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A multi-objective particle swarm optimization algorithm for business sustainability analysis of small and medium sized enterprises

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ANNALS OF OPERATIONS RESEARCH 2020年第2期293卷 557-586页

作者： Ben Abdelaziz, Fouad Alaya, Houda Dey, Prasanta Kumar NEOMA Business Sch Rouen CampusBlvd Andre Siegfried F-76130 Mont St Aignan France IHE Paris Logist & Innovat Technol Res Ctr Paris France Tunis Business Sch Tunis Tunisia Aston Univ Aston Business Sch Birminghan England

Sustainability is the major issue of small and medium sized enterprises (SMEs) all across the globe. Although SMEs contribute to GDP of any country their negative contribution to environment is also significant. Prior studies on SMEs' sustainability mainly classified into three categories-the correlation between environmental and social practices with economic performance, sustainable supply chain performance measurement, and empirical research on sustainability practices. There is no study that objectively derives the sustainable structure of SMEs through optimal combination of sustainability practices (inputs) and performance (outputs). Therefore, the main objective of this paper is to generate optimal structure of sustainable SMEs by combining neural network and particle swarm algorithm while considering multi-objective framework. The study uses data from 54 SMEs of Normandy in France and 30 SMEs of Midlands in the UK. The data was gathered through questionnaire survey. As we do not have the explicit expression of our objective functions, we train a neural network on our databases in order to enable the generation of value of the different objectives for any profile. We design and run a multi-objective version of particle swarm optimization (MPSO) to generate efficient companies' structures. The weighted sum method is then used for different weights. The comparison of observed data and the results of the PSO analysis facilitates to derive improvement measures for each individual SME.

关键词： Neural network Particle swarm optimization multi-objective programming Sustainability practices and performance

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An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems

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APPLIED MATHEMATICS AND COMPUTATION 2013年第18期219卷 9652-9669页

作者： Mavrotas, George Florios, Kostas Natl Tech Univ Athens Sch Chem Engn Lab Ind & Energy Econ Athens 15780 Greece

Generation (or a posteriori) methods in multi-objective Mathematical programming (MOMP) is the most computationally demanding category among the MOMP approaches. Due to the dramatic increase in computational speed and the improvement of Mathematical programming algorithms the generation methods become all the more attractive among today's decision makers. In the current paper we present the generation method AUGMECON2 which is an improvement of our development, AUGMECON. Although AUGMECON2 is a general purpose method, we will demonstrate that AUGMECON2 is especially suitable for multi-objective Integer programming (MOIP) problems. Specifically, AUGMECON2 is capable of producing the exact Pareto set in MOIP problems by appropriately tuning its running parameters. In this context, we compare the previous and the new version in a series of new and old benchmarks found in the literature. We also compare AUGMECON2's performance in the generation of the exact Pareto sets with established methods and algorithms based on specific MOIP problems (knapsack, set packing) and on published results. Except from other Mathematical programming methods, AUGMECON2 is found to be competitive also with multi-objective Meta-Heuristics (MOMH) in producing adequate approximations of the Pareto set in multi-objective Combinatorial Optimization (MOCO) problems. (C) 2013 Elsevier Inc. All rights reserved.

关键词： multi-objective programming epsilon-Constraint method Exact pareto set

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Interactive approach to bi-level integer multi-objective fractional programming problem

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APPLIED MATHEMATICS AND COMPUTATION 2013年 223卷 17-24页

作者： Emam, O. E. Helwan Univ Fac Comp Sci & Informat Dept Informat Syst Cairo Egypt

This paper proposes an interactive approach for solving bi-level integer multi-objective fractional programming problem. At the first phase of the solution approach, we begin by finding the convex hull of its original set of constraints using the cutting-plane algorithm then the two level decision makers use the Charnes and Cooper transformation to convert the fractional objective functions to equivalent linear functions. At the second phase, the algorithm simplifies the equivalent problem by transforming it into separate multi-objective decision-making problem and solving it by using the e-constraint method. In addition, the theoretical results are illustrated with the help of a numerical example. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Integer programming multi-objective programming Fractional programming Bi-level programming

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Bi-Level multi-objective Optimization Scheduling for Regional Integrated Energy Systems Based on Quantum Evolutionary Algorithm

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ENERGIES 2021年第16期14卷 4740页

作者： Fan, Wen Liu, Qing Wang, Mingyu North China Elect Power Univ Baoding State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China

Integrated energy systems have become an important research topic in the pursuit of sustainable energy development. This paper examines regional integrated energy systems, presents the typical architecture of regional integrated energy systems, and builds an integrated energy system model. Two evaluation indexes are proposed: the integrated energy self-sufficiency rate and the expected energy deficiency index. Based on these evaluation indexes and taking into account the uncertainty of wind power generation, a bi-level optimization model based on meta-heuristic algorithms and multi-objective programming is established to solve the problem of regional integrated energy system planning under different load structures and for multi-period and multi-scenario operation modes. A quantum evolutionary algorithm is combined with genetic algorithms to solve the problem.

