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Modelling and solving the airport slot-scheduling problem with multi-objective, multi-level considerations

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 124卷 102914-102914页

作者： Katsigiannis, Fotios A. Zografos, Konstantinos G. Fairbrother, Jamie Univ Lancaster Ctr Transport & Logist CENT Dept Management Sci Management Sch Lancaster LA1 4YX England

In overly congested airports requests for landing and take-off slots are allocated according to the IATA World Scheduling Guidelines (WSG). A central concept of these guidelines is the prioritization of the satisfaction of the requested slots according to a hierarchy that recognizes historic usage rights of slots. A number of criteria have been proposed in the literature to optimize airport slot allocation decisions. multi-objective programming models have been proposed to investigate the trade-offs of the slot allocation objectives for the same level of the slot hierarchy. However, the literature currently lacks models that can study in a systematic way the trade-offs among the scheduling objectives across all levels of the hierarchy and the airport schedule as a whole. To close the existing literature gap, we are proposing a new tri-objective slot allocation model (TOSAM) that considers total schedule displacement, maximum schedule displacement and demand-based fairness, and we introduce a multi-level, multi-objective algorithm to solve it. We are using real world slot request and airport capacity data to demonstrate the applicability of the proposed approach. Our computational results suggest that the systematic consideration of the interactions among the objectives of the different levels of the slot hierarchy, results to improved schedule-wide slot scheduling performance. In particular, we found that small sacrifices made for the attainment of the scheduling objectives of the upper echelons of the slot hierarchy, result in significant improvements of the schedule-wide objectives.

关键词： Airport slot allocation multi-objective programming Airport demand management Airport scheduling multi-level decision-making

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A modified TOPSIS approach for solving stochastic fuzzy multi-level multi-objective fractional decision making problem

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OPSEARCH 2020年第4期57卷 1374-1403页

作者： El Sayed, M. A. Baky, Ibrahim A. Singh, Pitam Benha Univ Dept Basic Engn Sci Fac Engn Elqalyoubia Egypt Tabuk Univ Fac Sci Dept Math Tabuk Saudi Arabia Motilal Nehru Natl Inst Technol Dept Math Allahabad Prayagraj 211004 India

This paper presents a new modified technique for order preference by similarity to ideal solution (M-TOPSIS) approach for unraveling stochastic fuzzy multi-level multi-objective fractional decision making problem (ML-MOFDM) problem. In the proposed model the coefficients and the scalars of the fractional objectives have a fuzzy nature. The right-hand sides are stochastic parameters also, both of the left-hand side coefficients and the tolerance measures are fuzzy kind. In this manner, the deterministic-crisp ML-MOFDM model of stochastic fuzzy ML-MOFDM can be gotten utilizing chance constrained strategy with predominance plausibility criteria and the alpha-cut methodology. In literature, almost all works on multi-level fractional programming are the crisp version, in which they convert the fractional functions into a linear one using a first order Taylor series which causes rounding off error. The proposed M-TOPSIS approach presents a new method for solving such problem without approximating or changing the nature of the problem. An algorithm to clear up the M-TOPSIS approach, just as illustrative numerical model is displayed.

关键词： multi-level optimization multi-objective programming TOPSIS Fractional programming Chance constrained programming Fuzzy sets

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On the spectrum of achievable targets in cross-efficiency evaluation and the associated secondary goal models

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 177卷 114927-114927页

作者： Davtalab-Olyaie, Mostafa Ghandi, Fatemeh Asgharian, Masoud Univ Kashan Fac Math Sci Dept Appl Math Kashan *** Iran McGill Univ Dept Math & Stat Burnside HallRoom 1224805 Sherbrooke St West Montreal PQ H3A 0B9 Canada

The cross-efficiency (CE) evaluation method was introduced to improve the discriminatory power of DEA and eliminate unrealistic DEA weighting schemes. One important issue in CE evaluation is the non-uniqueness of the CE scores. Several secondary goal models based on different targets for cross-efficiencies (CEs) of each DMU with respect to other DMUs were proposed to address this issue. However, the suggested targets, fixed value 1 and the CCR efficiency score, are not achievable for all CEs. Moreover, the proposed secondary goal models based on these targets are sensitive to outlier DMUs, and may generate unrealistic CE scores. In this manuscript, we prove that the spectrum of achievable targets of CEs can be obtained using the most resonated appreciative (MRA) model, proposed by Oral et al. (2015), and the least resonated appreciative (LRA) model that we introduce. To this end, we propose a general secondary goal model using multi-objective programming and show that the CEs generated using MRA (LRA) model for each DMU is greater (less) than the corresponding CEs obtained by any other model that can be derived from the proposed benevolent (aggressive) general model. Using this achievable spectrum, we then propose several benevolent, aggressive and neutral secondary goal, and a weighted average CE evaluation model. Using two real examples, we compare the results of the proposed CE methods with those obtained from several other CE methods. Our data analyses indicate that our proposed methods are less sensitive to outliers, less biased towards 1, has better discriminatory power and can identify pseudo-efficient DMUs.

