Optimal Product Line Research Articles

Industrial Metabolism Analysis and Green Optimization of a Painting Production Line

This article takes a coating production line as an example, uses industrial metabolism analysis method to quantitatively analyse its material metabolism process, and forms a material balance sheet to evaluate resource efficiency and environmental efficiency. The results show that the resource efficiency of the coating production line is 0.4%, and the environmental efficiency is 0.51%, indicating that there is a serious waste of resources and greater environmental pressure. The reason is the old spraying method, the use of highly volatile paints and cleaning agents, and the higher VOC content of the paint. In response to these problems, this article combines the input and output content of the production line and proposes several green optimization approaches from the perspectives of cleaner production, circular economy and ecological industrial parks.

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.

Layout optimization of signal control box production line based on SLP

Taking the production process of the signal control box as the research object, aiming at the problem of the unreasonable layout of the production line, the Systematic Layout Planning (SLP) method is used to obtain the relationship and position relationship diagrams between the various operation units, and the optimal layout plan is given, combined with the factory building Conditions and funding constraints, determined and implemented the production layout improvement plan, reduced the material handling distance, shortened the delivery cycle, and realized the rapid delivery of products.

Product Line Optimization Considering Network Effects

Products exhibit network effects when the utility a customer obtains from buying a product depends not only on its attributes but also on the sales of the same or similar products. In this article, we study product line optimization problem considering network effects. We first characterize consumer choice model considering network effects. Then we investigate the product line optimization problem under such model. In the homogeneous case with the same products, we find that the optimal choice probabilities and optimal prices may be different for each product. In the heterogeneous case with different products, there is at most one product that takes the “high” market share while all the other products take “low” market share. In particular, when only the network effects are heterogeneous, the larger the network effect, the lower the optimal pricing and the higher the market share. Through numerical experiment, finally, we demonstrate the importance of considering network effects and test the robustness of solutions.

Optimizing Production Line Using the Rank Positional Weight (RPW) Method at PT. X

Line balancing is a series of work stations (machinery and equipment) that are used in manufacturing products. Line balancing usually consists of a number of work areas called work stations handled by one or more operators and it is possible to be handled using a variety of tools. The purpose of research in the case of the production line is to obtain the lowest RPW weight value from the production PT. X with Rank Potential Weights method which is optimal for production lines without bottlenecks at the work station. The allocation of elements at work stations is limited by main constraints, namely zoning constraints. This method is usually better known as ranked positional weight system (RPW). The first step is to make a precedence diagram and precedence matrix. Calculate the positional weights for each element obtained from working time of that element. Calculating the production process time, each work element requires a cycle time using stopwatch time study. The method of ranking positional weight system (RPW) in this case the Production Track is in the initial condition with a cycle time of 1857 seconds and there are 5 work station. The known value of balance delay is 26.74%, efficiency is 37.34%.

Special Issue “Design and Optimization of Production Lines”

The classical models for designing production lines follow the objective of balancing the line so as to improve the throughput [...]

Optimization of Solar Cell Production Lines Using Neural Networks and Genetic Algorithms

To keep improving the efficiency-to-cost ratio of photovoltaic solar cells, manufacturing lines must be continuously improved. Efficiency optimization is usually performed process-wise and can be s...

Research on multi-effect evaporation salt prediction based on feature extraction

In the multi-effect evaporation salt making process, the smooth operation of the salt making process is crucial. As the salt production process continues, many unstable factors will cause the salt production process not to proceed smoothly. These factors can be discovered in advance by predicting the salt production data, thus, it is of great significance to predict the multi-effect evaporation salt production data. In the process of multi-effect evaporation and salt production, the multiple salt-making devices make the influence between the parameters closer, and the influence of a single parameter on itself is sometimes ductile. Therefore, the data of multi-effect evaporation and salt production have the characteristics of high dimensions, high complexity and temporal information. If the historical salt production data is used for data prediction directly, the prediction model will take a long time and the prediction effect is not good. Thus, how to predict the multi-effect evaporation salt production data is the main research problem of this paper. In view of the above problems, according to the characteristics of multi-effect evaporation salt production data, this paper analyzes and improves the self encoder for feature extraction of multi effect-evaporation salt production data, so as to solve the problem of high dimensions and high complexity of salt production data. On this basis, combined with the time-series information contained in the salt production data, a multi-effect evaporation salt production data prediction model is proposed based on long-term and short-term memory cycle neural network to solve the prediction problem of time-series salt production data. Experiments show that the prediction model can predict and prevent the problems in salt production line in advance. It has a certain theoretical research value and application value in the intelligent production process and production line optimization of salt chemical industry.

