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Model and algorithm of routes planning for emergency relief distribution in disaster management with disaster information update

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2019年第1期38卷 208-223页

作者： Zhu, Jianming Liu, Shuyue Ghosh, Smita Univ Chinese Acad Sci Sch Engn Sci 19A Yuquan Rd Beijing Peoples R China Univ Texas Dallas Dept Comp Sci Richardson TX 75083 USA

Emergency relief distribution plays a vital role during disaster responding. This paper studies routes selection and transportation time moment selection for emergency relief distribution along with disaster information updating. This paper derives a new definition for route reliability, which is a combination of multiple routes according to three factors: reliability of each edge, the number of dissimilar detour paths, and dissimilarity of multiple routes. Transportation time moment is determined according to disaster information updating accuracy rate. A multi-objective mathematical programming is formulated for route planning problem. The objective function consists route reliability and information accuracy rate. Then we present an ant colony optimization algorithm to solve this problem. Disaster information update process is clarified, as well as propose a new way to evaluate multiple routes reliability and applying an improved ant colony optimization algorithm. Finally, an earthquake around Ludian in Yunnan Provence is considered as a disaster scenario to analyze our model and algorithm, include the optimal solution, parameters' impact on objective function, and performance of our algorithms.

关键词： multiple routes reliability Relief distribution multi-objective programming Ant colony algorithm Information updates

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A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 92卷 27-38页

作者： Babbar, Chirag Amin, Saman Hassanzadeh Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada

With the emerging trend of green supply chain management, supplier selection and order allocation based on green criteria have become very important in this competitive world. During the selection process of the eligible suppliers, qualitative as well as quantitative factors should be considered. In this paper, a novel mathematical model is developed to select a set of suppliers, and assign the order quantity. Due to the importance of environmental concerns, both qualitative and quantitative environmental criteria are taken into account in this research. The proposed model comprises two phases namely a two-stage QFD, and a stochastic multi-objective mathematical model. The stochastic (scenario) approach helps to manage the uncertainty in the order allocation process. Furthermore, trapezoidal fuzzy numbers are utilized to handle the vagueness in human thoughts. The application of the proposed model is shown in beverages industry. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Supplier selection Order allocation Fuzzy sets theory Quality Function Deployment (QFD) multi-objective programming

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Evaluation of the performance of the multi-objective scalarization methods for the aircraft sequencing and scheduling problem using multi-criteria decision-making

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2023年第4期95卷 501-511页

作者： Donmez, Kadir Samsun Univ Dept Air Traff Control Samsun Turkey

Purpose This study aims to evaluate the performance of the most popular multi-objective programming scalarization methods in the literature for the aircraft sequencing and scheduling problem (ASSP). These methods are the weighted sum method, weighted goal programming, the epsilon-constraint method, the elastic constraint method, weighted Tchebycheff and augmented weighted Tchebycheff. Design/methodology/approach First, the ASSP for a single runway case was modeled using mixed-integer programming considering the safety and operational constraints and the objectives of the minimization of total delay and total flight time for a sample airport. The objectives were then combined by using the multi-objective programming scalarization methods and various expected times of arrival-departure samples were run for the mathematical models. Finally, the methods were evaluated in terms of the number of nondominated solutions, superior nondominated solution and the average solution time using the Measurement of Alternatives and Ranking according to Compromise Solution method, which is a popular multi-criteria decision-making method. Findings Augmented Weighted Tchebycheff was found to be the most effective approach to ASSP in terms of the evaluation criteria followed by Weighted Tchebycheff and then weighted sum method. Practical implications The methodology presented in this study could provide more efficient air traffic management in terminal maneuvering areas when multiple objectives need to be optimized. Originality/value Although there are studies including the comparison of several scalarization methods for other problems, the comparison of the methods for ASSP has not yet been handled in the literature. As there are several stakeholders in the air traffic system, ASSP includes several objectives, and as a result, this problem can benefit from analyses using this comparison.

