Articles de revues sur le sujet « Production planning. Multidisciplinary design optimization. New products »

One-of-a-kind production (OKP) is a new manufacturing paradigm to produce customized products based on requirements of individual customers while maintaining the quality and efficiency of mass production. In this research, a computer-aided optimal product design and process planning approach is developed to support OKP product design and manufacture to satisfy individual customer requirements with near to mass production efficiency. In this work, a hybrid AND-OR graph is developed to model the variations of design configurations/parameters and manufacturing processes/parameters in generic product family. Since different design configurations and parameters can be created from the same customer requirements, and each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical optimization are employed to identify the optimal product design configuration/parameters and manufacturing process/parameters. A case study to identify the optimal design configuration/parameters and manufacturing process/parameters of custom window products in an industrial company is introduced to demonstrate the effectiveness of the developed method.

Today production and logistic systems are getting more complex. This is a problem which the planning and design of such systems have to deal with. One main issue of production system development in series production is the planning of production processes and systems under uncertainty. New and existing production technologies are often not fully adoptable to new products. This is why some of the main characteristics, like, for example, cost, time, or quality, are not definable at the beginning. Only value ranges and probabilities can be estimated. However, the adaptation process is controllable, which means that the adaptation results are depending on the existing development budget and its resources. This paper presents an approach for the optimized allocation of development resources regarding the adaptation risks of production technologies and processes. The modeling concept GRAMOSA is used for integrated modeling and discrete event-based simulation of the aspired production system. To this end a domain-specific modeling language (DSML) is applied. The further risk-based analysis of the simulation results and the optimized allocation of the development budget are done by use of mathematical optimization.

The task to optimize the life cycle of new technique (aerospace, engineering products, etc.) in difficult economic conditions is stated and solved. The aim of the study is to develop a method to reduce the life cycle of complex technique creation. The subject of the research is the planning and management of the life cycle of complex technique in a highly competitive environment and stochastic behavior of high-tech products market. The paper shows the contradictions between the planned nature of modern production, which operates in conditions of Industry 4.0 and the stochastic behavior of the market. This contradiction leads to the relevance of short-term production plans with minimal risks. Therefore, the planning of the production system is carried out based on the portfolio that can be done in the short term. When planning new orders, it is necessary to shorten the life cycle of new equipment creation by analyzing the main stages: design, preparation of production, production. The optimization model to select the measures (project actions) at the initial stage of the life cycle is proposed. In order to generate the set from which the alternative options for activities are selected, expert assessments being ordered on the basis of the importance of time indicators, competitiveness, innovation, costs and risks are used. Simulation of the shortening of the new technique creation life cycle at a time of capacity constraints of production that creates high-tech products is carried out. The following mathematical models and methods are used: system analysis, optimization using integer programming, multi-criteria optimization, expert assessments, simulation modeling, agent-based modeling, risk assessment. The method allows to create competitive products at a time of capacity constraints by means of planning to shorten the life cycle of new technique creation and resources management.

With increasingly stringent environmental regulations on emission standards, enterprises and investigators are looking for effective ways to decrease GHG emission from products. As an important method for reducing GHG emission of products, low-carbon product family design has attracted more and more attention. Existing research, related to low-carbon product family design, did not take into account remanufactured products. Nowadays, it is popular to launch remanufactured products for environmental benefit and meeting customer needs. On the one hand, the design of remanufactured products is influenced by product family design. On the other hand, the launch of remanufactured products may cannibalize the sale of new products. Thus, the design of remanufactured products should be considered together with the product family design for obtaining the maximum profit and reducing the GHG emission as soon as possible. The purpose of this paper is to present an optimization model to concurrently determine product family design, remanufactured products planning and remanufacturing parameters selection with consideration of the customer preference, the total profit of a company and the total GHG emission from production. A genetic algorithm is applied to solve the optimization problem. The proposed method can help decision-makers to simultaneously determine the design of a product family and remanufactured products with a better trade-off between profit and environmental impact. Finally, a case study is performed to demonstrate the effectiveness of the presented approach.

