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Optimal schemes for resubmitted lot acceptance using previous defect count data

COMPUTERS & INDUSTRIAL ENGINEERING 2015年 87卷 66-73页

作者： Fernandez, Arturo J. Univ La Laguna Dept Matemat Estadist & Invest Operat San Cristobal la Laguna 38071 Camay Islands Spain

Optimal defects-per-unit inspection schemes for screening batches of manufactured material are obtained by minimizing the expected sampling effort. Nonaccepted lots may be resubmitted for resampiing inspection, whereas the Poisson model is used to describe the random behavior of the number of nonconformities per sampled unit. A coefficient is presented to assess the similarity degree between the available previous information and the current inspection, and truncated gamma distributions are adopted to quantify the natural prior uncertainty about the defect rate using past count data and expert opinions. A step-by-step computational procedure is proposed to solve the underlying integer nonlinear programming problem in order to find the best resubmitted lot sampling plan with controlled expected producer and consumer risks based on previous objective and subjective knowledge. In many practical cases, the inclusion of lot resubmissions and past information into the inspection process provides substantial savings in sample size, as well as more reliable evaluations of the existing producer and consumer risks. The proposed approach allows the practitioners to consider a restricted interval for the defect rate, which is reasonable in practice and unfeasible under the frequentist perspective. Moreover, a mechanism is suggested to update the prior distribution based on past performance of the inspection plan. For illustrative purposes, the methodology developed is applied to the manufacturing of glass. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Expected producer and consumer risks Industrial quality control integer nonlinear programming Poisson distribution Truncated gamma prior model

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Multi-stage optimization for periodic inspection planning of geo-distributed infrastructure systems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第3期245卷 797-804页

作者： Phan, Dzung T. Zhu, Yada IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA

This paper develops a multi-stage optimization framework for determining periodic inspection intervals for geo-distributed infrastructure systems subject to hidden failures. We assume that the unit's failure can only be rectified at periodic inspection when a perfect repair is carried out to restore the unit to as-good-as-new condition. For large-scale systems such as fire hydrants in a city, water supply and power transformer distribution networks in urban areas, firstly, we partition the units of a system into groups based on their pre-specified features by a clustering algorithm;then compute an optimal periodic inspection interval for each group based on the expected cost per cycle;and finally, we find the inspection schedule for the whole system that not only minimizes the total inspection and maintenance cost but also satisfies the workforce and budget resource constraints. An integer nonlinear programming formulation and a heuristics are introduced to determine the inspection schedule. The proposed approach is applied to scheduling periodic inspection of large amounts of fire hydrants in a major U.S. city. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Periodic inspection Hidden failures integer nonlinear programming Clusters Maintenance cost

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System-Centric Minimum-Time Paths for Battery-Powered Vehicles in Networks with Charging Nodes

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IFAC-PapersOnLine 2015年第27期48卷 111-116页

作者： Pourazarm, Sepideh Cassandras, Christos G. Division of Systems Engineering Center for Information and Systems Engineering Boston University United States

We study the routing problem for vehicle flows through a road network that includes both battery-powered Electric Vehicles (EVs) and Non-Electric Vehicles (NEVs). We seek to optimize a system-centric (as opposed to user-centric) objective aiming to minimize the total elapsed time for all vehicles to reach their destinations considering both traveling times and recharging times for EVs when the latter do not have adequate energy for the entire journey. Extending prior work where we considered only EVs entering the network, we formulate the problem by grouping all vehicles into a set of "subflows"and provide solutions based on both a Mixed integer Non-Linear programming (MINLP) approach and an alternative flow optimization problem. Since the problem size increases with the number of subflows, its proper selection is essential to render the problem manageable, thus reflecting a trade-off between proximity to optimality and computational effort needed to solve the problem. We propose a criterion and procedure leading to a "good" choice for the number of subflows. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Secondary batteries Charging (batteries) Economic and social effects Electric vehicles integer programming nonlinear programming Problem solving Battery powered Battery powered electric vehicles Computational effort Flow optimization Mixed integer nonlinear programming Routing problems System centric Total elapsed time

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Deterministic Bundle Pricing of Two Products with Limited Stock

Deterministic Bundle Pricing of Two Products with Limited St...

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Iranian Conference on Intelligent Systems (ICIS)

作者： Azadeh, A. Songhori, M. H. Saberi, M. Hosseini, S. Salarvand, F. Pazhoheshfar, P. Univ Tehran Dept Ind Engn Tehran Iran Univ Tafresh Dept Ind Engn Tafresh Iran Islamic Azad Univ Sci & Res Branch Dept Ind Engn Arak Iran Shazand Petrochem Co Dept Human Resource Arak Iran

ISBN: (纸本)9781479933501

In this study, we consider a firm in a monopoly environment which sells two types of perishable products using a commodity bundling practice. Our goal is to find the optimal strategy of selling these two products. We determine whether they should be offered separately or in a bundle, define their optimal prices and determine the initial amount of the bundle which should be made from components with limited stocks. We consider a benefit lost cost in a case that the customer segment does not find his desirable product. We also consider a shortage cost in a case that the customer segment selects a product or the bundle but there is not enough of it to satisfy all of the customer segment demands. We assume there are several different customer segments which have different behaviors with different reservation prices. Customer reservation prices are known for each product. Reservation prices for the bundle are determined by degree of contingency. We first solve this problem using integer nonlinear programming by LINGO software but this method gives hand local optimum results so we decide to solve it with a genetic algorithm to be able to find better results. We have 2 different approaches in using GA. First we suppose that the inventory levels are fixed and second we suppose that the prices are fixed and then we find the optimum result.

