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An interactive multi-objective programming approach for project portfolio selection and scheduling

COMPUTERS & INDUSTRIAL ENGINEERING 2022年 169卷

作者： Zorluoglu, Ozge Sahin Kabak, Ozgur Istanbul Tech Univ Fac Management Ind Engn Dept TR-34356 Istanbul Turkey

Organizations have to allocate resources, time, and workforce in many projects at the same time. Selection and scheduling of the projects have a significant impact on effective project management. However, most of the studies in the literature do not solve the selection and scheduling problems simultaneously. This study aims to design an interactive process to integrate selection and scheduling processes in the project management. For this purpose, a new multi objective programming model is proposed. The project scores are presented as belief de-grees (i.e., distributions to linguistic term levels) that are gathered as a result of the weighted cumulative belief degree approach. By the use of the belief degrees, projects could be selected and scheduled based on the satis-faction level of the problem owner. The proposed model considers conditions and restrictions in management of business development projects such as the progress percentage of the projects in a period, the complementary and mutual exclusive relations between projects, etc. An interactive solution procedure is developed in order to solve the proposed model. The proposed model and the solution procedure are applied in an information technology company for their business development projects

关键词： Project scheduling Project portfolio selection multiple objective programming Interactive decision support

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An alternative optimization technique for interval objective constrained optimization problems via multiobjective programming

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Journal of the Egyptian Mathematical Society 2014年第2期22卷 292-303页

作者： Samiran Karmakar Asoke Kumar Bhunia Department of Business Mathematics and Statistics St. Xavier’s College (Autonomous) Kolkata 700016 India Department of Mathematics The University of Burdwan Burdwan 713104 India

An alternative optimization technique via multiobjective programming for constrained optimization problems with interval-valued objectives has been proposed. Reduction of interval objective functions to those of noninterval (crisp) one is the main ingredient of the proposed technique. At first, the significance of interval-valued objective functions along with the meaning of interval-valued solutions of the proposed problem has been explained graphically. Generally, the proposed problems have infinitely many compromise solutions. The objective is to obtain one of such solutions with higher accuracy and lower computational effort. Adequate number of numerical examples has been solved in support of this technique.

关键词： 90C29 90C30 90C59 90C90 multiple objective programming Interval objective Uncertainty in optimization Interval numbers Interval mathematics

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A multi-objective stochastic programming model for designing supply chain networks

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IFAC Proceedings Volumes 2013年第9期46卷 1956-1960页

作者： Amir Azaron department of Industrial Engineering Istanbul Sehir University Istanbul Turkey Michael Smurfit Graduate School of Business University College Dublin Dublin Ireland

A multi-objective stochastic programming model is developed for supply chain design under uncertainty using an interactive approach. This is a comprehensive model, which includes both the strategic and tactical levels. The uncertainty regarding demands, supplies, processing and transportation costs is captured by generating discrete scenarios with given probabilities of occurrences. The objective functions involved are the expected total cost (min), the variance of the total costs (min) to get a robust design, and the probability of not meeting a certain budget (min). Then, an interactive multi-objective technique with explicit trade-off information given named surrogate worth trade-off (SWT) method is used to solve the multi-objective model.

关键词： Supply chain management multiple objective programming Stochastic programming Risk management

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Lead time control in multi-class multi-stage assembly systems with finite capacity

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第4期66卷 808-817页

作者： Yaghoubi, Saeed Noori, Siamak Azaron, Amir Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Univ Coll Dublin Michael Smurfit Grad Sch Business Dublin 2 Ireland Istanbul Sehir Univ Dept Ind Engn Istanbul Turkey

In this paper, we model multi-class multi-stage assembly systems with finite capacity as queueing networks. It is assumed that different classes (types) of products are produced by the production system and products' orders for different classes are received according to independent Poisson processes. Each service station of the queueing network specifies a manufacturing or assembly operation, in that processing times for different types of products are independent and exponentially distributed random variables with service rates, which are controllable, and the queueing discipline is First Come First Served (FCFS). Different types of products may be different in their routing sequences of manufacturing and assembly operations. For modeling multi-class multi-stage assembly systems, we first consider every class separately and convert the queueing network of each class into an appropriate stochastic network. Then, by using the concept of continuous-time Markov processes, a system of differential equations is created to obtain the distribution function of manufacturing lead time for any type of product, which is actually the time between receiving the order and the delivery of finished product. Furthermore, we develop a multi-objective model with three conflicting objectives to optimally control the service rates, and use goal attainment method to solve a discrete-time approximation of the original multi-objective continuous-time problem. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Assembly systems Markov processes multiple objective programming

