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Soft time-windows for a bi-objective vendor selection problem under a multi-sourcing strategy: Binary-continuous differential evolution

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COMPUTERS & OPERATIONS RESEARCH 2016年第0期76卷 43-59页

作者： Niknamfar, Amir Hossein Niaki, Seyed Taghi Akhavan Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran Sharif Univ Technol Dept Ind Engn POB 11155-9414Azadi Ave Tehran *** Iran

This paper introduces a novel and practical integration of the inventory control and vendor selection problems for a manufacturing system that provides multiple products for several stores located in different places. The replenishment policy of each store is the economic order quantity under a multi-sourcing strategy in which the demand rate decreases as the selling price increases. In this strategy, the ordered quantity of each store for each product can be replenished by a set of selected vendors among all. In addition, the selected vendors can deliver the required products within a certain time window based on a soft time-window mechanism. The aim is to minimize the total system cost and delivery schedule violations, simultaneously. A trade-off between the two objectives is generated using the min-max approach to obtain near fair non-dominated solutions. As the problem is known to be NP-hard, a novel meta-heuristic algorithm called binary-continuous differential evolution (BCDE) is developed to make the original differential evolution capable of solving both binary and continuous optimization problems. Moreover, an improved genetic algorithm with a multi-parent crossover operator is designed to solve the problem. While the applicability of the proposed approach and the solution methodologies are demonstrated, the solution algorithms are tuned and their performances are analyzed and compared statistically. Finally, sensitivity analyses on the size of the soft time-window and the bandwidth factor of the BCDE algorithm are conducted. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multiple objective programming Vendor selection Soft time window Multi-sourcing strategy Binary-continuous differential evolution

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multiple objective minimum cost flow problems: A review

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期176卷 1404-1422页

作者： Hamacher, Horst W. Pedersen, Christian Roed Ruzika, Stefan Univ Kaiserslautern Fachbereich Math D-67653 Kaiserslautern Germany Univ Aarhus Dept Operat Res Aarhus Denmark

In this paper, theory and algorithms for solving the multiple objective minimum cost flow problem are reviewed. For both the continuous and integer case exact and approximation algorithms are presented. In addition, a section on compromise solutions summarizes corresponding results. The reference list consists of all papers known to the authors which deal with the multiple objective minimum cost flow problem. (c) 2005 Elsevier B.V. All rights reserved.

关键词： multiple objective programming network flows efficient solution approximation

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Weighted sum of maximum regrets in an interval MOLP problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2018年第5期25卷 1659-1676页

作者： Rivaz, S. Yaghoobi, M. A. Shahid Bahonar Univ Kerman Fac Math & Comp Dept Appl Math Kerman Iran

This paper presents a multiobjective linear programming problem with interval objective function coefficients. Considering the concept of maximum regret, the weighted sum problem of maximum regrets is introduced and its properties are investigated. It is proved that an optimal solution of the weighted sum problem of maximum regrets is at least possibly weakly efficient. Further, the circumstances under which the optimal solution is necessarily efficient (necessarily weakly efficient or possibly efficient) are discussed. Moreover, using a relaxation procedure, an algorithm is proposed, which for a given set of weights finds one feasible solution that minimizes the weighted sum of maximum regrets. A numerical example is provided to illustrate the proposed algorithm.

关键词： multiple objective programming linear programming interval programming uncertainty modeling

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A novel portfolio selection model based on fuzzy goal programming with different importance and priorities

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第20期42卷 6898-6912页

作者： Kocadagli, Ozan Keskin, Ridvan Mimar Sinan Fine Arts Univ Dept Stat TR-34380 Istanbul Turkey Celal Bayar Univ Fac Econ & Adm Sci Dept Econometr TR-45030 Manisa Turkey

Despite the risk return tradeoff is main concern of financial theory;the rational investment decisions requires considering many criteria simultaneously. In addition to determining a certain importance and priority among these criteria, modeling the investor behaviors in accordance with market trends provides much more realistic approach. However, the researchers mostly overlook to evaluate these concepts simultaneously. This article introduces a novel fuzzy portfolio selection model that takes into accounts the risk preferences in accordance with the market moving trends as well as the risk return tradeoff, and allows the decision makers to define a certain importance and priority among their objectives. To construct this model, firstly the portfolio return, risk and beta coefficient are assumed as main objectives including the possibilistic uncertainties. To define possibilistic uncertainty, the specific fuzzy membership functions are constituted for these objectives with respect to the risk preferences of investors and market moving trends. By means of the fuzzy goal programming techniques, a novel portfolio selection model is developed using these specific fuzzy membership functions. In the application section, three investment terms are examined in the Istanbul Stock Exchange National 30 Index. While ISE30 index has the upward (bullish) and the downward (bearish) moving trends in the first two implementations, the third implementation includes a scenario in which the investors desire to chase the ISE30 index. In the analyses, the proposed model is compared with the classical Mean Variance, Mean-Absolute-Deviation and Maxmin models in terms of their portfolio returns based on the selling prices in the test periods. As a result, the proposed model gives superior performance than the classical models because it takes into account the investor preferences in accordance with market moving trend. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： multiple objective programming Fuzzy goal programming Portfolio selection model Risk preferences of investors Capital Asset Pricing Model