Reducing channel inefficiency in product line design

It is well known that the optimal product line design in a manufacturer-retailer distribution channel is different from the optimal design achieved under a centralized channel. Channel inefficiencies are observed due to the potential misalignment of the manufacturer's and the retailer's segment targeting strategies. We show that this result in the literature is based on a crucial assumption that the product is comprised of only one attribute, and that one segment's preference structure dominates the other segment's preference structure. In this paper, we generalize the problem by considering a multiple-attribute product line design problem that includes the case of a non-dominating preference structure. Interestingly, we show that channel inefficiencies may become an issue in this non-dominating preference structure only when the market segments are significantly different in the overall part worth across the product attributes. We also show the potential of two important operations management concepts firms can use as an attempt to improve channel efficiency. Contrary to the predominant result in the literature on commonality in the centralized channel, we show that offering commonality in the decentralized channel may actually improve channel efficiency even in the absence of cost saving. Furthermore, firms can benefit from the implementation of decentralized product design where the retailer also takes part in designing product attributes. The overall part worth difference between the two segments appears to have a strong influence on the change in channel profits. These findings give rise to previously unrecognized opportunities for firms to redesign their product lines.

Product line design with vertical and horizontal consumer heterogeneity: the effect of distribution channel structure on the optimal quality and customization levels

Purpose The purpose of this study is to analyze the problem of optimal product line design in marketing channels where consumers are heterogeneous in both horizontal and vertical dimensions. Design/methodology/approach This paper develops a model to evaluate when it is preferable for a firm to extend the product line in a vertical or horizontal direction. Consumers are modeled as being vertically heterogeneous with respect to their valuation of quality and horizontally heterogeneous with respect to their preference on the esthetic component of the product. These model characteristics allow us to consider a broader set of product line extension strategies. By considering both a vertically integrated channel and a decentralized channel, this study investigates how channel structure influences optimal product line design. The problem with supplemental numerical analyses is mathematically analyzed. Findings The analysis shows that a horizontal product line extension strategy that offers the customized product can be used as an alternative to a vertical product line extension strategy. If the fixed cost is not too high, offering the customized product with low quality may be preferred to the quality-based segmentation strategy. Furthermore, the analysis shows that the channel structure is influential as the preference for the horizontal product line extension strategy is more pronounced in the decentralized channel than in the centralized channel. Research limitations/implications The analysis presented in this paper is limited by the consideration of full market coverage. Further research is needed to see how the results can be generalized to the case with partial market coverage. Practical implications The analysis suggests that a firm may consider product customization as part of its product line strategy. Information regarding market characteristics and channel structure is important when deciding on the optimal product line design. Originality/value The model reflects a more realistic marketing strategy and channel structure than previous studies that typically consider product line extension in only one direction and focus on the centralized distribution channel. Combining the standard product line extension and customization strategies also represents an important contribution to the literature. These extensions produce interesting new results and insights into a firm’s optimal product line design strategy.

Simulation design and optimization of production line of a cross axis machining based on Plant Simulation

In order to study the operation process and results of an actual intelligent production line, Plant Simulation was used to simulate the production line of a cross shaft. First of all, the basic layout is modeled, 3D model is used, and the actual machine model is imported or designed to make the effect more realistic. This study analyzed the production process, created orders according to customer requirements, used Simtalk language to complete the simulation logic of the model, and displayed the simulation data in the form of charts after the operation, so as to make the results more intuitive. Finally, the results are analyzed and optimized.

Decentralized optimization of the flexible production lines

Decentralized optimization of the flexible production lines

Multifidelity Modeling for Analysis and Optimization of Serial Production Lines

Recent advances in sensing, data analytics, and manufacturing technologies (e.g., 3-D printing, soft robotics, nanotechnologies, etc.) provide the potential to produce highly customized products by allowing flexible system design, endless device configurations, and unprecedented information flows. These opportunities also increase the complexity of controlling such systems optimally, which typically requires fast exploration of an increasingly large number of alternative operation strategies. Simulation and stochastic models have been particularly successful to support control and optimization of production systems, and methods have been developed to exploit them separately. Herein, we argue that the simultaneous use of these models can allow for better control and optimization by balancing the simulation accuracy, and related high computational costs, with the computational efficiency and lower accuracy of stochastic models. In this article, we assume that high fidelity models have higher accuracy and computational costs, and we present a novel multifidelity approach, which utilizes several models at different levels of fidelity to efficiently and effectively estimate and optimize the performance of asynchronous serial production lines with machines suffering from multiple failure types. Experimental results show that the multifidelity approach leads to better estimations, requiring less computational effort for optimization compared with the use of only high fidelity simulations.

A digital twin-based flexible cellular manufacturing for optimization of air conditioner line

In a personalized and various production mode, the production line needs to be updated quickly to meet market demand. The Optimization of production line is taken as the object. To address the coupling problems, such as unreasonable production line layout, unbalanced process capability, inaccurate logistics distribution and unintelligent equipment testing, a method of flexible cellular manufacturing based on digital twin is put forward. Decoupling based on event mechanisms and multi-objective optimization will be used in the design of methods, which will be continuously optimized in the simulation and will eventually be validated. After the implementation of an air conditioner line, the production capacity increased by 58.3 %, the WIP decreased by 77.8 %, the balance rate of the production line increased by 25.2 %, and the per capita production capacity increased by 29.8 %. The number of operators decreased by 28.3 %. The results show that the optimization method of flexible cellular manufacturing based on digital twin has practical value and guiding significance to improve the efficiency of production line.