Economic instability and stochastic behavior of the market lead to the need to develop new methods and models to reduce the life cycle of high-tech products. Therefore, the urgent task is a detailed consideration and analysis of each main stage of the life cycle of creating complex technical products. The current study develops a method for optimizing production processes. The subject of research is a production planning and management as the main stage of the life cycle. The method proposed in the publication is based on the component architecture of the created innovative high-tech product, which is obtained because of decomposition at the initial stage of design. Modern production is of distributed nature. While planning production, it is necessary to pay attention to the management and synchronization of material and other types of flows in the logistics chain of distributed production. To form a set of basic and subsidiary operations of distributed production, it is necessary to consider the logistical features, the level of decomposition of the component architecture of the created product, as well as the type of component in the production cycle. Logistics operations are conducted following the requirements for the relevant indicators, namely, the cost of the operation, quality, competitiveness, innovation, the risk of the operation. The main criterion for optimization is the duration of operations. The work uses multi-agent simulation modeling of a sequential logistics chain of production processes, as well as mathematical models and methods: system analysis, optimization using integer programming, multi-criteria optimization, and expert evaluations. The method reduces the duration of the production cycle for the manufacture of individual components of the complex architecture of high-tech products by determining the optimal set of production and ancillary logistics operations for each component, considering the requirements for key production indicators, which minimize production time of high-tech products.

For original equipment manufacturers (OEMs), producing a line of new and remanufactured products can be an effective strategy for improving the sustainability of their business. The potential cannibalization of new product sales and the technological obsolescence of used products, however, can create barriers for OEMs to embrace remanufacturing. In order to address these challenges in OEM remanufacturing, this paper proposes a mixed-integer programming model for the optimal line design of new and remanufactured products. Aiming at two objectives, i.e., maximizing the total profit and maximizing the total market share, the model simultaneously optimizes a line of new and remanufactured products in terms of their (1) design specifications (including an upgrade plan for the remanufactured product), (2) selling prices, and (3) production quantities and the detailed production plan. With the simultaneous optimization, the model suggests an optimal way of differentiating the new and remanufactured products in order to overcome the cannibalization and obsolescence effects and to maximize the total profit and/or market share. The model also accounts for environmental impact, stipulating that the total environmental impact of manufacturing remains under a certain limit. To demonstrate the applicability and effectiveness of the model, a case study is presented using the example of a desktop computer.

The article presents an idea of a production process control system. Advanced automation and control of production processes play a key role in maintaining competitiveness. The proposed solution consists of sensor networks for measurement process parameters, production resources and equipment state. The system uses wired and wireless communication, which gives possibility to acquisition data from existing in enterprise sensors and systems as well as acquisition data from new systems and sensors used to measure all processes, starting from production preparation to the final product. The solution contains process tomography sensors based on electrical capacitance tomography, electrical impedance tomography and ultrasound tomography. The use of tomographic methods enables to manage the intelligent structure of the companies in terms of processes and products. Industrial tomography enables observation of physical and chemical phenomena without the need to penetrate inside. It will enable the optimization and auto-optimization of design processes and production. Such solutions can operate autonomously, monitor and control measurements. All sensors return to the system continuous data about state of processes in some technologically closed objects like fermenters. Process tomography can also be used to acquisition data about a flow of liquids and loose ingredients in pipeline based on transport systems. Data acquired from sensors are collected in data warehouses in order to future processing and building the knowledge base. The results of the data analysis are showed in user control panels and are used directly in the control of the production process to increase the efficiency and quality of the products. Control methods cover issues related to the processing of data obtained from various sensors located at nodes. Monitoring takes place within the scope of acquired and processed data and parameter automation.

In today’s competitive environment, organizations, in addition to trying to improve their production conditions, have a special focus on their supply chain components. Cooperation between supply chain members always reduces unforeseen costs and speeds up the response to customer demand. In the new situation, according to the category of return products and their reprocessing, supply chains have found a closed-loop structure. In this research, the aim was to design a closed-loop supply chain in competitive conditions. For this purpose, the key decisions of this chain included locating retail centers, adjusting the inventory of chain members, and selling prices of final products, optimally determined. For this purpose, a nonlinear integer mathematical model is presented. One of the most important innovations of this research was considering the variable value for return products. Then, in order to solve the proposed model, a whale optimization algorithm was developed. Numerical results from the sample examples showed that the whale algorithm had a very good performance in terms of response quality and speed-of-action in finding the optimal solution to this problem.

