35 research outputs found
Resolving forward-reverse logistics multi-period model using evolutionary algorithms
© 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management
Partner selection for reverse logistics centres in green supply chains: a fuzzy artificial immune optimisation approach
The design of reverse logistics networks has now emerged as a major issue for manufacturers, not only in developed countries where legislation and societal pressures are strong, but also in developing countries where the adoption of reverse logistics practices may offer a competitive advantage. This paper presents a new model for partner selection for reverse logistic centres in green supply chains. The model offers three advantages. Firstly, it enables economic, environment, and social factors to be considered simultaneously. Secondly, by integrating fuzzy set theory and artificial immune optimization technology, it enables both quantitative and qualitative criteria to be considered simultaneously throughout the whole decision-making process. Thirdly, it extends the flat criteria structure for partner selection evaluation for reverse logistics centres to the more suitable hierarchy structure. The applicability of the model is demonstrated by means of an empirical application based on data from a Chinese electronic equipment and instruments manufacturing company
Reverse Logistics Network Design with Centralized Return Center
Natural resources and landfills have been overused and exhausted, resulting in the necessity of product recovery. Today, as a growing number of producers engage in product recovery, the need for efficient reverse logistics networks has become more significant than ever.
An optimization modeling approach is used to develop a generic integrated forward and reverse logistics network for a firm involved in product recovery. The proposed modeling framework demonstrates and compares the performance of centralized return centers (CRC) and conventional collection centers in the reverse logistics network. Several case studies are used to analyze the sensitivity of the network structures and performances to various modeling parameters including product return ratio, product disposition ratios, and processing and handling costs at collection centers. Lastly, recommendations are made to remove model limitations and improve reverse logistics network models
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
La métaheuristique CAT pour le design de réseaux logistiques déterministes et stochastiques
De nos jours, les entreprises dâici et dâailleurs sont confrontĂ©es Ă une concurrence mondiale sans cesse plus fĂ©roce. Afin de survivre et de dĂ©velopper des avantages concurrentiels, elles doivent sâapprovisionner et vendre leurs produits sur les marchĂ©s mondiaux. Elles doivent aussi offrir simultanĂ©ment Ă leurs clients des produits dâexcellente qualitĂ© Ă prix concurrentiels et assortis dâun service impeccable. Ainsi, les activitĂ©s dâapprovisionnement, de production et de marketing ne peuvent plus ĂȘtre planifiĂ©es et gĂ©rĂ©es indĂ©pendamment. Dans ce contexte, les grandes entreprises manufacturiĂšres se doivent de rĂ©organiser et reconfigurer sans cesse leur rĂ©seau logistique pour faire face aux pressions financiĂšres et environnementales ainsi quâaux exigences de leurs clients. Tout doit ĂȘtre rĂ©visĂ© et planifiĂ© de façon intĂ©grĂ©e : sĂ©lection des fournisseurs, choix dâinvestissements, planification du transport et prĂ©paration dâune proposition de valeur incluant souvent produits et services au fournisseur. Au niveau stratĂ©gique, ce problĂšme est frĂ©quemment dĂ©signĂ© par le vocable « design de rĂ©seau logistique ». Une approche intĂ©ressante pour rĂ©soudre ces problĂ©matiques dĂ©cisionnelles complexes consiste Ă formuler et rĂ©soudre un modĂšle mathĂ©matique en nombres entiers reprĂ©sentant la problĂ©matique. Plusieurs modĂšles ont ainsi Ă©tĂ© rĂ©cemment proposĂ©s pour traiter diffĂ©rentes catĂ©gories de dĂ©cision en matiĂšre de design de rĂ©seau logistique. Cependant, ces modĂšles sont trĂšs complexes et difficiles Ă rĂ©soudre, et mĂȘme les solveurs les plus performants Ă©chouent parfois Ă fournir une solution de qualitĂ©. Les travaux dĂ©veloppĂ©s dans cette thĂšse proposent plusieurs contributions. Tout dâabord, un modĂšle de design de rĂ©seau logistique incorporant plusieurs innovations proposĂ©es rĂ©cemment dans la littĂ©rature a Ă©tĂ© dĂ©veloppĂ©; celui-ci intĂšgre les dimensions du choix des fournisseurs, la localisation, la configuration et lâassignation de mission aux installations (usines, entrepĂŽts, etc.) de lâentreprise, la planification stratĂ©gique du transport et la sĂ©lection de politiques de marketing et dâoffre de valeur au consommateur. Des innovations sont proposĂ©es au niveau de la modĂ©lisation des inventaires ainsi que de la sĂ©lection des options de transport. En deuxiĂšme lieu, une mĂ©thode de rĂ©solution distribuĂ©e inspirĂ©e du paradigme des systĂšmes multi-agents a Ă©tĂ© dĂ©veloppĂ©e afin de rĂ©soudre des problĂšmes dâoptimisation de grande taille incorporant plusieurs catĂ©gories de dĂ©cisions. Cette approche, appelĂ©e CAT (pour collaborative agent teams), consiste Ă diviser le problĂšme en un ensemble de sous-problĂšmes, et assigner chacun de ces sous-problĂšmes Ă un agent qui devra le rĂ©soudre. Par la suite, les solutions Ă chacun de ces sous-problĂšmes sont combinĂ©es par dâautres agents afin dâobtenir une solution de qualitĂ© au problĂšme initial. Des mĂ©canismes efficaces sont conçus pour la division du problĂšme, pour la rĂ©solution des sous-problĂšmes et pour lâintĂ©gration des solutions. Lâapproche CAT ainsi dĂ©veloppĂ©e est utilisĂ©e pour rĂ©soudre le problĂšme de design de rĂ©seaux logistiques en univers certain (dĂ©terministe). Finalement, des adaptations sont proposĂ©es Ă CAT permettant de rĂ©soudre des problĂšmes de design de rĂ©seaux logistiques en univers incertain (stochastique)
