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第27届中国控制与决策会议

作者： Ying Ai Min Huang Licai Lu School of Science Shenyang Jianzhu University College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern University

ISBN: (纸本)9781479970186

The price decision and return policy for e-business is studied in a fuzzy *** demand and cost are uncertain,which are described as fuzzy variable.A maximize expectation profit model with risk neutral e-tailer and a chance-constrained programming model with risk averse e-tailer are *** a numerical example some insights to management are obtained.

关键词： return policy pricing e-business chance-constrained programming fuzzy variable

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Bi-objective supply chain planning in a fuzzy environment

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第1期26卷 153-164页

作者： Ahmadizar, Fardin Zeynivand, Mehdi Univ Kurdistan Dept Ind Engn Sanandaj Iran

This paper proposes a fuzzy bi-objective mixed integer linear programming formulation and solution methodology for a multi-echelon, multi-product and multi-period supply chain planning. The supply chain is a network of suppliers, plants, distribution centers, cross-docks and retailers. Products are delivered to retailers through cross-docks or directly from manufacturing plants. Cross-docking as an efficient logistic strategy is an intermediate level that involves receiving products from various resources (distribution centers), sorting and then shipping them to their destinations (retailers). The aim of this paper is to present a model that minimizes the total cost and develops a just-in-time (JIT) distribution for the supply chain. The proposed model integrates procurement, production and distribution plans in the tactical level under fuzzy supply, production and demand by considering cross-docking and direct shipments simultaneously. Triangular fuzzy numbers are adapted to represent fuzzy parameters. Moreover, the fuzzy chance-constrained programming is applied to transform the fuzzy model into an auxiliary crisp model.

关键词： Supply chain planning cross-docking JIT fuzzy numbers chance-constrained programming

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IB-ICCMSP: An Integrated Irrigation Water Optimal Allocation and Planning Model Based on Inventory Theory under Uncertainty

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WATER RESOURCES MANAGEMENT 2014年第1期28卷 241-260页

作者： Li, M. Guo, P. Yang, G. Q. Fang, S. Q. China Agr Univ Ctr Agr Water Res China Beijing 100083 Peoples R China Taiyuan Univ Sci & Technol Inst Environm Sci Taiyuan 030024 Shanxi Peoples R China

In this study, an inventory-theory-based inexact chance-constrained multi-stage stochastic programming (IB-ICCMSP) model under multi-uncertainties is developed. IB-ICCMSP integrates inventory theory into an inexact chance-constrained multi-stage stochastic optimization framework. This method can not only effectively address system multiple uncertainties (e.g. discrete intervals and probability density functions) and dynamic features, but also provide water transferring and allocating schemes among multiple stages. The developed model is applied to irrigation water allocation optimization system in Zhangye City, Gansu province, China. Based on the runoff simulation prediction of Yingluo Gorge and water supply-demand balance analysis of the 12 irrigation areas in Zhangye City, different optimal irrigation water measures are generated under different flow levels and different probabilities in the planning year. The obtained results are valuable for supporting the adjustment of the existing irrigation patterns and identifying desired water-allocation plans for irrigation under multi-uncertainties.

关键词： EOQ model for backorder Inexactmulti-stage stochastic programming chance-constrained programming Runoff simulation Optimal irrigation water allocation Uncertainty

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A new chance-constrained DEA model with birandom input and output data

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2014年第12期65卷 1824-1839页

作者： Tavana, Madjid Shiraz, Rashed Khanjani Hatami-Marbini, Adel La Salle Univ Philadelphia PA 19141 USA Univ Paderborn D-33098 Paderborn Germany Tabriz Univ Tabriz Iran Catholic Univ Louvain Louvain Belgium

The purpose of conventional Data Envelopment Analysis (DEA) is to evaluate the performance of a set of firms or Decision-Making Units using deterministic input and output data. However, the input and output data in the real-life performance evaluation problems are often stochastic. The stochastic input and output data in DEA can be represented with random variables. Several methods have been proposed to deal with the random input and output data in DEA. In this paper, we propose a new chance-constrained DEA model with birandom input and output data. A super-efficiency model with birandom constraints is formulated and a non-linear deterministic equivalent model is obtained to solve the super-efficiency model. The non-linear model is converted into a model with quadratic constraints to solve the non-linear deterministic model. Furthermore, a sensitivity analysis is performed to assess the robustness of the proposed super-efficiency model. Finally, two numerical examples are presented to demonstrate the applicability of the proposed chance-constrained DEA model and sensitivity analysis.

