A bill of materials-based approach for end-of-life decision making in design for the environment

Re-use, recycling or remanufacturing of products and components are good alternatives for reducing the environmental problems resulting from the huge amounts of waste currently arriving at landfills. A new approach is proposed in this paper for enhancing these alternatives from the earliest stages of product design. Given the product structure (obtained from its bill of materials (BOM)) and the joining and geometrical relationships among the components (obtained from the three-dimensional, computer-aided design representation), a model is proposed that will determine the EOL (EOL) strategy, i.e. the depth of disassembly inside the structure and the final end (re-use, recycle, remanufacture or disposal) for each disassembled part leading to the highest profit. A scatter search (SS) metaheuristic is used to determine the disassembly cost at each level of the BOM. The model presents a number of major improvements with respect to previous research. It addresses the problems of simultaneously determining both the best EOL strategy and the disassembly sequence, as well as allowing removal of components not only over the two or three Cartesian axes and affording the possibility of modifying the encountered strategy in a further step so as to fulfil other business criteria (such as disassembly time, resources availability or maximum waste generation rate).     

A new directed graph approach for automated setup planning in CAPP

The task of setup planning is to determine the number and sequence of setups and the machining features or operations in each setup. Now there are three main methods for setup planning, i.e., the knowledge-based approach, the graph-based approach and the intelligence algorithm-based approach. In the knowledge-based and graph-based approaches reported in the literature, the main problem is that there is no guarantee that all precedence cycles between setups can be avoided during setup formation. The methods to break precedence cycles between setups are to split one setup into smaller setups. However, the implementation of this method is difficult and complex. In the intelligence algorithm-based approach, the method to handle the precedence constraints is a penalty strategy, which does not reflect the influence of precedence constraints on setup plans explicitly. To deal with the above deficiencies, a new directed graph approach is proposed to describe precedence constraints explicitly, which consists of three parts: (1) a setup precedence graph (SPG) to describe precedence constraints between setups. During the generation of the SPG, the minimal number of tolerance violations is guaranteed preferentially by the vertex clusters algorithm for serial vertices and the minimal number of setups is achieved by using variants of the breadth-first search. Precedence cycles between setups are avoided by checking whether two serial vertex clusters can generate a cycle; (2) operation sequencing to minimise tool changes in a setup; and (3) setup sequencing to generate optimal setup plans, which could be implemented by a topological sort. The new directed graph approach will generate many optimal or near-optimal setup plans and provide more flexibility required by different job shops. An example is illustrated to demonstrate the effect of the proposed approach.     

A framework for designing robust supply chains considering product development issues

The primary objective of the design for supply chain (DFSC) is the selection of an appropriate product family. Moreover, it deals with the selection of the optimal combination among the different conflicting criteria while making a trade-off between the supply chain cost, sales profit and the product design complexities. In this research, to address the DFSC issues a product platform approach has been proposed which amalgamates the component modularity as well as the function modularity in the product design. The optimisation model proposed in this paper for the product development and the supply chain design is based on a generic bill of materials (GBOM) representation. The complete framework includes vital decision-making needed for designing a robust supply chain such as locating plants to alleviate the likely dominance of production cost and market mediation cost on product variety and imparting process flexibility of the located plants. The optimisation model proposed in this paper, models the supply chain cost, sales profit and product design complexity as three criteria that altogether determine the robustness of the supply chain and the underlying product development approach. Certain parameters like process flexibility, flow types and drivers of the product variety dominance have been controlled in the design framework. To resolve the complexity of the proposed model a genetic algorithm (GA) technique has been proposed. The proposed GA adopts an arithmetic crossover, a dynamic mutation and a variable penalty strategy to produce optimal results in a very short computational time. To validate the proposed model, a simulated case study of the wiring harness supplier of an AGV manufacturer has been studied.     

A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.     

A fuzzy approach for sequencing interrelated activities in a DSM

Production and manufacturing systems often involve a myriad of interrelated activities. How these activities are organised and scheduled has a significant effect on the success of a system. Recently, the Design Structure Matrix (DSM) has been regarded as an effective tool for modelling and scheduling interrelated activities. Based on fuzzy set theory, this study explicitly addresses the uncertain activity dependencies in our formulation and develops a mathematical model for sequencing interrelated activities in a DSM. Because of the complexity of the model, a new approach, which embeds an exact algorithm within a framework of a local search heuristic, is presented for solving large problem instances. Testing results demonstrate that relatively good solutions can be easily obtained by our approach, thereby providing managers with an effective tool for scheduling a large number of interrelated activities with uncertain dependencies.     

