检索结果-内蒙古大学图书馆

Connections between multiple-objective programming and weight restricted data envelopment analysis: The role of the ordering cone

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期323卷 571-582页

作者： Korhonen, Pekka Soleimani-damaneh, Majid Wallenius, Jyrki Aalto Univ Sch Business POB 11000 Aalto 00076 Finland Univ Tehran Coll Sci Sch Math Stat & Comp ICOL Ind & Computat Optimizat Lab Tehran Iran

This paper explores some new, important and interesting connections between multiple-objective programming (MOP) and Data Envelopment Analysis (DEA). We show that imposing weight restrictions in DEA corresponds to changing the ordering cone in MOP in a specific way. The new ordering cone is constructed and its properties are proved, providing useful insights about the connections between MOP and DEA. After providing several theoretical results, we illustrate them on a real-world data set. In addition to their theoretical appeal, our results hold significant practical importance for several reasons which are addressed in the paper.

关键词： Data envelopment analysis (DEA) multiple-objective programming Weight restrictions Pareto solution Ordering cone Decision models

来源：评论

学校读者我要写书评

暂无评论

Geometric compromise programming: application in portfolio selection

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第5期30卷 2571-2594页

作者： Salas-Molina, Francisc Pla-Santamaria, David Vercher-Ferrandiz, Maria Luisa Garcia-Bernabeu, Ana Univ Politecn Valencia Ferrandiz & Carbonell S-N Alcoy 03801 Alicante Spain

Compromise programming (CP) aims to find solutions by minimising distances to an ideal point with maximum achievement which is usually infeasible. A common assumption in CP is that it is highly unlikely that the optimum decision will lie out of the bounds of the compromise set given by metrics p=1$p=1$ and p=infinity$p=\infty$ of the Minkowski distance function. This assumption excludes the use of multiplicative functions as a measure of achievement. We propose geometric CP (GCP) to provide alternative solutions based on multiplicative functions to overcome this limitation. This methodology is an extension of CP that allows to incorporate the principle of limited compensability. An additional interesting feature of GCP is that, under reasonable assumptions, characterises extreme seekers' behaviour with non-concave utility functions (expressing no preference for any of the extremes). We discuss the practical implications of our approach and present three numerical illustrations in the context portfolio selection.

关键词： multiple-objective programming multiplicative utility functions extreme seekers compromise set

来源：评论

学校读者我要写书评

暂无评论

Inverse data envelopment analysis with stochastic data

引用

RAIRO-OPERATIONS RESEARCH 2021年第5期55卷 2739-2762页

作者： Ghomi, Ali Ghobadi, Saeid Behzadi, Mohammad Hassan Rostamy-Malkhalifeh, Mohsen Islamic Azad Univ Sci & Res Branch Dept Stat Tehran Iran Islamic Azad Univ Khomeinishahr Branch Dept Math Esfahan Iran Islamic Azad Univ Sci & Res Branch Dept Math Tehran Iran

The inverse Data Envelopment Analysis (InvDEA) is an exciting and significant topic in the DEA area. Also, uncertain data in various real-life applications can degrade the efficiency results. The current work addresses the InvDEA in the presence of stochastic data. Under maintaining the efficiency score, the inputs/outputs-estimation problem is investigated when some or all of its outputs/inputs increase. A novel optimality concept for multiple-objective programming problems, stochastic (weak) Pareto optimality in the level of significance alpha is an element of[0,1], is introduced to derive necessary and sufficient conditions for input/output estimation. Furthermore, the performance of the developed theory in a banking sector application is verified.

关键词： Data envelopment analysis inverse DEA stochastic data efficiency multiple-objective programming

来源：评论

学校读者我要写书评

暂无评论

Multi-objective programming for energy system based on the decomposition of carbon emission driving forces: A case study of Guangdong, China

引用

JOURNAL OF CLEANER PRODUCTION 2021年 309卷 127410-127410页

作者： Zhang, Yang Fu, Zhenghui Xie, Yulei Li, Zheng Liu, Yanxiao Hu, Qing Guo, Huaicheng Peking Univ Coll Environm Sci & Engn Beijing 100871 Peoples R China Chinese Res Inst Environm Sci Beijing 100871 Peoples R China Univ Sci & Technol Sch Energy & Environm Engn Beijing 100083 Peoples R China

