2,940 research outputs found
Decision support for build-to-order supply chain management through multiobjective optimization
This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review
The traditional manufacturing sectors (footwear, textiles and clothing, furniture and toys, among others) are based on small and medium enterprises with limited capacity on investing in modern production technologies. Although these sectors rely heavily on product customization and short manufacturing cycles, they are still not able to take full advantage of the fourth industrial revolution. Industry 4.0 surfaced to address the current challenges of shorter product life-cycles, highly customized products and stiff global competition. The new manufacturing paradigm supports the development of modular factory structures within a computerized Internet of Things environment. With Industry 4.0, rigid planning and production processes can be revolutionized. However, the computerization of manufacturing has a high degree of complexity and its implementation tends to be expensive, which goes against the reality of SMEs that power the traditional sectors. This paper reviews the main scientific-technological advances that have been developed in recent years in traditional sectors with the aim of facilitating the transition to the new industry standard.This research was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) under the project CloudDriver4Industry TIN2017-89266-R
A Stackelberg game theoretic model for optimizing product family architecting with supply chain consideration
Planning of an optimal product family architecture (PFA) plays a critical role in defining an organization's
product platforms for product variant configuration while leveraging commonality and variety. The focus
of PFA planning has been traditionally limited to the product design stage, yet with limited consideration
of the downstream supply chain-related issues. Decisions of supply chain configuration have a profound
impact on not only the end cost of product family fulfillment, but also how to design the architecture of
module configuration within a product family. It is imperative for product family architecting to be
optimized in conjunction with supply chain configuration decisions. This paper formulates joint optimization of PFA planning and supply chain configuration as a Stackelberg game. A nonlinear, mixed
integer bilevel programming model is developed to deal with the leaderâfollower game decisions
between product family architecting and supply chain configuration. The PFA decision making is
represented as an upper-level optimization problem for optimal selection of the base modules and
compound modules. A lower-level optimization problem copes with supply chain decisions in accordance with the upper-level decisions of product variant configuration. Consistent with the bilevel
optimization model, a nested genetic algorithm is developed to derive near optimal solutions for PFA and
the corresponding supply chain network. A case study of joint PFA and supply chain decisions for power
transformers is reported to demonstrate the feasibility and potential of the proposed Stackelberg game
theoretic joint optimization of PFA and supply chain decisions
Integrated open source architectural design for high density housing with computational control and management engineering the paradoxes of chinese housing architecture
Session V (Room D): Methodology IHousing is a collection of individual units based on negotiation between global standardization by the designers and local customization by the users after occupation. Due to the economic, industrial and time constrains, it is impossible to reflect usersâ different needs in the design stage for high density housing. In response to this challenge, this research paper argues that the high density housing design can adopt the individual customization by the users in the design stage without paying significantly extra cost, hence the design process could be an open-ended evolutionary and transparent process rather than deterministic execution. To overcome the deficiency in addressing the future uncertainty by different users and the one-off development without the interactive mechanism for usersâ feedback in the sub-sequential housing design and procurement, This essay proposes Integrated Open Source Design for Architecture (IOSDA) for housing design practice based on collective data and parametric connectivity between the end users and the designers, discussing how to integrate top-down mechanism with designerâs empirical inputs and the bottom-up ecosystems with usersâ participation in high density housing design. IOSDA reflects a different attitude to design the future, which shifts from heroic prediction of the future to engaging the present grassroots, from board proactive reaction to the capacities for new possibilities.postprin
An interactive product development model in remanufacturing environment: a chaos-based artificial bee colony approach
This research presents an interactive product development model in re-manufacturing environment. The product development model defined a quantitative value model considering product design and development tasks and their value attributes responsible to describe functions of the product. At the last stage of the product development process, re-manufacturing feasibility of used components is incorporated. The consummate feature of this consideration lies in considering variability in cost, weight, and size of the constituted components depending on its types and physical states.