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A reference set based many-objective co-evolutionary algorithm with an application to the knapsack problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期300卷 405-417页

作者： Sahinkoc, H. Mert Bilge, Umit Bogazici Univ Dept Ind Engn TR-34342 Istanbul Turkey

Despite the growing interest on many-objective evolutionary algorithms, studies on combinatorial problems are still rare. In this study, we choose many-objective knapsack problem (MaOKP) as the benchmark and target the challenges imposed by many-objectives in discrete search spaces by investigating several reference set handling approaches and combining several prominent evolutionary strategies in an innovative fashion. Our proposed algorithm uses elitist nondominated sorting and reference set based sorting, however reference points are mapped onto a fixed hyperplane obtained at the beginning of the algorithm. All evolutionary mechanisms are designed in a way to complement the reference set based sorting. Reference point guided path relinking is proposed as the recombination scheme for this purpose. Repair and local improvement procedures are also guided by reference points. Moreover, the reference set co-evolves simultaneously with the solution set, using both cooperative and competitive interactions to balance diversity and convergence, and adapts to the topology of the Pareto front in a self-adaptive parametric way. Numerical experiments display the success of the proposed algorithm compared to state-of-art approaches and yield the best results for MaOKP. The findings are inspiring and encouraging for the use of co-evolutionary reference set based techniques for combinatorial optimization. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Evolutionary computations multiple objective programming Combinatorial optimization Many-objective Co-evolution

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A bi-objective two-stage robust location model for waste-to-energy facilities under uncertainty

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DECISION SUPPORT SYSTEMS 2017年 99卷 37-50页

作者： Hu, Chenlian Liu, Xiao Lu, Jie Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai Peoples R China Natl Univ Singapore Dept Ind & Syst Engn Singapore Singapore Univ Technol Fac Engn & Informat Technol Ctr Artificial Intelligence Decis Syst & eServ Intelligence Lab Sydney NSW Australia

Waste-to-energy (WTE) facilities have begun to play an increasingly important role in the management of municipal solid waste (MSW) worldwide. However, due to the environmental and economic impacts they impose on urban sustainability, the location of WTE facilities is always a sensitive issue. With the frequent involvement of private investors in WTE projects in recent years, the uncertainties associated with MSW generation often impose a huge financial risk on both the private investors involved and the government. Therefore, decision support for the location planning of WTE facilities is necessary and critical. A bi-objective two-stage robust model has been developed to help governments identify cost-effective and environmental friendly WTE facility location strategies under uncertainty, in which one objective is to minimize worst-case annual government spending, while the other minimizes environmental disutility. To efficiently solve the model, a novel solution method has been developed based on a combination of the c-constraint method and the column-and-constraint generation algorithm. The proposed model is demonstrated via a case study in the city of Shanghai where the government plans to locate incinerators to release pressure on sanitary land-fills. The computational results show that the proposed model and solution method can effectively support decision-makers. A further sensitivity analysis reveals several useful MSW management insights. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Facility location Waste-to-energy multiple objective programming Robust optimization Solid waste management

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Biobjective robust simulation-based optimization for unconstrained problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期299卷 249-262页

作者： Zheng, Liang Bao, Ji Xu, Chengcheng Tan, Zhen Cent South Univ Sch Traff & Transportat Engn Changsha Peoples R China Univ Nottingham Ningbo China Nottingham Univ Business Sch China Ningbo Peoples R China

We propose a biobjective robust simulation-based optimization (BORSO) method to solve unconstrained problems involving implementation errors and parameter perturbations. We adopt the notion that a solution is robust efficient (RE) if the region that dominates its worst-case realizations of the biobjectives under uncertainty does not contain (all) the worst-case realizations of the biobjectives of any other solution under uncertainty. Our algorithm aims to efficiently find a set of RE solutions through a series of function evaluations or simulations. First, we design surrogate-model guided search mechanisms for the worst-case neighbors of the current iterate. Subsequently, we determine the iteration distance along an effective local move direction, which excludes the worst-case neighbors from the neighborhood of the new iterate. Depending on the practical need for solution diversity, multiple initial solutions can be specified in our algorithm, and the final iterates of these solutions generate a set of RE solutions. The test results of a synthetic biobjective nonconvex optimization problem show the effectiveness of the BORSO method and its superior performance against a sampling-based robust multiobjective optimization solver. Furthermore, when the proposed algorithm is applied to a real-world biobjective traffic signal timing problem, satisfactory solutions can be obtained under a limited computational budget. These results indicate that the proposed BORSO method can solve unconstrained biobjective simulation-based optimization problems under uncertainties.

