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Modeling asset allocation and liability composition for Indian banks

MANAGERIAL FINANCE 2014年第7期40卷 700-723页

作者： Viswanathan, P. Ranganatham, M. Balasubramanian, G. Great Lakes Inst Management Chennai Tamil Nadu India Univ Madras Chennai Tamil Nadu India Inst Financial Management & Res IFMR Chennai Tamil Nadu India

Purpose - Asset liability management is a multi-dimensional set of activities. Against this backdrop, the purpose of this paper is to build a goal programming model for optimally determining the asset allocation and liability composition for Indian Banks. Design/methodology/approach - The conceptual model framework has been developed and then tested for four banks that typically represent the Indian banking sector. Published balance sheet data were used for the model that span over 1995-2009. The veracity of the model has been tested in terms of its ability to project the optimum asset allocation and liability composition for the year 2010. Findings - The model has been able to generate the optimum asset and liability mix that meets the goals set on the key drivers. The solution provided is realistic and compatible with the actual figures. Sensitivity analysis including current and savings account and interest rate changes has been successfully performed to study impact they cause on profitability. Research limitations/implications - The model provides an overall approach to asset allocation and liability composition based on past data reflecting the preferences and priorities of the banks with regard to their outlook on setting targets. This may change. The variables like return and risk are stochastic in nature. Practical implications - The model demonstrated in this paper would be useful to the policy makers in any bank for decision support and planning in view of its ability to incorporate a large number of constraints. Changes in profit could be instantaneously captured through sensitivity analysis. Originality/value - The goal programming model used here is invariant to the type of bank and year of consideration.

关键词： Risk Sensitivity analysis ALM CASA Goal programming multi-objective programming Drivers of profitability

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multi-objective optimization in dial-a-ride public transportation

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Transportation Research Procedia 2014年 3卷 299-308页

作者： Guerriero, Francesca Pezzella, Ferdinando Pisacane, Ornella Trollini, Luigi Dipartimento di Ingegneria Meccanica Energetica e Gestionale Università della Calabria Via Pietro Bucci Rende 87036 Italy Dipartimento di Ingegneria dell'Informazione Università Politecnica Delle Marche Via Brecce Bianche 12 Ancona 60131 Italy

In the Dial-a-Ride public transportation systems, each customer requirement is specified in terms of a pickup (origin), of a delivery (destination) and of a time window within it has to be satisfied. The aim is to find a set of routes, each assigned to a vehicle, in order to satisfy the set of requests, under capacity, time windows, precedence and pairing conditions. It is usually assumed that the demand of a request, picked up at its origin, is exactly delivered at its destination (one-to-one service) and that the fleet of the vehicles is based at a single depot. From a modelling point of view, the problem could be addressed as a one-to-one capacitated Pickup and Delivery Problem with Time Windows (PDPTW) and therefore, the mathematical formulation presents, beyond the traditional capacity constraints on the vehicles, also the pairing, the precedence and the time windows conditions. In particular, the pairing conditions guarantee that each couple (pickup, delivery) has to belong to the same route while the precedence constraints impose that each pickup has to be served before the associated delivery. This paper addresses the problem with the aim of optimizing, at the same time, the maximum total ride time and the total waiting time. Then, a bi-objective PDPTW with a constraint on the maximum duration of each route is proposed and solved by a two-step approach. In particular, the first step determines a set of feasible routes by meta-heuristics. These routes are used in second step in a bi-objective set partitioning formulation solved by the epsilon-constraint method to generate efficient solutions. The parameters of the meta-heuristics are properly set by a racing procedure. Computational experiments on some benchmark instances are carried out to assess the performance of the proposed approach. © 2014 The Authors.