A mathematical programming approach to fuzzy queues with batch arrivals

This paper proposes a mathematical programming approach for constructing the membership functions of the performance measures in batch-arrival queueing systems with constant batch size and the arrival rate and service rate being fuzzy numbers. The basic idea underlying the proposed method is to transform a fuzzy batch-arrival queue to a family of conventional crisp queues with batch arrivals by applying the α-cut approach. Then the family of crisp batch-arrival queues is described by formulating a pair of parametric nonlinear programs, through which the membership functions of the performance measures can be derived. A numerical example is solved successfully to demonstrate the validity of the proposed approach. Since the performance measures are completely expressed by membership functions rather than by crisp values, more information is provided for designing queueing systems. The successful extension of batch-arrival queues to fuzzy environments permits queueing models to have wider applications in the real world.     

A mathematical formulation for interface-based modular product design with geometric and weight constraints

Since customer preferences change rapidly, there is a need for design processes with shorter product development cycles. Modularization plays a key role in achieving mass customization, which is crucial in today's competitive global market environments. Standardized interfaces among modularized parts have facilitated computational product design. To incorporate product size and weight constraints during computational design procedures, a mixed integer programming formulation is presented in this article. Product size and weight are two of the most important design parameters, as evidenced by recent smart-phone products. This article focuses on the integration of geometric, weight and interface constraints into the proposed mathematical formulation. The formulation generates the optimal selection of components for a target product, which satisfies geometric, weight and interface constraints. The formulation is verified through a case study and experiments are performed to demonstrate the performance of the formulation.     

Game-theory approach for multi-objective optimal design of stationary flat-plate solar collectors

A game-theory approach has been used for the multi-objective optimum design of stationary flat-plate solar collectors. The clear-day solar-beam radiation and diffuse radiation at the location of the solar collector (Miami) are estimated. Three objectives are considered in the optimization-problem formulation: maximization of the annual average incident solar energy; maximization of the lowest month incident solar energy; and minimization of costs. The game-theory methodology is used for the solution of the three objective-constrained optimization problems to find a balanced solution. This solution represents the best compromise in terms of the super-criterion selected. Two types of sensitivity analyses are conducted on the optimum solution in this work. The sensitivity analysis with respect to the design variables indicates which design valuables are more important to different objective functions. The sensitivity analysis with respect to the solar constant shows that small fluctuations of solar constant experienced in practice affect the various objectives very little, thereby indicating that the mathematical model is robust. This work represents the first work aimed at the application of multi-objective optimization strategy, particularly the game theory approach, for the solution of the solar collector design problem.     

A risk assessment methodology for incorporating uncertainties using fuzzy concepts

This paper proposes a new methodology for incorporating uncertainties using fuzzy concepts into conventional risk assessment frameworks. This paper also introduces new forms of fuzzy membership curves, designed to consider the uncertainty range that represents the degree of uncertainties involved in both probabilistic parameter estimates and subjective judgments, since it is often difficult or even impossible to precisely estimate the occurrence rate of an event in terms of one single crisp probability.It is to be noted that simple linguistic variables such as ‘High/Low’ and ‘Good/Bad’ have the limitations in quantifying the various risks inherent in construction projects, but only represent subjective mental cognition adequately. Therefore, in this paper, the statements that include some quantification with giving specific value or scale, such as ‘Close to any value’ or ‘Higher/Lower than analyzed value’, are used in order to get over the limitations.It may be stated that the proposed methodology will be very useful for the systematic and rational risk assessment of construction projects.     

An evolutionary approach for cooling system optimization in plastic injection moulding

The cooling process is of great importance in plastic injection moulding as it has a direct impact on both productivity and product quality. Cooling process optimization is a sophisticated task which includes not only the design of cooling channels but also the selection of process parameters. Most existing optimization systems focus on either cooling channel design or process parameter selection but not both. This paper explores an approach to optimize both cooling channel design and process condition selection simultaneously through an evolutionary algorithm. The prototype system proposed in this paper is an integration of the genetic algorithm and CAE (Computer-Aided Engineering) technology. The aim is to launch a computerized system that can guide the optimization of the cooling process in plastic injection moulding. The objective is to achieve the most uniform cavity surface temperature to assure product quality.     