关键词： Aircraft sequencing and scheduling problem multi-objective scalarization methods multi-criteria decision-making Mixed-integer programming multi-objective programming

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An integrated fuzzy-grey relational analysis approach to portfolio optimization

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APPLIED INTELLIGENCE 2023年第4期53卷 3804-3835页

作者： Mehlawat, Mukesh Kumar Gupta, Pankaj Khan, Ahmad Zaman Univ Delhi Dept Operat Res New Delhi 110007 India Flat 01 Kamayani Kunj Apartment Delhi 110092 India

This paper combines two approaches (Fuzzy set theory and Grey Relational Analysis) for modelling an investor's imprecise linguistic expectations and the uncertain returns of assets. We propose a novel maximization-type risk measure capable of incorporating the investor's individual preferences. The investor provides the expectations of what is considered the "ideal" return from the portfolio. We use Credibility theory to capture the investors' subjective and imprecise expectations in a precise mathematical form. We construct a portfolio return sequence using the assets' actual return data and an ideal sequence based on investors' preferences. Subsequently, we calculate the Grey similitude and the closeness incidence degree between the two sequences. The closer the portfolio return is to the ideal return, the better. In this manner, we develop a new risk measure that can quantify an investor's perception of risk. This measure is intuitive and easy to calculate. It does not involve estimating many parameters, something which would increase the estimation risk. We use a genetic algorithm to solve the resulting portfolio optimization model. We illustrate this method with two case studies: (i) a case study of 100 assets of the U.S. stock market's NASDAQ-100 index and (ii) a case study of 50 assets of the Indian stock market's NIFTY-50 index. We comprehensively analyze the model's out-of-sample performance and discuss its implications. The portfolios obtained using the proposed approach exhibit healthy growth outside the in-sample period. We also compare the out-of-sample performance of the proposed model with several approaches in the literature to establish its superiority.

关键词： Portfolio optimization Credibility theory Grey incidence analysis multi-objective programming Fuzzy set theory Genetic algorithm

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Fuzzy stochastic price scenario based portfolio selection and its application to BSE using genetic algorithm

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APPLIED SOFT COMPUTING 2018年 62卷 867-891页

作者： Dutta, S. Biswal, M. P. Acharya, S. Mishra, Rajashree KILT Univ Sch Appl Sci Dept Math Bhubaneswar Orissa India Indian Inst Technol Dept Math Kharagpur W Bengal India

This paper is concerned with portfolio selection problem using a fuzzy stochastic price scenario. In this scenario, a ratio factor (k) is calculated from the historical data to generated the future price of the stocks of Bombay Stock Exchange. The ratio factor k of different stocks are treated as a fuzzy numbers, which in turn gives future fuzzy prices of the stocks. Returns on the stocks are calculated from the future price of the stocks. Rejection of the assets are done based on returns calculated from the worst case scenario. If the returns of an asset exceed the investor's risk tolerance then the asset are not included in the portfolio. The definition of capital budget has been reformed to include the transaction cost with the capital budget. This process is implemented in two stage multi-objective fuzzy probabilistic programming problem which is then solved using a fuzzy genetic algorithm to obtain maximum short term and long term returns. A case study of Bombay Stock Exchange is provided to illustrate the above model. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Fuzzy portfolio selection Stochastic process Genetic algorithm Fuzzy stochastic programming Two stage programming multi-objective programming

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multi-reservoir design using Pontryagin principle

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ADVANCES IN WATER RESOURCES 2002年第7期25卷 829-839页

作者： Mousavi, H Ramamurthy, AS Seattle Publ Util Water Resource Planning Div Seattle WA 98104 USA Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ H3G 1M8 Canada

Several successful applications of optimal control theory (OCT) based on the Pontryagin's minimum principle have been recorded in literature. These applications were focused on optimizing the operating policy of multi-reservoir systems. In this study, the performance of OCT algorithm in designing multi-reservoir system is investigated. Three deterministic optimization models based on the OCT were developed to design the best storage strategies in a multi-reservoir system to supply water. multi-objective programming methods were implemented in the three models in order to consider the two non-commensurate objectives of minimizing cost and water deficit. The applications of these models to a multi-reservoir system were compared to an existing dynamic programming model. The result of this study showed that in all cases, the developed OCT models presented sub-optimal solution in designing multi-reservoir systems. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： reservoirs water supply storage optimization design cost modeling multi-objective programming

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Stochastic optimization for flow-shop scheduling with on-site renewable energy generation using a case in the United States

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106812-106812页

作者： Wang, Shasha Mason, Scott J. Gangammanavar, Harsha Clemson Univ Dept Ind Engn Clemson SC 29634 USA Southern Methodist Univ Dept Engn Management Informat & Syst Dallas TX USA

On-site renewable energy provides great opportunities for manufacturing plants to reduce energy costs when faced with time-varying electricity prices. To efficiently utilize on-site renewable energy generation, production schedules and energy supply decisions need to be well investigated. In this paper, we present a two-stage, multi-objective stochastic program for flow shops with sequence-dependent setup. The first stage provides optimal schedules to minimize the total completion time. The second stage determines the energy supply decisions to minimize energy costs under a time-of-use electricity pricing scheme. The power demand of the production is met by on-site renewable generation, supply from the main grid, and energy storage system. An epsilon-constraint algorithm integrated with L-shaped method is proposed to analyze the problem. Sets of Pareto optimal solutions are provided for decision-makers. Our results show that the energy cost of setup operations is relatively high such that it cannot be ignored. Further, using solar or wind energy saves energy costs significantly. While, utilizing solar energy can reduce more.

