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نتیجه جستجو - مدل سازی بهینه سازی

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Malaysia scenario of biomass supply chain-cogeneration system and optimization modeling development: A review
سناریوی مالزی سیستم تولید همزمان زنجیره تأمین زیست توده و توسعه مدل سازی بهینه سازی: یک مرور-2021
The development of biomass-based cogeneration energy systems in Malaysia is progressing to meet the circular economy concept and sustainability goal. This comprehensive review aims to report recent advancements in biomass-based cogeneration/biomass co-firing technology in Malaysia correlated with the optimization modeling role. First, this work presents the outlook and current scenario of cogeneration systems in Malaysia by observing performance and the challenges confronted by the technologies. Next, investigation of technical issues concerning the key players of the technologies and the biomass supply chain. This work had prepared using quantitative content-based analysis-meta-analysis. The practical implication of this review enables a complex optimization model that integrates biomass-based cogeneration and biomass supply chain considering economic and environmental viability. It will further enhance progress toward the Malaysian “Industry 4.0-driven” energy initiative. A novel optimization model grounded on Industry 4.0 parameters will foster new opportunities for researchers.
Keywords: Biomass-based cogeneration system | Biomass co-firing | Optimization modeling | Renewable energy | Economic and operational viability
مقاله انگلیسی
2 A strategic and global manufacturing capacity management optimisation model: A Scenario-based multi-stage stochastic programming approach
یک مدل بهینه سازی مدیریت تولید استراتژیک و جهانی: یک رویکرد برنامه نویسی تصادفی چند مرحله ای مبتنی بر سناریو-2020
Large-scale multinational manufacturing firms often require a significant investment in production capacity and extensive management efforts in strategic planning in an uncertain business environment. In this research we first discuss what decision terms and boundary conditions a holistic capacity management model for the manufacturing industry must contain. To better understand how these decision terms and constraints have been employed by the recent model developers in the area of capacity and resource management modelling for manufacturing, 69 optimisation-based (deterministic and stochastic) models have been carefully selected from 2000 to 2018 for a brief comparative analysis. The results of this comparison shows although applying uncertainty into capacity modelling (in stochastic form) has received a greater deal of attention most recently (since 2010), the existing stochastic models are yet very simplistic, and not all the strategic terms have been employed in the current model developments in the field. This lack of a holistic approach although is evident in deterministic models too, the existing stochastic counterparts proved to include much less decision terms and inclusive constraints, which limits them to a limited applications and may cause sub-optimal solutions. Employing this set of holistic decision terms and boundary conditions, this work develops a scenario-based multi-stage stochastic capacity management model, which is capable of modelling different strategic terms such as capacity level management (slight, medium and large capacity volume adjustment to increase/decrease capacity), location/relocation decisions, merge/decomposition options, and product management (R&D, new product launch, productto-plant and product-to-market allocation, and product phase-out management). Possibility matrix, production rates, different financial terms and international taxes, inflation rates, machinery depreciation, investment lead-time and product cycle-time are also embedded in the model in order to make it more practical, realistic and sensitive to strategic decisions and scenarios. A step-by-step open-box validation has been followed while designing the model and a holistic black-box validation plan has been designed and employed to widely validate the model. The model then has been verified by deploying a real-scaled case of Toyota Motors UK (TMUK) decision of mothballing one of their production lines in the UK after the global recession in 2010.
Keywords: Stochastic programming | Optimisation modelling | Capacity management | Manufacturing
مقاله انگلیسی
3 Allocating human resources in organizations operating under crisis conditions: A fuzzy input-output optimization modeling framework
اختصاص منابع انسانی در سازمان هایی که در شرایط بحران قرار دارند: چارچوب مدل سازی بهینه سازی ورودی / خروجی فازی-2018
Ensuring the resilience of industrial systems to perturbations is a vital strategy for climate change adapta tion to maintain sustainable consumption and production. Business decision-making models are essential in providing rational support for choices made by managers and industry practitioners under crisis condi tions that may result from climatic disruptions. To date, most of the techniques proposed in the literature focus on the disruptions of physical resources that propagate through supply chain linkages; nonetheless, a significant research gap remains on mitigating impacts caused by disruptions in workforce availability. In this work, a fuzzy input-output optimization model is developed for allocating scarce labor resources within a business enterprise or organization. This model uses the input-output framework to take into account organizational interdependencies that exist among workers or departmental units, to ensure minimal loss of vital services delivered to external clients. The model is demonstrated using two illustra tive case studies. The first case study involves medical staff deployment in a hospital during a pandemic event; while the second case study involves allocation of personnel in a business process outsourcing firm during an adverse weather event. The examples illustrate how the proposed fuzzy input-output optimization model can provide decision support for practitioners in industry, in order to mitigate the impacts of human resource shortage on business continuity during a crisis.
Keywords: Resilience ، Fuzzy optimization ، Human resource allocation ، Crisis operations ، Input-output model
مقاله انگلیسی
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