Driven Rapid Customized Design of Board-Type Furniture Production Line

Abstract The mass personalization paradigm requires manufacturing enterprises to adapt to market changes quickly to meet customer demands. It also imposes higher requirements for designing intelligent production lines. Based on the digital twin (DT) technology, a rapid customized design method is proposed for developing new board-type furniture production lines. The DT has the characteristics of interactive virtual-reality mapping and fusion. It could provide design guidance and decision-support services in the design phase, yield the engineering analyzing ability to solve coupled problems, and finally generate the authoritative design scheme of the manufacturing system. A production line design platform is developed based on the DT model, which can parallelize the design process and reduce the design cycle. The parallel control between the physical world and digital space is achieved by establishing the DT network and perceiving the status of the physical equipment. Five key enabling technologies are introduced to provide the theoretical fundamentals for implementing the DT-based manufacturing system design approach. A board-type furniture production line is presented as a case study to verify the effectiveness of the method.

Multiobjective optimization for modular unit production lines focusing on crew allocation and production performance

A flexible production duration is necessary because of the increase in the demand for modular construction; further, the benefits of modular construction, such as reduced duration and labor requirement, are also considered important. Such construction requires various production line design alternatives that consider the benefits with equal priority. To address this issue, a multiobjective optimization model for modular unit production line (MOMUPL) was developed based on a genetic algorithm. To this end, differences between unit production line design and scheduling methods in the manufacturing industry were identified. Then, to rank solutions in optimization processes, the nondominated sorting genetic algorithm II (NSGA-II) was applied to the model. The MOMUPL suggests various optimization results, thereby allowing the project manager to select a solution based on specific project characteristics. Therefore, using MOMUPL, the duration for scheduling is expected to be reduced along with a reduction in project labor cost and duration.

The impact of the optimal buffer configuration on production line efficiency: A VNS-based solution approach

Intelligent design of manufacturing systems is an important research area because it directly affects the profitability of companies and is very costly. Production lines are the most encountered systems in manufacturing and optimal design of production lines is one of the basic research areas for both academy and practitioners. In this study, the buffer allocation problem (BAP), one of the most important optimization problems in designing production lines, is handled and a variable neighborhood search (VNS) based solution approach is proposed to solve the problem with the objectives of throughput maximization and total buffer size minimization. In addition to proposing a new solution approach, two initialization heuristics are employed to enhance the search efficiency. The existing benchmark problems were used to performance evaluation of the proposed approach. The computational study showed that the proposed VNS-based solution approach was highly effective in finding good-quality solutions for all the problem sets examined.

Investigation of low- and high cycle fatigue in Al 2024-T4 alloy

The article presents the results of low-(LCF) and high (HCF) fatigue in samples of aluminum alloy 2024-T4, which is used in aircraft construction, mainly for highly loaded structural components, including for plating and fuselage frames and girders rotor blades of helicopters. This alloy is used where it is required a high strength to weight ratio of the product and high resistance to fatigue. Moreover, it is poorly weldable and has a low corrosion resistance. The tests were performed on hourglass and cylindrical samples, with a parallel and perpendicular orientation relative to the "direction of embodiment" or blank sheet rolling direction. Material for the study was collected from the production line design elements PZL-130 Orlik TC-II. There have been a description of the analytical results in the form of Manson-Coffin and Morrow equations.

Data-Enabled Real-Time Modeling for Production Systems with Variable Cycle Time

Cycle time is one of the essential parameters that could lead to a significant improvement on the productivity of a manufacturing system. In the traditional manufacturing systems, cycle time for each station is recommended by the vendor and determined based on production line design and through repeated experiments and simulations. Once determined, the cycle time is typically unchangeable during the production operation. Nowadays, the development of smart manufacturing enables a flexible production operation, and the cycle time of each machine can be made changeable to quickly respond to market demand fluctuation and to further improve the system efficiency. To evaluate the performance of a serial production line with variable cycle time, a novel data-enabled manufacturing system model is developed, in which sensor data inputs are allowed and cycle times are modeled as input variables. In addition, an opportunity window concept, which has been studied in the previous study and has been treated as a real-time system status measurement, is extended to the variable cycle time scenario. Numerical case studies are presented to validate the fidelity of the data-enabled model and demonstrate the influence of variable cycle time of individual machines on the production line throughput and the opportunity windows.

