Optimization and Engineering最新文献_第3页

Towards efficient waste management: identification of waste flow chains in micro-regional detail through monitored data 实现高效废物管理：通过监测数据确定微观区域废物流动链的细节

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-06-18 DOI: 10.1007/s11081-024-09897-1

Lucie Němcová, Jaroslav Pluskal, Radovan Šomplák, Jakub Kůdela

{"title":"Towards efficient waste management: identification of waste flow chains in micro-regional detail through monitored data","authors":"Lucie Němcová, Jaroslav Pluskal, Radovan Šomplák, Jakub Kůdela","doi":"10.1007/s11081-024-09897-1","DOIUrl":"https://doi.org/10.1007/s11081-024-09897-1","url":null,"abstract":"<p>Countries around the world are gradually implementing the transition to a circular economy in waste management. This effort should be initiated already at the waste producers. It is necessary to plan and monitor waste management in as much detail as possible, i.e. at the level of micro-regions. At present, only indicators at the national level are analysed, as more detailed data at the micro-regional level are often not available or are burdened with significant errors and inconsistencies. The calculation of waste management indicators for micro-regions will allow to identify the potential for increasing material or energy recovery and to plan the necessary infrastructure directly to these locations instead of blanket and often ineffective legislative actions. This paper presents an approach for determining the producer-treatment linkage, i.e., provides information about each produced waste, where it was treated, and in what way. Such information is often not available based on historical waste management data as there are repeated waste transfers and often aggregated within a micro-region. The network flow approach is based on an iterative procedure combining a simulation with multi-criteria optimization. The chosen criteria replicate expert estimates in investigated issue such as minimum flow splitting, and minimum transfer micro-regions. A data reconciliation is performed where the deviation from all simulations is minimized, given that the capacity constraints of nodes and arcs resulting from the database must be satisfied. The approach is tested on a generated sample task to evaluate the precision and time complexity of the developed tool. Finally, the presented approach is applied to address a case study in the Czech Republic, within which it is possible to identify treatment location and methods for waste from individual regions.</p>","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"20 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective topology optimization design of thermal-mechanical coupling structure based on FPTO method 基于 FPTO 方法的热机械耦合结构多目标拓扑优化设计

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-05-25 DOI: 10.1007/s11081-024-09890-8

Dengfeng Huang, Shunshun Zhou, Xiaolei Yan

{"title":"Multi-objective topology optimization design of thermal-mechanical coupling structure based on FPTO method","authors":"Dengfeng Huang, Shunshun Zhou, Xiaolei Yan","doi":"10.1007/s11081-024-09890-8","DOIUrl":"https://doi.org/10.1007/s11081-024-09890-8","url":null,"abstract":"<p>The coupling problem between the structural field and temperature field is widely encountered in engineering applications and holds significant research importance. In the context of thermo-mechanical coupling topology optimization, the thermal stress resulting from the temperature field can cause structural deformation, consequently impacting the structural performance. Therefore, it is crucial to conduct rational optimization designs to ensure favorable mechanical behavior and heat dissipation. This study utilizes thermo-mechanical coupling theory to perform multi-objective topology optimization, aiming to minimize compliance and heat dissipation weakness concurrently, thereby obtaining a more comprehensive design scheme with enhanced overall performance. Initially, a topological optimization model for the coupled thermo-mechanical problem is established. Subsequently, the objective functions of structural compliance and heat dissipation weakness are normalized, and their sensitivities are derived. Next, a multi-load case and multi-objective optimization algorithm based on Floating Projection Topological Optimization (FPTO) is proposed to minimize both structural compliance and heat dissipation weakness. By comparing the topological configuration and objective function values obtained using the Solid Isotropic Material with Penalization (SIMP) method, it is evident that the FPTO method achieves clear and smooth boundaries in the topological configuration, while yielding smaller objective function values. Additionally, under appropriate trade-off factors, the FPTO method achieves a more balanced topological structure and optimizes material distribution without increasing the structural volume, thus enabling lightweight structures, providing novel ideas and methods for engineering applications.</p>","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"60 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient bounds tightening based on SOCP relaxations for AC optimal power flow 基于 SOCP 松弛的交流优化功率流的高效边界收紧

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-05-25 DOI: 10.1007/s11081-024-09891-7

Yuanxun Shao, Dillard Robertson, Michael Bynum, Carl D. Laird, Anya Castillo, Joseph K. Scott

引用次数: 0

Iterative MILP algorithm to find alternate solutions in linear programming models 在线性规划模型中寻找替代解决方案的迭代 MILP 算法