关键词： Production scheduling Flow shop Renewable energy Stochastic programming multi-objective programming

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A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 450-471页

作者： Samani, Mohammad Reza Ghatreh Hosseini-Motlagh, Seyyed-Mandi Ghannadpour, Seyed Farid Iran Univ Sci & Technol Sch Ind Engn Univ Ave Namiak 16846 Iran

Motivated by a real-life case study, this paper proposes a multilateral perspective for blood supply chain network design as an intricate decision-making problem. The proposed approach utilizes a novel multi-objective mathematical model by incorporating both quantitative and qualitative factors to comprehensively model the investigated case study. In the proposed model, the quantitative factors aim to minimize the loss of product freshness and total cost of the network. Moreover, the number of collection facilities, which are capable of collecting blood, are maximized through a formulation based upon qualitative criteria. Moreover, to cope with the uncertainty, a robust optimization approach is utilized, and an interactive fuzzy solution approach is applied to solve the proposed multi-objective model. The computational results demonstrate the capability of the proposed model in designing the blood supply chain network in practice. Moreover, some sensitivity analyses are conducted to show the findings observed in a real case study. The results show that the decision makers should be aware of quality-based requirements, because of significant impact on blood facility location, besides the cost oriented consideration. Moreover, concerning of uncertainty, the results obtained from the robust approach outweigh those of the deterministic one. Finally, based on the finding, helpful managerial insights are provided for the decision makers in the blood supply chain network design.

关键词： Blood supply chain multi-objective programming multi-attribute group decision-making Robust optimization

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Evaluation of cloud service industry with dynamic and network DEA models

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APPLIED MATHEMATICS AND COMPUTATION 2017年 315卷 188-202页

作者： Kao, Han-Ying Wu, Dong-Jyun Huang, Chia-Hui Natl Dong Hwa Univ Dept Comp Sci & Informat Engn 1Sec 2Da Hsueh Rd Hualien 97401 Taiwan Natl Taipei Univ Business Dept Business Adm 321Sec 1Ji Nan Rd Taipei 100 Taiwan

Recently, cloud services and computing have revolutionized both academic research and industrial practices. Numerous hardware and software providers have joined the intensely competitive cloud services market. At the same time, a corresponding focus on evaluation of the industry is growing apace. Investigations are currently under way into how to incorporate the dynamic and intermediate processes in cloud service companies' business models. This paper intends to develop some alternative models of network data envelopment analysis (NDEA) for evaluating cloud service businesses. By considering various internal functions and processes of the services in multi-period settings, we design three evaluation models: (1) dynamic black-box data envelopment analysis (DBDEA);(2) static network data envelopment analysis (SNDEA);and (3) dynamic network data envelopment analysis (DNDEA). Using multi-objective programming (MOP) techniques, the three NDEA models are formulated and solved for the cloud service industry. An empirical study is conducted to evaluate the performance of the cloud service industry. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Network data envelopment analysis Cloud service industry Efficiency evaluation multi-objective programming Empirical study

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An indefinite quadratic optimization over an integer efficient set

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OPTIMIZATION 2018年第8期67卷 1143-1156页

作者： Moulai, Mustapha Drici, Wassila USTHB LaROMaD Lab Algiers Algeria USTHB Dept Operat Res Fac Math Algiers Algeria

In this paper, a new exact method is proposed for solving the maximization problem, say, of an indefinite quadratic utility function over the efficient set of a multi-objective integer linear programming (MOILP) problem. Indeed, we develop a branch and cut algorithm based on a continuous indefinite quadratic optimization, for reaching an integer optimal solution of problem without having to enumerate explicitly all integer efficient solutions of MOILP problem. The branch and bound process, strengthened by efficient cuts and tests, allows us to fathom considerably nodes in the tree. Thus, a large number of feasible and non-efficient solutions can be avoided. An experimental study is reported to validate the theoretical results.

关键词： multi-objective programming integer programming quadratic programming branch and cut

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