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A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2011年第2期209卷 104-112页

作者： Liefooghe, Arnaud Jourdan, Laetitia Talbi, El-Ghazali Univ Lille 1 Lab Informat Fondamentale Lille UMR CNRS 8022 INRIA Lille Nord Europe F-59650 Villeneuve Dascq France Univ Coimbra CISUC Dept Informat Engn P-3000 Coimbra Portugal King Saud Univ Riyadh Saudi Arabia

This paper presents a general-purpose software framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO. A concise overview of evolutionary algorithms for multiobjective optimization is given. A substantial number of methods has been proposed so far, and an attempt of conceptually unifying existing approaches is presented here. Based on a fine-grained decomposition and following the main issues of fitness assignment, diversity preservation and elitism, a conceptual model is proposed and is validated by regarding a number of state-of-the-art algorithms as simple variants of the same structure. This model is then incorporated into the ParadisEO-MOEO software framework. This framework has proven its validity and high flexibility by enabling the resolution of many academic, real-world and hard multiobjective optimization problems. (C) 2010 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Evolutionary algorithms Conceptual unified model Algorithm design and implementation Software framework

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A multiobjective evolutionary algorithm for approximating the efficient set

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期176卷 1723-1734页

作者： Hanne, Thomas Fraunhofer Inst Ind Math Dept Optimizat ITWM D-67663 Kaiserslautern Germany

In this article, a new framework for evolutionary algorithms for approximating the efficient set of a multiobjective optimization (MOO) problem with continuous variables is presented. The algorithm is based on populations of variable size and exploits new elite preserving rules for selecting alternatives generated by mutation and recombination. Together with additional assumptions on the considered MOO problem and further specifications on the algorithm, theoretical results on the approximation quality such as convergence in probability and almost sure convergence are derived. (c) 2005 Elsevier B.V. All rights reserved.

关键词： evolutionary computation multiple objective programming evolutionary algorithms continuous optimization efficient set stochastic convergence

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Multiobjective programming for sizing and locating biogas plants: A model and an application in a region of Portugal

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COMPUTERS & OPERATIONS RESEARCH 2017年第Jul.期83卷 189-198页

作者： Silva, Sandra Alcada-Almeida, Luis Dias, Luis C. Polytech Inst Viana do Castelo Praca Gen Barbosa P-4900347 Viana Do Castelo Portugal Inst Syst Engn & Comp Rua Antero de Quental 199 P-3000033 Coimbra Portugal Univ Coimbra CeBER Av Dias da Silva 165 P-3004512 Coimbra Portugal Univ Coimbra Fac Econ Av Dias da Silva 165 P-3004512 Coimbra Portugal

The location of undesirable facilities involves economic, environmental and social impacts. The costs associated and the rejection of facilities by nearby population are crucial concerns. This paper introduces a Multiobjective Mixed-Integer Linear programming (MMILP) approach to identify locations and capacities of biogas plants to treat animal waste from dairy farms, and assign each farm to a subset of the opened biogas plants. Three objectives were considered in the mathematical model: minimizing initial investment, operation and maintenance costs;minimizing transportation cost;and minimizing social rejection. The proposed model was applied to the Entre-Douro-e-Minho Region in Portugal. The approach provided as output a set of Pareto optimal solutions, represented by maps using a Geographic Information System, each one achieving a unique combination of economic and social performance. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Location multiple objective programming Bioenergy Geographical information systems

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House selection via the internet by considering homebuyers' risk attitudes with S-shaped utility functions

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第1期241卷 188-201页

作者： Ho, Hui-Ping Chang, Ching-Ter Ku, Cheng-Yuan Chien Kuo Technol Univ Dept Int Business Adm Changhua Taiwan Chang Gung Univ Dept Informat Management Taoyuan Taiwan Natl Chiao Tung Univ Dept Informat Management & Finance Hsinchu Taiwan