关键词： Epsilon-constraint Method multi-objective programming Pickup and Delivery Problem with Time Windows Racing Methods Vehicle Routing

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Semi-structured Fuzzy Decision-making Approach to multi-objective programming Problems

Semi-structured Fuzzy Decision-making Approach to Multi-obje...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Yu, Xuefeng Zhang, Xiaowei Mu, Lili Heilongjiang Univ Sch Hydraul & Elect Engn Harbin 150086 Peoples R China

ISBN: (纸本)9781424447541

This paper applies the semi-structured decision-making model (SDM) to the multi-objective linear programming Firstly, the general form of the multi-objective programming is introduced Then the SDM is proposed After that, the weight of decision-making objective is determined, and also the objective function is established combining with the SDM Finally, the algorithm is discussed with an example

关键词： fuzzy decision-making multi-objective programming economic benefits social benefits environmental benefits

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Optimization Modelof Passenger Train Service Plan Based onthe Degreeof Passenger Satisfaction

Optimization Modelof Passenger Train Service Plan Based onth...

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3rd International Conference on Civil Engineering and Transportation (ICCET 2013)

作者： Liu, Pei Han, Baoming Zhang, Qi Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

ISBN: (纸本)9783038350064

The passenger train service plan which is the basis of passenger transportation organization shows how to organize passenger flow into train flow. The degree of passenger satisfaction reflects the difference between perceived service and expected service. Due to the deficiency of transportation supply, a minority of passengers have to take their "originally reluctant" trains instead of "preferred" trains. This paper firstly analyses extra passenger transportation costs in high-speed rail line. Secondly,a multiobjective optimization model of passenger train service plan based on the degree of passenger satisfaction is proposed,in which minimizing the total passenger train stop times and minimizing the extra passenger transportation costs are the two planning objectives. Finally,a case study on the Beijing-Tianjin intercity railway line shows that the model can be solved by the genetic algorithm and can achieve satisfactory results.

关键词： High-speed Railway theDegree of Passenger Satisfaction the Passenger Train Service Plan multi-objective programming

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Compromise programming with Tchebycheff norm for discrete stochastic orders

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ANNALS OF OPERATIONS RESEARCH 2013年第1期211卷 433-446页

作者： Sitarz, Sebastian Univ Silesia Inst Math PL-40007 Katowice Poland

This paper presents a method of decision making with returns in the form of discrete random variables. The proposed method is based on two approaches: stochastic orders and compromise programming used in multi-objective programming. Stochastic orders are represented by stochastic dominance and inverse stochastic dominance. Compromise programming uses the augmented Tchebycheff norm. This norm, in special cases, takes form of the Kantorovich and Kolmogorov probability metrics. Moreover, in the paper we show applications of the presented methodology in the following problems: projects selections, decision tree and choosing a lottery.

关键词： multi-objective programming Compromise programming Tchebycheff norm Stochastic dominance

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Industrial Structure Adjustment of Shaanxi Province under the Limited Water Resources Condition

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Frontiers of Engineering Management 2014年第4期1卷 378-384页

作者： Xiao-jun Liu Zhen-yu Guo Chao Ding Han-liang Fu School of Management Xi’an University of Architecture and Technology

Shaanxi Province is a region poor of water resources in China, and the lack of water resources is becoming a key factor which restricts the social and economic development of Shaanxi Province. In the perspective of ef ficient utilization of water resources,Shaanxi Province is committed to the transformation of the economic growth and the industrial structure, and treats these measures as important guarantees for the future development of Shaanxi Province. This paper focuses on building multi-objective programming model of industrial structure optimization under the limited water resources condition, indicates the direction of the industrial transformation and water consumption structural adjustment by solving the model, and furthermore points out the key factors that affect the development of Shaanxi Province in the future. Finally, the authors give some advices of promoting the reasonable and effective utilization of water resources of Shaanxi Province.

关键词： water resources capacity of water resources industrial transformation multi-objective programming

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Optimization of Integrated Risk in Commercial Banking based on Financial Statements

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Procedia Computer Science 2014年 31卷 501-510页

作者： Chunbing Bao Jianping Li Dengsheng Wu Xiaoqian Zhu Changzhi Liang Chang Liu Institute of Policy & Management Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Integrated risk control and asset optimization is an important issue in commercial bank industry. This paper combines balance sheet with income statement, and forms a structure measuring each asset's risk based on the method using income statement only, having a better use of the data resource. Considering the commercial bank's diversified pursuit of low risk and high profit, we solve the problem using the method of multiple objective programming, and we give the Pareto surface to support selection decisions. The analysis framework of the integrated risk optimization based on financial statements provides a feasible idea for commercial banks’ asset optimization research with limited data resource.

