Графо-аналитическое моделирование дискретно-непрерывных поточных линий лесопромышленных складов

This chapter discusses digital mechanisms for optimizing the management system in the forest industry, which includes organizational, legal, socio-economic, and environmental aspects. Efficient forest management is considered as an integral part of efficient nature management and includes the use of forest resources, their protection, and reproduction of forests. Digital management mechanisms in forest management in general and in the forest industry in particular are based on platform solutions. Platform solutions are based on the formation and processing of data on the basis of a single automated information system, which acts as the foundation for the development of digitalization in forestry. Such a digital platform is designed to provide informational, analytical, consulting, and other support to the activities of all subjects of relations in the field of use, conservation, protection, and reproduction of forest resources.

The Maximal Profit Model for reaching an optimal design of the production line undergoing the limitations of obtainable resources is presented in this paper. This model is treated as an integer programming problem, and an efficient step-by-step algorithm to solve this problem is also constructed. In addition, it is discussed that the operation cost of a machine does not include idle and breakdown situations while the maintenance cost for a broken machine should be considered. This study offers a better tool for achieving the optimal design of a flexible production line and reveals the special applicability of the shortest path in production line design.

In order to meet the requirements of automatic processing for mechanical components manufacturing enterprises, the paper describes a design method of automatic production line. Firstly, the paper made the overall design of the automatic production line, takt time is calculated according to the annual production demand, and the analysis of device configuration and position layout of the production line was carried out based on the annual production requirement. Secondly, the paper selects types of CNC machines and industrial robots, and design structural of the silo, the temporary storage platform, the conveyor belt, the fuel injection and water removal. 3D model of each component and space layout of the production line are drawn by Solidworks software. Thirdly, the process flow of automatic production line was designed, and the CNC intelligent management system was developed to meet the requirements, which can monitor the process information such as takt time. Test is carried out after establishment of automatic production line, the test run finally shows that the automatic production line works stably and can meet the requirements of enterprise.

Product line design is a key decision area that a product development team has to deal with in the early stages of product development. Previous studies of product line design have focused on single-objective optimization. However, several optimization objectives may be simultaneously pursued, and the solutions that can address the objectives are required in many practical scenarios. In this research, we propose a one-step multiobjective optimization approach for product line design. The proposed optimization model has three objectives: 1) maximizing the market share of a company's products; 2) minimizing the total product development cost of a product line; and 3) minimizing the total product development cycle time. A curve-fitting method is introduced into the part-worth utility models so that the optimization model can be applied to products with level-based attributes and attributes that have continuous values. A multiobjective genetic algorithm is adopted to solve the optimization model, obtaining a set of nondominated solutions. With the solutions, a new product development team can select a preferred solution interactively in a 2-D graph. An example of the optimal design of a product line of digital cameras is used to illustrate the proposed approach.

Product line design is one of the most important decisions for an organization in today’s hypercompetitive world. Product line designs are NP-hard, which implies that it requires an unacceptable amount of time to obtain the guaranteed optimal solution to a problem of reasonable scale. Machine learning techniques such as genetic algorithms can provide very “good” solutions to these problems. In this chapter, we describe the architecture and user interface of a multi-feature decision support system, PRODLINE, which allows the decision maker to address the decision problem of product line designs. A key feature of the system is its ability to provide users with solutions using different solution techniques as well as the ability to change easily the algorithm parameters to assess if improvements in the solution are possible. A final novel and major advantage of the PRODLINE system is that it permits the user to consider strategic competitive responses to the optimal product line design problem.KeywordsProduct LineDynamic ProgrammingDecision Support SystemAttribute LevelConjoint AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The article discusses the problems of modeling and simulation in the design of production lines, mainly from an educator's perspective. Nowadays, there is a wide range of computer programs that can be used to design production lines and to simulate various aspects of their operation. However, the programs being available vary considerably as to their functionality, the approach to production system design, and the visualization tools. Therefore we demonstrate and evaluate in this paper four simulation programs, focusing on the easiness of system design, area of the possible applications in education of engineers, and the limitations imposed by versions dedicated for students. We evaluate three of programs for digital factory simulation on a common, simple assembly task, then demonstrate that these programs may be also used for more specialized simulations in various areas of production, and compare with a specialized program for simulation of robotised production lines and work cells.

The use of automated guided vehicles (AGVs) as transporters and mobile workstations in automated assembly lines is on the increase. This is due in part to the flexibility AGVs introduce in the design of production lines. In this paper, models for the management and control of material flow through AGV based production lines are developed. The use of the models permits the line designer to optimally determine the launch times of successive unit loads of parts into the line to minimize production makespan while balancing between line idleness and the idleness of vehicles employed on the line. The use of the models for AGV based production line design purposes is demonstrated through example problems.

