Optimal Product Line Research Articles

A flexible smart manufacturing system in mass personalization manufacturing model based on multi-module-platform, multi-virtual-unit, and multi-production-line

Since the 21st century, with the gradual improvement of customers' living standards and autonomy, customers are increasingly pursuing personalized products. In order to adapt to this environment, enterprises implement mass personalization manufacturing model. This new manufacturing model needs a complex production system to support, which should adaptive flexible configuration according to the dynamic needs of customers. This configurable and rapid adjustment production system is a flexible smart manufacturing system. This paper studies the flexible layout and optimization of resources for flexible smart manufacturing system in mass personalization manufacturing model, and solves the flexible production preparation problem of sales order based on customer personalization. The main research contents include: flexible layout of production platform, flexible layout of virtual unit, flexible layout and optimization of production line and equipment. As the result of this research, the utilization rate of production layout space and equipment can be improved and the total production cost can be saved. At the same time, the system framework, model algorithm, method and theory studied in this paper have reference value for the research of flexible smart manufacturing system in a new manufacturing model facing mass personalization under the current adaptable industrial trend.

Robust product line pricing under the multinomial logit choice model

Incorporating consumer choice behavior into a product line design optimization model enhances the understanding of consumer choices and improves the opportunities to increase profit. Most product line optimization problems assume that parameters are precisely known in consumer choice model. However, the decision maker does not precisely know the model parameters because of insufficient sample data, measurement problems, and other factors. We investigate the problem of establishing robust product line pricing under a multinomial logit model to account for the uncertainty of the valuation parameter. First, we present a nominal product line model to maximize profit. We then establish a robust product line model to maximize the worst-case expected profit, where the valuation parameter lies in an uncertainty set. We consider both single and multiple products development and derive the optimal prices’ closed-form expressions. Through numerical experiments, we illustrate the benefit of robust product line pricing to address parameter uncertainty. We demonstrate that the difference between the expected nominal profit and the worst-case profit increases with the increase of the interval of the uncertainty set, and the robust profit relative to the worst-case nominal profit improves. The robust product line design can ensure steadier, even higher profit.

Simulation Model for Wire Harness Design in the Car Production Line Optimization Using the SimPy Library

The automotive industry is one of the most important economic sectors in the world. At the beginning, vehicles only had mechanical components, so the use of an automotive wire harness was not indispensable. Cars today are equipped with electronic components that, in addition to the basic operations of moving, turning, and stopping, perform more and more functions every day. Wiring harnesses are indispensable for controlling these electronic components. Automotive wiring harnesses have hundreds of variants, are principally manufactured with customized designs, and are measured specifically for each car. A large number of production variants increase labor hours, as well as rework, inventory, and manufacturing costs. Even when technologies exist to assist in the design of production lines, today, the design of production lines is mainly based on experience from previous cases. This paper aims to show how a discrete event simulation permits support for decision making for the proper design of assembly lines, as well as identifying possible unbalances in production lines and overloaded processes. In our work, we design and implement a discrete event simulation model of this production using the SimPy Python library. Finally, a case study in the automotive sector is presented, a production week is simulated, and the current plant configuration and possible improvement scenarios are analyzed.

Multichannel retailers' prominent attribute and product positioning strategies

PurposeThis paper studies how multichannel retailers choose the product quality level and decide which attribute to make prominent in their physical store in a competitive environment.Design/methodology/approachThis paper develops a game theoretic model, in which multichannel retailers' decisions are made in three stages. Using prominent experiential attributes (e.g. functionality) in their offline store and product quality decisions, multichannel retailers are capable of transferring the sales between different channels.FindingsThis analysis shows that making different attributes prominent in their physical store may be an equilibrium, and each multichannel retailer chooses the highest quality level for the prominent attribute. However, the prominent attribute of the highest quality level is not always optimal. Under certain conditions, multichannel retailers may make the experiential attribute prominent in their respective physical stores, which can result in equilibrium.Practical implicationsThe results indicate that multichannel retailers should avoid blindly highlighting high-quality attributes in a competitive environment, or falling into price completion.Originality/valueFrom the perspective of prominent attributes, this study designs the optimal product line based on channel characteristics. The results of the research can provide practical implications for multichannel retailers to increase sales.

Research on optimization and simulation of sand casting production line based on VSM

Purpose This paper aims to provide feasible countermeasures for the application of lean manufacturing in automatic sand casting workshops to optimize production line balance. Design/methodology/approach A production line balance optimization model for sand casting workshops is proposed. The value stream mapping (VSM) approach is applied to diagnose the problems in the production process. An optimization scheme is established based on eliminate, combine, rearrange, simplify and increase theory and Kanban management method. Further, simulation is done to compare current VSM and future VSM. Findings After implementing the proposed model, findings indicated that the idle time of equipment was effectively reduced, the line of balance of the production line was increased by 44.7%, the production lead time was shortened by 60.3% and the production capacity was increased by 50.0%. Research limitations/implications Application of the optimization model in this study is limited to sand casting workshops that have realized automatic or semi-automatic production. Originality/value This paper provides an optimization model for the implementation of lean manufacturing in sand casting workshops and provides a reference case that reflects the actual application of lean manufacturing tools in a real situation.

