Modelling of material handling systems for facility design in manufacturing environments with job-specific routing

Traditionally, flow times distance is used as a surrogate for cost in facility design. However, this performance measure does not fully capture the impact of facility design decisions on operational performance measures such as cycle time and work-in-progress in the system, which are often more meaningful for managers, especially in a manufacturing environment. To better measure operational performance, modelling of material handling systems using a queueing network must be integrated in the facility design process. A number of approaches are discussed in the facility design literature for modelling material flow using queueing networks. In these approaches, Poisson arrival or Markovian job routing assumptions have been used. However, for many manufacturing environments, these assumptions lead to an inaccurate estimation of the material handling system's performance and thus lead to poor facility designs. Incorporating more general queueing results for non-Markovian systems is difficult, however, because the facility design process must investigate a large number of potential solutions and thus the results from the queueing models for the material handling system must be quickly obtained. In this paper, the need for more general queueing models of material handling systems in facility design is confirmed. Then, an approach based on multi-class queueing models is adapted to capture the change in variability of the system performance caused by both different arrangements of workstations in the facility and different arrival processes to the workstations due to the job routing in a computationally efficient manner. The proposed modelling approach is shown to provide more accurate results than previous methods used in facility design based on numerical comparisons with results from discrete-event simulation.     

An engineering design knowledge reuse methodology using process modelling

This paper describes an approach for reusing engineering design knowledge. Many previous design knowledge reuse systems focus exclusively on geometrical data, which is often not applicable in early design stages. The proposed methodology provides an integrated design knowledge reuse framework, bringing together elements of best practice reuse, design rationale capture and knowledge-based support in a single coherent framework. Best practices are reused through the process model. Rationale is supported by product information, which is retrieved through links to design process tasks. Knowledge-based methods are supported by a common design data model, which serves as a single source of design data to support the design process. By using the design process as the basis for knowledge structuring and retrieval, it serves the dual purpose of design process capture and knowledge reuse: capturing and formalising the rationale that underpins the design process, and providing a framework through which design knowledge can be stored, retrieved and applied. The methodology has been tested with an industrial sponsor producing high vacuum pumps for the semiconductor industry.     

An argumentation-based method for managing complex issues in design of infrastructural systems

The many interacting and conflicting requirements of a wide range of stakeholders are the main sources of complexity in the infrastructure and utility systems. We propose a systemic methodology based on negotiation and argumentation to help in the resolution of complex issues and to facilitate options appraisal during design of such systems. A process-based approach is used to assemble and propagate the evidence on performance and reliability of the system and its components, providing a success measure for different scenarios or design alternatives. The reliability of information sources and experts opinions are dealt with through an extension of the mathematical theory of evidence. This framework helps not only in capturing the reasoning behind design decisions, but also enables the decision-makers to assess and compare the evidential support for each design option.     

Safety assessment in plant layout design using indexing approach: implementing inherent safety perspective. Part 2-Domino Hazard Index and case study

The design of layout plans requires adequate assessment tools for the quantification of safety performance. The general focus of the present work is to introduce an inherent safety perspective at different points of the layout design process. In particular, index approaches for safety assessment and decision-making in the early stages of layout design are developed and discussed in this two-part contribution. Part 1 (accompanying paper) of the current work presents an integrated index approach for safety assessment of early plant layout. In the present paper (Part 2), an index for evaluation of the hazard related to the potential of domino effects is developed. The index considers the actual consequences of possible escalation scenarios and scores or ranks the subsequent accident propagation potential. The effects of inherent and passive protection measures are also assessed. The result is a rapid quantification of domino hazard potential that can provide substantial support for choices in the early stages of layout design. Additionally, a case study concerning selection among various layout options is presented and analyzed. The case study demonstrates the use and applicability of the indices developed in both parts of the current work and highlights the value of introducing inherent safety features early in layout design.     