A new, multi-dimensional, additive manufacturing process for fine ceramics was proposed and developed as part of a national project in Japan. The process consists of three-dimensional printing and two-dimensional coating of fine ceramics. A new coating process, hybrid aerosol deposition (HAD), was proposed as the ceramic coating process. The HAD process is a hybrid of aerosol deposition (AD) and plasma spray. Such new technologies, however, usually take a long time to move from first discovery to use in producing a commercial product. For example, a past study showed that it took nearly 15 years from the invention of the AD process to the time it became a technology used at an industrial company. Therefore, it is very important to consider how to accelerate the learning and technological transfer of a new process to industry in addition to how to develop new processes once they emerge. In this study, a new scheme, a coating hub, is proposed to promote the transfer of the HAD process to industrial adoption. In the coating hub, a collaboration scheme for companies to get interest of the technology, even in the early stages of technological development, is considered. Here, needs-seeds matching, reliable relationships, intellectual property, and the generalization of technology are considered. Another important scheme of the coating hub is to try to couple design with manufacturing. Here, product design tools for agile production are provided. In order to attract and evaluate consumers for targeted products, a Kansei delight design based on the Kano model is introduced. A delight map viewer is provided to visualize potential consumers’ delight factors. Detailed planning from the early trial stage is introduced with the viewer. A topology optimization tool is also provided in the coating hub as a design tool. In order to validate this coating hub concept, a ceramic frying pan is designed as a case study. The delight map viewer proves effective for those who are not design professionals to consider the attractiveness of products based on user evaluation. The coupling of the topology optimization tool is also useful for the multidimensional additive manufacturing of ceramics proposed in this study. This case study implies that even a small manufacturer could design a new product by utilizing the coating hub concept. It would give many new opportunities not only to big manufactures interested in high-end business-to-business components but also to supporting industries and even to individuals to utilize new emerging coating technologies.

Purpose The product family assembly line (PFAL) is a mixed model-assembly line, which is widely used in mass customization and intelligent manufacturing. The purpose of this paper is to study the problem of PFAL, a flexible (evolution) planning method to respond to product evolution for PFAL, to focus on product data analysis and evolution planning method. Design/methodology/approach The evolution balancing model for PFAL is established and an improved NSGA_II (INSGA_II) is proposed. From the perspective of data analysis, dynamic characteristics of PFAL are researched and analyzed. Especially the tasks, which stability is considered, can be divided into a platform and individual task. In INSGA_II algorithm, a new density selection and a decoding method based on sorting algorithms are proposed to compensate for the lack of traditional algorithms. Findings The effectiveness and feasibility of the method are validated by an example of PFAL evolution planning for a family of similar mechanical products. The optimized efficiency is significantly improved using INSGA_II proposed in this paper and the evolution planning model proposed has a stronger ability to respond to product evolution, which maximizes business performance over an effective period of time. Originality/value The assembly line designers and managers in discrete manufacturing companies can obtain an optimal solution for PFAL planning through the evolution planning model and INSGA-II proposed in this paper. Then, this planning model and optimization method have been successfully applied in the production of small wheel loaders.

With the rapid development of e-commerce industry, online shopping has become a craze. With the rapid growth of transaction volume on e-commerce platforms, a large amount of transaction data has been accumulated. From the transaction information of these users, a lot of very valuable information can be mined, such as the defects of products and the actual needs of users. In view of the existing e-commerce transaction information collection method is not mature, in this paper, the electric business platform system architecture planning and design increases the function management module. In this paper, a new Naive Bayes model is established by using HBase distributed database instead of traditional database. Based on the optimization and extraction of the important transaction information in the product, the dataset of e-commerce transaction information is updated. Through the efficiency test of the collection method, the information scalability ability test, and the accuracy test, the important context was sorted out after integration, the sources of trading information were sorted out, and the data analysis of the collected information was conducted to optimize the information collection method and verify the feasibility of the method.

Purpose The purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced. Design/methodology/approach As the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes. Findings Numerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms. Practical implications Scheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper. Originality/value This paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.

Delineation and understanding the geology and the hydrogeology of a contaminated site, considering its chemical and its biological aspects, are fundamental requirements for successful environmental remediation. The aim of this research is to provide some evidence about the effectiveness of a hydrogeochemical geodatabase to facilitate the integrated management, representation and analysis of heterogeneous data, enabling the appropriate selection, design and optimization of an effective remediation strategy. This study investigates a new technology for the remediation of a dense non-aqueous phase liquid aged source zone, with the aim of enhancing in situ bioremediation by coupling groundwater circulation wells with a continuous production system of electron donors. The technology was verified through a pilot test carried out at an industrial site highly contaminated by chlorinated aliphatic hydrocarbons. The multidisciplinary conceptual model confirmed a complex hydrogeological situation, with the occurrence of active residual sources in low permeability layers. The pilot test results clearly demonstrate a significant mobilization of contaminants from the low permeability zone, and the possibility of favoring the in situ natural attenuation mechanisms based upon biological reductive dechlorination. Different information related to the hydrogeochemical sphere must be integrated and taken into consideration when developing a reliable remediation strategy for contaminated sites.

