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An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP)

APPLIED MATHEMATICS AND COMPUTATION 2008年第1期200卷 167-177页

作者： Chaharsooghi, S. K. Kermani, Amir H. Meimand Department of Industrial Engineering School of Engineering Tarbiat Modares University Iran

The multi-objective resource allocation problem (MORAP) addresses the important issue which seeks to find the expected objectives by allocating the limited amount of resource to various activates. Resources may be manpower, assets, raw material or anything else in limited supply which can be used to accomplish the goals. The goals may be objectives (i.e., minimizing costs, or maximizing efficiency) usually driven by specific future needs. In this paper, in order to obtain a set of Pareto solution efficiently, we proposed a modified version of ant colony optimization (ACO), in this algorithm we try to increase the efficiency of algorithm by increasing the learning of ants. Effectiveness and efficiency of proposed algorithm was validated by comparing the result of ACO with hybrid genetic algorithm (hGA) which was applied to MORAP later. (C) 2007 Elsevier Inc. All rights reserved.

关键词： ant colony optimization multi-objective optimization model multi-objective resources allocation problem (MORAP)

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Optimal Size of Auxiliary Storage Reservoir for Rain Water Harvesting and Better Crop Planning in a Minor Irrigation Project

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WATER RESOURCES MANAGEMENT 2009年第2期23卷 265-288页

作者： Mishra, A. Adhikary, A. K. Panda, S. N. Water Technol Ctr Eastern Reg ICAR Bhubaneswar 751023 Orissa India Indian Inst Technol Dept Agr & Food Engn Kharagpur 721302 W Bengal India

The scope and feasibility of auxiliary storage reservoir in the outlet command of a flow based minor irrigation project was studied to overcome the inadequate irrigation water availability during the dry season. A multi objective optimization model was developed to determine the optimal size of auxiliary storage reservoir and optimal cropping pattern. Assuming that about 50% main reservoir capacity water will be available for irrigating dry season crops and fixing the first priority level of the objective function as maximization of net seasonal benefit and maximization of cropped area, the optimal surface area for auxiliary storage reservoir as the percentage of the command area was obtained as 17.40% and 10.92%, respectively. The performance of the minor irrigation project significantly increased due to provision of auxiliary storage reservoir. The economic analysis also revealed that the intervention is economically viable.

关键词： Minor irrigation project Auxiliary storage reservoir multi-objective optimization model Hydraulic performance Economic analysis

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multi-objective based economic operation and environmental performance of PV-based large industrial consumer

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SOLAR ENERGY 2017年 157卷 227-235页

作者： Rezaeipour, Roshanak Zahedi, Ahmad Islamic Azad Univ Tabriz Branch Dept Elect Engn Tabriz Iran James Cook Univ Dept Elect & Comp Syst Engn Townsville Qld Australia

Provision of electrical energy for consumers with different consumption levels is not responsibility of power plants anymore. In other words, distributed energy resources are integrated to help consumers supply their own demands by themselves. Also, utilization of distributed energy resources including renewable ones would be a great step for satisfaction of environmental concerns imposed by environment friendly organizations. In this paper, a multi-objective model has been proposed to optimize economic operation and environmental performance of a large consumer in the presence of DRP. Pareto front set is achieved through solving the proposed model by C-constraint method and the best solution amid the obtained solutions is selected by max-min fuzzy satisfying technique. Positive effects of DRP has been also used for cost reduction and emission mitigation of large consumer. The proposed problem is modeled using a mixed-integer linear program and solved by GAMS software. Several case studies have been simulated and the corresponding results have been presented to confirm effectiveness of proposed techniques. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization model Large electricity consumer epsilon-constraint method and fuzzy satisfying approach Demand response program (DRP)

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multi-objective optimization of train routing problem combined with train scheduling on a high-speed railway network

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2014年第Jul.期44卷 1-20页

作者： Sun, Yahua Cao, Chengxuan Wu, Chao Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

Based on train scheduling, this paper puts forward a multi-objective optimization model for train routing on high-speed railway network, which can offer an important reference for train plan to provide a better service. The model does not only consider the average travel time of trains, but also take the energy consumption and the user satisfaction into account. Based on this model, an improved GA is designed to solve the train routing problem. The simulation results demonstrate that the accurate algorithm is suitable for a small-scale network, while the improved genetic algorithm based on train control (GATC) applies to a large-scale network. Finally, a sensitivity analysis of the parameters is performed to obtain the ideal parameters;a perturbation analysis shows that the proposed method can quickly handle the train disturbance. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Train routing problem Train scheduling problem multi-objective optimization model Genetic algorithm

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Incentive-aware virtual machine scheduling in cloud computing

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JOURNAL OF SUPERCOMPUTING 2018年第7期74卷 3016-3038页