Benefits of considering inventory in service parts logistics network design problems with time-based service constraints

We study the integrated logistics network design and inventory stocking problem as characterized by the interdependency of the design and stocking decisions in service parts logistics. These two sets of decisions are usually considered sequentially in practice, and the associated problems are tackled separately in the research literature. The overall problem is typically further complicated due to time-based service constraints that provide lower limits on the percentage of demand satisfied within specified time windows. We introduce an optimization model that explicitly captures the interdependency between network design (location of facilities, and allocation of demands to facilities) and inventory stocking decisions (stock levels and their corresponding stochastic fill rates), and present computational results from our extensive experiments that investigate the effects of several factors including demand levels, time-based service levels and costs. We show that the integrated approach can provide significant cost savings over the decoupled approach (solving the network design first and inventory stocking next), shifting the whole efficient frontier curve between cost and service level to superior regions. We also show that the decoupled and integrated approaches may generate totally different solutions, even in the number of located facilities and in their locations, magnifying the importance of considering inventory as part of the network design models.     

An integrated framework for multi-scale materials simulation and design

In this paper, we describe initial results of an ongoing research activity involving materials scientists, computer scientists, mathematicians, and physicists from academia, industry and a national laboratory. The present work aims to develop a set of integrated computational tools to predict the relationships among chemistry, microstructure and mechanical properties of multicomponent materials systems. It contains a prototype grid-enabled package for multicomponent materials design with efficient information exchange between structure scales and effective algorithms and parallel computing schemes within individual simulation/modeling stages. As part of our multicomponent materials design framework, this paper reports the materials simulation segment in developing materials design knowledgebase, which involves four major computational steps: (1) Atomic-scale first-principles calculations to predict thermodynamic properties, lattice parameters, and kinetic data of unary, binary and ternary compounds and solutions phases; (2) CALPHAD data optimization approach to compute thermodynamic properties, lattice parameters, and kinetic data of multicomponent systems; (3) Multicomponent phase-field approach to predict the evolution of microstructures in one to three dimensions (1–3D); and (4) Finite element analysis to generate the mechanical response from the simulated microstructure. These four stages are to be integrated with advanced discretization and parallel algorithms and a software architecture for distributed computing systems.     

Multidisciplinary design methodology for mechatronic systems based on interface model

Mechatronic system is considered as the resulting integration of electrical/electronic system, mechanical parts and information processing. Therefore, to enable a systematic design process of mechatronic systems with a high-level integration, the so-called multidisciplinary integrated design is required. However, neither academia nor industry has yet provided an effective solution, which can fully support the whole design process to achieve such multidisciplinary integrated design. In order to organise the design activities from different disciplines and to aid the designers to achieve the multidisciplinary integrated design, the authors propose a design methodology based on a multidisciplinary interface model. In line with the systems engineering practices, an extended V-model is used as the macro-level process in the proposed design methodology. It starts with identification of requirements on the entire system and ends with a user-validated system. The hierarchical design model is adopted as the micro-level process. It supports the specific design phases where individual designers can structure design sub-tasks and proceed and react in unforeseen situations. To ensure the consistency and traceability between the two levels, the multidisciplinary interface model is proposed. This design methodology is demonstrated by studying the design process of a quadrotor.     

A functional failure reasoning methodology for evaluation of conceptual system architectures

In this paper, we introduce a new methodology for reasoning about the functional failures during early design of complex systems. The proposed approach is based on the notion that a failure happens when a functional element in the system does not perform its intended task. Accordingly, a functional criticality is defined depending on the role of functionality in accomplishing designed tasks. A simulation-based failure analysis tool is then used to analyze functional failures and reason about their impact on overall system functionality. The analysis results are then integrated into an early stage system architecture analysis framework that analyzes the impact of functional failures and their propagation to guide system-level architectural design decisions. With this method, a multitude of failure scenarios can be quickly analyzed to determine the effects of architectural design decisions on overall system functionality. Using this framework, design teams can systematically explore risks and vulnerabilities during the early (functional design) stage of system development prior to the selection of specific components. Application of the presented method to the design of a representative aerospace electrical power system (EPS) testbed demonstrates these capabilities.     

Multi-criteria fuzzy decision support for conceptual evaluation in design of mechatronic systems: a quadrotor design case study

Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.     

