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نتیجه جستجو - بهینه سازی قوی

تعداد مقالات یافته شده: 15
ردیف عنوان نوع
1 A multi-objective robust optimization model for upstream hydrocarbon supply chain
یک مدل بهینه سازی قوی چند هدفه برای زنجیره تأمین هیدروکربن بالادست-2021
The hydrocarbon supply chain (HCSC) is a significant part of the world’s energy sector. The energy market has experienced erratic behavior over the last few years results in financial risks such as exceeding certain limits of the budget or not achieving the desired levels of cash in-flow, i.e., revenue. In this work, robust optimization and multi-objective mathematical programming are used to develop a model that eliminates or at least mitigates the impact of uncertain market behavior. Robust optimization provides tactical plans that are feasible and robust over market scenarios. The model assesses the trade-offs between alternatives and guides the decision-maker towards the effective management of the HCSC. The economic objectives are to minimize total cost and maximize revenue, while the non-economic objective is to minimize the depletion rate. The model considers the environmental aspect by limiting the emission of CO2 and the sustainability aspect by reducing the depletion rate of natural resources. Uncertain behavior of the oil market is modeled on scenario representation. A case study based on real data from Saudi Arabia HCSC is provided to demonstrate the model’s practicality, and a sensitivity analysis is conducted to provide some managerial insights. The results indicate that Saudi Arabia can cover its entire expenditure, break-evenpoint, by producing oil at 7.18 MMbbld and gas at 3,543.48 MMcftd. Besides, the robust approach provides a preferred plan with the highest cash inflow and the lowest sustainability over other approaches, e.g., deterministic, stochastic, and risk-based. The differences show that the robust model increases oil production to compensate for the variability of the scenario.
KEYWORDS: Hydrocarbon supply chain | Multi-objective optimization | Robust optimization | Scenario-Based Optimization | Tactical planning
مقاله انگلیسی
2 Data Driven Robust Optimization for Handling Uncertainty in Supply Chain Planning Models
بهینه سازی قوی مبتنی بر داده ها برای مدیریت عدم قطعیت در مدل های برنامه ریزی زنجیره تامین-2021
While addressing supply chain planning under uncertainty, Robust Optimization (RO) is regarded as an efficient and tractable method. As RO involves calculation of several statistical moments or maximum / minimum values involving the objective functions under realizations of these uncertain parameters, the accuracy of this method significantly depends on the efficient techniques to sample the uncertainty parameter space with limited amount of data. Conventional sampling techniques, e.g. box/budget/ellipsoidal, work by sampling the uncertain parameter space inefficiently, often leading to inaccuracies in such estimations. This paper proposes a methodology to amalgamate machine learning and data analytics with RO, thereby making it data-driven. A novel neuro fuzzy clustering mechanism is implemented to cluster the uncertain space such that the exact regions of uncertainty are optimally identified. Subsequently, local density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling to sample the uncertain parameter space more accurately. The proposed technique is utilized to explore the merits of RO towards addressing the uncertainty issues of product demand, machine uptime and production cost associated with a multiproduct, and multisite supply chain planning model. The uncertainty in supply chain model is thoroughly analysed by carefully constructing examples and its case studies leading to large scale mixed integer linear and nonlinear programming problems which were efficiently solved in GAMS framework. Demonstration of efficacy of the proposed method over the box, budget and ellipsoidal sampling method through comprehensive analysis adds to other highlights of the current work.
Keywords: Uncertainty Modelling | Supply chain Management | Data driven Robust Optimization | Neuro Fuzzy Clustering | Multi-Layered Perceptron
مقاله انگلیسی
3 Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization
زنجیره تامین حلقه بسته پایدار برای صنایع لبنی با بهینه سازی قوی و ابتکاری-2021
This paper supplements the augmented ε-constraint approach with linearization using robust optimization and heuristics with an improved algorithm to maximize the total profit and minimize the environmental effects of a sustainable closed-loop supply chain (CLSC) in the dairy industry. The resultant mixed-integer linear programming (MILP) model is applied to a case from the dairy industry and evaluated against several test problems. The pessimistic, optimistic, and worst-case scenarios are considered along with the sensitivity analysis on the profitability of the CLSC concerning the product lifetimes. Our results inform that applying the heuristic on large- scale problems yields a 25% improvement in runtime. Furthermore, products with a longer lifetime under the worst-case scenario yield greater profit than those products with a shorter lifetime under an optimistic scenario.
