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نتیجه جستجو - بهینه سازی تصادفی

تعداد مقالات یافته شده: 15
ردیف عنوان نوع
1 A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network
یک مدل تصادفی محکم فازی چند هدفه برای طراحی یک شبکه زنجیره تأمین پایدار ، قابل انعطاف و پاسخگو-2021
This study proposes a multi-objective mixed-integer programming model to configure a sustainable supply chain network while considering resilience and responsiveness measures. The model aims at minimizing the total costs and environmental damages while maximizing the social impacts, as well as the responsiveness and resilience levels of the supply chain network. An improved version of the fuzzy robust stochastic optimization approach is proposed to tackle the uncertain data arising in the dynamic business environment. Furthermore, a new version of meta-goal programming named the multi-choice meta-goal programming associated with a utility function is developed to solve the resulting multi-objective model. A case study in the water heater industry is investigated to illustrate the application of the proposed model and its solution approach. The numerical results validate the proposed model and the developed solution method. Finally, interactions between the sustainability, responsiveness, and resilience dimensions are investigated and several sensitivity analyses are performed on critical parameters by which useful managerial insights are provided.
Keywords: Supply chain network design | Sustainability | Resilience | Responsiveness | Fuzzy robust stochastic optimization | Multi-choice meta-goal programming
مقاله انگلیسی
2 Data, data flows, and model specifications for linking multi-level contribution margin accounting with multi-level fixed-charge problems
داده‌ها، جریان‌های داده، و مشخصات مدل برای پیوند حسابداری حاشیه سهم چندسطحی با مشکلات شارژ ثابت چندسطحی-2021
This article describes the data, data flows, and spreadsheet implementations for linking multi-level contribution margin accounting as a subsystem in cost accounting with several versions of a multi-level fixed-charge problem (MLFCP), the latter based on the optimization approach in operations research. This linkage can reveal previously hidden optimization potentials within the framework of multi-level contribution margin accounting, thus providing better information for decision making in companies and other organizations. For the data, plausible fictitious values have been assumed taking into consideration the calculation principles in cost accounting where applicable. They include resourcerelated data, market-related data, and data from cost accounting needed to analyze the profitability of a companys´ products and organizational entities in the presence of hierarchically structured fixed costs. The data are processed and analyzed by means of mathematical optimization techniques and sensitivity analysis. The linkage between multi-level contribution margin accounting and MLFCP is implemented in three spreadsheet files, including versions for deterministic optimization, stochastic optimization, and robust optimization. This paper provides specifications for compatible solver add-ins and for executing sensitivity analysis. The data and spreadsheet implementations described in this article were used in a research article entitled “Making better decisions by applying mathematical optimization to cost accounting: An advanced approach to multi-level contribution margin accounting” [1]. The data sets and the spreadsheet implementations may be reused a) by researchers in management and cost accounting as well as in operations research and quantitative methods for verification and for further development of the linkage concept and of the underlying optimization models; b) by practitioners for gaining insight into the data requirements, methods, and benefits of the proposed linkage, thus supporting continuing education; and c) by instructors in academia who may find the data and spreadsheets valuable for classroom use in advanced courses. The complete spreadsheet implementations in the form of three ready-touse Excel files (deterministic, stochastic, and robust version) are available for download at Mendeley Data. They may serve as customizable templates for various use cases in research, practice, and education.