Product line optimization in the presence of preferences for compromise alternatives

Recent advances in customer choice analysis demonstrated the strong impact of compromise alternatives on the behaviour of decision-makers in a wide range of decision situations. Compromise alternatives are characterized by an intermediate performance on some of the relevant attributes. For instance, price compromises are well known in the sense that customers tend to buy neither the cheapest, nor the most expensive alternative, but the mid-priced one. However, thus far, the literature on product line optimization has not considered such context effects.In this paper, we propose a model-based approach for optimal product line selection which incorporates customers’ preferences for compromise alternatives. We consider customer choice in a realistic, sophisticated fashion by applying an established utility model that integrates compromise variables into a multinomial logit model. We formulate the resulting optimization problem as a mixed-integer linear program. The challenging feature for modelling – making the formulation substantially more complicated than existing ones without compromises – are the endogenous effects of selected products on other alternatives’ utilities that need to be adequately captured via compromise variables. Based on data we collected by a stated choice experiment in a retail setting, we perform a computational study and demonstrate the superiority of our product line selection approach compared to a reference model that does not take compromises into account. Even under uncertainty of the estimated utility parameters, profit gains of, on average, 23% can be achieved in our experimental setting.

Fuzzy self-tuning differential evolution for optimal product line design

Designing a successful product line is a critical decision for a firm to stay competitive. By offering a line of products, the manufacturer can maximize profits or market share through satisfying more consumers than a single product would. The optimal Product Line Design (PLD) problem is classified as NP-hard. This paper proposes a Fuzzy Self-Tuning Differential Evolution (FSTDE) for PLD, which exploits Fuzzy Logic to automatically calculate the parameters independently for each solution during the optimization, thus resulting to a settings-free version of DE. The proposed method is compared to the most successful mutation strategies of the algorithm as well as previous approaches to the PLD problem, like Genetic Algorithm and Simulated Annealing, using both actual and artificial data of consumer preferences. The comparison results demonstrate that FSTDE is an attractive alternative approach to the PLD problem.

Digital twin-based production scheduling system for heavy truck frame shop

Existing scheduling systems cannot respond quickly and efficiently to the appearance of uncertain variable interference factors in the scheduling process. The efficiency of the enterprise is hence seriously affected by these factors. The concept of the digital twin provides new research directions for scheduling systems. In order to improve the overall performance of the production scheduling system in a frame shop, virtual-real fusion technology utilizing the digital twin is first introduced to integrate the information and logistics flows in the scheduling process as part of the manufacturing execution system-based production scheduling mechanism. The balance optimization of the mixed-flow production line is then carried out, and the mixed-flow production line balance and sequencing models are established. A scheduling system for the frame shop combining various system functions is finally designed based on the sequencing model and production status. The scheduling system comprehensively considers the two uncertain factors of materials and orders to generate scheduling information through an intelligent scheduling mode while realizing real-time synergy between the information and logistics flows. The system has achieved excellent implementation results in the frame shop and provides a reference for the application of digital twins in production workshops.

Product2Vec: Understanding Product-Level Competition Using Representation Learning

Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. We introduce Product2Vec, a method based on representation learning, to study product-level competition when the number of products is large. The proposed model takes shopping baskets as inputs and, for every product, generates a low-dimensional vector that preserves important product information. Using these product vectors, we present several findings. First, we show that these vectors can recover analogies between product pairs. Second, we create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Third, we combine these vectors with traditional choice models to study product-level competition. To accurately estimate price elasticities, we modify the representation learning algorithm to remove the influence of price from the product vectors. We show that, compared with state-of-the-art choice models, our approach is faster and can produce more accurate demand forecasts and price elasticities. Fourth, we present two applications of Product2Vec to marketing problems: 1) analyzing intra- and inter-brand competition and 2) analyzing market structure. Overall, our results demonstrate that machine learning algorithms, such as representation learning, can be useful tools to augment and improve traditional marketing methods.

Identification of reference multi criteria domain model - Production line optimization case study

Abstract The methods of multi-criteria decision analysis are widely used for problems of selecting the best solution or ordering their sets. In the vast majority of cases, solving multi-criteria decision-making problems focuses on finding solutions for a given set of alternatives. When, for one or more decisional variants, the values of their attributes change, or a new alternative for assessment appears, the problem occurs. Therefore, in this paper, we focus on identifying the full decision-making model in the issue related to the study case of production line optimization. In this work, the Characteristic Objects METhod (COMET) has been applied. In effect, it identifies the full decision-making model. The decision problem has been identified, taking into account six decision criteria, and its operation has been used in organizing four decision alternatives.

Multi-criteria decision making approach to production line optimization

Abstract The problem of optimization assembly lines for rarely repeatable products with low volumes will be addressed in this paper. In this kind of production each product should be treated as a project with different cycle time on each station. Balance of assembly lines processing separate projects is impacted by uncertainty, assembly methods are not fully automated. Daily operations in this kind of activity needed an effective and fast method to support decision making by assembly line managers and production planners. For this purpose Analytical Hierarchy Method (AHP) and TOPSIS methods are used. The empirical study presents four possible optimization configurations for comparison and evaluation using the AHP and TOPSIS methods.