Keywords: Robust optimization | Closed-loop supply chain | Augmented ε-constraint | Diary
مقاله انگلیسی
4 A hybrid modeling approach for green and sustainable closed-loop supply chain considering price, advertisement and uncertain demands
یک روش مدل سازی ترکیبی برای زنجیره تامین حلقه بسته سبز و پایدار با در نظر گرفتن قیمت ، تبلیغات و تقاضاهای نامشخص-2021
The closed-loop supply chain network design (CLSCND) has garnered a lot of attention since it can handle economic and environmental issues. Likewise, supply chain coordination (SCC) tools can play an important role in enhancing the performance of the supply chains. This paper proposes a new hybrid method, in which SCC decisions and CLSCND objectives are simultaneously involved. First, this approach makes price, greenness, and advertisement decisions, and then it aims at maximizing profit and minimizing CO2 emission. A new nonlinear programming (NLP) model is developed based on the sensitivity of the return rate to green quality and the customers’ maximum tolerance, while the demands are uncertain. In order to overcome the uncertain demands, a robust optimization (RO) model is used. A Lagrangian relaxation algorithm is also employed to solve large-scale instances in a logical running time. The applicability of the proposed approach is corroborated through several examples. The results indicate an improvement in the performance of economic and environmental objectives under greening and advertising decisions. Furthermore, the proposed RO model outperforms the model that does not consider a robust approach.
Keywords: Closed-loop supply chain | Supply chain coordination | Supply chain network design | Robust optimization | Lagrangian relaxation | Uncertain demand
مقاله انگلیسی
5 Multi-objective sustainable opened- and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation
چندین هدف پایدار حلقه بسته و حلقه بسته تحت عدم اطمینان مخلوط در طی وضعیت همه گیر COVID-19-2021
Logistics problems play a significant role in an emergency situation. During and after a critical circumstance (like pandemic COVID-19), it is an important task to active the opened- and closed-loop system through an efficient and resilient supply chain network. This paper considers a multi-objective multi-product multi-period two-stage sustainable opened- and closed-loop supply chain planning to maintain supply among production centers and various hospitals during COVID-19 pandemic situation. To build a less contagious network, transportation problem and pick-up-delivery vehicle routing problem are designed as two stages, respectively to carry out distribution. We allow a mixed uncertain environment by considering uncertain-random parameters in the proposed model to express ambiguity in real-life data. A multi-attribute decision making approach is suggested to determine the priorities of affected areas, according to their urgency in terms of entropy weights. Moreover, a robust optimization approach for uncertain-random parameter is developed to cope with uncertainty in different scenarios, and thereafter augmented weighted Tchebycheff method is applied to solve the model. To demonstrate the practicability of the proposed model and solving approach, three test problems with reasonable sizes are considered and results are discussed through some sensitivity analyses.
Keywords: Sustainable opened- and closed-loop supply chain | Mixed uncertainty | Multi-attribute decision making | Transportation problem | Pick-up-delivery vehicle routing problem | Robust optimization.
