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IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Wang, Gang He, Zhenhuan Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

ISBN: (纸本)9781538678879

It is a new trend in the freight delivery industry to develop express freight transportation. Operation plan is both the basis of transportation organization and the important way to realize the satisfaction of shipper time. This paper fully considers the shipper's requirements on time value, establishes a model to design the express freight train operation plan which maximizes the satisfaction of shipper on delivered time and minimizes the operation cost of railway enterprise, and validates the feasibility by a case study. The results show that the model is a meaningful exploration in the field of express freight train operation planning.

关键词： express freight train operation plan shipper time satisfaction multi-objective programming

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A Recourse Stochastic Goal programming Approach for the multi-objective Stochastic Vehicle Routing Problem

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JOURNAL OF multi-CRITERIA DECISION ANALYSIS 2016年第1-2期23卷 3-14页

作者： Masri, Hatem Ben Abdelaziz, Fouad Alaya, Houda Univ Bahrain Coll Business Adm POB 32038 Sakhir Bahrain NEOMA Business Sch Mont St Aignan France Univ Tunis Inst Super Gest Le Bardo Tunisia

This paper addresses a multi-objective stochastic vehicle routing problem where several conflicting objectives such as the travel time, the number of vehicles in use and the probability of an accident are simultaneously minimized. We suppose that demands and travel durations are of a stochastic nature. In order to build a certainty equivalent program to the multi-objective stochastic vehicle routing problem, we propose a solution strategy based on a recourse approach, a chance-constrained approach and a goal-programming approach. The resulting certainty equivalent program is solved to optimality using CPLEX. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： vehicle routing problem multi-objective programming goal programming stochastic programming

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REAL TIME UNMANNED AIR VEHICLE ROUTING

REAL TIME UNMANNED AIR VEHICLE ROUTING

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作者： NAIL KARABAY MIDDLE EAST TECHNICAL UNIVERSITY

学位级别：硕士

In this thesis, we study real-time routing of an unmanned air vehicle (UAV) in a two- dimensional dynamic environment. The UAV starts from a base point, visits all targets and returns to the base point, while all targets change their locations during the mission period. We find the best route for the route planner (RP) considering two objectives; minimization of distance and minimization of radar detection threat. We develop a real-time algorithm to find the UAV's most preferred route for a RP who has an underlying linear or quadratic preference function. In this algorithm, we structure the nondominated frontiers of the trajectories between each target pair and find a route using these trajectories. The algorithm updates the route of the UAV each time the UAV arrives at a target. As the UAV must return to the base target at the end of its journey, we solve a multi-objective shortest Hamiltonian path problem to find a route rather than a multi-objective traveling salesperson problem each time the UAV visits a target. To reduce the computational burden, we develop k-closest heuristic. In this heuristic, instead of structuring the nondominated frontiers between all target pairs, for each target, we select k closest targets and structure only the nondominated frontiers of these k targets. In addition, we develop an adaptive algorithm to determine the value of k. For the RP who has a quadratic preference function, we choose among? nondominated trajectories for each target pair to find a route. We consider the cases? = 1 and? > 1, seperately. We demonstrate all algorithms on different examples.

关键词： unmanned air vehicle routing real-time routing multi-objective programming linear preference function quadratic preference function

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An interactive satisficing approach for multi-objective optimization with uncertain parameters

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第3期28卷 535-547页

作者： Zhong, Shuya Chen, Yizeng Zhou, Jian Liu, Yuanyuan Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Uncertain variables are used to describe the phenomenon where uncertainty appears in a complex system. For modeling the multi-objective decision-making problems with uncertain parameters, a class of uncertain optimization is suggested for the decision systems in Liu and Chen (2013), which is called the uncertain multi-objective programming. In order to solve the proposed uncertain multi-objective programming, an interactive uncertain satisficing approach involving the decision-maker's flexible demands is proposed in this paper. It makes an improvement in contrast to the noninteractive methods. Finally, a numerical example about the capital budget problem is given to illustrate the effectiveness of the proposed model and the relevant solving approach.