作者： Xu, Heyang Liu, Yang Wei, Wei Zhang, Wenqiang Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Henan Peoples R China

As cloud computing is a market-oriented utility, optimal virtual machine (VM) scheduling in cloud computing should take into account the incentives for both cloud users and the cloud provider. However, most of existing studies on VM scheduling only consider the incentive for one party, i.e., either the cloud users or the cloud provider. Very few related studies consider the incentives for both parties, in which the cost, one of the most attractive incentives for cloud users, is not well addressed. In this paper, we investigate the problem of VM scheduling in cloud computing by optimizing the incentives for both parties. The problem is formulated as a multi-objective optimization model, i.e., maximizing the successful execution rate of VM requests and minimizing the combined cost (incentives for cloud users), and minimizing the fairness deviation of profits (incentive for the cloud provider). The proposed multi-objective optimization model can offer sufficient incentives for the two parties to stay and play in the cloud and keep the cloud system sustainable. A heuristic-based scheduling algorithm, called cost-greedy dynamic price scheduling, is then developed to optimize the incentives for both parties. Experimental results show that, compared with some popular algorithms, the developed algorithm can achieve higher successful execution rate, lower execution cost, smaller fairness deviation and most important, higher degree of user satisfaction in most cases.

关键词： Cloud computing Virtual machine scheduling Incentives multi-objective optimization model User satisfaction

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Analyzing the Impacts of Climate Change on Hydro-Environmental Conflict-Resolution Management

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WATER RESOURCES MANAGEMENT 2019年第4期33卷 1591-1607页

作者： Xu, Yuni Fu, Xiang Chu, Xuefeng Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Hubei Peoples R China North Dakota State Univ Dept Civil & Environm Engn Dept 2470 POB 6050 Fargo ND 58108 USA

Conflict-resolution methods have been applied to water resources management to balance conflicting interests of stakeholders. Due to the climate change impacts on hydrologic processes, the strategy selections of conflict-resolution methods can be influenced, resulting in different selection rules for historical and future periods. This study aims to quantify the impacts of climate change on the strategy-selection rules of the conflict-resolution methods for better long-term strategic decision-making. The methodology of this study consists of climatic, hydrological, environmental and multi-objective optimization models, two fuzzy social choice methods (FSCMs) and four game-theoretical bargaining methods (GTBMs). The hydro-environmental conflict-resolution management in the Yangtze River of China is selected as the case study. The results show that the strategy selection of GTBMs is more stable and results in a better balance between hydropower and environmental objectives, compared to that of FSCMs. Moreover, considering climate change, under the appropriate environmental flow pattern, the stabilities of the strategy selections of FSCMs and GTBMs are slightly influenced, and the average satisfied degrees of both objectives obtained by FSCMs and GTBMs in the future period (2021-2080) are lower than those in the base period (1950-2012). The findings from this study provide guidance for hydro-environmental conflict-resolution management from a sustainable development perspective.

关键词： Conflict-resolution method multi-objective optimization model Climate change Environmental protection Hydropower generation

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Allocation of fresh water recourses in China with nested probabilistic-numerical linguistic information in multi-objective optimization

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KNOWLEDGE-BASED SYSTEMS 2020年 188卷 105014-105014页

作者： Wang, Xinxin Xu, Zeshui Gou, Xunjie Sichuan Univ Business Sch State Key Lab Hydraul & Mt River Engn Chengdu 610064 Peoples R China

Fresh water resources are made of conventional water resources (CWRs) and unconventional water resources (UWRs), and they are one of the important natural resources that cannot be replaced. The purpose of this paper is to predict and allocate China's fresh water resources in 2025 under complex environment. According to historical data, we first conduct data reprocessing including data collection, data prediction and discussion. In order to achieve an appropriate tradeoff between ecological environment and economic development, a multi-objective optimization model is constructed based on market mechanism. Specifically, we establish two objective functions: one is to minimize the total cost, the other is to minimize the whole amount of CWRs, and then, we optimize the parameters in the model based on nested probabilistic-numerical linguistic information. After that, the solution and the strategy of fresh water resources are obtained, and the sustainable development and risk response by adjusting and adding the parameters are further analyzed. The results show that the model is effective, feasible and applicable. Finally, we make some discussions about the strengths and weakness of the model, and the suggestions for the fresh water resources in China. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Fresh water resources Conventional water resources Unconventional water resources Nested probabilistic-numerical linguistic term information multi-objective optimization model

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Matching patients and healthcare service providers: a novel two-stage method based on knowledge rules and OWA-NSGA-II algorithm

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JOURNAL OF COMBINATORIAL optimization 2019年第1期37卷 221-247页

作者： Chen, Xi Zhao, Liu Liang, Haiming Lai, Kin Keung Xidian Univ Sch Econ & Management Xian 710071 Shaanxi Peoples R China City Univ Hong Kong Dept Management Sci Hong Kong 999077 Peoples R China

