A robust optimization model under uncertain environment: An application in production planning

Singh, Shubham; Biswal, M. P.

doi:10.1016/j.cie.2021.107169

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…Robust optimization presents a different method to solve problems involving uncertain parameters. In a robust optimization problem, there is no need to know the distribution function of uncertain parameters, and having the range of changes of these parameters suffices [18,19]. Two important criteria were considered in designing this method: A).…”

Section: Robust Optimizationmentioning

confidence: 99%

“…There are six constraints in the nexus that were considered, which are examined in the following. The level of access to surface and groundwater in paddy fields and drylands must not exceed the value of surface and groundwater in the nexus as raised in Equations ( 18) and (19), respectively. We need electrical energy to collect surface and groundwater because of the use of electric pumps.…”

Section: Constraintsmentioning

confidence: 99%

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Sustainable Management of the Electrical-Energy–Water–Food Nexus Using Robust Optimization

et al. 2021

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The significance of the security of electrical energy, water, and food resources in the future, which are inextricably connected, has led to increasing attention to this important issue in studies. This is an issue inattention to which can have irreparable consequences in the future. One of the sectors where electrical energy, water, and food are very closely associated is agriculture. Undoubtedly, the ability to properly manage electrical energy, hydropower, and food resources that have many uncertainties brings about the development of agriculture on the one hand and the optimal allocation of electrical energy, water, and land resources on the other. Thus, while reaching the highest economic profit, the greenhouse gas emissions reach the minimum possible value too. In this study, via robust optimization and by precisely considering the existing uncertainties, a model was developed for the optimal allocation of electrical energy, water, and land resources for a region in the north of China. In addition to acknowledging the close relationship between electrical energy, water, and food sources, the results show the method’s effectiveness for sustainable management in agriculture.

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Section: Robust Optimizationmentioning

confidence: 99%

Section: Constraintsmentioning

confidence: 99%

Sustainable Management of the Electrical-Energy–Water–Food Nexus Using Robust Optimization

et al. 2021

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“…The production planning problem has subsequently been solved using many approaches, for instance, linear programming [15] and nonlinear programming [16], based on the considered conditions, such as random parameters. According to studies by Yazdani et al [17]- [20, sustainability problems could also be solved by implementing multiobjective optimization models.…”

Section: Introductionmentioning

confidence: 99%

The Joint Decision-Making Support through Piecewise Objective Optimization Model for Integrated Supplier Selection, Inventory Management, and Production Planning Involving Discounts

Widowati

Sutrisno

Utomo

2023

JOIV : Int. J. Inform. Visualization

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The decision-makers in manufacturing industries continuously optimize every supply-chain part to achieve optimal profit. In this paper, three crucial activities in the supply chain are observed as profit contributors: supplier selection, inventory management, and production planning. Decision-making support is needed to optimize those activities, especially when prices/costs involve discounts. Therefore, this study aims to develop integrated decision-making support for supplier selection, inventory management, and production planning involving discounted prices. The problem was considered with multi-supplier, multi-raw material, multi-product, and multi-observation time instant. The objective was based on maximizing the profit for the entire activity, i.e., from the raw material procurement and storage to the production. This supply chain was modeled as mixed-integer linear programming with a piecewise objective function representing the profit, which was maximized. It was also modeled with a bunch of constraint functions, including product demand satisfaction. The proposed model was tested with computational simulations using randomly generated supply chain data. The primal simplex algorithm was also employed to calculate the real value of the optimal decision, which was combined with the Branch-and-Bound approach to calculate the appropriate integer solution. The results showed that the optimal decision was achieved, namely (1) The optimal quantity of raw materials ordered to each supplier, (2) The optimal production quantity, and (3) The optimal inventory level, which provided the maximal profit for the whole optimization time horizon. This indicated that the proposed decision-making support model is implementable for industrial decision-makers.

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“…Robust optimization, which does not require the accurate probability distribution of uncertain parameters beforehand, has been used in energy transactions, unit commitment and dispatch of multi-energy systems and microgrids, and optimal load dispatch of the community energy hub . In traditional robust optimization methods, uncertainty sets are usually preset with a fixed model without enough flexibility to capture the characteristics of uncertain data . Hence, a data-driven approach has been introduced to approximate and optimize the underlying function using quantities of data. − Machine learning methods can model the uncertain data and obtain valid information for data analysis and decision making to form a closed loop .…”

Section: Introductionmentioning

confidence: 99%

“…42 In traditional robust optimization methods, uncertainty sets are usually preset with a fixed model without enough flexibility to capture the characteristics of uncertain data. 43 Hence, a datadriven approach has been introduced to approximate and optimize the underlying function using quantities of data. 44−46 Machine learning methods can model the uncertain data and obtain valid information for data analysis and decision making to form a closed loop.…”

mentioning

confidence: 99%

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

et al. 2022

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With the increasing challenges in environmental protection, product quality, and market profit, the development of a feasible optimization approach coupling accurate oil property online prediction has become crucial to the intelligent production of refineries. However, traditional property modeling with near-infrared (NIR) spectroscopy cannot dynamically extract the synergistic effect between wavelengths and cause inaccurate property prediction. The success rate of the data-driven robust optimization (DDRO) of the refining process is influenced by process uncertainty. Thus, we proposed a modified hybrid strategy on online NIR prediction modeling with adjustable characteristic wavelength selection and DDRO under uncertainty for the refining process. An adjustable feature space variable extraction (AFSVE) method was first proposed to dynamically select the characteristic wavelengths for constructing accurate property prediction models. A data-driven robust optimization of gasoline blending model was rendered through dual transformation using the uncertainty set of oil property derived from principal component analysis combined with robust kernel density estimation. An industrial application showed the best prediction accuracy of property models with the AFSVE method, and a high blending success rate was obtained by the proposed modified hybrid strategy, which hedges against uncertainties, including measuring error and blending effect, and boosts environmentally friendly and intelligent process operation.

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A robust optimization model under uncertain environment: An application in production planning

Cited by 12 publications

References 25 publications

Sustainable Management of the Electrical-Energy–Water–Food Nexus Using Robust Optimization

Sustainable Management of the Electrical-Energy–Water–Food Nexus Using Robust Optimization

The Joint Decision-Making Support through Piecewise Objective Optimization Model for Integrated Supplier Selection, Inventory Management, and Production Planning Involving Discounts

Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

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