关键词： Data envelopment analysis Cross-efficiency evaluation multi-objective programming Secondary goal model

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Conceptualising a supply and demand resilience methodology: A hybrid DEMATEL-TOPSIS-possibilistic multi-objective optimization approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 160卷 107589-107589页

作者： Mohammed, A. Naghshineh, B. Spiegler, V Carvalho, H. Muscat Univ Fac Transport & Logist Bldg 142 Muscat Oman Univ Kent Kent Business Sch Dept Analyt Operat & Syst Canterbury Kent England Univ NOVA Lisboa Fac Sci & Technol UNIDEMI Dept Mech & Ind Engn P-2829516 Caparica Portugal

Due to the growing globalisation and strategic sourcing, supply chains (SCs) are confronted with potential dis-ruptions. Companies need to make further efforts and investments to improve their supply chain resilience (SCR) in becoming more prepared to minimise disruption risks. Sourcing is one of the main, strategic, key factors towards SCR. Also, organisations require resilience in demand fulfilment to handle volatile marketplaces. This paper presents a methodology towards SCR to both supply and demand variations motivated by a real case study of a manufacturing company that works to improve its SCR. To this end, a hybrid integrated multi-attribute decision making-possibilistic bi-objective programming model (MADM-PBOPM) was developed. First, a new framework presenting pillars to assess suppliers' resilience was developed based on a thorough literature review and decision makers' input. Then, a DEMATEL-TOPSIS approach was proposed to quantify existing suppliers' resilience and assess its performance. It also helped in categorising resilience pillars (RPs) as causes and effects. Thereafter, the obtained weights of suppliers and pillars were integrated into the developed PBOPM. The latter helps the purchasing team to (1) order materials from suppliers based on their resilience and performance ef-ficiency;and (2) elevate the company's resilience to uncertain demands fulfilment. Therefore, the developed methodology can potentially be used by the purchasing teams to build up SCs that are resilient to supply disruption and demand uncertainty. This MADM-PBOPM model was validated as part of the case study inves-tigation. Furthermore, the suppliers' assessment output was validated by using two sensitivity analysis ap-proaches including criteria weight variation and other multi-attribute decision making (MADM) approaches.

关键词： COVID-19 disruption Resilient sourcing Supply chain resilience multi-objective programming MCDM

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Optimization of water resource dispatching for Huanghua Port under uncertain water usage scenario

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 751卷 141597-141597页

作者： Fang, Yong Wang, Yanjiao Liu, Qiang Luo, Kan Liu, Zhonghua Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China State Energy Grp Huanghua Port Co Ltd Informat Ctr Huanghua 061113 Hebei Peoples R China

In recent years, large-scale coal bulk cargo ports have been vigorously promoting the green and intelligent construction, it is an important problem for them to manage water resources scientifically and effectively to realize energy conservation and environmental protection under uncertain circumstances. Taking Huanghua Port of Shenhua Group in China as an example, firstly, through a systematic review of the water resource dispatching infrastructures and production operations, the four-level water resource dispatching framework of Huanghua Port was obtained. Secondly, an uncertain multi-objective programming model is constructed to comprehensively consider the cost of water purchase, the energy consumption of water diversion, and the uncertainty of water usage. Then, an algorithm is designed according to the characteristics of the model. Finally, the applicability and effectiveness of the water resource dispatching framework, optimization model, and solution algorithm are verified by the analysis of 8 typical production water scenarios. This study not only provides Huanghua Port with a water resource dispatching optimization solution but also provides a decision-making reference for the green and intelligent transformation and upgrading of other large-scale coal bulk ports. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Water resource dispatching Uncertainty multi-objective programming Decision-making support Huanghua Port

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Developing a two-stage model for a sustainable closed-loop supply chain with pricing and advertising decisions

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JOURNAL OF CLEANER PRODUCTION 2021年 309卷 127165-127165页

作者： Khorshidvand, Behrooz Soleimani, Hamed Sibdari, Soheil Esfahani, Mir Mehdi Seyyed Islamic Azad Univ Qazvin Branch Fac Ind & Mech Engn Qazvin Iran Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia Univ Massachusetts Charlton Coll Business Dartmouth MA USA Amirkabir Univ Technol Fac Ind Engn & Management Syst Tehran Polytech Tehran Iran

Closed-Loop Supply Chain (CLSC) has become a critical problem due to its effects on various factors including economic motivations, environmental concerns, and social impacts. Moreover, there are coordination tools, such as pricing and advertising, which impact its performance. In this paper, we offer a two-stage approach to model and solve a sustainable CLSC, taking into account pricing, green quality, and advertising. In the first stage, optimal decisions on pricing, greening, and advertising are made, while in the second stage, a fuzzy multiobjective Mixed Integer Linear programming (MILP) model is used to maximize the total profit, reduce CO2 emissions, and improve social impacts. Suitable solution methods are introduced according to the scale of the problem. For small-scale instances, an augmented epsilon-constraint method is used to solve the problem. For largescale instances, approximations are required, and a Lagrangian relaxation algorithm solves the problem in polynomial time. The performance of the proposed model is evaluated through various numerical examples. The results illustrate the applicability and efficiency of the model, while confirming significant improvements in sustainable objectives under optimal pricing, green quality, and advertising. Besides, the proposed Lagrangian relaxation method significantly reduces the computational time for large-scale instances, with only a 2.308% deviation from the optimal results.