The matching between patients and healthcare service providers is an important issue in healthcare. Searching an appropriate matching for both patients and healthcare service providers can not only facilitate efficiency of diagnosis and treatment, but also make both of them more satisfied with the matching results. This paper proposes a two-stage method for searching the optimal matching between the patients and healthcare service providers. In the first stage, where a large number of patients are involved in the matching problem, the knowledge rules are proposed to classify the patients with similar categories of disease into the same group. In the second stage, patients in each group are compared in terms of aspiration levels and the evaluation levels of the healthcare service providers, and satisfaction degrees of patients are calculated. Then, a multi-objective optimization model is built by maximizing the satisfaction degrees of patients, maximizing the number of treated patients and balancing the workload of healthcare service providers. To solve this model, the ordinal weighting average non-dominated sorting genetic algorithm II (OWA-NSGA-II) is developed. Furthermore, a practical example of service in rehabilitation therapy is used to illustrate the feasibility of the proposed method. Additionally, several simulation experiments in different large scale problems are conducted to test the performance of OWA-NSGA-II. Simulation results show that the proposed NSGA-II algorithm has better convergence in the large scale problem, yields a more stable distribution of non-dominated solutions, as well as non-dominated solutions much faster.

关键词： Matching patients and healthcare service providers Knowledge rules multi-objective optimization model Ordinal weighting average non-dominated sorting genetic algorithm II (OWA-NSGA-II)

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multi-echelon, multi-period Supply Chain Master Planning in the Food Process Industry: A Sustainability Concept

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PROCESS INTEGRATION AND optimization FOR SUSTAINABILITY 2022年第2期6卷 497-512页

作者： David, Dimkpa U. Aikhuele, Daniel O. Ughehe, Prosper O. Tamuno, Eli M. Univ Port Harcourt Fac Engn Sch Grad Studies Dept Mech Engn Port Harcourt Nigeria

This work presents a multi-echelon, multi-period supply chain master planning in the process industry;with a concept of sustainability, carried out on a real-life case study located in Port Harcourt in the South-South region of Nigeria. A supply chain master panning model (SCMPM) takes into account the most effective and efficient method to fulfil customer order and demand over a mid-term planning horizon;it also takes of bottlenecks by assigning demands to production. A mixed integer, multi-objective optimization deterministic model was formulated which aimed at minimizing the overall system costs, emissions, social factors and customers order fulfilment in the supply chain. The Cplex solver of GAMS, with branch and cut algorithm, was used on an Intel processor with 2.30 GHz speed to run the model. The results obtained from the model with real industry data from the case study gave the objective function values as 324, 854, 340 Naira for economic factor, 26.455 tonnes of CO2 emission (170.50 g CO2), for environmental factor, while those of the social factor and allowable backorder gave 10.433 and 105 units of product, respectively. The results showed the feasibility of the formulated models and the importance to have an effective master planning supply chain decision model in place. A significant correlation between four conflicting objectives was achieved by using the weighted-sum approach and the analytic hierarchy process (AHP) thereby converting them to a single linear optimization model.

关键词： Supply Chain Master Planning Sustainability multi-objective optimization model Process Industry Weighted Sum Analytic Hierarchy Process

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A novel hybrid entropy-clustering approach for optimal placement of pressure sensors for leakage detection in water distribution systems under uncertainty

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URBAN WATER JOURNAL 2020年第3期17卷 185-198页

作者： Taravatrooy, Narges Nikoo, Mohammad Reza Hobbi, Salma Sadegh, Mojtaba Izady, Azizallah Shiraz Univ Sch Engn Dept Civil & Environm Engn Shiraz Iran Boise State Univ Dept Civil Engn Boise ID 83725 USA Sultan Qaboos Univ Water Res Ctr Muscat Oman

This study presents a novel hybrid entropy-clustering framework for placing pressure sensors in water distribution systems (WDS) to detect leakage. Leakages are simulated at all potential nodes of WDS, and then potential pressure sensors (PPS) in WDS are classified using a K-means clustering algorithm. Transinformation entropy for each potential pair of PPS was also computed, which in turn helped to reduce redundant information. PPS locations were subsequently optimized using a multi-objective optimization model. Furthermore, to capture the sensitivity of sensors' layout in WDS to sensor error, a fuzzy-based analysis is integrated with a multi-objective optimization model. Finally, the best compromise solution of PPS placement in each category was selected using an ELECTRE multi-criteria decision making model. Reducing redundant information of pressure sensors based on information theory and choosing the best possible solution based on the ELECTRE model are the main novelties of this study. Results of C-Town WDS attest to the proposed framework' efficiency.

关键词： Water distribution system leakage transinformation entropy k-means clustering multi-objective optimization model fuzzy-set theory ELECTRE decision making model

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