关键词： commercial banks integrated risk financial statements multi-objective programming asset optimization

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A reduction dynamic programming algorithm for the bi-objective integer knapsack problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2013年第2期231卷 299-313页

作者： Rong, Aiying Figueira, Jose Rui ISEG Tech Univ Lisbon Cemapre Ctr Appl Math & Econ P-1200781 Lisbon Portugal Univ Tecn Lisboa CEG IST Ctr Management Studies Inst Super Tecn P-1049001 Lisbon Portugal

This paper presents a backward state reduction dynamic programming algorithm for generating the exact Pareto frontier for the hi-objective integer knapsack problem. The algorithm is developed addressing a reduced problem built after applying variable fixing techniques based on the core concept. First, an approximate core is obtained by eliminating dominated items. Second, the items included in the approximate core are subject to the reduction of the upper bounds by applying a set of weighted-sum functions associated with the efficient extreme solutions of the linear relaxation of the multi-objective integer knapsack problem. Third, the items are classified according to the values of their upper bounds;items with zero upper bounds can be eliminated. Finally, the remaining items are used to form a mixed network with different upper bounds. The numerical results obtained from different types of bi-objective instances show the effectiveness of the mixed network and associated dynamic programming algorithm. (C) 2013 Elsevier B.V. All rights reserved.

关键词： multi-objective programming Integer knapsack problem Dynamic programming Dominance relation Core concept State reduction

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Pareto-optimal solutions in fuzzy multi-objective linear programming

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FUZZY SETS AND SYSTEMS 2009年第18期160卷 2714-2721页

作者： Jimenez, Mariano Bilbao, Amelia Univ Basque Country Dpto Econ Aplicada 1 San Sebastian Spain Univ Oviedo Dpto Econ Cuantitat E-33006 Oviedo Spain

The problem of solving multi-objective linear-programming problems, by assuming that the decision maker has fuzzy goals for each of the objective functions, is addressed. Several methods have been proposed in the literature in order to obtain fuzzy-efficient solutions to fuzzy multi-objective programming problems. In this paper we show that, in the case that one of our goals is fully achieved, a fuzzy-efficient solution may not be Pareto-optimal and therefore we propose a general procedure to obtain a non-dominated solution, which is also fuzzy-efficient. Two numerical examples illustrate our procedure. (C) 2008 Elsevier B.V. All rights reserved.

关键词： multi-objective programming Goal programming Fuzzy mathematical programming Fuzzy-efficient solution Pareto-optimal solution Two-phase method

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Effective implementation of the ε-constraint method in multi-objective Mathematical programming problems

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APPLIED MATHEMATICS AND COMPUTATION 2009年第2期213卷 455-465页

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

As indicated by the most widely accepted classification, the multi-objective Mathematical programming (MOMP) methods can be classified as a priori, interactive and a posteriori, according to the decision stage in which the decision maker expresses his/her preferences. Although the a priori methods are the most popular, the interactive and the a posteriori methods convey much more information to the decision maker. Especially, the a posteriori (or generation) methods give the whole picture (i.e. the Pareto set) to the decision maker, before his/her final choice, reinforcing thus, his/her confidence to the final decision. However, the generation methods are the less popular due to their computational effort and the lack of widely available software. The present work is an effort to effectively implement the epsilon-constraint method for producing the Pareto optimal solutions in a MOMP. We propose a novel version of the method (augmented epsilon-constraint method - AUGMECON) that avoids the production of weakly Pareto optimal solutions and accelerates the whole process by avoiding redundant iterations. The method AUGMECON has been implemented in GAMS, a widely used modelling language, and has already been used in some applications. Finally, an interactive approach that is based on AUGMECON and eventually results in the most preferred Pareto optimal solution is also proposed in the paper. (C) 2009 Elsevier Inc. All rights reserved.

关键词： multi-objective programming epsilon-Constraint method GAMS

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