Payment cost minimization using Lagrangian relaxation and modified surrogate optimization approach

2011 ◽  
Author(s):  
Mikhail A. Bragin ◽  
Xu Han ◽  
Peter B. Luh ◽  
Joseph H. Yan
Keyword(s):  
Lagrangian Relaxation ◽  
Cost Minimization ◽  
Optimization Approach ◽  
Surrogate Optimization

Design of hydrogen supply chains under demand uncertainty – a case study of passenger transport in Germany

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Anton Ochoa Bique ◽  
Leonardo K. K. Maia ◽  
Ignacio E. Grossmann ◽  
Edwin Zondervan
Keyword(s):  
Stochastic Optimization ◽  
Demand Uncertainty ◽  
Programming Model ◽  
Cost Minimization ◽  
Water Electrolysis ◽  
Scenario Tree ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Strong Impact ◽  
Hydrogen Supply

Abstract A strategy for the design of a hydrogen supply chain (HSC) network in Germany incorporating the uncertainty in the hydrogen demand is proposed. Based on univariate sensitivity analysis, uncertainty in hydrogen demand has a very strong impact on the overall system costs. Therefore we consider a scenario tree for a stochastic mixed integer linear programming model that incorporates the uncertainty in the hydrogen demand. The model consists of two configurations, which are analyzed and compared to each other according to production types: water electrolysis versus steam methane reforming. Each configuration has a cost minimization target. The concept of value of stochastic solution (VSS) is used to evaluate the stochastic optimization results and compare them to their deterministic counterpart. The VSS of each configuration shows significant benefits of a stochastic optimization approach for the model presented in this study, corresponding up to 26% of infrastructure investments savings.

Payment Cost Minimization Auction for Deregulated Electricity Markets Using Surrogate Optimization

2006 ◽  
Vol 21 (2) ◽  
pp. 568-578 ◽  
Author(s):  
P.B. Luh ◽  
W.E. Blankson ◽  
Y. Chen ◽  
J.H. Yan ◽  
G.A. Stern ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Cost Minimization ◽  
Surrogate Optimization

scholarly journals A binary particle swarm optimization approach for power system security enhancement

2022 ◽  
Vol 12 (2) ◽  
pp. 1929
Author(s):  
Padmanabha Raju Chinda ◽  
Ragaleela Dalapati Rao
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Particle Swarm ◽  
System Security ◽  
Binary Particle Swarm Optimization ◽  
Optimization Approach ◽  
Power System Security ◽  
Swarm Optimization

Improvement of power system security manages the errand of making healing move against conceivable system overloads in the framework following the events of contingencies. Generation re-dispatching is answer for the evacuation of line overloads. The issue is the minimization of different goals viz. minimization of fuel cost, minimization of line loadings and minimization of overall severity index. Binary particle swarm optimization (BPSO) method was utilized to take care of optimal power flow issue with different targets under system contingencies. The inspiration to introduce BPSO gets from the way that, in rivalry with other meta-heuristics, BPSO has demonstrated to be a champ by and large, putting a technique as a genuine alternative when one needs to take care of a complex optimization problem. The positioning is assessed utilizing fuzzy logic. Simulation Results on IEEE-14 and IEEE-30 bus systems are presented with different objectives.

scholarly journals A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5449
Author(s):  
Saket Gupta ◽  
Narendra Kumar ◽  
Laxmi Srivastava ◽  
Hasmat Malik ◽  
Amjad Anvari-Moghaddam ◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Optimization Algorithms ◽  
Parameter Tuning ◽  
Electrical Network ◽  
Optimization Approach ◽  
Voltage Profile ◽  
Optimal Power ◽  
Voltage Stability Enhancement

This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.

scholarly journals An Optimization Approach to the Intermodal Transportation Network in Fruit Cold Chain, Considering Cost, Quality Degradation and Carbon Dioxide Footprint

2018 ◽  
Vol 25 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Qianli Ma ◽  
Wenyuan Wang ◽  
Yun Peng ◽  
Xiangqun Song
Keyword(s):  
Carbon Dioxide ◽  
Programming Model ◽  
Cost Minimization ◽  
Network Planning ◽  
Cold Chain ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Intermodal Transportation ◽  
Quality Degradation ◽  
Trade Offs

AbstractThis model optimizes port hinterland intermodal refrigerated container flows, considering both cost and quality degradation, which is distinctive from the previous literature content in a way that it quantifies the influence of carbon dioxide (CO2) emission in different setting temperature on intermodal network planning. The primary contribution of this paper is that the model is beneficial not only to shippers and customers for the novel service design, but also offer, for policy-makers of the government, insights to develop inland transport infrastructures in consideration of intermodal transportation. The majority of models of multimodal system have been established with an objective of cost minimization for normal commodities. As the food quality is possible to be influenced by varying duration time required for the storage and transportation, and transportation accompanied with refrigeration producing more CO2emission, this paper aims to address cost minimization and quality degradation minimization within the constraint of CO2footprint. To achieve this aim, we put the quality degradation model in a mixed-integer linear programming model used for intermodal network planning for cold chain. The example of Dalian Port and Yingkou Port offer insight into trade-offs between transportation temperature and transport mode considering CO2footprint. Furthermore, the model can offer a useful reference for other regions with the demand for different imported food, which requires an uninterrupted cold chain during the transportation and storage.