关键词： multi-objective optimization multi-objective programming Uncertain variable Uncertain programming Interactive satisficing approach

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Modelling and constructing membership function for uncertain portfolio parameters: A credibilistic framework

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 71卷 40-56页

作者： Jalota, Hemant Thakur, Manoj Mittal, Garima Indian Inst Technol Mandi Mandi 175001 Himachal Prades India Indian Inst Management Lucknow Lucknow 226013 Uttar Pradesh India

In this paper we attempt to automate the process of fitting the uncertain parameters of a multi-objective portfolio selection problem by generating L-R fuzzy numbers that belong to power reference function family. Such an approach is advantageous when the fuzzy parameters of the portfolio are best represented as general functional forms. We also propose four new portfolio selection models in a multi-criteria credibilistic framework. The key financial criteria considered are return, illiquidity (antagonistic to liquidity) and risk. In the absence of joint possibility distribution of these parameters, the return and illiquidity of the entire portfolio are considered as historical data set instead of return and illiquidity of the individual assets. In the process of fitting the most appropriate L-R fuzzy number, the vagueness within the information and deviation of the L-R fuzzy number from historical data is measured using entropy and cross-entropy respectively. These principles are embedded into the modelling process of proposed portfolio selection problems. One of the key contribution of this study is that we propose and design a sub-algorithm namely "Entropy-Cross Entropy (ECE) Algorithm" that is appended within an "MIBEXSM" genetic algorithm and is used to solve the proposed portfolio optimization problems. This proposed solution methodology results in an automated system that is intelligent enough to extract information required for fitting of L-R fuzzy number from the historical data and does not need any human intervention in terms of stating the parameters of the problem. We also conduct an empirical study to demonstrate the impact of the solution approach and applicability of the proposed models in practical applications of portfolio selection. For this purpose we collected historical data from National Stock Exchange (NSE), Mumbai, India. The data for a period of 2008-2013 is first used to train the models. Then the data sets of one-year period of 2013 - 201

关键词： Fuzzy portfolio selection Credibility measure L-R Fuzzy numbers multi-objective programming Credibilistic sharpe ratio Genetic algorithm

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multi-objective open location-routing model for relief distribution networks with split delivery and multi-mode transportation under uncertainty

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SCIENTIA IRANICA 2018年第6期25卷 3635-3653页

作者： Veysmoradi, D. Vahdani, B. Sartangi, M. Farhadi Mousavi, S. M. Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch POB *** Qazvin Iran PNU Dept Ind Engn POB 19395-3697 Tehran Iran Shahed Univ Fac Engn Dept Ind Engn POB 18155-159 Tehran Iran

In this study, the response phase of the management of natural disasters is investigated. One of the important issues in this phase is determining the distribution areas and timely distributing relief to affected areas in which transportation routing is of critical importance. In the event of disasters, especially flood and earthquake, terrestrial transportation is not fairly easy due to the damage to many infrastructures. For this reason, we propose that delivering relief from the distribution areas to disaster stricken places should be done by terrestrial and aerial transportation modes, simultaneously, to increase route reliability and reduce travel time. In this study, for relief allocation after earthquake, we offer a mixed-integer nonlinear open location-routing model in uncertain condition. This model includes several contradictory objectives and a variety of factors such as travel time, total costs, and reliability. In order to solve this model, a hybrid solution by combining robust optimization and fuzzy multi-objective programming has been used. The performance and effectiveness of the offered model and solution approach have been investigated through a case study of earthquake in East Azerbaijan, Iran. Our computational results show that the solution we have offered for real problems is effective. (C) 2018 Sharif University of Technology. All rights reserved.

关键词： Emergency logistics Relief distribution Location Routing Split delivery multi-objective programming Robust optimization

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Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2017年第4期55卷 979-996页

作者： Manopiniwes, Wapee Irohara, Takashi Sophia Univ Fac Sci & Technol Tokyo Japan Chiang Mai Univ Coll Arts Media & Technol Chiang Mai Thailand

This paper proposes a stochastic linear mixed-integer programming model for integrated decisions in the preparedness and response stages in pre- and post-disaster operations, respectively. We develop a model for integrated decisions that considers three key areas of emergency logistics: facility and stock prepositioning, evacuation planning and relief vehicle planning. To develop a framework for effective relief operations, we consider not only a cost-based but also an equity-based solution approach in our multiple objectives model. Then a normalised weighted sum method is used to parameterise our multiple objective programming model. This paper suggests a compromise between the cost, and the equity of relief victims. The experiments also demonstrate how time restrictions and the availability of relief vehicles impact the two objective functions.