Axiomatic approach to structural design

As civil engineering enters the 21st century, the demands on the profession will move toward complex, interdisciplinary tasks such as infrastructure rehabilitation, environmental cleanup, and the delivery of high-technology facilities (e.g., hospitals, R&D laboratories, and advanced manufacturing plants). The current structural design paradigm is a top-down process that includes a nonhomogeneous approach to decision-making. There is an apparent lack of basic principles to formalize and evaluate conceptual design decisions while preliminary and detailed design decisions reflect increasing formalization and reliance on computational methods. This nonhomogeneous approach to decision-making limits how well the practicing engineer can meet the impending design challenges; particularly since conceptual design decisions determine a significant portion of a project's total cost. Axiomatic design is presented as a systematic framework for structural design because it aids the designer in satisfying multiple design objectives in a homogeneous manner throughout the design process. It is also an effective framework for formalizing and evaluating conceptual design decisions. The design of a structural frame for an innovative mechanical parking system is presented as an illustrative case study. This paper represents an initial effort to apply the principles of axiomatic design to the domain of civil engineering structures.     

Configuration design of scalable reconfigurable manufacturing systems for part family

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.     

Analysis of fault tolerance and reliability in distributed real-time system architectures

Safety critical real-time systems are becoming ubiquitous in many areas of our everyday life. Failures of such systems potentially have catastrophic consequences on different scales, in the worst case even the loss of human life. Therefore, safety critical systems have to meet maximum fault tolerance and reliability requirements. As the design of such systems is far from being trivial, this article focuses on concepts to specifically support the early architectural design. In detail, a simulation based approach for the analysis of fault tolerance and reliability in distributed real-time system architectures is presented. With this approach, safety related features can be evaluated in the early development stages and thus prevent costly redesigns in later ones.     

An integrated IDEF0-3/CTPN/SFC approach for design and analysis of discrete event control systems

Due to its low cost and increased reliability, the programmable logical controller (PLC) plays an important role in industry automation. However, as systems become larger and more complex, efficient and systematic analysis and design of PLCs become a more important issue. In this research, an integrated IDEF0-3/CTPN/SFC (IPS) for developing discrete event control systems (DECSs) is presented. The proposed integrated methodology provides a complete and systematic development process for the DECSs. The development process is divided into four stages: functional analysis, system behaviour analysis, system design, and system implementation. The IDEF0-3, coloured timed Petri net (CTPN) and sequential function chart (SFC) are integrated into the corresponding stages based on their characteristics. In addition, the transformation rules between the IDEF0-3 and CTPN and between CTPN and SFC are also conducted for developing DECSs. The transformation rules provide a straightforward mapping from the IDEF0-3 to CTPN and from the CTPN to SFC. The result of the integrated IPS systematically leads to SFC for PLC implementation. Finally, a ball assorting system is given to illustrate how the integrated IPS approach is implemented for developing the DECSs.     

Systems design of hierarchically structured materials: Advanced steels

A systems approach integrates processing/structure/property/performance relations in the conceptual design of multilevel-structured materials. Using the example of high performance alloy steels, numerical implemen- tation of materials science principles provides a hierarchy of computational models defining subsystem design parameters which are integrated via computational thermodynamics in the comprehensive design of materials as interactive systems. Materials design class projects address application of the methods in metals, ceramics, and polymers for special applications.     

Integrated planning for product module selection and assembly line design/reconfiguration

To take full advantage of product modularity, modular product design and assembly system design/sreconfiguration have to be simultaneously addressed. The emerging reconfigurable production and flexible assembly techniques have made such an integrated approach possible. As such, this paper proposes an integrated approach to product module selection and assembly line design/reconfiguration problems. It further suggests that quality loss functions be used in a generic sense to quantify non-comparable and possibly conflicting performance criteria involved in the integrated problem. The complexity of the problem precludes the use of commercial software for solving meaningful sized problems in polynomial time. A genetic algorithm is therefore developed to provide quick solutions. An example problem is solved to illustrate the application of the proposed approach. Based on 72 randomly generated test problems, ANOVA analysis is further carried out to investigate the effects of genetic algorithm parameters. The convergence behaviour of the search processes is also examined by solving large problems with different numbers of operations and product modules.     