1. ABSTRACT Recently, developing sustainable architectural materials from renewable resources is gaining great interest. This interest is intended to alleviate the drawbacks of petroleum-based materials and their contribution in the escalation of CO2 emissions causing the current environmental deterioration. Achieving sustainability through developing efficient architectural materials have been always conditioned by technological advancements and economic potential. This has affected the architectural design and construction sectors, especially in times of disasters or economic crisis, resulting in paralysis in the architectural construction and material development. These effects were caused by the capitalization and centralization of architectural construction industries. The recent trend of self-sufficiency that had first emerged in environmental activities supporting recycling, environmental purification and conservation, oxygen, food, and electricity production, has extended to cover more sophisticated products, such as wearables, gadgets and architecture. Achieving self-sufficiency in architecture is of interest to multidisciplinary researchers who focus on developing both self-sufficient systems and materials as the two main components of the built environment. Developing architectural materials aims to provide cheap, recycled, renewable, environmentally friendly, durable and sustainable building material regardless of the possibility of the autonomous production of these materials on a popular democratic basis. Architectural building materials production was always and still is considered a massive industry that is centralized in major firms and LTDs, limiting the architectural construction process to the availability of major economic capacity. This centralization had its merits in forcing forward large-scale economies and vitalizing the architectural design and construction market, but only on the large scale; however, this centralization shows its drawbacks every time in disasters or economic crisis, causing almost total paralysis in the construction industry due to economic impotence caused by different reasons. Moreover, the centralization of the building and construction industry have affected developing communities, causing economic drawbacks and creating a ripple-like crisis in housing. In this paper, the authors propose the self-sufficiency approach in the development and production of sustainable architectural material from abundant and renewable microbial agents, in order to democratize and popularize material production on a domestic and personalized basis. The current work presents Bacterial Cellulose (BC) as a structural and membrane material in different architectural elements and applications, developed through simple and domestically applied procedures in order to create distributed and self-sufficient productive units for architectural materials production. The current study aims specifically at the easiness and simplification of the production practices and procedures of the biopolymers, and specifically bacterial cellulose for encouraging and establishing the popularization of self-sufficient production units of these renewable and abundant biopolymers. In this regard, the current study is part of the ongoing research on enhancing the mechanical properties of bacterial cellulose in order to use it for structural applications, that will be further developed in terms of medium optimization, bacterial cellulose production efficiency analysis, and material mechanical and physical properties testing. The following sections will contain a literature review on the chemical base and physical/mechanical properties of biopolymers including bacterial cellulose, followed by the experimental work conducted in this paper to develop bacterial cellulose as an architectural material. The results were further analyzed through formal and structural customization proposing possible applications in architectural design.

