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Pareto optimization of public-private partnership toll road contracts with government guarantees

TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2018年 117卷 158-175页

作者： Song, Jinbo Zhao, Yunpeng Jin, Lulu Sun, Yan Dalian Univ Technol Fac Econ & Management Dalian 116024 Peoples R China Dalian Univ Technol Fac Humanities & Social Sci Dalian 116024 Peoples R China

Government guarantees are frequently used in public-private partnership (PPP) toll road projects to attract private sector partners. In this paper, we propose a multi-objective programming model for Pareto-optimal decision in which the toll, quantity demanded, private firm's profit and social welfare are investigated, and compare the effectiveness of exclusivity guarantees and minimum demand guarantees in PPP contracts. Under certain assumptions, we find that for any government guarantee, the Pareto-optimal toll lies in between the toll set by private firms and socially optimal toll;in addition, the Pareto-optimal toll is higher and monopoly power is stronger under minimum demand guarantee if the relative negotiating power of private firms is sufficiently high. Both the private firm's profit and social welfare depend on relative negotiating power, buyback price, marginal social cost and minimum quantity demanded. In particular, if the minimum quantity demanded and the buyback price are sufficiently high, the government will tend to provide exclusivity guarantee but not minimum demand guarantee. A policy implication of this result is that it is not always the best choice for the government to provide a minimum demand guarantee to private firms.

关键词： Public-private partnership Toll roads Government guarantee Pareto optimization multi-objective programming

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Integrated bi-objective project selection and scheduling using Bayesian networks: A risk-based approach

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SCIENTIA IRANICA 2019年第6期26卷 3695-3711页

作者： Namazian, A. Yakhchali, S. Haji Rabbani, M. Univ Tehran Coll Engn Dept Ind Engn POB 14155-6619 Tehran Iran

This paper presents a novel formulation for the integrated bi-objective problem of project selection and scheduling. The first objective was to minimize the aggregated risk by evaluating the expected value of schedule delay and the second objective was to maximize the achieved benefit. To evaluate the expected aggregated impacts of risks, an objective function based on the Bayesian Networks was proposed. In the extant mathematical models of the joint problem of project selection and scheduling, projects are selected and scheduled without considering the risk network of the projects indicating the individual and interaction effects of risks impressing the duration of the activities. To solve the model, two solution approaches were developed, one exact and one metaheuristic approach. Goal programming (GP) method was adopted to optimally select and schedule projects. Since the problem was NP-hard (Non-deterministic Polynomial-time), an algorithm combining GP method and Genetic Algorithm (GA) was proposed, hence named GPGA. Finally, the efficiency of the proposed algorithm was assessed not only based on small-size instances, but also by generating and testing representative datasets of larger instances. The results of the computational experiments indicated that it had acceptable performance in handling large-size and more realistic problems. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： Project selection and scheduling Risk analysis Bayesian networks multi-objective programming Genetic algorithm

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Integrated blood supply chain planning for disaster relief

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2018年 27卷 168-188页

作者： Samani, Mohammad Reza Ghatreh Torabi, S. Ali Hosseini-Motlagh, Seyyed-Mandi Iran Univ Sci & Technol Sch Ind Engn Univ Ave Tehran 16846 Iran Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

This paper proposes a multi-objective mixed integer linear programming model for the design of an integrated blood supply chain network for disaster relief. The developed model accounts for all the special aspects of blood supply chains involving uncertain demand of blood products and their irregular supply, perishability of blood products and shortage avoidance. It also provides a trade-off analysis between the cost efficiency (via minimizing the total costs), responsiveness (through minimizing the maximum unsatisfied demand) and effectiveness of the designed network (by minimizing the time span between blood production in regional blood centers and consumption in demand zones so that their freshness is preserved). A hybrid framework based on the two-stage stochastic programming and possibilistic programming approaches is devised to deal with a mixture of random and epistemic uncertainties. Some numerical experiments are conducted to validate the proposed model and its solution approach. Also, a real case study is presented to demonstrate the practicality of the proposed model. Helpful managerial insights are also provided through conducting a number of sensitivity analyses.