scholarly journals Planning for urban water supply–demand portfolio using a hybrid robust stochastic optimization approach

2020 ◽  
Vol 20 (8) ◽  
pp. 3433-3448
Author(s):  
Maryam Shabani ◽  
Naser Shams Gharneh ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Water Management ◽  
Water Supply ◽  
Stochastic Optimization ◽  
Cost Minimization ◽  
Supply And Demand ◽  
Demand Management ◽  
Water Shortage ◽  
Optimization Approach ◽  
Urban Water ◽  
Management Options

Abstract Water management and preventing water shortage require accurate planning with attention to the importance of urban water. The problems ahead include the increase in demand and reduction in water supply resources due to factors that cause uncertainties and the high cost of water supply infrastructures. Most studies in urban water management consider only a single criterion. However, in this research, two objective functions, namely cost minimization and per capita water consumption maximization, were used simultaneously. A portfolio approach based on the balance of water supply and demand was developed taking uncertainty into account. Then, the problem was solved using a hybrid robust–stochastic optimization approach. The results showed the selected supply augmentation and demand management options in each stage under dry, normal, and wet year scenarios.

scholarly journals Crop Planning in Synchronized Crop-Demand Scenarios: A Biobjective Optimization Formulation

Horticulturae ◽  
2021 ◽  
Vol 7 (10) ◽  
pp. 347
Author(s):  
Belarmino Adenso-Díaz ◽  
Gabriel Villa
Keyword(s):  
Production Systems ◽  
Cost Minimization ◽  
Profit Maximization ◽  
Optimization Approach ◽  
New Approach ◽  
Crop Planning ◽  
Biobjective Optimization ◽  
New Type ◽  
Planning Problems

Crop planning problems have been extensively studied from different perspectives (profit maximization, optimizing available water use, sustainability, etc.). In this paper, a new approach is proposed that considers new forms of customer-producer relationship, involving long-term cooperation agreements where the product volumes are agreed, and the demand is guaranteed in advance. In this context, typical of manufacturing production systems, crop planning must guarantee a given production level on specific dates, thus becoming deterministic in nature. In that context, this paper introduces a lexicographic biobjective optimization approach that, in addition to cost minimization, aims at minimizing the risk of not meeting the agreed demands. The latter is done by maximizing the geographic dispersion of the crops so that weather risk is mitigated. A number of experiments have been carried out to test the proposed approach, showing the high complexity of the solution and opening the door to new solution procedures for a problem that results from interest given to the new type of relationships in the food logistics chain.

scholarly journals Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation

2021 ◽  
Author(s):  
Deniz Gurevin ◽  
Mikhail Bragin ◽  
Caiwen Ding ◽  
Shanglin Zhou ◽  
Lynn Pepin ◽  
...  
Keyword(s):  
Lagrangian Relaxation ◽  
Lane Detection ◽  
Fine Tuning ◽  
Model Parameters ◽  
Optimization Approach ◽  
Model Accuracy ◽  
Accuracy Requirement ◽  
Network Pruning ◽  
The Arts ◽  
Weight Pruning

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning and retraining (fine-tuning) significantly increases the overall training trails. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. We further accelerate the convergence of the SLR by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate the proposed method on image classification tasks using CIFAR-10 and ImageNet, as well as object detection tasks using COCO 2014 and Ultra-Fast-Lane-Detection using TuSimple lane detection dataset. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves higher compression rate than state-of-the-arts under the same accuracy requirement. It also achieves a high model accuracy even at the hard-pruning stage without retraining (reduces the traditional three-stage pruning to two-stage). Given a limited budget of retraining epochs, our approach quickly recovers the model accuracy.

scholarly journals An integrated model for aircraft routing and crew scheduling: Lagrangian Relaxation and metaheuristic algorithm

2020 ◽  
Vol 11 (1) ◽  
pp. 25
Author(s):  
Masoumeh Mirjafari ◽  
Alireza Rashidi Komijan ◽  
Ahmad Shoja
Keyword(s):  
Lagrangian Relaxation ◽  
Cost Minimization ◽  
Relaxation Method ◽  
Crew Scheduling ◽  
Aircraft Maintenance ◽  
Scheduling Problems ◽  
Aircraft Routing ◽  
Key Issues ◽  
Optimum Solution ◽  
New Framework

<p>Airline optimization is a significant problem in recent researches and airline industrial as it can determine the level of service, profit and competition status of the airline. Aircraft and crew are expensive resources that need efficient utilization. This paper focuses simultaneously on two major issues including aircraft maintenance routing and crew scheduling. Several key issues such as aircraft replacement, fairly night flights assignment and long-life aircrafts are considered in this model. We used the flight hours as a new framework to control aircraft maintenance. At first, an integrated mathematical model for aircraft routing and crew scheduling problems is developed with the aim of cost minimization. Then, Lagrangian relaxation and Particle Swarm Optimization algorithm (PSO) are used as the solution techniques. To evaluate the efficiency of solution approaches, model is solved with different numerical examples in small, medium and large sizes and compared with GAMS output. The results show that Lagrangian relaxation method provides better solutions comparing to PSO and also has a small gap to optimum solution.</p>

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