keywords: حسابداری هزینه | تحقیق در عملیات | مشکل ثابت شارژ | بهینه سازی | برنامه نویسی صحیح | تجزیه و تحلیل میزان حساسیت | بهینه سازی تصادفی | صفحه گسترده | Cost accounting | Operations research | Fixed-charge problem | Optimization | Integer programming | Sensitivity analysis | Stochastic optimization | Spreadsheet
مقاله انگلیسی
3 Making better decisions by applying mathematical optimization to cost accounting: An advanced approach to multi-level contribution margin accounting
تصمیم گیری های بهتر را با استفاده از بهینه سازی ریاضی به هزینه حسابداری: یک رویکرد پیشرفته به حسابداری حاشیه کمک چند سطح-2021
The purpose of multi-level contribution margin accounting in cost accounting is to analyze the profitability of products and organizational entities with appropriate allocation of fixed costs and to provide relevant information for short-term, medium- and longer-term decisions. However, the conventional framework of multi-level contribution margin accounting does not usually incorporate a mathematical optimization method that simultaneously integrates variable and fixed costs to determine the best possible product mix within hierarchically structured organizations. This may be surprising in that operations research provides an optimization model in the form of the fixed-charge problem (FCP) that takes into account not only variable costs but also fixed costs of the activities to be planned. This paper links the two approaches by expanding the FCP to a multi-level fixed-charge problem (MLFCP), which maps the hierarchical decomposition of fixed costs in accordance with multi-level contribution margin accounting. In this way, previously hidden optimization potentials can be made visible within the framework of multi-level contribution margin accounting. Applying the linkage to a case study illustrates that the original assessment of profitability gained on the sole basis of a multi-level contribution margin calculation might turn out to be inappropriate or even inverted as soon as mathematical optimization is utilized: products, divisions, and other reference objects for fixed cost allocation, which at first glance seem to be profitable (or unprofitable) might be revealed as actually unprofitable (or profitable), when the multi-level contribution margin calculation is linked to the MLFCP. Furthermore, the proposed concept facilitates assessment of the costs of an increasing variant diversity, which also demonstrates that common rules on how to interpret a multi-level contribution margin calculation may have to be revised in some cases from the viewpoint of optimization. Finally, the impact of changes in the fixed cost structure and other parameters is tested via sensitivity analyses and stochastic optimization.
keywords: حسابداری هزینه | حد مشارکت، محدوده مشارکت | هزینه های ثابت | نرم افزار | مخلوط محصول | تصمیم گیری | تحقیق در عملیات | مشکل ثابت شارژ | مشکل چند سطح قابل شارژ | بهینه سازی | برنامه نویسی صحیح | تجزیه و تحلیل میزان حساسیت | بهینه سازی تصادفی | صفحه گسترده | مطالعه موردی | Cost accounting | Contribution margin | Fixed costs | Profitability | Product mix | Decision making | Operations research | Fixed-charge problem | Multi-level fixed-charge problem | Optimization | Integer programming | Sensitivity analysis | Stochastic optimization | Spreadsheet | Case study
مقاله انگلیسی
4 An integrated queuing-stochastic optimization hybrid Genetic Algorithm for a location-inventory supply chain network
الگوریتم ترکیبی بهینه سازی صف-تصادفی ترکیبی برای یک شبکه زنجیره تأمین مکان-موجودی-2021
We consider a location-inventory optimization model for supply chain (SC) configuration. It includes a supplier, multiple distribution centers (DCs), and multiple retailers. Customer demand and replenishment lead time are considered to be stochastic. Two classes of customer orders, priority and ordinary, are assumed based on their demand. The goal is to find the optimal locations for DCs and their inventory policy simultaneously. For this purpose, a two-phase approach based on queuing theory and stochastic optimization was developed. In the first phase, the stock level of DCs is modeled as a Markov chain process and is analyzed, while in the second phase, a mathematical program is used to determine the optimal number and locations of DCs, the assignment of retailers to DCs, and the order quantity and safety stock level at DCs. As solving this problem is NP-hard, a hybrid Genetic Algorithm (GA) was developed to make the problem computationally tractable.
Keywords: Supply chain network design | Location-inventory planning | Stochastic optimization | Demand uncertainty | Lead-time uncertainty
مقاله انگلیسی
5 A new stochastic gain adaptive energy management system for smart microgrids considering frequency responsive loads
یک دستاورد تصادفی جدید سیستم مدیریت انرژی تطبیقی ​​برای میکروگریدهای هوشمند با توجه به بارهای پاسخگو فرکانس-2020
Islanded microgrids as flexible, adaptive and sustainable smart cells of distribution power systems should be operated in accordance to both techno-economic purposes. Motivated by this need, the microgrid operators are in charge to elevate the active accommodation of both demand-side and supply-side distributed energy resources. To that end, in this paper, a new flexible frequency dependent energy management system is proposed through which distributed generators have time varying droop controllers with a gain-adaptive strategy. Besides to cope economically with uncertainty arise frequency excursions, a new, comfort-aware and versatile frequency dependent demand response program is mathematically formulated and conducted to the energy management system. It is aimed to co-optimize the microgrid energy resources such a way the day-ahead operational costs are managed subject to a secure frequency control portfolio. The presented model is solved using a two-stage stochastic programming and by a tractable efficient mixed integer linear programming approach. The simulation results are derived in 24-h scheduling time horizon and implemented on a typical test microgrid. The effectiveness of the proposed hourly gain assignment and frequency responsive load management program has been verified thoroughly by analyzing the results.