Green Technologies for Production Processes
This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies
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Global supply chain optimization: a machine learning perspective to improve caterpillar's logistics operations
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Supply chain optimization is one of the key components for the effective management of a company with a complex manufacturing process and distribution network. Companies with a global presence in particular are motivated to optimize their distribution plans in order to keep their operating costs low and competitive. Changing condition in the global market and volatile energy prices increase the need for an automatic decision and optimization tool. In recent years, many techniques and applications have been proposed to address the
problem of supply chain optimization. However, such techniques are often too problemspecific or too knowledge-intensive to be implemented as in-expensive, and easy-to-use computer system. The effort required to implement an optimization system for a new instance of the problem appears to be quite significant. The development process necessitates the involvement of expert personnel and the level of automation is low. The aim of this project is to develop a set of strategies capable of increasing the level of
automation when developing a new optimization system. An increased level of automation is achieved by focusing on three areas: multi-objective optimization, optimization algorithm usability, and optimization model design. A literature review highlighted the great level of interest for the problem of multiobjective optimization in the research community. However, the review emphasized a lack of standardization in the area and insufficient understanding of the relationship between multi-objective strategies and problems. Experts in the area of optimization and artificial intelligence are interested in improving the usability of the most recent
optimization algorithms. They stated the concern that the large number of variants and parameters, which characterizes such algorithms, affect their potential applicability in real-world environments. Such characteristics are seen as the root cause for the low success of the most recent optimization algorithms in industrial applications. Crucial task for the development of an optimization system is the design of the optimization model. Such task is one of the most complex in the development process, however, it is still performed mostly manually. The importance and the complexity of the task strongly suggest the development of tools to aid the design of optimization models. In order to address such challenges, first the problem of multi-objective optimization is considered and the most widely adopted techniques to solve it are identified. Such techniques are analyzed and described in details to increase the level of standardization in the area. Empirical evidences are highlighted to suggest what type of relationship exists between strategies and problem instances. Regarding the optimization algorithm, a classification method is proposed to improve its usability and computational requirement by automatically tuning one of its key parameters, the termination condition. The algorithm understands the problem complexity and automatically assigns the best termination condition to minimize runtime. The runtime of the optimization system has been reduced by more than 60%. Arguably, the usability of the algorithm has been improved as well, as one of the key configuration tasks can now be completed automatically. Finally, a system is presented to aid the definition of the optimization model through regression analysis. The purpose of the method is to gather as much knowledge about the problem as possible so that the task of the optimization model definition requires a lower user involvement. The application of the proposed algorithm is estimated that could have saved almost 1000 man-weeks to complete the project. The developed strategies have been applied to the problem of Caterpillarâs global supply chain optimization. This thesis describes also the process of developing an optimization system for Caterpillar and highlights the challenges and research opportunities identified while undertaking this work. This thesis describes the optimization model designed for Caterpillarâs supply chain and the implementation details of the Ant Colony System, the algorithm selected to optimize the supply chain. The system is now used to design the distribution plans of more than 7,000 products. The system improved Caterpillarâs marginal profit on such products by a factor of 4.6% on average.Caterpillar Inc
An Optimisation-based Framework for Complex Business Process: Healthcare Application
The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can â when applied skilfully â improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success