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-04-26 DOI: 10.1007/s11081-024-09887-3

Dev A. Kakkad, Ignacio E. Grossmann, Bianca Springub, Christos Galanopoulos, Leonardo Salsano de Assis, Nga Tran, John M. Wassick

引用次数: 0

Generalized measure Black–Scholes equation: towards option self-similar pricing 广义计量布莱克-斯科尔斯方程：走向期权自相似定价

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-04-05 DOI: 10.1007/s11081-024-09885-5

Nizar Riane, Claire David

引用次数: 0

Direct and inverse problems of fractional Sturm–Liouville equation 分数 Sturm-Liouville 方程的直接和逆问题

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-03-25 DOI: 10.1007/s11081-024-09881-9

Zahra Kavousi Kalashmi, Hanif Mirzaei, Kazem Ghanbari

引用次数: 0

An ensemble of artificial neural network models to forecast hourly energy demand 预测每小时能源需求的人工神经网络模型组合

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-03-25 DOI: 10.1007/s11081-024-09883-7

{"title":"An ensemble of artificial neural network models to forecast hourly energy demand","authors":"","doi":"10.1007/s11081-024-09883-7","DOIUrl":"https://doi.org/10.1007/s11081-024-09883-7","url":null,"abstract":"<h3>Abstract</h3> <p>We propose an ensemble artificial neural network (EANN) methodology for predicting the day ahead energy demand of a district heating operator (DHO). Specifically, at the end of one day, we forecast the energy demand for each of the 24 h of the next day. Our methodology combines three artificial neural network (ANN) models, each capturing a different aspect of the predicted time series. In particular, the outcomes of the three ANN models are combined into a single forecast. This is done using a sequential ordered optimization procedure that establishes the weights of three models in the final output. We validate our EANN methodology using data obtained from a A2A, which is one of the major DHOs in Italy. The data pertains to a major metropolitan area in Northern Italy. We compared the performance of our EANN with the method currently used by the DHO, which is based on multiple linear regression requiring expert intervention. Furthermore, we compared our EANN with the state-of-the-art seasonal autoregressive integrated moving average and Echo State Network models. The results show that our EANN achieves better performance than the other three methods, both in terms of mean absolute percentage error (MAPE) and maximum absolute percentage error. Moreover, we demonstrate that the EANN produces good quality results for longer forecasting horizons. Finally, we note that the EANN is characterised by simplicity, as it requires little tuning of a handful of parameters. This simplicity facilitates its replicability in other cases. </p>","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"21 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simple and effective meta relational learning for few-shot knowledge graph completion 用于完成少量知识图谱的简单有效的元关系学习

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-03-25 DOI: 10.1007/s11081-024-09880-w

Shujian Chen, Bin Yang, Chenxing Zhao

{"title":"Simple and effective meta relational learning for few-shot knowledge graph completion","authors":"Shujian Chen, Bin Yang, Chenxing Zhao","doi":"10.1007/s11081-024-09880-w","DOIUrl":"https://doi.org/10.1007/s11081-024-09880-w","url":null,"abstract":"<p>Conventional knowledge graph completion methods are effective for completing knowledge graphs (KGs), but they face significant challenges when dealing with relations with only a limited number of associative triples. To address the issue of incompleteness and long-tail distribution of relations in KGs, few-shot knowledge graph completion emerges as a promising solution. This approach predicts new triplets about a relation by leveraging only a handful of associated triples. Previous methods have focused on aggregating neighbor information and imposing sequential dependency assumptions. However, these methods can be counterproductive when they involve unrelated neighbors and rely on unrealistic assumptions, which hinders the learning of meta-representations. This paper proposes a simple and effective meta relational learning model (SMetaR) for few-shot knowledge graph completion that maintains the complete feature information of few-shot relations through a linear model. This approach effectively learns the meta-representation of few-shot relations and enhances meta-relational learning capabilities. Extensive experiments on two public datasets reveal that the model outperforms existing few-shot knowledge graph completion methods, demonstrating its effectiveness.</p>","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"234 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Service-oriented operational decision optimization for dry bulk shipping fleet under stochastic demand 随机需求下以服务为导向的干散货船队运营决策优化

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-03-22 DOI: 10.1007/s11081-024-09884-6

引用次数: 0

Regularized dynamics for monotone inverse variational inequalities in hilbert spaces 希尔伯特空间中单调逆变不等式的正规化动力学

IF 2.1 3区工程技术

Optimization and Engineering Pub Date : 2024-03-18 DOI: 10.1007/s11081-024-09882-8

Pham Ky Anh, Trinh Ngoc Hai

引用次数: 0