关键词： integrated energy system quantum evolutionary algorithm multi-objective programming bi-level model uncertainty

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A multi-objective optimization approach for the selection of overseas oil projects

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 151卷 106977-106977页

作者： Chen, Hao Li, Xi-Yu Lu, Xin-Ru Sheng, Ni Zhou, Wei Geng, Hao-Peng Yu, Shiwei Renmin Univ China Sch Appl Econ Beijing 100872 Peoples R China China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China Macau Univ Sci & Technol Sch Business Macau 999078 Peoples R China Univ Cambridge Energy Policy Res Grp EPRG Cambridge CB2 1AG England

Most good quality overseas oil projects, high in investment returns and abundant in resources, are located in politically unstable regions, where competing objectives present great challenges for investors to make informed decisions. Moreover, most of the existing models are single objective and do not adequately incorporate the unique characteristics of overseas oil investment. To bridge these gaps, this study develops a Non-linear multi-objective Binary Program (NMBP) to optimize the investment portfolios under three competing objectives. A solution algorithm is developed to solve this multiple objective program by integrating Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). NSGA-II searches for the pareto set of optimal investment portfolios and TOPSIS determines the best compromise solution based on the investors' preferences. Finally, China's oil investment in the Belt and Road Initiative countries is taken as a case study to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Overseas oil investment Portfolio choices multi-objective programming NSGA-II TOPSIS Belt and road initiative

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Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study

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ANNALS OF OPERATIONS RESEARCH 2020年第1期289卷 123-152页

作者： Khalilpourazari, Soheyl Soltanzadeh, Shima Weber, Gerhard-Wilhelm Roy, Sankar Kumar Concordia Univ Dept Mech Ind & Aerosp Engn MIAE Montreal PQ Canada Sharif Univ Technol Dept Ind Engn Tehran Iran Poznan Univ Tech Fac Engn Management Ul Strzelecka 11 PL-60965 Poznan Poland Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 W Bengal India Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada Middle East Tech Univ Inst Appl Math TR-06800 Ankara Turkey

In recent years, attention to blood supply chain in disaster circumstances has significantly increased. Disasters, especially earthquakes, have adverse consequences such as destruction, loss of human lives, and undermining the effectiveness of health services. This research considers a six-echelon blood supply chain which consists of donors, blood collection centers (permanent and temporary), regional blood centers, local blood centers, regional hospitals, and local hospitals. For the first time, we considered that helicopters could carry blood from regional hospitals to local hospitals and return injured people that cannot be treated in local hospitals to regional hospitals due to the limited capacity. In addition to the above, different transportations with limited capacities regarded, where the optimal number of required transportations equipment determined after the solution process. This research aims to avoid the worst consequences of a disaster using a neural-learning process to gain from past experiences to meet new challenges. For this aim, this article considers three objective functions that are minimizing total transportation time and cost while minimizing unfulfilled demand. The model implemented based on a real-world case study from the most recent earthquake in the Iran-Iraq border which named the deadliest earthquake of 2017. Based on our results, we learned how to design an efficient blood supply chain that can fulfill hospitals blood demand quickly with the lowest cost using simulation and optimization processes. Moreover, we performed in-depth analyses and provided essential managerial insights at last.

关键词： Blood supply chain Disaster management Humanitarian relief Lexicographic weighted Tchebycheff method Neural learning Operational research multi-objective programming

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Research on the Recovery of Irregular Flights under Uncertain Conditions

Research on the Recovery of Irregular Flights under Uncertai...

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作者： Xiangfei Meng Xiaoyu Lin Hualei Zhao Jing Yang Department of Service Supporting Air Force Command College

This paper mainly studies the problem of irregular flights recovery under uncertain *** on the analysis of the uncertain factors affecting the flight, taking the total delay time and the total cost of flight delay as the objective function, and considering the constraints of flight plan and passenger journey, an uncertain objective programming model is ***, taking OVS airport temporarily closed due to bad weather as an example, the results show that better quality optimization scheme can be obtained by integrating passenger recovery with narrow sense flight recovery stage and implementing integrated recovery.