Optimal product line design with reference price effects

A reference price is the price expectation of consumers for a product. When considering whether to buy a product, consumers usually compare its price with the reference price, which leads to psychological gain-loss utility affecting the consumers’ willingness to pay for the product. In this paper, we incorporate a reference price into the product line design problem and investigate the strategic implications of reference price effects for both the firm and consumers. We first study the optimal quality-price bundle design problem of a monopolistic firm and investigate the impact of reference price effects on the optimal quality and price decision when the number of products in the line is given. We show that as the importance of price comparison increases, the optimal range of qualities in the product line narrows. Specifically, the lowest quality in the product line is distorted upward, while the quality gap between adjacent products narrows. In particular, when consumers attach great importance to price comparison, the reference price effects may cause the firm to abandon discrimination among consumers and instead offer just a single product. We also investigate the optimal product variety setting problem considering a fixed setup cost incurred for each product in the choice set. Our numerical studies using synthetic and real-world data demonstrate that the demand model with reference price has better goodness of fit and prediction accuracy. The impact of neglecting reference price in the product line design problem is significant, potentially leading to substantial profit loss. Finally, we extend our work through general discussions of the model and through the dynamic pricing problem incorporating temporal reference price effects.

An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization

The load forecasting is generally based on historical data extrapolation in most forecasting models, resulting in a poor correlation with the production information and significant application limitations. To improve this, the production information-based backpropagation neural network (BPNN) combined with genetic algorithm (GA) and particle swarm optimization (PSO) hybrid forecasting model was established for a papermaking enterprise with the three-level electric data collected. Based on this, shift electric consumption quotas and air compressor transformation energy-saving predictions were proposed. The results show that when the production management information is included, the average mean absolute percent error (MAPE) of the six forecasting results can be 1.2%, an improvement of 18.3% on average, indicating the high accuracy of our proposed model. The unit energy consumption of paper products can be reduced by 3.26% through management optimization using the proposed shift electric consumption quotas. Under the guidance of energy-saving predictions, the overall power saving rate of the production line after the enterprise reforms the air compressor is 3%. The proposed load-forecasting model and energy optimization methods have high accuracy and practical applicability.

Mixed Production Line Optimization of Industrialized Building Based on Ant Colony Optimization Algorithm

Prefabricated components production line optimization is critical for improving industrialized building construction efficiency; however, few studies focus on the production line optimization problem in context of industrialized building construction. In order to optimize the large random orders in the prefabricated components production process, this research proposes a model to minimize variance of the production capacity utilization of prefabricated components in the production cycle, and the ant colony optimization algorithm is introduced to solve the mixed production line sequencing optimization problem. By optimizing the sequence, the production capacity of the component production is balanced, and the capacity utilization rate in the industrialized building construction process is improved. Finally, the effectiveness of the method is verified through a real case of fabricated building components production. The results show that the variance of daily production capacity utilization rate of the optimized hybrid component production line has reduced to 0.53%, which is significantly lower than the 2.45% before optimization. The proposed model could effectively achieve the production capacity balance of prefabricated components production line.

Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line

Bottleneck identification is a relevant tool for continuous optimization of production lines. In this work, we implement a data-driven discrete-event simulator (DDS) based on experimental distributions, obtained from real historical data. The DDS allows to analyse the behavior of a balanced manufacturing line at Bosch Thermotechnology, under different hypotheses. It shows that some scenarios perceived as likely to increase output may actually decrease production metrics, reveals the importance of line injection rates, and leads to the need for adequate real time bottleneck forecasting tools, which allow shift managers intervention in a useful time frame. Eleven prediction models are tested, where a random forest and a multi-layer perceptron attain the best performances (above 95% in all metrics). This data flow is operationalized through a micro-services pipeline which is briefly discussed.

Balanced Optimization Design of Aluminum Alloy Impeller Rapid Casting Production Line

The improvement and design of the production line is the key to improve the overall efficiency and reduce the total cost of each enterprise. The main purpose of the current rapid casting aluminum alloy impeller is through the determination of reasonable distribution center, production facilities, and the location of the relationship between the warehouse, so as to improve the overall efficiency of the logistics industry, promote interest rate. Reasonable aluminum alloy impeller rapid casting scheme, guiding the future transportation power, logistics innovation is an important factor. The manufacturing industry will continue to advance, and the optimization of production lines for more advanced innovations will be a critical research goal in the future.