关键词： Sustainable closed-loop supply chain multi-objective programming Supply chain pricing Augmented epsilon-constraint Lagrangian relaxation CO2 emissions

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Research on the Recovery of Irregular Flights under Uncertain Conditions

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World Journal of Engineering and Technology 2021年第4期9卷 755-764页

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

This paper mainly studies the problem of irregular flights recovery under uncertain conditions. Based 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 constructed. Finally, 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 TWO-STAGE BI-LEVEL DECISION APPROACH FOR GREEN SUPPLY CHAIN DESIGN AND CLOUD VIRTUAL MACHINE PLACEMENT

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2021年第5期28卷 529-540页

作者： Kao, Han-Ying Lan, Zih-Heng Huang, Chien-Hao Huang, Chia-Hui Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Hualien Taiwan Natl Taipei Univ Business Dept Business Adm Taipei Taiwan

This study aims to establish an integrated model of green supply chain network design in cloud computing environments. In this study, two levels are defined: supply chain design and virtual machine allocation. In the proposed two-stage model, the supply chain network design decision (leader level) is solved in the first stage by considering three objectives . minimizing the total costs, minimizing the carbon emission, and maximizing the satisfaction level of service (minimizing the transportation lead time). In the second stage, considering the information demand from the leader level, the placement of virtual machines to the supply chain servers (follower level) is established by minimization of energy consumption and maximization of the effectiveness (minimization of the power wastage) of the physical machine. A case study and experiments are conducted to verify the performance of the proposed methods.

关键词： green supply chain design virtual machine placement bi-level decision modes two-stage decision approaches multi-objective programming

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multi-period portfolio optimization using coherent fuzzy numbers in a credibilistic environment

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 167卷 114135-114135页

作者： Gupta, Pankaj Mehlawat, Mukesh Kumar Khan, Ahmad Zaman Univ Delhi Dept Operat Res Delhi 110007 India

In this paper, we use an extension of fuzzy numbers, called coherent fuzzy numbers, to model asset returns and an investor's perception of the stock market (pessimistic, optimistic, or neutral) simultaneously. Two multi-period multi-objective portfolio optimization models are formulated using mean absolute semi-deviation and Conditional Value-at-Risk (CVaR) as risk measures, respectively. We aim to provide more flexibility to the investor in specifying the risk tolerance and devise optimum investment plans for different investment horizons. The proposed models also incorporate bound, cardinality, and skewness constraints for each investment period to capture various stock market scenarios. A real-coded genetic algorithm is employed to solve the resultant models. Two real-life case studies involving 20 assets of the National Stock Exchange (NSE), India, and another involving 50 assets listed in the S&P 500 and NASDAQ-100 indexes have been provided to illustrate the efficacy and advantages of the models. An in-sample and out-of-sample analysis have been done for both the models to analyze the performance in the real-world scenario. The conclusion drawn from the analysis strongly emphasizes on accurately assessing the current stock market prospects, i.e., adopting the right attitude (pessimistic, optimistic, or neutral), is of paramount importance and must be included in the portfolio optimization problem.

关键词： Fuzzy multi-period portfolio optimization multi-objective programming Fuzzy set theory Coherent fuzzy numbers Conditional Value-at-Risk Real-coded genetic algorithm

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Stochastic energy management in a renewable energy-based microgrid considering demand response program

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 129卷 106791-106791页

作者： Hajiamoosha, Pouria Rastgou, Abdollah Bahramara, Salah Sadati, S. Muhammad Bagher Islamic Azad Univ Dept Elect Engn Kermanshah Branch Kermanshah Iran Islamic Azad Univ Dept Elect Engn Sanandaj Branch Sanandaj Iran

Climate changes increase concerns about global warming caused by greenhouse gases and have also increased the focus and implementation of renewable energy sources (RESs) planning. One of the important RESs is tidal energy or tidal power, which is a form of hydropower that converts the energy obtained from tides into the electrical power. Although tidal power is still not widely used, this energy resource has the potential for the future electricity generation. This paper addresses the stochastic energy management in a microgrid considering RESs such as solar, wind and tidal sources in the presence of the demand response program and storage devices. The uncertainty of the RESs, demand, and electricity price is handled by Monte Carlo simulation (MCS). The model is a linear multi-objective optimization which the first objective aims to reduce the cost and the second aims to reduce the emission. Augmented ?-constraint approach is applied to solve the problem in the CPLEX/ GAMS software environment. The interactive fuzzy decision-making is applied to choose the best answer among the Pareto answers according to the planner criteria.

关键词： multi-objective programming Tidal energy Renewable energy sources Augmented ?-constraint method Pollution emission

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