Further, this research focuses on reverse logistics paradigm to drive environmental management and economic concerns of the manufacturing industry after the product launching and selling in the market. Moreover, the model is extended by integrating it with RFID technology. This RFID embedded model is aimed at analyzing the economical impact on the account of having advantage of a real time system with reduced inventory shrinkage, reduced processing time, reduced labor cost, process accuracy, and other directly measurable benefits.
Consideration the computational complexity involved in product development process reverse logistics, this research proposes; Self-Guided Algorithms & Control (S-CAG) approach for the product development model, and Chaos-based Interactive Artificial Bee Colony (CI-ABC) approach for re-manufacturing model. Illustrative Examples has been presented to test the efficacy of the models. Numerical results from using the S-CAG and CI-ABC for optimal performance are presented and analyzed. The results clearly reveal the efficacy of proposed algorithms when applied to the underlying problems. --Abstract, page iv
Optimisation of the concurrent product and process configuration: an approach to reduce computation time with an experimental evaluation
International audienceConcurrent configuration of a product and its associated production process is a challenging problem in customer/supplier relations dealing with customisable or configurable products. It gathers in a single model multiple choices and constraints which come simultaneously from products (choices of components or functionalities), from processes (choices of resources and quantities) and from their mutual interrelations. Considering this problem as a Constraint Satisfaction Problem (CSP), the aim of this article is to improve its optimisation, while considering multiple objectives. Using an existing evolutionary optimisation algorithm as a basis, we propose an approach that reduces the computation time required for optimisation. The idea is first to quickly compute a rough Pareto of solutions, then ask the user to select an area of interest, and finally to launch a second computation on this restricted area. After an introduction to the problem, the approach is explained and the algorithm adaptations are presented. Then various computation experiments results demonstrate that computation times are significantly reduced while keeping the optimality level
On the selection and analysis of software product line implementation components using intelligent techniques
En los Ăşltimos aĂąos y con el creciente avance tecnolĂłgico, las empresas
ya no se centran exclusivamente en diseĂąar un producto para un cliente (por
ejemplo, el diseĂąo de un sitio web para el Hotel Decameron), sino en producir
para un dominio (por ejemplo, el diseĂąo de sitios web para hoteles); es decir,
el diseĂąo de un producto que pueda adaptarse fĂĄcilmente a las diferentes variaciones
que puedan existir para un mismo producto y que se adapte a los
gustos individuales de los clientes.
En la ingenierĂa de software, esto puede lograrse a travĂŠs de la gestiĂłn de
lĂneas de productos de software (SPL). Una SPL se define como un conjunto de
sistemas que comparten un conjunto comĂşn de caracterĂsticas que satisfacen
la demanda de un mercado especĂfico. Una SPL intenta reducir el esfuerzo y
el costo de implementar y mantener en el tiempo un conjunto de productos de
software similares; sin embargo, manejar la variabilidad en estos sistemas es
una tarea dif´Ĺcil, a mayor n´umero de productos m´as complejo se hace manejarlos.
Los modelos de caracter´Ĺsticas (FMs) se emplean para representar gr´aficamente
las partes comunes y variables de una SPL. Dada la gran cantidad de
caracter´Ĺsticas que se pueden derivar de un modelo de caracter´Ĺstica (FM), resulta
dif´Ĺcil de gestionarlos. Para hacer frente a estos problemas se ha propuesto
el An´alisis Autom´atico de Modelos de Caracter´Ĺsticas (AAFM) que mediante
el uso de herramientas asistidas por ordenador, se ocupa de la extracci ´on
de informaciĂłn de los modelos de caracterĂsticas. No obstante, existen ciertos
escenarios en los que la configuraciĂłn de un producto se convierte en una
actividad compleja dado el nĂşmero de componentes que existen para implementar
una determinada caracterĂstica.