Simultaneous Recovery of High Quality Black Sesame Oil and Defatted Meal by a New Aqueous Method: Optimization and Comparison with Other Methods.

A method able to simultaneously obtain oil and defatted meal (rich in proteins) with high quality is preferable to others for processing black sesame seeds, which should also be green, healthy, highly efficient and sustainable. Methods including solvent extraction and hot-pressing currently available for the commercial production of oils are not able to meet all criteria just mentioned above. Therefore, development of new aqueous method of extracting black sesame oil has been promoted. In our study, we developed a new aqueous method using 1.95:10 aqueous salt solution-to-ground black sesame seed ratio which simultaneously recovered 96.54% black sesame oils and defatted meal with only 3.89% residual oils and 50.1% proteins (on dry weight basis). The oil produced had low acid value at 0.43 mgKOH/kg and peroxide value 3.37 mmol/kg and good other quality indexes. We found that proper amount of water added was essential for efficiently recover black sesame oils while other factors including temperature and time of baking raw materials to deactivate lipase activity, pore size of the sieve for ground black sesame seeds to pass through, addition of salt as well as temperature and time of agitating significantly affected the recovery efficiency. As compared with other methods, the new aqueous method had higher oil recovery rate or quality and was more environmentally friendly. No waste water was discharged during separation of oils. The experimental data can be applied to guide the design and manufacture of production line of black sesame oilseeds on a pilot or commercial scale.

Development of an Efficient Prediction Model for Optimal Design of Serial Production Lines

One of the problems encountered in the design and implementation of a serial production line (SPL) is the buffer size between the machine tools. The buffer size of the SPL has an important impact on the productivity of the whole production system. The machine tools’ characteristics including their uptimes and downtimes and the process parameters are the main factors that affect the decision regarding the buffer size, and thus the productivity of the SPL. Due to the dynamic nature of this problem, it is complex to find the optimal buffer size in SPL. Thus, in this paper, an Efficient Prediction Model (EPM) is developed using Artificial Neural Network (ANN). The purpose of the developed EPM is to find the buffer size between each succeeding pair of machine tools in SPL at any given uptimes and downtimes of machine tools. An optimization model based on genetic algorithms (GA) is used to generate the learning data for the prediction model to find the optimal or near optimal buffer size of the bay of each machine tool in SPL. The proposed approach integrates the optimization and prediction methodologies to evaluate, and predict the optimal buffer sizes for maximum productivity. Including uptime and downtime parameters enable the proposed method to be used to improve the design of running SPL as well as to design a new SPL. Numerical examples for five and fifteen machine tools were conducted independently in this research and the results show the ability of the proposed method to determine the optimal buffer sizes in a reasonable amount of time. In particular, the results of case studies show that the developed model accurately predict the optimal buffer size, especially for the case of five machines and even for a higher number of machine tools yet with acceptable but less accuracy. Finally, the performance of the proposed approach was compared with some results of the state of the art methods reported in the literature. The comparison shows the superiority of the present approach to identify buffer sizes for higher throughput under the same uptimes and downtimes.

Intelligent Manufacturing Production Line Simulation of Super Capacitor

With the improvement of sensing technology, the improvement of software and hardware technology and computer operation ability, the digital twin technology has been further developed with the promotion of industrial intelligence and industrial information system. Digital twin technology is gradually applied to the simulation design and optimization of complex production line. The production line of super capacitor is a kind of complex production lines, so many factors need to be considered in the design process. Therefore, the digital twin technology is used to design the production line. In this paper, the plant simulation software of Siemens company is used to simulate the super capacitor production line, so as to verify the rationality of the application of digital twin technology in the complex production line design process, and study the influence of AGV car characteristics on the production line. Simulation in virtual space can well demonstrate the actual production process. The failure and other factors in the actual production process can be replaced in the form of failure rate in the simulation environment. Data statistics and other functions can directly see the changes of various parameters in the production line, to get a better optimization scheme.