Building intelligent systems has been one of the great challenges since the early days of human culture. From the second half of the 18th century, two revolutionary changes played the key role in technical development, hence in creating engineering and intelligent engineering systems. The industrial revolution was made possible through technical advances, and muscle power was replaced by machine power. The information revolution of our time, in turn, canbe characterized as the replacement of brain power by machine intelligence. The technique used to build engineering systems and replace muscle power can be termed "Hard Automation"1) and deals with industrial processes that are fixed and repetitive in nature. In hard automation, the system configuration and the operations are fixed and cannot be changed without considerable down-time and cost. It can be used, however, particularly in applications calling for fast, accurate operation, when manufacturing large batches of the same product. The "intelligent" area of automation is "Soft Automation," which involves the flexible, intelligent operation of an automated process. In flexible automation, the task is programmable and a work cell must be reconfigured quickly to accommodate a product change. It is particularly suitable for plant environments in which a variety of products is manufactured in small batches. Processes in flexible automation may have unexpected or previously unknown conditions, and would require a certain degree of "machine" intelligence to handle them.The term machine intelligence has been changing with time and is machinespecific, so intelligence in this context still remains more or less a mysterious phenomenon. Following Prof. Lotfi A. Zadeh,2) we consider a system intelligent if it has a high machine intelligence quotient (MIQ). As Prof. Zadeh stated, "MIQ is a measure of intelligence of man-made systems," and can be characterized by its well defined dimensions, such as planning, decision making, problem solving, learning reasoning, natural language understanding, speech recognition, handwriting recognition, pattern recognition, diagnostics, and execution of high level instructions.Engineering practice often involves complex systems having multiple variable and multiple parameter models, sometimes with nonlinear coupling. The conventional approaches for understanding and predicting the behavior of such systems based on analytical techniques can prove to be inadequate, even at the initial stages of setting up an appropriate mathematical model. The computational environment used in such an analytical approach is sometimes too categoric and inflexible in order to cope with the intricacy and complexity of real-world industrial systems. It turns out that, in dealing with such systems, one must face a high degree of uncertainty and tolerate great imprecision. Trying to increase precision can be very costly.In the face of the difficulties above, Prof. Zadeh proposes a different approach for Machine Intelligence. He separates Hard Computing techniques based Artificial Intelligence from Soft Computing techniques based Computational Intelligence.•Hard computing is oriented toward the analysis and design of physical processes and systems, and is characterized by precision, formality, and categorization. It is based on binary logic, crisp systems, numerical analysis, probability theory, differential equations, functional analysis, mathematical programming approximation theory, and crisp software.•Soft computing is oriented toward the analysis and design of intelligent systems. It is based on fuzzy logic, artificial neural networks, and probabilistic reasoning, including genetic algorithms, chaos theory, and parts of machine learning, and is characterized by approximation and dispositionality.In hard computing, imprecision and uncertainty are undesirable properties. In soft computing, the tolerance for imprecision and uncertainty is exploited to achieve an acceptable solution at low cost, tractability, and a high MIQ. Prof. Zadeh argues that soft rather than hard computing should be viewed as the foundation of real machine intelligence. A center has been established - the Berkeley Initiative for Soft Computing (BISC) - and he directs it at the University of California, Berkeley. BISC devotes its activities to this concept.3) Soft computing, as he explains2),•is a consortium of methodologies providing a foundation for the conception and design of intelligent systems,•is aimed at formalizing of the remarkable human ability to make rational decision in an uncertain, imprecise environment.The guiding principle of soft computing, given by Prof. Zadeh2) is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality.Fuzzy logic is mainly concerned with imprecision and approximate reasoning, neurocomputing mainly with learning and curve fitting, genetic computation mainly with searching and optimization and probabilistic reasoning mainly with uncertainty and propagation of belief. The constituents of soft computing are complementary rather than competitive. Experience gained over the past decade indicates that it can be more effective to use them combined, rather than exclusively.Based on this approach, machine intelligence, including artificial intelligence and computational intelligence (soft computing techniques) is one pillar of Intelligent Engineering Systems. Hundreds of new results in this area are published in journals and international conference proceedings. One such conference, organized in Budapest, Hungary, on September 15-17, 1997, was titled'IEEE International Conference on Intelligent Engineering Systems 1997' (INES'97), sponsored by the IEEE Industrial Electronics Society, IEEE Hungary Section, Bá{a}nki Doná{a}t Polytechnic, Hungary, National Committee for Technological Development, Hungary, and in technical cooperation with the IEEE Robotics & Automation Society. It had around 100 participants from 29 countries. This special issue features papers selected from those papers presented during the conference. It should be pointed out that these papers are revised and expanded versions of those presented.The first paper discusses an intelligent control system of an automated guided vehicle used in container terminals. Container terminals, as the center of cargo transportation, play a key role in everyday cargo handling. Learning control has been applied to maintaining the vehicle's course and enabling it to stop at a designatedlocation. Speed control uses conventional control. System performance system was evaluated by simulation, and performance tests slated for a test vehicle.The second paper presents a real-time camera-based system designed for gaze tracking focused on human-computer communication. The objective was to equip computer systems with a tool that provides visual information about the user. The system detects the user's presence, then locates and tracks the face, nose and both eyes. Detection is enabled by combining image processing techniques and pattern recognition.The third paper discusses the application of soft computing techniques to solve modeling and control problems in system engineering. After the design of classical PID and fuzzy PID controllers for nonlinear systems with an approximately known dynamic model, the neural control of a SCARA robot is considered. Fuzzy control is discussed for a special class of MIMO nonlinear systems and the method of Wang generalized for such systems.The next paper describes fuzzy and neural network algorithms for word frequency prediction in document filtering. The two techniques presented are compared and an alternative neural network algoritm discussed.The fifth paper highlights the theory of common-sense knowledge in representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning, and experimental results using this method presented.The next paper introduces an expert consulting system that employs software agents to manage distributed knowledge sources. These individual software agents solve users' problems either by themselves or thorough mutual cooperation.The last paper presents a methodology for creating and applying a generic manufacturing process model for mechanical parts. Based on the product model and other up-to-date approaches, the proposed model involves all possible manufacturing process variants for a cluster of manufacturing tasks. The application involves a four-level model structure and Petri net representation of manufacturing process entities. Creation and evaluation of model entities and representation of the knowledge built in the shape and manufacturing process models are emphasised. The proposed process model is applied in manufacturing process planning and production scheduling.References:1) C. W. De Silva, "Automation Intelligence," Engineering Application of Artificial Intelligence, 7-5, 471-477, (1994).2) L. A. Zadeh, "Fuzzy Logic, Neural Networks and Soft Computing," NATO Advanced Studies Institute on Soft Computing and Its Application, Antalya, Turkey, (1996).3) L. A. Zadeh, "Berkeley Initiative_in Soft Computing," IEEE Industrial Electronics Society Newsletter. 41-3, 8-10, (1994).