Parameter tolerance design for electrical circuits

Realistic circuit design requires that unavoidable tolerances on component parameters be taken into account, particularly in situations where a circuit is to be mass-produced. Since specifications are normally imposed on circuit performance, parameter tolerances can have the undesirable effect of reducing manufacturing yield (i.e. the percentage of circuits which meet specifications) to values below unity, thereby effectively increasing circuit cost. Approaches have been developed to electrical circuit design which incorporate aspects of parameter tolerance variations at the various stages of design, thus enabling tolerance effects to be assessed and minimized. There are two principal approaches: statistical and deterministic. The first uses probabilistic techniques to predict variations in circuit performance, whereas the second uses deterministic (i.e. non-stochastic) methods. Within each group, three types of problems are important: first, the maximization of yield, secondly, the minimization of circuit unit cost and, thirdly, the minimization of performance variability. This paper discusses some important advances in the statistical approach to tolerance design. Monte Carlo analysis is almost invariably an important component of the procedure: random fluctuations in parameter values are simulated according to some probability density function and inserted into a computer circuit simulation program which computes corresponding circuit performance variations. The procedure — also referred to as tolerance analysis — not only allows the designer to predict expected performance fluctuations but also presents him with information regarding the relative location of acceptable and non-acceptable circuits in component parameter space. The Monte Carlo method can handle without difficult any number of component parameters and performance functions; moreover, statistical dependence among parameters is readily handled. The algorithm presented here is experimentally validated through successful design of practical circuits and is applicable to both discrete and integrated circuits. Strategies which ensure computational efficiency of the methods are discussed and a cost/benefit analysis carried out for a typical circuit.     

An integration design optimization framework of robust design,axiomatic design,and reliability‐based design

Robust design, axiomatic design, and reliability‐based design provide effective approaches to deal with quality problems, and their integration will achieve better quality improvement. An integration design optimization framework of robust design, axiomatic design, and reliability‐based design is proposed in this paper. First, the fitted response model of each quality characteristic is obtained by response surface methodology and the mean square error (MSE) estimation is given by a second‐order Taylor series approximation expansion. Then the multiple quality characteristics robust design model is developed by the MSE criteria. Finally, the independence axiom constraints for decoupling and reliability constraints are integrated into the multiple quality characteristics robust design model, and the integration design optimization framework is formulated, where the weighted Tchebycheff approach is adopted to solve the multiple objective programming. An illustrative example is presented at the end, and the results show that the proposed approach can obtain better trade‐offs among conflicting quality characteristics, variability, coupling degree and reliability requirements. Copyright © 2011 John Wiley & Sons, Ltd.     

Systems design of high performance stainless steels II. Prototype characterization

Within the framework of a systems approach, the design of a high performance stainless steel integrated processing/structure/property/performance relations with mechanistic computational models. Using multicomponent thermodynamic and diffusion software platforms, the models were integrated to design a carburizable, secondary-hardening, martensitic stainless steel for advanced gear and bearing applications. Prototype evaluation confirmed the predicted martensitic transformation temperature and the desired carburizing and tempering responses, achieving a case hardness of R _c 64 in the secondary-hardened condition without case primary carbides. Comparison with a commercial carburizing stainless steel demonstrated the advantage of avoiding primary carbides to resist quench cracking associated with a martensitic start temperature gradient reversal. Based on anodic polarization measurements and salt-spray testing, the prototype composition exhibited superior corrosion resistance in comparison to the 440C stainless bearing steel, which has a significantly higher alloy Cr concentration. This revised version was published online in June 2006 with corrections to the Cover Date.     

Computer-aided design by optimization in architecture

This paper shows that optimization concepts are particularly useful in design because of their direct assistance in decision making. In this they subsume evaluation or appraisal techniques. One approach based on dynamic programming is presented as being directly applicable in computer-aided architectural design. Multi-attribute objectives in design can be handled using optimization concepts. Finally, multi-objective design, including multi-attribute multi-objective design, can be handled via the use of Pareto optimality approaches. The result of such processes is a solution database which the designer searches. The solution database contains information about the design decisions themselves as well as the performance of each solution in its various objectives. The designer still assumes responsibility for selecting particular solutions since there is no unique solution produced. It is suggested that any problem which can be manipulated quantitatively can be solved using these concepts.     

An integrated approach to product design, materials selection and cost estimation

An integrated approach to the interrelated activities of product design, materials selection and cost estimation is proposed. The wide range of engineering materials is first narrowed to a limited number of candidates using design limitations and performance requirements. Each of the candidate materials is used to develop an optimum design which is then used in cost estimation. An optimization technique, such as benefit-cost analysis, is used to select the optimum design-material combination. A case study is presented to illustrate the use of the integrated approach.