A fuzzy QFD program modelling approach using the method of imprecision

The use of quality function deployment (QFD) to aid decision making in product planning has gained extensive international attention, but current QFD approaches are unable to cope with complex product planning (CPP) characterized by involving multiple engineering characteristics (ECs) associated with significant uncertainty. To tackle this difficulty, in this paper, fuzzy set theory is embedded into a QFD framework and a novel fuzzy QFD program modelling approach to CPP is proposed to optimize the values of ECs by taking the design uncertainty and financial considerations into account. In the proposed methodology, fuzzy set theory is used to account for design uncertainty, and the method of imprecision (MoI) is employed to perform multiple-attribute synthesis to generate a family of synthesis strategies by varying the value of s, which indicates the different compensation levels among ECs. The proposed methodology will allow QFD practitioners to control the distribution of their development budget by presetting the value of s to determine the compensation levels among ECs. An illustrative example of the quality improvement of the design of a motor car is provided to demonstrate the application and performance of the modelling approach.     

Design for manufacturing: application of collaborative multidisciplinary decision-making methodology

Design for manufacturing is often difficult for mechanical parts, since significant manufacturing knowledge is required to adjust part designs for manufacturability. The traditional trial-and-error approach usually leads to expensive iterations and compromises the quality of the final design. The authors believe the appropriate way to handle product design for manufacturing problems is not to formulate a large design problem that exhaustively incorporates design and manufacturing issues, but to separate the design and manufacturing activities and provide support for collaboration between engineering teams. In this article, the Collaborative Multidisciplinary Decision-making Methodology is used to solve a product design and manufacturing problem. First, the compromise Decision Support Problem is used as a mathematical model of each engineering teams’ design decisions and as a medium for information exchange. Second, game-theoretic principles are employed to resolve couplings or interactions between the teams’ decisions. Third, design-capability indices are used to maintain design freedom at the early stages of product realization in order to accommodate unexpected downstream design changes. A plastic robot-arm design and manufacturing scenario is presented to demonstrate the application of this methodology and its effectiveness for solving a complex design for manufacturing problem in a streamlined manner, with minimal expensive iterations.     

Mixture design approach for early stage formulation development of a transdermal delivery system

Transdermal delivery systems (TDS) consisting of mixtures of adhesives also named multiple polymer adhesive systems are rarely found in the market and research has only been performed on a few of them. Following the principles of ICH Q8, a Design of Experiments (DOE) approach was selected for the formulation development. For evaluation of the statistical method of “mixture design”, blends of silicon adhesive, acrylic adhesive, oleyl alcohol as a surfactant and ibuprofen as a model drug were considered to be combined at different concentrations. A randomized design of 16 runs with five replicates and five runs to estimate the lack of fit (LOF) was generated. Samples were tested for adhesion properties, stability of the wet mixes, solubility of the API in the matrix and appearance of the matrix. After performing an ANOVA with the results, response surfaces of tack, shear adhesion, extent of creaming, crystallization behavior, droplet size and droplet size range were derived as contour plots. It could be shown that crystal growth of ibuprofen correlates well with droplet size and droplet size range, where lowest values for crystallization were found with mixtures containing small droplets. However, it was observed that oleyl alcohol showed no positive effect on the miscibility of the polymers and no improvement of the solubility of ibuprofen in the mixtures. With a reasonable number of experiments, the development of a design space for a TDS via mixture design gave valuable information on the product as well as on the interactions of the components.     

A new methodological framework for design for additive manufacturing

Additive manufacturing (AM) offers numerous benefits for innovative design solutions. However, engineers are currently not supported in identifying and incorporating these potentials systematically in their design solutions. In this paper, previous Design for Additive Manufacturing (DfAM) approaches are first reviewed comprehensively and classified into distinct categories according to their main purpose and application. They are then analysed further by being related to conventional design methodologies like VDI 2221. Since previous DfAM approaches only provide selective assistance at single steps in the product development process, a new framework for DfAM is proposed. Existing methods and tools, both from DfAM and from general design methodologies, are integrated into the modular framework structure. A concept for using the framework is presented to provide design engineers with continuous support in all product development phases, thereby fostering the complete exploitation of AM potentials and the development of AM-conformal designs.     