关键词： multiple objective programming Simulation-based optimization Robust optimization Uncertainties

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Multiobjective Integer programming: Synergistic Parallel Approaches

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INFORMS JOURNAL ON COMPUTING 2020年第2期32卷 461-472页

作者： Pettersson, William Ozlen, Melih Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Lanark Scotland RMIT Univ Sch Sci Melbourne Vic 3000 Australia

Exactly solving multiobjective integer programming (MOIP) problems is often a very time-consuming process, especially for large and complex problems. Parallel computing has the potential to significantly reduce the time taken to solve such problems but only if suitable algorithms are used. The first of our new algorithms follows a simple technique that demonstrates impressive performance for its design. We then go on to introduce new theory for developing more efficient parallel algorithms. The theory utilises elements of the symmetric group to apply a permutation to the objective functions to assign different workloads and applies to algorithms that order the objective functions lexicographically. As a result, information and updated bounds can be shared in real time, creating a synergy between threads. We design and implement two algorithms that take advantage of such a theory. To properly analyse the running time of our three algorithms, we compare them against two existing algorithms from the literature and against using multiple threads within our chosen integer programming solver, CPLEX. This survey of six different parallel algorithms, to our knowledge the first of its kind, demonstrates the advantages of parallel computing. Across all problem types tested, our new algorithms are on par with existing algorithms on smaller cases and massively outperform the competition on larger cases. These new algorithms, and freely available implementations, allow the investigation of complex MOIP problems with four or more objectives.

关键词： multiple objective programming parallel computing integer programming combinatorial optimisation

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An outranking-based general approach to solving group multi-objective optimization problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2013年第3期225卷 497-506页

作者： Fernandez, Eduardo Olmedo, Rafael Autonomous Univ Sinaloa Fac Ingn Ciudad UnivBlvd Las Amer S-N Culiacan Mexico

This paper presents a general approach to solving multi-objective programming problems with multiple decision makers. The proposal is based on optimizing a bi-objective measure of "collective satisfaction". Group satisfaction is understood as a reasonable balance between the strengths of an agreeing and an opposing coalition, considering also the number of decision makers not belonging to any of these coalitions. Accepting the vagueness of "collective satisfaction", even the vagueness of "person satisfaction", fuzzy outranking relations and other fuzzy logic models are used. Our method transforms a group multi-objective optimization problem into a group choice problem on a decision set composed of a relatively small set of alternatives. This set contains the possible acceptable consensuses in the parameter space. Once such a set has been identified, other well-known techniques can be used to reach the final choice. Main advantages: (a) Each individual decision maker is concerned with his/her own multi-objective optimization problem, only sharing decision variables;own constraints and own mapping between decision variables and objective space are allowed;(b) the search for the best agreement is not limited to portions of the Pareto frontiers;(c) no voting rule is used by the optimization algorithm;no to some extent arbitrary way of handling collective preferences is needed;(d) no assumptions of transitivity and comparability of preference relations are needed;and (e) the concepts of satisfaction/non-satisfaction do not depend on distance measures or other to some extent arbitrary norms. Very good performance of the whole proposal is illustrated by a real-size example. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Group decisions and negotiations multiple objective programming Fuzzy outranking relations

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Exploring the trade-off between generalization and empirical errors in a one-norm SVM

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第3期218卷 667-675页

作者： Aytug, Haldun Sayin, Serpil Koc Univ Coll Adm Sci & Econ TR-34450 Istanbul Turkey Univ Florida Warrington Coll Business Dept Informat Syst & Operat Management Gainesville FL 32611 USA

We propose a one-norm support vector machine (SVM) formulation as an alternative to the well-known formulation that uses parameter C in order to balance the two inherent objective functions of the problem. Our formulation is motivated by the E-constraint approach that is used in bicriteria optimization and we propose expressing the objective of minimizing total empirical error as a constraint with a parametric right-hand-side. Using dual variables we show equivalence of this formulation to the one with the trade-off parameter. We propose an algorithm that enumerates the entire efficient frontier by systematically changing the right-hand-side parameter. We discuss the results of a detailed computational analysis that portrays the structure of the efficient frontier as well as the computational burden associated with finding it. Our results indicate that the computational effort for obtaining the efficient frontier grows linearly in problem size, and the benefit in terms of classifier performance is almost always substantial when compared to a single run of the corresponding SVM. In addition, both the run time and accuracy compare favorably to other methods that search part or all of the regularization path of SVM. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Data mining multiple objective programming Support vector machines One-norm Regularization path

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