En esta tesis, exploramos tĂŠcnicas inteligentes para resolver dos problemas
que surgen al manejar una SPL:
i. Por un lado, hemos identificado los problemas que surgen cuando un
desarrollador desea mantener sus aplicaciones al d´Ĺa con los últimos
avances tecnol´ogicos. La estrecha relaci ´on entre las caracter´Ĺsticas de
aplicaci ´on y los componentes de plataforma es dif´Ĺcil de rastrear. Los desarrolladores deben ser conscientes de las consecuencias que podr´Ĺan
traer a las aplicaciones existentes cuando cambia el hardware donde
se va a ejecutar; por ejemplo, cuando una aplicaci ´on se traslada de un
smartphone a una computadora/tablet, o cuando una plataforma se actualiza
a una nueva versi´on. Los diferentes tamaËnos y resoluciones de
pantalla, la posible ausencia de un radio celular o el aumento de la cantidad
de memoria pueden tener impactos positivos o negativos en una
aplicaci ón. En este contexto, dado que las caracter´Ĺsticas de aplicaci ´on y
de plataforma estån conceptualmente separadas, sus caracter´Ĺsticas pueden
modelarse en dos modelos distintos. Por consiguiente, manejar la
trazabilidad entre estas dos capas y c´omo los posibles cambios en ciertas
caracter´Ĺsticas puedan afectar a la otra capa, es un problema que est´a por
resolver.
ii. Por otro lado, hemos encontrado lo complicado que es para el desarrollador
de aplicaciones configurar un producto cuando hay una variedad
de componentes de implementaciĂłn para cada caracterĂstica. Por ejemplo,
un desarrollador web en WordPress busca manualmente aquellos
componentes (plugins) que son factibles y mĂĄs Ăłptimos para cada sitio
web. Esta tarea lleva tiempo y no siempre garantiza que los componentes
seleccionados sean los m´as adecuados (en tÊrminos de calidad) para
la aplicaciĂłn requerida. Dos escenarios podrĂan surgir durante esta
configuraci´on: primero, la selecci ´on emp´Ĺrica de un componente, en la
pr´actica, puede no proporcionar los resultados esperados; adem´as, no
tener criterios basados en la experiencia de otros usuarios con respecto a
estos componentes, podr´Ĺa inducir una mala selecci ´on y lograr una mala
experiencia para el usuario final. En este contexto, el manejo de la relaci
´on entre los componentes de implementaci´on y sus caracter´Ĺsticas es
otro problema a resolver.
Concretamente, las contribuciones de esta tesis se detallan a continuaci´on;
Modelos de caracter´Ĺsticas en m´ ultiples capas: En esta ´area introducimos un
framework para el an´alisis de modelos de caracter´Ĺsticas de m´ ultiples
capas, llamado MAYA. Los objetivos que perseguimos con esta soluci´on
son: i) modelar la variabilidad de los sistemas software en dos capas, incluyendo
sus respectivas interdependencias; ii) definir un conjunto de
operaciones que puedan imponerse a dichos modelos; iii) una implementaci
´on de referencia para el an´alisis de m´ ultiples capas basado en un
caso de estudio en Android, y finalmente; iv) dos evaluaciones emp´Ĺricas
que demuestran la viabilidad de nuestra propuesta en la pr´actica.
Componentes de implementaci´on: La configuraci´on de un producto es una de las actividades m´as propensas a errores, m´as a ´un cuando para cada
caracter´Ĺstica hay m´as de un componente que la implemente. Para
gestionar estas configuraciones, introducimos un sistema de recomendaci
´on basado en componentes llamado RESDEC que facilita la selecci ´on
de componentes de implementaci´on al crear productos en una SPL. Concretamente
las contribuciones que se presentan con esta propuesta son:
i) modelado del problema de selecci ´on de componentes de implementaci
´on como una tarea de recomendaci´on utilizando algoritmos de filtrado
colaborativo y por contenido; ii) dise Ëno de un prototipo de herramienta
de sistema de recomendaci´on basada en componentes lista para ser
utilizada y extendida a otros entornos a partir de la selecci ´on de componentes
de implementaci´on y, finalmente; iii) una evaluaci´on emp´Ĺrica
basado en sitios web de comercio electr ´onico enWordPress
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