关键词： Data Envelopment Analysis chance-constrained programming birandom variables quadratic constraints super-efficiency

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A multiple objective stochastic approach to vehicle routing problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第5-8期74卷 1149-1158页

作者： Mehrjerdi, Yahia Zare Yazd Univ Dept Ind Engn Yazd Iran

This research incorporates the concept of chance-constrained programming and multiple objective goal programming in the area of vehicle routing problem. The research led to the development of the model of the goal programming (GP) stochastic vehicle routing problem (SVRP) that allows decision-makers' involvement in the solution process of problem for obtaining satisfactory solutions. Due to the complexity of the SVRP, a set of stations to be visited by a fleet of vehicles needs to be partitioned into feasible sets of routes, one for each vehicle enabling the application of the multiple objectives GP technique to each of the vehicle routes. The multiple objectives SVRP then consisted of the following two major stages: (1) route construction stage (RCS) and (2) route improvement stage (RIS). The RCS of the problem consists of the problem formulation and partitioning a set of stations into feasible sets of vehicle routes. Due to the complexity of the problem, only heuristic methods for solving SVRP are considered in this stage. On the other hand, the RIS of the problem consists of (1) problem formulation in which goals and probabilistic constraints are identified, (2) transformation of problem in (1) into an equivalent deterministic form, and (3) GP formulation of RIS in which priorities of various goals are identified. To solve the problems stated in the RCS and RIS of the problem, two interactive computer programs coded in Fortran computer language are developed. To test and validate the algorithm proposed in this research, two test problems, usually employed by other researchers, were took into consideration. A modification of the Clarke and Write algorithm is developed to determine the most favorable vehicle routes of the SVRP for the "E"-type problem. Additionally, two heuristic algorithms which are the modification of the Clarke and Wright "savings" approach are developed for solving the "F"-type problem. The E- and F-type problems are the name given to the equiv

关键词： Vehicle routing problem chance-constrained programming Goal programming Heuristic Optimization

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Capacity-constrained supplier selection model with lost sales under stochastic demand behaviour

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NEURAL COMPUTING & APPLICATIONS 2014年第2期24卷 347-356页

作者： Kesen, Saadettin Erhan Selcuk Univ Dept Ind Engn TR-42075 Konya Turkey

In today's market conditions, volume of demand is quite uncertain and thus it is hard to estimate. In many cases, buyer is prone to use supply chain flexibility rather than inventory holding strategy to withstand demand uncertainty. We assume that the buyer releases a replenishment order to the supplier for each cycle (or period) under the contract which is mainly composed of four parameters: (1) supply cost per unit, (2) minimum order quantity, (3) order quantity reduction penalty and (4) maximum capacity of the supplier. Based on these parameters, there are two flexibility options that buyer should evaluate in the order of cycle (1) issue an order smaller than the minimum order quantity and pay the related penalty and (2) place no order and lose the sales. Hence, Q (lost) emerges as a critical buyer decision, the order quantity, below which no order is placed. Total expected supply cost plus lost sales, as a function of Q (lost) is presented. We derive the optimal Q (lost) that minimises the total cost function. Since capacity of each supplier is finite, we then develop a supplier selection model with total cost minimisation over the suppliers subject to capacity constraint that has a stochastic nature stemming from demand behaviour. Linearisation on the model is performed using chance-constrained programming approach. From a given set of supply bids from the potential supply chain partners, the buyer is able to make a quantifiable choice.

关键词： Supply chain flexibility Lost sales Supplier selection chance-constrained programming Triangular distribution

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Two-stage Combinatory Planning Method for Efficient Wind Power Integration in Smart Distribution Systems Considering Uncertainties

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2014年第15期42卷 1661-1672页

作者： Zeng, Bo Zhang, Jianhua Ouyang, Shaojie Yang, Xu Dong, Jun Zeng, Ming North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Econ & Management Sch Beijing 102206 Peoples R China

-A crucial factor that may hinder the usage of renewable energies is the mismatch to load demand in the temporal dimension. In the smart-grid environment, demand response is regarded as a feasible solution to mitigate such a problem. In this study, a combinatory planning method is proposed for efficient integration of wind generation. The problem is formulated as a two-stage fuzzy chance-constrained programming model, which co-optimizes the installation of distributed wind generation along with network transformation and determination of optimal real-time prices to achieve the minimization of the total cost over planning horizons. The impact of both aleatory and epistemic uncertainties associated with wind generation, load growth, and responsive behaviors of customers has been considered. Results of the case study demonstrate the effectiveness of the proposed method and the necessity of considering different uncertainties in the planning.