Research on Lean Production Management Strategy Based on Extension Innovation Method

Aiming at the problems of low innovation efficiency and waste in domestic manufacturing enterprises, this paper, on the basis of lean improvement, combines the principle of conjugate analysis to solve the contradictions in production, and uses the extension innovation method to optimize the production line optimization. E-type engine is the research object,By implementing extension transformation, impossible problems can be transformed into possible problems, infeasible problems can be transformed into feasible problems, and unknown problems can be transformed into knowable problems.First of all, this paper finds the existing problems by collecting data, establishes the employee model, inventory model, working time model, and conducts correlation analysis and divergence analysis to get the methods and approaches to solve the conflicting problems, and establishes the compatibility function of the waste problem. , and quantify the degree of waste. Secondly, the extension analysis of the E-type engine extension model is carried out, and the relationship between them is found from the angle of soft and hard conjugate. Thirdly, the conjugate transformation is carried out on the model, and the basic elements are transformed by the basic transformation method and the extension transformation operation method. Finally, generate a variety of improvement strategies, evaluate the goodness of the extension strategy, and select the extension strategy greater than 0 as the optimal extension strategy according to the company's requirements. The extension method can solve the contradictions in production management.

Multi-objective optimization of energy-efficient production schedules using genetic algorithms

The optimization of production schedules to be more energy efficient while still meeting production goals is a difficult task: How to schedule and distribute production tasks to meet production goals, while making best use of fluctuating energy market prices and availability of locally installed energy sources? Although a large body of related work exists in this domain, most of those seem to focus on individual aspects and not the whole picture. In this paper, a genetic algorithm for optimization of production schedules with respect to energy consumption, peak shaving, and makespan is presented, that also takes into account that tasks can be performed in different ways, having different characteristics. The algorithm has been successfully employed within the SPEAR project by applying it for optimization of an automotive production line and for the pathway of an automated guided vehicle.

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.

Production line optimization based on Man-Machine collaborative improvement

In many cases industrial companies’ efficiency is limited by bad man-machine collaboration. In the time of intelligent manufacturing, more data can be collected and help companies to improve their efficiency. Therefore, a production line optimization method based on Man-Machine collaboration with the help of big data tools and sensors is introduced. At first, the production line will be analyzed and main data as machine cycle time, personnel operation time, task distribution etc. will be collected. Then based on these data, a simulation model will be made in plant simulation. After that, the performance of the production line will be evaluated considering the man-machine collaboration, work task distribution etc. and the optimization will be made. Afterwords, the machine cycle time, operator motion track and labor operation time in real production situation will be collected to compare and validate the optimization model.

Hybrid Petri nets-based Flow modeling and application on hybrid system.

Flow management is necessary in several application areas, in the optimization of industrial production lines, in IT to manage data flows and in the automation of industrial systems. Physical systems in general consist of continuous processes interacting with discrete processes forming a hybrid dynamic system constituted by continuous dynamic type models and discrete events. The application of the hybrid Petri nets tool in the modeling, study and performance evaluation of these systems helps to analyze the dynamic properties by acting on the parameters and the structure of the models in order to evaluate their behavior. This work is focused on the application of this tool to model a material flow management system between a rotary kiln and a clinker cooler in a production line (cement process). The implementation of the modeling and the analysis of the results obtained by simulation on a software platform (Visual Object Net ++), aims to study industrial processes with mathematical tools and to follow their behavior on software, this allows us an optimal analysis of complex systems in dangerous environments, and to try practical and effective solutions by simple means before moving on to the implementation and programming of actions that require more expensive means.

Hybrid Petri nets-based Flow modeling and application on hybrid system.

Flow management is necessary in several application areas, in the optimization of industrial production lines, in IT to manage data flows and in the automation of industrial systems. Physical systems in general consist of continuous processes interacting with discrete processes forming a hybrid dynamic system constituted by continuous dynamic type models and discrete events. The application of the hybrid Petri nets tool in the modeling, study and performance evaluation of these systems helps to analyze the dynamic properties by acting on the parameters and the structure of the models in order to evaluate their behavior. This work is focused on the application of this tool to model a material flow management system between a rotary kiln and a clinker cooler in a production line (cement process). The implementation of the modeling and the analysis of the results obtained by simulation on a software platform (Visual Object Net ++), aims to study industrial processes with mathematical tools and to follow their behavior on software, this allows us an optimal analysis of complex systems in dangerous environments, and to try practical and effective solutions by simple means before moving on to the implementation and programming of actions that require more expensive means.

Simulation and optimization of a factory automation production line based on Plant Simulation

Model an automated production line in a factory in order to understand and improve the automated production line. The layout and function of this automated production line were simulated on Plant Simulation, and the statistical results were obtained according to the actual situation. According to the results, the shortcomings of the production line were analyzed, an optimization method was proposed, and the results were simulated again by using genetic algorithm for comparison.

Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events

The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.

A digital twin framework for the simulation and optimization of production systems

Industry 4.0 has raised the expectations on productivity, automation, and resource efficiency of manufacturing systems. This paper proposes a digital twin framework for the simulation and optimization of production lines and cells that can be used in the design and operation stages. The framework is supported by an architecture that connects manufacturing and machine tool data (digital shadow), the discrete event simulation model and the optimization engine, allowing for a variety of functionalities to plan and manage the production system. A use case is provided to demonstrate this framework, implemented in an automated line for the manufacturing of railway axles.