On the product line selection problem under attraction choice models of consumer behavior

Introduction: Three-dimensional (3D)-printed custom pelvic implants have become a clinically viable option for patients undergoing pelvic cancer surgery with resection of the hip joint. However, increased clinical utilization has also necessitated improved implant durability, especially with regard to the compression screws used to secure the implant to remaining pelvic bone. This study evaluated six different finite element (FE) screw modeling methods for predicting compression screw pullout and fatigue failure in a custom pelvic implant secured to bone using nine compression screws. Methods: Three modeling methods (tied constraints (TIE), bolt load with constant force (BL-CF), and bolt load with constant length (BL-CL)) generated screw axial forces using functionality built into Abaqus FE software; while the remaining three modeling methods (isotropic pseudo-thermal field (ISO), orthotropic pseudo-thermal field (ORT), and equal-and-opposite force field (FOR)) generated screw axial forces using iterative physics-based relationships that can be implemented in any FE software. The ability of all six modeling methods to match specified screw pretension forces and predict screw pullout and fatigue failure was evaluated using an FE model of a custom pelvic implant with total hip replacement. The applied hip contact forces in the FE model were estimated at two locations in a gait cycle. For each of the nine screws in the custom implant FE model, likelihood of screw pullout failure was predicted using maximum screw axial force, while likelihood of screw fatigue failure was predicted using maximum von Mises stress. Results: The three iterative physics-based modeling methods and the non-iterative Abaqus BL-CL method produced nearly identical predictions for likelihood of screw pullout and fatigue failure, while the other two built-in Abaqus modeling methods yielded vastly different predictions. However, the Abaqus BL-CL method required the least computation time, largely because an iterative process was not needed to induce specified screw pretension forces. Of the three iterative methods, FOR required the fewest iterations and thus the least computation time. Discussion: These findings suggest that the BL-CL screw modeling method is the best option when Abaqus is used for predicting screw pullout and fatigue failure in custom pelvis prostheses, while the iterative physics-based FOR method is the best option if FE software other than Abaqus is used.

Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.

Reprint of “The impact of development cost on product line design and its environmental performance”

<p style='text-indent:20px;'>Due to the fierce market competition, enterprises try to satisfy customers' requirements for personalized products in order to maximize profit or market share of their products. This not only needs to determine the product variants through product line design, but also needs to pay attention to resource allocation in the manufacturing process. This paper proposes a cellular manufacturing optimization model that considers the market and production. If the company excessively pursues the satisfaction of customers' personalized needs, the manufacturing time and cost may increase accordingly. Of course, with the restriction of production capacity in manufacturing cells and the expectation of reducing cost, managers cannot design attributes' levels of a product line casually, which may result in its unstable marketing share and profit. Therefore, the product demand influenced by customers' preferences could be a key factor to link market and production. The objective of propose model is to maximize product profit which consists of revenue and miscellaneous costs (material, processing, transportation, final assembly and fixed costs). A revised imperialist competitive algorithm (RICA) is developed to optimize the discrete problem. Extensive numerical experiments and t-test are carried out to verify the effect of this method. The results demonstrate the proficiency of RICA over another imperialist competitive algorithm based method and genetic algorithm in terms of solution quality.</p>

The existing product line design literature devotes little attention to the effect of demand uncertainty. Due to demand uncertainty, the supply‐demand mismatch is inevitable which leads to different degrees of lost sales depending on the configuration of product lines. In this article, we adopt a stylized two‐segment setup with uncertain market sizes and illustrate the interplay between two effects: risk pooling that mitigates the impact of demand uncertainty and market segmentation that facilitates consumer differentiation. Compared to downward substitution, inducing bidirectional substitution through product line decisions including quality levels and prices can yield greater risk pooling effects. However, we show that the additional benefit from the risk pooling effect cannot compensate for the reduced market segmentation effect. We demonstrate that the presence of demand uncertainty can reduce the benefit of market segmentation and therefore the length of product lines in terms of the difference between products. We also propose three heuristics that separate product line and production decisions; each of these heuristics corresponds to one particular form of demand substitution. Our numerical studies indicate that the best of the three heuristics yields performance that is close to optimality. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 143–157, 2015

Renewable synthetic methanol system design based on modular production lines

Li-ion battery is used widely in many industrial areas, and formation is a necessary process in battery manufacture. After formation, the performance of Li-ion power batteries can be activated. In the process of production, the key technology in the formation has been changed. The technology of formation and holding independently for each battery is turning into the formation after being clamped and then holding now. Therefore, the design of the automatic production line must base on the battery groups after being clamped. This text will introduce a new production line for the formation of Li-ion battery, mainly discuss the formation of clamped batteries, and how to realize the automation of the production line. The line uses the three-dimensional multi-layer frameworks, speed-fold chains and stoppers. The speed-fold chain is a new type of conveyor chain, and known as "flexible manufacturing unit". The battery groups can be stopped in the manufacturing line by the stoppers, and then they can be charged or discharged. The line can be connected by the lifting machine.