关键词： price-responsive demand distributed wind generation combinatory planning smart grid uncertainties chance-constrained programming

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chance-constrained Optimization of Reliable Fixed Broadband Wireless Networks

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INFORMS JOURNAL ON COMPUTING 2014年第4期26卷 893-909页

作者： Classen, Grit Koster, Arie M. C. A. Coudert, David Nepomuceno, Napoleao Rhein Westfal TH Aachen Lehrstuhl Math 2 D-52062 Aachen Germany Inria Project Team Mascotte F-06103 Sophia Antipolis France Univ Nice Sophia Antipolis F-06103 Sophia Antipolis France Univ Fortaleza Programa Posgrad Informat Aplicada BR-60811905 Fortaleza Ceara Brazil

In this paper, we extend our former investigation on conceiving reliable fixed point-to-point wireless networks under outage probability constraints. We consider the problem of determining the minimum cost bandwidth assignment of a network, while guaranteeing a reliability level of the solution. If the optimal bandwidth assignment and routing of traffic demands are accomplished, the reliability criterion requires that network flows remain feasible with high probability, regarding that the performance of microwave links is prone to variations due to external factors, e.g., weather. We introduce a chance-constrained programming approach to tackle this problem and we present reformulations to standard integer linear programming models, including a budget-constrained formulation. To improve the solving performance, we propose new valid inequalities and a primal heuristic. Computational results present a performance analysis of the valid inequalities and the heuristic. Further, the outperformance of the novel model compared to more traditional approaches is documented.

关键词： fixed wireless networks capacitated network design network reliability chance-constrained programming integer programming

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Fuzzy chance-constrained Multiobjective Portfolio Selection Model

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2014年第3期22卷 653-671页

作者： Mehlawat, Mukesh Kumar Gupta, Pankaj Univ Delhi Dept Operat Res Delhi 110007 India

This paper addresses the problem of portfolio selection with fuzzy parameters from a perspective of chance-constrained multiobjective programming. The key financial criteria used here are conventional, namely, return, risk, and liquidity;however, we use short- and long-term variants of return rather than a single measure of an investor's expectations in respect thereof. The proposed model aims to achieve the maximal return (short term as well as long term) and liquidity of the portfolio. It does so at a credibility, which is no less than the confidence levels defined by the investor. Further, to capture uncertain behavior of the financial markets more realistically, fuzzy parameters used here are such as those characterized by general functional forms. To solve the problem, we rely on a specially developed algorithm that hybridizes fuzzy simulation and real-coded genetic algorithm. Numerical experiments are included to showcase the applicability and efficiency of the model in a real investment environment.

关键词： chance-constrained programming credibility measure fuzzy portfolio selection multiobjective optimization real-coded genetic algorithm (RCGA)

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Multi-objective solid transportation problems with budget constraint in uncertain environment

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2014年第8期45卷 1668-1682页

作者： Kundu, Pradip Kar, Samarjit Maiti, Manoranjan Natl Inst Technol Dept Math Durgapur India Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore India

This paper investigates multi-objective solid transportation problems (MOSTP) under various uncertain environments. The unit transportation penalties/costs are taken as random, fuzzy and hybrid variables respectively, in three different uncertain multi-objective solid transportation models and in each case, the supplies, demands and conveyance capacities are fuzzy. Also, apart from source, demand and capacity constraints, an extra constraint on the total budget at each destination is imposed. chance-constrained programming technique has been used for the first two models to obtain crisp equivalent forms, whereas expected value model is formulated for the last. We provide an another approach using the interval approximation of fuzzy numbers for the first model to obtain its crisp form and compare numerically two approaches for this model. Fuzzy programming technique and a gradient based optimisation - generalised reduced gradient (GRG) method are applied to beget the optimal solutions. Three numerical examples are provided to illustrate the models and programming.

关键词： solid transportation problem multiple objective programming uncertain variable chance-constrained programming fuzzy programming technique

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