Keywords: Hierarchical control structure | Islanded microgrids | Droop gain scheduling | Frequency responsive loads | Two-stage stochastic optimization
مقاله انگلیسی
6 AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling
بهینه سازی مبتنی بر هوش مصنوعی لایه های کاتالیزور سلول سوختی PEM برای حداکثر چگالی توان از طریق مدل سازی جایگزین داده محور-2020
Catalyst layer (CL) is the core electrochemical reaction region of proton exchange membrane fuel cells (PEMFCs). Its composition directly determines PEMFC output performance. Existing experimental or modeling methods are still insufficient on the deep optimization of CL composition. This work develops a novel artificial intelligence (AI) framework combining a data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variables global optimization for improving the maximum power density of PEMFCs. Simulation results of a three-dimensional computational fluid dynamics (CFD) PEMFC model coupled with the CL agglomerate model constitutes the database, which is then used to train the data-driven surrogate model based on Support Vector Machine (SVM), a typical AI algorithm. Prediction performance shows that the squared correlation coefficient (R-square) and mean percentage error in the test set are 0.9908 and 3.3375%, respectively. The surrogate model has demonstrated comparable accuracy to the physical model, but with much greater computation- resource efficiency: the calculation of one polarization curve will be within one second by the surrogate model, while it may cost hundreds of processor-hours by the physical CFD model. The surrogate model is then fed into a Genetic Algorithm (GA) to obtain the optimal solution of CL composition. For verification, the optimal CL composition is returned to the physical model, and the percentage error between the surrogate model predicted and physical model simulated maximum power densities under the optimal CL composition is only 1.3950%. The results indicate that the proposed framework can guide the multi-variables optimization of complex systems.
Keywords: Proton exchange membrane fuel cell | Catalyst layer composition | Agglomerate model | Data-driven surrogate model | Stochastic optimization algorithm
مقاله انگلیسی
7 Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources
مدل تصادفی بازار برق در ریز شبکه ها با توجه به برنامه های پاسخ به تقاضا و منابع انرژی تجدید پذیر-2020
In this paper, a cooperative market mechanism is proposed to define the energy transactions and market price in multi-microgrids (MMGs). The proposed model can be used for both the grid-connected and isolated MMG as well as models with several MG owners. We use a cooperative approach that guarantees the existence of the optimal solution, while the Nash equilibrium points cannot ensure the Pareto optimality of the solution in the competitive approaches. Microgrids (MGs) send their bids/offers to the market operator, and the devoted energy will be announced to MGs in order to set the production of their resources. Various energy production units such as renewable energy resources (Photovoltaic, and wind), dispatchable energy resources, Energy Storage Systems (ESS), and demand response program have been taken into account. Moreover, an incentive-based demand response program motivates the consumers to take part in the market and benefit from the deployed market. Scenario generation and reduction methods are used to consider various uncertainties in the power system. The proposed model is formulated as a Mixed Integer Linear Programming (MILP) and solved by GAMS software. Several case studies are tested and the simulation results show the efficiency of the proposed model.