Energy-related carbon emissions are increasing the rate of climate change, and controlling carbon emissions is a common challenge for the international public. Despite attempts to restrict the utilization of fossil energy and advancing technology for cleaner production, there has been little discussion on the determinants of change in carbon emissions for future scenarios and planning energy systems according to the analysis of low carbon development. In this study, a comprehensive energy optimization planning framework under a low-carbon mode is established. A framework based on the gray model (GM) and logarithmic mean Divisia index (LMDI) method are constructed to predict the emission mitigation potential and decompose the carbon emission driving factors. The decomposition results are key input prerequisites for the following energy optimization model: An interval parameter multiple-objective programming (IPMOP) optimization model, which is developed to support regional energy system administration by seeking the trade-offs among economic development, energy utilization, and environmental protection under multiple uncertainties. Furthermore, the proposed approach is applied to a case study in Guangdong, China. The results reveal that (a) the clean production effect (GDP per unit of atmospheric pollutants emission) would become the primary positive force for carbon emission increase, and the pollutant reduction effect (total atmospheric pollutants emission) would play the primary negative role;(b) the coaldominated energy structure in Guangdong is expected to be transformed to a petroleum-dominated energy structure;(c) the GDP in Guangdong would steadily increase over time, but the pace of economic growth will decelerate, and the annual average growth rate of GDP for the coming fifteen years will be [3.67%, 4.26%]. This study provides a new pathway for policymakers to identify the determinants of carbon emission increase and to generate optimal solutions on a regio

关键词： Carbon emissions Driving factors LMDI Energy optimization model multiple-objective programming Uncertainties

来源：评论

学校读者我要写书评

暂无评论

Metaheuristics for solving the biobjective single-path multicommodity communication flow problem

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2019年第2期26卷 589-614页

作者： Masri, Hela Krichen, Saoussen Guitouni, Adel Univ Tunis Tunis Business Sch Tunis Tunisia Univ Tunis Inst Super Gest Tunis Tunis Tunisia Univ Victoria Peter B Gustavson Sch Business Victoria BC Canada

Single-path multicommodity flow problem (SMCFP) is a well-known combinatorial optimization problem, in which the flow of each commodity can be transmitted using only one path linking its destination to an appropriate origin within the addressed network. In this paper, we study the SMCFP in a multiobjective context by considering the simultaneous optimization of paths' delay and average reliability. The network is modeled as a finite set of nodes that can communicate using preestablished connections where each connection is characterized by a capacity, a lead time, and a reliability. A node can be an information producer or/and information consumer. The contention problem is solved by assigning a path and a dedicated bandwidth to each flow. The problem is formulated as a biobjective nonlinear optimization problem. This biobjective problem has not been considered in the literature. We design three alternative procedures for approximating the Pareto front. We proposed an MGA based on NSGA-II, a multiobjective variable neighborhood search and a new distance-based hybrid metaheuristic. The hybridization integrates a local search into the framework of genetic algorithm to effectively drive the search toward a better approximating of the Pareto front. The propounded algorithms' efficiencies are experimentally investigated on a test bed of instances applied to a planar and a grid network. A comparative study is conducted based on different multiobjective performance indicators.

关键词： multiple-objective programming metaheuristics network flows nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

A bi-objective study of the minimum latency problem

引用

JOURNAL OF HEURISTICS 2019年第3期25卷 431-454页

作者： Arellano-Arriaga, N. A. Molina, J. Schaeffer, S. E. Alvarez-Socarras, A. M. Martinez-Salazar, I. A. Univ Autonoma Nuevo Leon PISIS FIME San Nicolas De Los Garza Mexico Univ Malaga Programa Doctorado Econ & Empresa Malaga Spain Univ Malaga Dept Econ Aplicada Matemat Malaga Spain

We study a bi-objective problem called the Minimum Latency-Distance Problem (mldp) that aims to minimise travel time and latency of a single-vehicle tour designed to serve a set of client requests. This tour is a Hamiltonian cycle for which we aim to simultaneously minimise the total travel time of the vehicle and the total waiting time (i.e., latency) of the clients along the tour. This problem is relevant in contexts where both client satisfaction and company profit are prioritise. We propose two heuristic methods for approximating Pareto fronts for mldp: SMSA that is based on a classic multi-objective algorithm and EiLS that is based on a novel evolutionary algorithm with intelligent local search. We report computational experiments on a set of artificially generated problem instances using an exact method and the two proposed heuristics, comparing the obtained fronts in terms of various quality metrics.

关键词： Combinatorial optimisation Distance Genetic algorithms Latency Meta-heuristics Multi-objective optimisation multiple-objective programming

来源：评论

学校读者我要写书评

暂无评论

Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2018年第3期25卷 1053-1079页

作者： Mejia-Argueta, Christopher Gaytan, Juan Caballero, Rafael Molina, Julian Vitoriano, Begona Tech Univ Eindhoven Dept Ind Engn & Innovat Sci 3169 Den Dolech 2 NL-5612 AZ Eindhoven Nord Brabant Netherlands Univ Autonoma Estado Mexico Sch Engn Toluca 225104 Estado De Mexic Mexico Univ Malaga Appl Econ Math Campus El Ejido E-29071 Malaga Spain Univ Complutense Madrid Comunidad Madrid Dept Stat & Operat Res Madrid 16734 Spain Univ Complutense Madrid Comunidad Madrid Inst Interdisciplinary Math Madrid 16734 Spain

This paper addresses frequent and foreseeable floods in the short-term preparedness of an imminent event using a multicriteria optimization model integrated with a geographical information system to simulate flood levels, determine the best strategies, and update information. The proposed model takes into account the four main relief operations: location of emergency facilities (i.e., distribution centers, shelters, and meeting points), prepositioning of humanitarian aid, evacuation, and distribution of humanitarian aid. Three criteria are considered in the formulation to minimize: the maximum evacuation flow-time, the maximum distribution flow-time, and total cost of relief operations. The approximation to the efficient frontier is built using multiobjective programming through the use of commercial software. The usefulness and robustness of the model are verified using data from one of the worst Mexican floods considering various flood levels created from three key elements in humanitarian logistics. The strategies provided by the proposed methodology are compared with those implemented by the Mexican authorities during the studied disaster.