The widespread use of the Internet has significantly changed the behavior of homebuyers. Using online real estate agents, homebuyers can rapidly find some modern houses that meet their needs;however, most current online housing systems provide limit features. In particular, existing systems fail to consider homebuyers' housing goals and risk attitudes. To increase effectiveness, online real estate agents should provide an efficient matching mechanism, personalized service and house ranking with the aim of increasing both buyers' satisfaction and deal rate. An efficient online real estate agent should provide an easy way for homebuyers to find (rank) a suitable house (alternatives) with consideration of their different housing philosophies and risk attitudes. In order to comprehend these ambiguous housing goals and risk attitudes, it is also indispensable to determine a satisfaction level for each fuzzy goal and constraint. In this study, we propose fuzzy goal programming with an S-shaped utility function as a decision aid to help homebuyers in choosing their preferred house via the Internet in an easy way. With the use of a decision aid, homebuyers can specify their housing goals and constraints with different priority levels and thresholds as a matching mechanism for a fuzzy search, while the matching mechanism can be translated into a standard query language for a regular relational database. Moreover, a laboratory experiment is conducted on a real case to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed method provides better customer satisfaction than manual systems in housing selection service. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Decision support systems multiple objective programming Fuzzy goal programming Utility functions Risk attitude

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An integrated bi-objective optimization model accounting for the social acceptance of renewable fuel production networks

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第1期315卷 354-367页

作者： Becker, Tristan Wolff, Michael Linzenich, Anika Engelmann, Linda Arning, Katrin Ziefle, Martina Walther, Grit Tech Univ Dresden Fac Business & Econ Jr Professorship Management Sci Dresden Germany Rhein Westfal TH Aachen Chair Operat Management Sch Business & Econ Aachen Germany Rhein Westfal TH Aachen Chair Commun Sci Human Comp Interact Ctr Aachen Germany Rhein Westfal TH Aachen Risk Percept & Commun Aachen Germany

Renewable liquid fuels produced from biomass, hydrogen, and carbon dioxide play an important role in reaching climate neutrality in the transportation sector. For large-scale deployment, production facilities and corresponding logistics have to be established. However, the implementation of such a large-scale renewable fuel production network requires acceptance by citizens. To gain insights into the structure of efficient and socially accepted renewable fuel production networks, we propose a bi-objective mixed -integer programming model. In addition to an economic objective function, we consider social acceptance as a second objective function. We use results from a conjoint analysis study on the acceptance and preference of renewable fuel production networks, considering the regional topography, facility size, production pathway, and raw material transportation to model social acceptance. We find significant trade-offs between the economic and social acceptance objective. The most favorable solution from a social acceptance perspective is almost twice as expensive as the most efficient economical solution. However, it is possible to strongly increase acceptance at a moderate expense by carefully selecting sites with preferred regional topography.

关键词： multiple objective programming Conjoint analysis Social acceptance Renewable fuels

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An efficient Differential Evolution based algorithm for solving multi-objective optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第2期217卷 404-416页

作者： Ali, Musrrat. Siarry, Patrick Pant, Millie. Univ Paris Est Creteil LiSSi EA3956 F-94010 Creteil France Indian Inst Technol Roorkee Dept Paper Technol Roorkee 247667 Uttar Pradesh India

In the present study, a modified variant of Differential Evolution (DE) algorithm for solving multi-objective optimization problems is presented. The proposed algorithm, named Multi-objective Differential Evolution Algorithm (MODEA) utilizes the advantages of Opposition-Based Learning for generating an initial population of potential candidates and the concept of random localization in mutation step. Finally, it introduces a new selection mechanism for generating a well distributed Pareto optimal front. The performance of proposed algorithm is investigated on a set of nine bi-objective and five tri-objective benchmark test functions and the results are compared with some recently modified versions of DE for MOPs and some other Multi objective Evolutionary Algorithms (MOEA5). The empirical analysis of the numerical results shows the efficiency of the proposed algorithm. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Evolutionary computation Global optimization multiple objective programming Opposition-Based Learning Random localization

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