scholarly journals Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 507
Author(s):  
Lima ◽  
Relvas ◽  
Barbosa-Póvoa ◽  
Morales
Keyword(s):  
Robust Optimization ◽  
Oil Industry ◽  
Computational Effort ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Optimization Approach ◽  
Solution Quality ◽  
Inaccurate Information ◽  
Computational Performance ◽  
Adjustable Robust Optimization

The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.

Robust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods

2015 ◽  
Vol 35 (1) ◽  
pp. 81-93 ◽  
Author(s):  
Masoud Rabbani ◽  
Neda Manavizadeh ◽  
Niloofar Sadat Hosseini Aghozi
Keyword(s):  
Mathematical Model ◽  
Robust Optimization ◽  
Production Planning ◽  
Production System ◽  
Demand Uncertainty ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Optimization Approach ◽  
Content Type ◽  
Evolutionary Methods

Purpose – This paper aims to consider a multi-site production planning problem with failure in rework and breakdown subject to demand uncertainty. Design/methodology/approach – In this new mathematical model, at first, a feasible range for production time is found, and then the model is rewritten considering the demand uncertainty and robust optimization techniques. Here, three evolutionary methods are presented: robust particle swarm optimization, robust genetic algorithm (RGA) and robust simulated annealing with the ability of handling uncertainties. Firstly, the proposed mathematical model is validated by solving a problem in the LINGO environment. Afterwards, to compare and find the efficiency of the proposed evolutionary methods, some large-size test problems are solved. Findings – The results show that the proposed models can prepare a promising approach to fulfill an efficient production planning in multi-site production planning. Results obtained by comparing the three proposed algorithms demonstrate that the presented RGA has better and more efficient solutions. Originality/value – Considering the robust optimization approach to production system with failure in rework and breakdown under uncertainty.

Feasibility Robust Optimization via Scenario Generation and Reduction

2018 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Solution

Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains without any known probability distributions. The proposed approach integrates a new sampling-based scenario generation scheme with a new scenario reduction approach in order to solve feasibility robust optimization problems. An analysis of the computational cost of the proposed approach was performed to provide worst case bounds on its computational cost. The new proposed approach was applied to three test problems and compared against other scenario-based robust optimization approaches. A test was conducted on one of the test problems to demonstrate that the computational cost of the proposed approach does not significantly increase as additional uncertain parameters are introduced. The results show that the proposed approach converges to a robust solution faster than conventional robust optimization approaches that discretize the uncertain parameters.

A Comparison between Three Meta-Modeling Optimization Approaches to Design a Tube Hydroforming Process

2012 ◽  
Vol 504-506 ◽  
pp. 607-612 ◽  
Author(s):  
Giuseppe Ingarao ◽  
Laura Marretta ◽  
Rosa di Lorenzo
Keyword(s):  
Polynomial Regression ◽  
Tube Hydroforming ◽  
Local Approximation ◽  
Computational Effort ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Modeling Techniques ◽  
Hydroforming Process ◽  
Meta Modeling ◽  
Research Challenge

Computer aided procedures to design and optimize forming processes have become crucial research topics as the industrial interest in cost and time reduction has been increasing. A standalone numerical simulation approach could make the design too time consuming while meta-modeling techniques enables faster approximation of the investigated phenomena, reducing the simulation time. Many researchers are, nowadays, facing such research challenge by using various approaches. Response surface method (RSM) is probably the most known one, since its effectiveness was demonstrated in the past years. The effectiveness of RSM depends both on the definition of the Design of Experiments (DoE) and the accuracy of the function approximation. The number of numerical simulations can be strongly reduced if a proper optimization approach is implemented: one of the main issues about optimization techniques is related to the design necessity of performing either global or local approximation. This paper aims to test the efficacy of some meta-modeling techniques in the optimization of a T-shaped hydroforming process. In this paper three optimization approaches based on different meta-modeling techniques are implemented. In particular, classical Polynomial Regression approach (PR), Moving Least Squares approximation (MLS) and Kriging method are applied. The results showed that, thanks to the peculiarities of MLS and Kriging methods, it is possible to strongly reduce the computational effort in sheet metal forming optimization, particularly in comparison with a classical PR approach. Differences were highlighted and quantified.

Adjustable robust optimization approach for two-stage operation of energy hub-based microgrids

Energy ◽  
2021 ◽  
Vol 222 ◽  
pp. 119894
Author(s):  
Mohammad H. Shams ◽  
Majid Shahabi ◽  
Mohammad MansourLakouraj ◽  
Miadreza Shafie-khah ◽  
João P.S. Catalão
Keyword(s):  
Robust Optimization ◽  
Optimization Approach ◽  
Two Stage ◽  
Energy Hub ◽  
Adjustable Robust Optimization

scholarly journals Non-Convex Feasibility Robust Optimization Via Scenario Generation and Local Refinement

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case

Abstract Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains. The proposed approach is based on an integration of two techniques: (i) a sampling-based scenario generation scheme and (ii) a local robust optimization approach. An analysis of the computational cost of this integrated approach is performed to provide worst-case bounds on its computational cost. The proposed approach is applied to several non-convex engineering test problems and compared against two existing robust optimization approaches. The results show that the proposed approach can efficiently find a robust optimal solution across the test problems, even when existing methods for non-convex robust optimization are unable to find a robust optimal solution. A scalable test problem is solved by the approach, demonstrating that its computational cost scales with problem size as predicted by an analysis of the worst-case computational cost bounds.

Sequential Cooperative Robust Optimization (SCRO) for Multi-Objective Design Under Uncertainty

2015 ◽  
Author(s):  
J. M. Hamel
Keyword(s):  
Robust Optimization ◽  
Uncertainty Analysis ◽  
Computational Efficiency ◽  
System Optimization ◽  
Surrogate Models ◽  
Optimization Techniques ◽  
Robust Design Optimization ◽  
Optimization Approach ◽  
Deterministic System ◽  
Multi Objective

The optimal design of systems under uncertainty is a critical challenge faced by design engineers. Robust optimization is a well-studied and widely used technique for the design of engineering systems that possess uncertainty, and numerous robust optimization techniques have been presented in recent years. The majority of the robust optimization techniques presented in the literature suffer from a computational efficiency challenge, either due to the expense of obtaining objective or constraint function uncertainty information, or due to the fact that many robust optimization approaches (with a few notable exceptions) require that a potentially expensive uncertainty analysis calculation (e.g. Monte-Carlo simulation) be nested within an already potentially expensive optimization solver (e.g. a genetic algorithm). Additionally, many robust optimization approaches focus solely on design problems that possess a single design objective, and the robust techniques that do consider problems with multiple design objectives often require various simplifying assumptions or are even more computationally expensive to implement. Clearly there are opportunities for improvement in the area of robust optimization, and this paper presents a new robust design Optimization approach called Sequential Cooperative Robust Optimization (SCRO), which uses both a sequential approach and multi-objective optimization techniques in an effort to decouple the deterministic system optimization problem from the associated uncertainty analysis problem. The SCRO approach first fits surrogate models to the system objective and constraint functions, in addition to system sensitivity functions, using as few function calls as possible in order to improve computational efficiency. The approach then performs a series of sequential multi-objective optimizations using the developed surrogate models. These optimizations work to find points in the design space that are optimal with respect to deterministic performance and both objective and feasibility robustness metrics based on predicted system sensitivities. The SCRO approach has the potential to find solutions not available to other robust optimization approaches, and can be more efficient than other more traditional robust optimization techniques due to its use of surrogate approximation and a sequential framework.

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