关键词： supply chain management humanitarian logistics multi-objective programming stochastic programming optimisation

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Thermo-economic analysis and optimization selection of ORC system configurations for low temperature binary-cycle geothermal plant

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APPLIED THERMAL ENGINEERING 2017年 125卷 153-164页

作者： Liu, Xiaomin Wei, Ming Yang, Luona Wang, Xing Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Fluid Machinery & Engn 28 West Xianning Rd Xian 710049 Shaanxi Peoples R China Chinese Acad Sci Inst Engn Thermophys Energy Storage R&D Ctr Beijing 100190 Peoples R China

multi-objective programming is used to analyze the performance of different organic Rankine cycle (ORC) plant layouts with different working fluids for low temperature binary-cycle geothermal plant. The studied results show that for the considered ORC plant layouts, the optimal overall performance indices increase with the geothermal temperature increasing. For a specific working fluid, the optimal ORC system for overall performance always remains unchanged despite the increase in the geothermal temperature. The optimal schemes of ORC systems vary with the performance indices of ORC system. The optimal scheme for comprehensive performance index is simple cycle with R123 when the geothermal temperature increases from 80 degrees C to 95 degrees C. The optimal scheme for thermal efficiency is regenerative cycle with R123, while the optimal scheme for capital cost is superheated cycle with R123. For the work output and exergy efficiency, the optimal scheme is superheated cycle with R152a when the geothermal temperature varies from 80 degrees C to 85 degrees C, while the scheme of superheated cycle with R134a is better for work output and exergy efficiency when the geothermal temperature is greater than 90 degrees C. This study provides useful references for the researchers selecting the optimal configuration of ORC system for low temperature binary-cycle geothermal plant. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Organic Rankine cycle multi-objective programming Working fluid Geothermal plant

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A multi-stage and multi-supplier inventory model allowing different order quantities

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APPLIED MATHEMATICAL MODELLING 2017年 52卷 613-625页

作者： Chang, Shu-Chin Chang, Ching-Ter Chung Yuan Christian Univ Dept Accounting Taoyuan Taiwan Chang Gung Univ Dept Informat Management Taoyuan Taiwan Chang Gung Mem Hosp Dept Thorac Med Taoyuan Taiwan Ming Chi Univ Technol Dept Ind Engn & Management New Taipei Taiwan

This paper addresses a multi-stage inventory model that allows different order quantities among the selected suppliers to obtain the optimal solutions. To achieve the objective of the study, the single-objective and multi-objective methods are adopted for suitable real world applications. With respect to a single-objective method, this paper aims to minimize the total ordering costs, holding costs, and purchasing costs, subject to the price, quality, and capacity. With respect to a multi-objective method, it focuses on cost minimization, as well as quality and capacity maximization. The proposed model not only considers the allocation of different order quantities among the selected suppliers, but also incorporates the multi-stage inventory problem. Furthermore, a numerical example is provided to illustrate the usefulness of the proposed model and a comparative understanding of various methods. In addition, a simulation test is performed to effectively validate the proposed model which outperforms the previous works. Finally, a sensitivity analysis carried out to investigate the impact of supply chain cost. (C) 2017 Elsevier Inc. All rights reserved.

关键词： multi-objective programming multi-stage inventory model Supply chain system

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Evaluation of multi-objective optimization approaches for solving green supply chain design problems

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年 68卷 168-184页

作者： Kadzinski, Milosz Tervonen, Tommi Tomczyk, Michaf K. Dekker, Rommert Poznan Univ Tech Inst Comp Sci Piotrowo 2 PL-60965 Poznan Poland Evidera Ltd London England Erasmus Univ Erasmus Sch Econ Econometr Inst NL-3000 DR Rotterdam Netherlands

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker's (DM's) indirect preferences into the search process. Within this case study, the CO2 emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Supply chain management Green logistics multi-objective programming Indirect preference information Evolutionary algorithms Interactive evolutionary multi-objective optimization

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