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ردیف | عنوان | نوع |
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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 |
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 |
4 |
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 |
5 |
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 |
6 |
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 |
7 |
Energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique
مدیریت انرژی مصرف کننده بزرگ برق مبتنی بر شبکه بادی و PV با استفاده از روش بهینه سازی قدرتمند -2020 Large electricity consumers (LEC) can purchase energy from various energy resources such as bilateral contracts,
pool market, micro-turbines, battery storage systems, wind turbines, photovoltaic panels (PV). The uncertainty
of market price leads to uncertainty in the total cost to the LEC. Therefore, in this article, the robust optimization
(RO) technique is provided to investigate the uncertainty of the pool market price in the presented problem.
Also, demand response program (DRP) is provided to decrease the purchased cost to the LEC as much as possible.
According to the obtained results, without considering DRP, purchased cost is approximately $40,253.252 and
$42,586.984, respectively in the risk-neutral strategy (ideal condition) and robust strategy (worst condition).
Furthermore, the purchased cost is reduced nearly $36,945.362 and $39,789.267 in the risk-neutral and robust
strategies with considering DRP. So, it can be concluded that the purchased cost to the LEC with considering DRP
is reduced 8.2% and 6.5% in risk-neutral and robust strategies, respectively. Keywords: Large electricity consumers (LEC) | Wind turbine | Photovoltaic panel (PV) | Demand response program (DRP) | Robust optimization approach (RO) |
مقاله انگلیسی |
8 |
Multi-objective robust energy management for all-electric shipboard microgrid under uncertain wind and wave
مدیریت انرژی چند منظوره قدرتمند برای ریز شبکه برد حامل تمام برقی تحت باد و موج نامشخص-2020 An all-electric ship (AES) uses diesel generators and energy storage system (ESS) to meet both propulsion and
service loads. Thus, it can be viewed as a mobile microgrid. During the operation of an AES, significant uncertainties
such as water wave and wind introduce considerable speed loss, which may lead to severe voyage
delays. To fully address this issue, a new robust energy management model is proposed to coordinately schedule
an AES’s power generation and voyage considering the uncertain wave and wind. Two objectives are minimized
simultaneously: the fuel consumption (FC) and energy efficiency operational indicator (EEOI). The problem is
formulated as a bi-level robust optimization model after certain constraint decomposition. Normal boundary
intersection method is utilized to solve this multi-objective programming. Compared with existing joint scheduling
methods, the proposed method can fully guarantee the on-time rates of AES in various uncertain scenarios
and providing high-quality Pareto solutions. Keywords: All-electric ship | Mobile microgrid | Robustness | Energy management system | Joint generation and voyage scheduling | Uncertain wave and wind |
مقاله انگلیسی |
9 |
Multi-energy-storage energy management with the robust method for distribution networks
مدیریت انرژی ذخیره سازی چند منظوره با روش قوی برای شبکه های توزیع-2020 The randomness, volatility and anti-peaking characteristic from distributed renewable energy generation rise
great challenges for the safe and economic operation of the distribution networks (DNs). To address this problem,
this paper proposes a novel multi-energy-storage energy management system (EMS) to co-optimize the
electricity-driven mobile energy storage (MES) and inverter air-conditioning (AC)-based thermal energy storage
(TES). To facilitate the energy management of the DN, the MES that considers the delay factors and the TES that
regulates reactive power have been developed into a unified analytic model capable of charging and discharging.
In addition, considering the impact of the forecasting uncertainties and the risk-aversion of the dispatcher, a
novel robust optimization method is proposed to obtain more accurate “worst scenario”. The dispatching model
is then converted into a mixed integer second-order cone programming problem (MI-SOCP) and a mixed integer
linear programming problem (MILP), and linearized techniques and an iteration method are used to efficiently
solve these problems. Simulation studies on a 41-node DN in Ontario indicate that the operational cost and
power loss of the DN can be reduced by no less than 1% and 8% using the proposed EMS, respectively, while a
safer voltage level with a voltage deviation of 5% can be obtained. The results confirm the effectiveness of the
MES and TES for peak shaving, valley filling and voltage supporting. Keywords: Distribution network | Mobile energy storage | Thermal energy storage | Energy management system | Iterative method |
مقاله انگلیسی |
10 |
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 |