مقاله انگلیسی
6
یک مدل بهینه سازی کارآمد برای تعهد واحد و اعزام سیستم های چند انرژی و ریز شبکه-2020
Multi-energy systems and microgrids can play an important role in increasing the efficiency of distributed energy systems and favoring an increasing penetration from renewable sources, by serving as control hubs for the optimal management of Distributed Energy Resources. Predictive operation planning via Mixed Integer Linear Programming is an effective way of tackling the optimal management of these systems. However, the uncertainty of demand and renewable production forecasts can hinder the optimality of the scheduling solution and even lead to outages. This paper proposes a new Affinely Adjustable Robust Formulation of the day-ahead scheduling problem for a generic multi-energy system/microgrid subject to multiple uncertainty factors. Piece-wise linear decision rules are considered in the robust formulation, and their potential use for real-time control is assessed. Novel features include an ad hoc characterization of the polyhedral uncertainty space aimed at reducing solution conservativeness, aggregation of uncertain factors and partial-past recourse which allows speeding up the computational time. The advantages and limitations of the Affinely Adjustable Robust Formulation are thoroughly discussed and quantified through artificial and real-world test cases. The comparison with a conventional deterministic approach shows that, despite the limitations of the affine decision rules, the adjustable robust formulation can ensure full system reliability while attaining at the same time better performance
Keywords: Energy Management System | Robust Optimization | Combined Heat and Power | Multi Energy System | Uncertain Scheduling Optimization | Off-grid Microgrid
مقاله انگلیسی
7 Adaptation and approximate strategies for solving the lot-sizing and scheduling problem under multistage demand uncertainty
سازگاری و راهبردهای تقریب زنی برای حل مشکل اندازه بندی و زمان بندی تحت عدم قطعیت تقاضای چند مرحله ای -2018
This work addresses the lot-sizing and scheduling problem under multistage demand uncertainty. A flexible production system is considered, with the possibility to adjust the size and the schedule of lots in every time period based on a rolling-horizon planning scheme. Computationally intractable multistage stochastic programming models are often employed on this problem. An adaptation strategy to the multistage setting for two-stage programming and robust optimization models is proposed. We also present an approximate heuristic strategy to address the problem more efficiently, relying on multistage stochastic programming and adjustable robust optimization. In order to evaluate each strategy and model proposed, a Monte Carlo simulation experiment under a rolling-horizon scheme is performed. Results show that the strategies are promising in solving large-scale problems: the approximate strategy based on adjustable robust optimization has, on average, 6.72% better performance and is 7.9 times faster than the deterministic model.
keywords: Lot-sizing and scheduling problem |GLSP |Adjustable robust optimization |Multistage stochastic programming |Rolling-horizon
مقاله انگلیسی
8 Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era
بهینه سازی قوی تصادفی مبتنی بر داده ها: چارچوب محاسباتی عمومی و الگوریتم استفاده از یادگیری ماشین برای بهینه سازی تحت عدم اطمینان در دوران داده های بزرگ-2018
A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization un der uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for uncer tainty modeling. A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure. The outer optimization problem follows a two-stage stochastic programming approach to optimize the expected objective across different data classes; adaptive robust optimization is nested as the inner problem to ensure the robustness of the solution while maintaining computational tractability. A decomposition-based algorithm is further developed to solve the resulting multi-level optimization problem efficiently. Case studies on process network design and planning are presented to demonstrate the applicability of the proposed framework and algorithm.
Keywords: Big data ، Optimization under uncertainty ، Bayesian model ، Machine learning ، Process design and operations
مقاله انگلیسی
9 Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs
توسعه زنجیره های تامین پاسخگو و ضعیف: یک مدل قوی برای استراتژی های کاهش خطر در طرح های زنجیره تامین-2017
This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC’s network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet.
Keywords: Supply chain management | Network design | Risk management | Robust optimization | Responsiveness
مقاله انگلیسی
10 A robust optimization model for cellular manufacturing system into supply chain management
یک مدل بهینه سازی قوی برای سیستم تولید سلولی در مدیریت زنجیره تامین-2017
In this paper, a new mathematical model is presented for a cellular manufacturing system into supply chain design with labor assignment. This paper considers important manufacturing features thoroughly such as multiple plant locations, multi-market allocations with production planning and various part mix. The proposed model aims at minimizing the total cost of holding, inter-cell material handling, external transportation, fixed cost for producing each part in each plant, machine and labor salaries. It is assumed that the demands of products are uncertainty in three scenarios: optimistic, pessimistic and normal. Also, a robust optimization approach is then developed to solve the proposed model and find the best solution. The robustness and performance of the proposed model are explained in terms of an industrial case from a typical equipment manufacturer. This case study provides the researchers and practitioners to better understand the importance of designing robust optimization and cell formation in the supply chain management from a practical point of view.
Keywords: Cellular manufacturing | Supply chain | Labor assignment | Robust optimization | Market allocation
مقاله انگلیسی
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بازدید امروز: 1994 :::::::: بازدید دیروز: 2317 :::::::: بازدید کل: 4311 :::::::: افراد آنلاین: 13