关键词： Blood supply chain Disaster relief multi-objective programming Two-stage stochastic programming Possibilistic programming

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On the increasing importance of multiple criteria decision aid methods for portfolio selection

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2018年第10期69卷 1525-1542页

作者： Aouni, Belaid Doumpos, Michalis Perez-Gladish, Blanca Steuer, Ralph E. Qatar Univ Coll Business & Econ Doha Qatar Tech Univ Crete Sch Prod Engn & Management Financial Engn Lab Univ Campus Khania Greece Univ Oviedo Dept Quantitat Econ Fac Econ & Business Asturias Spain Univ Georgia Dept Finance Athens GA 30602 USA

In 1952, Markowitz published his famous paper on portfolio selection that transformed the field of finance. Although over 65 years have passed since then, the mean-variance model remains today the predominant model in portfolio selection. Having endured many criticisms over this period, the one that has perhaps been the most persistent is the fact that mainstream mean-variance theory is unable to accommodate additional criteria beyond expected return and variance. With investment decision-making having become more complex, this is a real problem as many problems with additional criteria exist and are only increasing in number and importance. In this paper, we review the papers that have been published that apply methods and procedures in an exact (as opposed to evolutionary) sense to address problems in portfolio selection with criteria beyond mean and variance. We also analyse the methodologies that allow the solution of the problem in a multiple criteria context, thus extending the features of the mean-variance approach that have caused portfolio theory to have such impact.

关键词： multiple criteria decision aid multi-objective programming portfolio selection security selection portfolio optimisation

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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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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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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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Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2018年 27卷 290-306页

作者： Vahdani, Behnam Veysmoradi, D. Noori, F. Mansour, F. Islamic Azad Univ Dept Ind Engn Qazvin Branch Fac Ind & Mech Engn Qazvin Iran

The accidental and unpredictable nature of disasters such as earthquake brings about some plans to deal with critical problems in order to reduce the dangers at the time of their occurrence. Effective distribution of relief goods and supplies plays an important role in the rescue operation after an earthquake. Therefore, a two-phase, multi-objective mixed integer, multi-period and multi-commodity mathematical modeling in the three-level relief chain was offered, in which, locating of the distribution centers and warehouses with various levels of capacity, related decisions to the stored goods in the warehouses and established distribution centers were considered in the first phase, and considering the limited hard time windows, in the second phase, operational programming was performed for vehicle routing and distribution of goods to the affected areas, so minimizing the total cost and travel time also increased the reliability of the route. In addition to the features considered in this model, in special cases, it is possible that each critical area receives service more than once;to consider split delivery assumption in the problem, a different model will be presented for this purpose. Since some parameters are uncertain during the crisis, in order to let the model approach the reality, using a robust optimization approach, the model was developed in an uncertain condition. Two meta-heuristic algorithms of NSGAII and MOPSO were used to solve the given problem, in which the accuracy of the mathematical model and the proposed algorithms efficiency were assessed through numerical examples. the results of algorithms were presented for 35 various problems.

关键词： Relief distribution Locating-routing Inventory Time windows multi-objective programming Meta-heuristic algorithms Robust optimization

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Optimizing a linear fractional function over the integer efficient set

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期267卷 135-151页

作者： Drici, Wassila Ouail, Fatma Zohra Moulai, Mustapha USTHB LaROMaD Lab Fac Math BP 32 Algiers 16111 Algeria USTHB Dept Operat Res Fac Math BP 32 Algiers 16111 Algeria

In this article, a new exact method is proposed to solve a problem, say , of maximizing a linear fractional function over the integer efficient set of multi-objective integer linear programming problem (MOILP). The method is developed through the branch and cut technique and the continuous linear fractional programming, to come up with an integer optimal solution for problem without having to explicitly list all efficient solutions of problem (MOILP). The branching process is strengthened by an efficient cut as well as an efficiency test so that a large number of non-efficient feasible solutions can be avoided. Illustrative example and an experimental study are reported to show the merit of this new approach.

关键词： multi-objective programming Integer programming Linear fractional programming Branch and cut

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Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

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INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2016年第3期45卷 286-310页

作者： Du, Jiaoman Yu, Lean Li, Xiang Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

关键词： Hazardous materials transportation multi-objective programming fuzzy chance-constrained programming hybrid intelligent algorithm

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