关键词： multiple-objective programming efficient solution weighted-sum method epsilon-constraint humanitarian operations

来源：评论

学校读者我要写书评

暂无评论

Humanitarian relief supply chain: a multi-objective model and solution

引用

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2017年第7期42卷 1167-1174页

作者： Jha, Aditya Acharya, Damodar Tiwari, M. K. Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

This paper models a humanitarian relief chain that includes a relief goods supply chain and an evacuation chain in case of a natural disaster. Optimum network flow is studied for both the chains by considering three conflicting objectives, namely demand satisfaction in relief chain, demand satisfaction in evacuation chain and overall logistics cost. The relief goods supply chain consists of three echelons: suppliers, relief camps and affected areas. The evacuation chain consists of two echelons: evacuation camps and affected areas. The model has been made more resilient by considering multiple paths between any two locations and disruption of camps and paths due to natural factors. The Mixed Integer programming problem has been solved using NSGA-III and results have been compared to those from benchmark algorithms. The model has been successfully tested on generated real-life-like data.

关键词： Humanitarian logistics supply chain resilience multiple-objective programming evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

SHELTER SITE LOCATION UNDER DEMAND UNCERTAINTY: A CHANCE-CONSTRAINED MULTI-objective MODELING FRAMEWORK

SHELTER SITE LOCATION UNDER DEMAND UNCERTAINTY: A CHANCE-CON...

引用

作者： Omer Burak Kinay Bilkent University

学位级别：硕士

Shelters have a very critical role in disaster relief since they provide accommo- dation and necessary services for the disaster victims who lost their homes. The selection of their locations among many candidate points is a task that should be carried out with a proper methodology that generates applicable and fairness- based plans. Since this selection process is done before the occurrence of disas- ters, it is important to take demand variability into account. Motivated by this, the problem of determining shelter site locations under demand uncertainty is addressed. In particular, a chance-constrained mathematical model that takes demand as a stochastic input is developed. By using a linearization approach that utilizes special ordered set of type 2 (SOS2) variables, a mixed-integer linear programming model is formulated. Using the proposed formulation, instances of the problem using data associated with Istanbul are solved. The results in- dicate that capturing uncertainty in the shelter site location problem by means of chance constraints may lead to solutions that are much different from those obtained from a deterministic setting. During these computational analysis, it is observed that the single-objective model is prone to generate many alternative so- lutions with different characteristics of important quality measures. Motivated by this, a multi-objective framework is developed for this problem in order to have a stronger modeling approach that generates only non-dominated solutions for the selected performance measures. The ε-constraint method is used for scalar- ization of the model. Bi-objective and 3-objective algorithms are presented for detecting all the efficient solutions of a given setting. Unlike the single-objective configuration, the decision makers could be supplied with much richer informa- tion by reporting many non-dominated solutions and allowing them to evaluate the trade-offs based on their preferences.

关键词： Disaster Management Shelter Site Location Discrete Facility Location Stochastic programming Chance-Constraints multiple-objective programming ε-constraint Method

来源：评论

学校读者我要写书评

暂无评论

Train Platforming Problem 11

Train Platforming Problem

引用

11th Symposium on Geoinformatics for Intelligent Transportation (GIS)

作者： Janosikova, L'udmila Krempl, Michal Univ Zilina Fac Management Sci & Informat Dept Math Methods & Operat Res Zilina 01026 Slovakia Tech Univ Ostrava Inst Transport Fac Mech Engn Ostrava 70833 Czech Republic

ISBN: (纸本)9783319114637;9783319114620

The train platforming problem consists in the allocation of passenger trains to platforms in a railway station. One of the important problems a dispatcher has to solve, especially in a large railway station, is to decide, at which platform track an approaching train should arrive. There is a tool helping him in his job called the track occupancy plan. The plan specifies for each arriving or departing train the platform track along with the time slot during which the track will be occupied by the train. This paper deals with a method for computer-aided design of the track occupancy plan. The problem is formulated as a bi-criterion mixed integer programming problem. The first objective is to minimise the deviations of the arrival and departure times proposed by the model from the times specified by the timetable. The second criterion maximises the desirability of the platform tracks to be assigned to the trains. The model is solved using a lexicographic approach and the local branching algorithm. The model was verified by using the real data of Prague main station. Results of the experiments are included.

关键词： Train platforming Scheduling Mixed integer mathematical programming multiple-objective programming

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：