Study on process of chemical fiber filament automatic doffing system based on simulation platform and machine learning

In recent years, a large number of automatic equipment has been introduced into the chemical fiber filament doffing production line, but the related research on the fully automatic production line technology is not yet mature. At present, it is difficult to collect data due to test costs and confidentiality. This paper proposes to develop a simulation platform for a chemical fiber filament doffing production line, which enables us to effectively obtain data and quantitatively study the relationship between the number of manual interventions and other process parameters of the production line. Considering that the parameter research is a multi-factor problem, an orthogonal test was designed by using SPSS software and was carried out by using a simulation platform. The multiple linear regression (MLR) and the neural network optimized by genetic algorithm were adopted to fit the relationship between the number of manual interventions and other parameters of the production line. The SPSS software was applied to obtain the standardized coefficients of the multiple linear regression fitting and the neural network mean impact value (MIV) algorithm was applied to obtain the magnitude and direction of the impact of different parameters on the number of manual interventions. The above results provide important reference for the design of similar new production lines and for the improvement of old production lines.

Reconfigurable Production Line Design Method to Ensure High Efficiency in Both Steady and Emergency Situations

Reconfigurable Production Line Design Method to Ensure High Efficiency in Both Steady and Emergency Situations

Multiobjective optimization of buffer capacity allocation in multiproduct unreliable production lines using improved adaptive NSGA-II algorithm

Buffer capacity allocation plays a key role in the design of production lines. By means of polymorphism analysis on production capacity and capability, we investigate the buffer allocation problem in multistage production lines with unreliable machines. The allocated buffers simultaneously maximize the system theoretical production rate and minimize the system state entropy. A mathematical model for buffer capacity optimization was established and optimized using an extended vector universal generating function (UGF) and an improved adaptive nondominated sorting genetic algorithm II (NSGA-II). The feasibility and effectiveness of the proposed method were verified by applying the model to a production line of engine heads.

Multi-criteria spine layout design of a mixed production line considering additional processes required for a new product

This research is based on a case study of an automotive company with a mixed production line system facing on redesigning layout due to an additional new product in the current facilities for cost improvement. The objective of this research is twofold (i) to fulfil space required for additional machines and (ii) to redesign layout which is the best layout for producing the current and new products. Heuristic approach combining construction and improvement algorithms is used for redesigning a manufacturing mixed production line layout, and this results in four alternative layouts. The four alternative layouts are evaluated comprehensively by using MCDM (Multi Criteria Decision Making) and the multi criteria considered are material handling cost, total re-layout cost, takt time process, numbers of shutdown period for relocation and comply with management policy related with safety. The best layout is the layout fulfilling required space, 1,120 m2 for new processes, and meet with the five criteria considered.

Buffer Allocation via Bottleneck-Based Variable Neighborhood Search

This study addresses the challenging problem of efficient buffer allocation in production lines. Suitable locations for buffer allocation are determined to satisfy the desired throughput, while a suitable balance between solution quality and computation time is achieved. A throughput calculation approach that yields the state probability of production lines is adopted to evaluate the effectiveness of candidate buffer allocation solutions. To generate candidate buffer allocation solutions, an active probability index based on state probability is proposed to rapidly detect suitable locations of buffer allocations. A variable neighborhood search algorithm is used to maintain acceptable solution quality; an additional neighborhood structure is used in the case where no satisfactory solution is generated in the initial neighborhood structure. Extensive numerical experiments demonstrate the efficacy of the proposed approach. The proposed approach can facilitate agile design of production lines in industry by rapidly estimating production line topologies.