Development of a family of products that satisfies different market niches introduces significant challenges to today’s manufacturing industries—from development time to aftermarket services. A product family with a common platform paradigm offers a powerful solution to these daunting challenges. This paper presents a new approach, the Comprehensive Product Platform Planning (CP3) framework, to design optimal product platforms. The CP3 framework formulates a generalized mathematical model for the complex platform planning process. This model (i) is independent of the solution strategy, (ii) allows the formation of sub-families of products, (iii) allows the simultaneous identification of platform design variables and the determination of the corresponding variable values, and (iv) seeks to avoid traditional distinctions between modular and scalable product families from the optimization standpoint. The CP3 model yields a mixed integer nonlinear programming problem, which is carefully reformulated to allow for the application of continuous optimization using a novel Platform Segregating Mapping Function (PSMF). The PSMF can be employed using any standard global optimization methodology (hence not restrictive); particle swarm optimization has been used in this paper. A preliminary cost function is developed to represent the cost of a product family as a function of the number of products manufactured and the commonality among these products. The proposed CP3 framework is successfully implemented on a family of universal electric motors. Key observations are made regarding the sensitivity of the optimized product platform to the intended production volume.

Assembly system configuration determines the topological arrangement of stations with defined logical material flow among them. The design of assembly system configuration involves (1) subassembly planning that defines subassembly tasks and between-task material flows and (2) workload balancing that determines the task-station assignments. The assembly system configuration should be flexibly changed and updated to cope with product design evolution and updating. However, the uncertainty in future product evolution poses significant challenges to the assembly system configuration design since the higher cost can be incurred if the assembly line suitable for future products is very different from that for the current products. The major challenges include (1) the estimation of reconfiguration cost, (2) unavailability of probability values for possible scenarios of product evolution, and (3) consideration of the impact of the subassembly planning on the task-station assignments. To address these challenges, this paper formulates a concurrent optimization problem to design the assembly system configuration by jointly determining the subassembly planning and task-station assignments considering uncertain product evolution. A new assembly hierarchy similarity model is proposed to estimate the reconfiguration effort by comparing the commonalities among different subassembly plans of current and potential future product designs. The assembly system configuration is chosen by maximizing both assembly hierarchy similarity and assembly system throughput under the worst-case scenario. A case study motivated by real-world scenarios demonstrates the applicability of the proposed method including scenario analysis.

The issue of industrial sector efficiency improvement is very relevant nowadays. The qualitative development of competitive advantages among domestic enterprises of instrument-making and engineering is possible nowadays mainly due to the effective use of available resources, the faster growth of labor productivity and enterprise innovative activity increase. At that it is important to pay a special attention to the development of an effective mechanism for innovative project competitive portfolio of production design and technological preparation (PDTP), since it is the quality, the duration and the cost of PDTP that largely determine the innovativeness and financial profitability of products. The article presents an economic-mathematical model of production capacity optimization, which allows to increase the efficiency of labor resource use and to optimize the time of new product development at machine-building enterprises (the load model for interchangeable resource groups). The model aims to minimize the total cost of documentation development, subject to simultaneous loading of employees in several projects within the matrix management structure. Thus, the competent planning of project portfolio financial efficiency concerning design and technological preparation in the modern dynamically developing world is one of the main attributes for a successful innovation and investment policy of an enterprise. The calculation of the proposed model serves the strategic and the tactical goals of an enterprise: it is necessary during the change of project participant composition and number and should be taken into account during personnel policy and enterprise overall strategy development.