Keywords: Demand response | Electricity market | Microgrid scheduling | Multi-microgrids | Stochastic optimization
مقاله انگلیسی
8 A hierarchical energy management system for islanded multi-microgrid clusters considering frequency security constraints
یک سیستم مدیریت انرژی سلسله مراتبی برای خوشه های چند ریزشبکه جزیره با توجه به محدودیت های امنیتی فرکانس-2020
With the widespread development of microgrids (MGs) in future smart distribution networks, a number of neighboring MGs can be connected and form a multi-microgrid (MMG) cluster. In this regard, the energy management of a MMG is challenging due to more complex components and higher degrees of uncertainty in a small region of power system. Likewise, in the islanded MMG (IMMG) clusters, due to the low-inertia and high intermittent energy delivery of renewable resources, the frequency security should be considered in the energy management. To address this issue, this paper proposes an energy management system (EMS) in which hierarchical control structure of IMMG clusters is precisely modeled. The proposed EMS aims to minimize total operation cost of IMMG cluster while sufficient primary and secondary reserves are scheduled to preserve frequency security in a predefined range. Besides, the proposed EMS provides optimal strategies for MGs to exchange energy and reserves during scheduling horizon. To consider operational uncertainties, the proposed EMS is formulated as a two-stage stochastic mixed-integer linear programming problem that guaranties the global optimal solution. The obtained results verify that through the proposed EMS, total operation cost of the IMMG cluster is minimized while the frequency can be cost-effectively preserved within a pre-defined secure range.
Keywords: Energy and reserve scheduling | Energy management system | Islanded multi-microgrid clusters | Hierarchical control | Two-stage stochastic optimization
مقاله انگلیسی
9 Product assortment and space allocation strategies to attract loyal and non-loyal customers
مجموعه ای از محصولات و استراتژی های تخصیص فضا برای جذب مشتری های وفادار و غیر وفادار-2020
Assortment planning deserves much attention from practitioners and academics due to its direct impact on retailers’ commercial success. In this paper we focus on the increasingly popular retail practice to use combined product assortments with both “standard”and more fashionable and short-lived “variable”products for building up store traffic of “loyal”and “non-loyal”heterogeneous customers and enlarging the sales due to the potential cross-selling effect. Addressing the assortment planning as a bilevel optimization problem, we focus on decision-dependent uncertainties: the retailer’s binary decision about product inclusion influences the distribution of the product’s demand. Furthermore, our model accounts for customers’ optimal purchase quantities, which depend on budget constraints limiting the basket that a customer is able to purchase. We propose iterative heuristics using optimal quantization of demand and customers budget distributions to define the total assortment and the inventory level per product. These heuristics provide lower bounds on the optimal value. We conduct a comparison to other existing lower bounds and we formulate upper bounds via linear (LP) and semidefinite (SDP) relaxations for the performance evaluation of the heuristics and for an efficient numerical solution in high-dimensional cases. For managerial insights, we compare the proposed approach with three assortment planning strategies: (1) the retailer does not carry variable products; (2) the retailer ignores the cross-selling effect; and (3) the maximum space allocated to each product is fixed. Our results suggest that variable assortment boosts the retailers profits if the cross-selling effect is not neglected in the decision about products quan- tities.
Keywords: OR in marketing | Assortment planning | Inventory management | Bilevel optimization | Stochastic optimization
مقاله انگلیسی
10 A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics
یک سیستم مدیریت انرژی ریز شبکه مبتنی بر بهینه سازی تصادفی محدود شده و تحلیل داده های بزرگ-2020
A Microgrid (MG) is a promising distributed technology to solve todays energy challenges. They are changing how electricity is produced, transmitted, and distributed, enabling to capture massive amounts of data from sensors, and other electrical infrastructures. However, recent advances in modeling and optimization of MG neither integrate the use of big data technologies aggressively nor focus on developing an optimal operational strategy for a single building. To bridge this gap, this research proposes to use Apache Spark to enhance the performance of a scalable stochastic optimization model for an MG for multiple buildings, and to ensure that a significant portion of the wind power output will be utilized. The decision model is formulated as a chance constraint two-stage optimization problem to obtain operation decisions for a behind-the-meter topology. The comparison between the current practice of using historical data and integrating Apache Spark technologies demonstrates the superiority of the streaming data as energy management strategy. Experiments under different settings show that using big data strategy, the model can (1) achieve more cost savings of the total system, (2) increase resiliency to power disturbances, and (3) build a data analytics framework to enhance the decisionmaking process.
Keywords: Sustainability | Microgrid | Big data | Spark streaming | Stochastic optimization | Wind power
مقاله انگلیسی
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