A topological model of limitations in design for manufacturing

Design for manufacturing (DFM) is a methodology that requires the use of specific manufacturing information at all stages of design. The method relies on a collection of informal and often controversial principles that seem to have eluded the benefits of formal analysis. The transition from design to manufacturing can be modeled as a mathematical mapping, and it has been previously shown how the discontinuity of this mapping formally captures the folklore that small design changes can lead to significantly increased manufacturing cost. We study the properties of the transition map in the presence of design and manufacturing variations, and show that its continuity is closely related to the structure of design and manufacturing topological spaces. The main result of this paper establishes conditions on these spaces under which design for manufacturing cannot be described by any continuous transition map. In practical terms, our study reveals the limitations of many DFM systems and approaches in their ability to relate design and manufacturing knowledge, and explains these limitations in terms of a basic incompatibility between the underlying design and manufacturing representations. We discuss how our model applies to DFM relative to traditional manufacturing methods (such as casting and stamping) and we speculate what changes might occur for alternative manufacturing technologies (such as electrical discharge machining (EDM), stereolithography, laser machining, and particle deposition).     

Decision monotonicity in incremental design: A case study of design for manufacture

Design has often been described as searching the space of solutions to a given problem for either a feasible or optimal solution. Usually, this search is conducted in an incremental, iterative manner. Unfortunately, there is a tendency for the feasible space in some domains to exhibit such poor structure that the incremental design process becomes both difficult and time consuming. This is especially true in the domain of design-for-manufacture by machining. In this paper we present the view that in such cases the sources of ill-structure must be aggressively eliminated by making strategic improvements and modifications to the target technology. To illustrate this point, we show that the domain of design-for-manufacture is ill-structured due to a class of interactions known as global interactions. We then show how global interactions can be virtually eliminated by a combination of a new workholding technology and a new interfacing technique. In the absence of global interactions, we show how the design structure exhibits a property of monotonicity. We describe the ramifications of this monotonicity on the design process, and show how design can then be considered to be deterministic. As a backdrop, we draw examples from other fields where similar strategies have resulted in greater designability.     

A polychromatic sets approach to the conceptual design of machine tools

Currently very few computer-aided conceptual design systems are available to support the initial development of a machine tool and its structure (concept, calculation, design) and the verification of the prototype. In recent years, polychromatic sets have emerged as a promising approach for mathematical modelling. The theory, techniques, and approaches of polychromatic sets are capable of playing an important role in the conceptual design of a variety of products including machine tools. In this paper, the concept of polychromatic sets is introduced, and a polychromatic sets approach for the conceptual design of machine tools is proposed. The usefulness of the proposed method is illustrated by a conceptual design example of a machining centre of drilling, milling, and boring. Applying the proposed approach to machine tools helps to make right decisions in the conceptual design stage. Due to its powerful mathematical modelling capability, the polychromatic sets technique proposed in this paper may possibly be extended and applied to design evaluation problems encountered in other design tasks in manufacturing systems including process planning.     

A heuristic design support methodology based on graph theory for district metering of water supply networks

The management of existing water supply networks can be substantially improved by permanent water district metering (WDM) which is one of the most efficient techniques for water loss detection and pressure management. However, WDM may compromise water system performance, since some pipes are usually closed to delimit districts in order not to have too many metering stations, to decrease costs and simplify water balance. This may reduce the reliability of the whole system and not guarantee the delivery of water at the different network nodes. In practical applications, the design of district meter areas (DMAs) is generally based on empirical approaches or on limited field experiences. In this work a design support methodology (DSM) is proposed, which helps to identify the position of flow meters and of boundary valves needed to define permanent DMAs. The DSM is based on graph theory and is applied to a test case.     

基于图着色理论的异构车联网时隙分配方案

针对异构车联网系统车辆较多导致时隙分配困难的问题,提出了一种基于图着色理论的时隙分配方案。该方案以考虑两跳内节点的图为模型,通过图着色的方法分配时隙,有效降低了隐藏终端带来的丢包;另外给出了一种高效实用的时隙重用分配算法。该算法根据度定义权值以确定车辆分配时隙数目,保证了公平性,提高了时隙重用,进而提高了消息发送的可靠性,同时也适用于网络拓扑多变的车联网场景。仿真结果表明,在车辆数为200、时隙数为100时,与传统时隙分配方法相比,该方案的车辆平均收包率获得大幅提升。此外,随着时隙重用的增加,车辆间干扰增强,从而导致平均收包率降低。研究还发现,增加车辆发射功率时,由于接收端信干噪比先增加后趋于不变,所以平均收包率也先增加后趋于不变。