关键词： Recovery of Irregular Flights Uncertainty Theory multi-objective programming

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A note on solving multi-objective integer indefinite quadratic fractional programs

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ANNALS OF OPERATIONS RESEARCH 2020年第2期289卷 459-462页

作者： Kushwah, Prerna Sharma, Vikas Thapar Inst Engn & Technol Sch Math Patiala Punjab India

In this note we have discussed that a simplex like algorithm to solve a indefinite quadratic fractional programming problem proposed by Mekhilef et al. (Ann Oper Res, 2019. https://***/10.1007/s10479-019-03178-2) fails to find its optimal solution and so it may not generate the actual set of efficient points of the corresponding multi-objective integer indefinite quadratic fractional programs. A counter example in support of this argument is also given.

关键词： multi-objective programming Integer programming Linear programming Quadratic programming Fractional programming Efficient cut Branch and cut

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An integrated approach for reducing spatially coupled water-shortage risks of Beijing-Tianjin-Hebei urban agglomeration in China*

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JOURNAL OF HYDROLOGY 2021年第PartD期603卷 127123-127123页

作者： Cai, Yanpeng Wang, Hao Yue, Wencong Xie, Yulei Liang, Qingtian Guangdong Univ Technol Inst Environm & Ecol Engn Guangdong Prov Key Lab Water Qual Improvement & E Guangzhou 510006 Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzho Guangzhou 511458 Peoples R China Beijing Normal Univ State Key Lab Water Environm Simulat Beijing 100875 Peoples R China Dongguan Univ Technol Res Ctr Ecoenvironm Engn Dongguan 523808 Peoples R China Qingdao Coastal Architectural Design Co Ltd Qingdao 266011 Peoples R China

Growing agricultural and economic activities have brought tremendous pressure to regional water resources management. Considering the interactions of water resources allocation among adjacent regions with connected water systems, it is necessary to pay attention to the unification of socio-economic development and environmental protection between the regions. This is of significance to many agglomeration areas that are composed of multiple large cities such as the Beijing-Tianjin-Hebei urban agglomeration (BTH) in China. In this research, an ensemble approach was proposed to tackle water resources allocation problems under the constraints of water resources to reflect interdependence of the composed three areas, i.e., Beijing, Tianjin and Hebei. The approach included ARIMA (Autoregressive Integrated Moving Average) method, ANN (Artificial Neural Network) modelling, copula functions, and multi-objective chance-constrained programming model. It can 1) reflect the variations characteristic of regional water supply and demands, 2) solve the interactions between multi-regional water resources, and 3) obtain desired water resources allocation strategies under multi- scenario and scale conditions. Then, the approach was applied in typical regions of China, i.e., Beijing-Tianjin-Hebei urban agglomeration. Considering 27 scenarios of the nexus water shortage risk, multi-objective joint opportunity constraint programming model aimed at maximizing economic benefits and ecological benefits, in combination with economic, agricultural, water resources and environmental constraints. As the optimal strategies indicated, the maximum GDP in BTH would remain at 8514 billion yuan. The minimum amount of water allocation and wastewater discharge would be 24-26 billion m3 and 2100-2101 million m3 respectively.

关键词： Copula function multi-objective programming Nexus risks Beijing-Tianjin-Hebei urban agglomeration

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Smart Optimization Design of Urban Group Chain Travel Routes 20

Smart Optimization Design of Urban Group Chain Travel Routes

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20th COTA International Conference of Transportation Professionals (CICTP) - Advanced Transportation, Enhanced Connection

作者： Zhang, Wen Hu, Xiaowei Zeng, Yongquan Harbin Inst Technol Sch Transportat Sci & Engn Huanghe Rd Harbin Heilongjiang Peoples R China

ISBN: (数字)9780784482933

ISBN: (纸本)9780784482933

With the improvement of people's living standards and the desire to pursue quality of travel, the number of people who choose self-guided itineraries is increasing sharply. However, the problem is that planning routes during the trip are exhausting, and the efficiency of transportation. In response to this and considering the psychology of people making the most of their holidays along with current boom in chain travel, the paper proposes a frame and an algorithm that aims to optimize the travel route. This paper studies route planning for chain travel of city groups within a specific range based on cluster analysis. The algorithm refers to the solution of TSP, knowledge in Graph Theory, and is suitable for optimizing travel itineraries for multiple cities for several days, characterized by providing users with time and money-saving options. It reduces waste on the road, waiting times for travelers, and provides convenience for self-guided itineraries.

关键词： City groups Chain travel Transportation big data multi-objective programming TSP

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