关键词： Bundle pricing Degree of contingency integer nonlinear programming Genetic algorithm

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Development of a thermal and electrical energy management in residential building micro-grid

Development of a thermal and electrical energy management in...

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Journal of Renewable and Sustainable Energy

作者： Esmaeili, S. Vahidi, B. Parvizimosaed, M. Brahman, F. Department of Electrical Engineering Amirkabir University of Technology Tehran 15875-4413 Iran Department of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Department of Electrical Engineering Iran University of Science and Technology Tehran 16846-13114 Iran

Global warming and pressing concern about CO2emission along with increasing fuel and oil cost have brought about great challenges for energy companies and homeowners. In this regard, a potential candidate solution is widely used for Distributed Energy Resources, which are capable of providing high quality, low-cost heat and power to off-grid or remote facilities. To appropriately manage thermal and electrical energy, a Smart Energy Management System (SEMS) with hierarchical control scheme has been presented. The developed SEMS model results in mixed integer non-linear programming optimization problem with the objective function of minimizing the operation cost as well as considering emissions. Moreover, the optimization problem has been solved for deterministic and stochastic scheduling algorithms. The novelty of this work is basically reliant on using data mining approach to reduce forecasting error. Several case studies have been carried out to evaluate the performance of proposed data mining method on both energy cost and expected cost. © 2014 AIP Publishing LLC.

关键词： Data mining Costs Electric power transmission networks Energy management Energy management systems Energy resources Global warming integer programming nonlinear programming Scheduling algorithms Stochastic systems Distributed Energy Resources Electrical energy management Hierarchical control scheme Mixed integer nonlinear programming Objective functions Optimization problems Residential building Stochastic scheduling

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A hybrid method of fuzzy simulation and genetic algorithm to optimize constrained inventory control systems with stochastic replenishments and fuzzy demand

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INFORMATION SCIENCES 2013年 220卷 425-441页

作者： Taleizadeh, Ata Allah Niaki, Seyed Taghi Akhavan Aryanezhad, Mir-Bahador Shafii, Nima Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Raja Univ Dept Ind Management Qazvin Iran Sharif Univ Technol Teharn Iran Univ Porto Fac Engn P-4100 Oporto Portugal

Multi-periodic inventory control problems are mainly studied by employing one of two assumptions. First, the continuous review, where depending on the inventory level, orders can happen at any time, and next the periodic review, where orders can only be placed at the beginning of each period. In this paper, we relax these assumptions and assume the times between two replenishments are independent random variables. For the problem at hand, the decision variables (the maximum inventory of several products) are of integer-type and there is a single space-constraint. While demands are treated as fuzzy numbers, a combination of back-order and lost-sales is considered for the shortages. We demonstrate the model of this problem is of an integer-nonlinear-programming type. A hybrid method of fuzzy simulation (FS) and genetic algorithm (GA) is proposed to solve this problem. The performance of the proposed method is then compared with the performance of an existing hybrid FS and simulated annealing (SA) algorithm through three numerical examples containing different numbers of products. Furthermore, the applicability of the proposed methodology along with a sensitivity analysis on its parameters is shown by numerical examples. The comparison results show that, at least for the numerical examples under consideration, the hybrid method of FS and GA shows better performance than the hybrid method of FS and SA. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Multiproduct inventory control Partial back-ordering Stochastic replenishment integer nonlinear programming Fuzzy simulation Genetic algorithm

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A Joint Link and Channel Assignment Routing Scheme for Cognitive Radio Networks

A Joint Link and Channel Assignment Routing Scheme for Cogni...

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作者： Zhao, H. S. Brno University of Technology

Cognitive radio (CR) is a promising technology that enables opportunistic utilization of the temporarily vacant spectrum to mitigate the spectrum scarcity in wireless communications. Since secondary users (SUs) should vacate the channel in use immediately after detecting the reappearances of primary users (PUs) in cognitive radio networks (CRNs), the route reliability is a distinctive challenge for routing in CRNs. Furthermore, the throughput requirement of an SU session should be satisfied and it is always preferable to select a route with less negative influence on other current or latish sessions. To account for the route reliability challenge, we study the joint link and channel assignment routing problem for CRNs. It is formulated in a form of integer nonlinear programming (INLP), which is NP-hard, with the objective of minimizing the interference of a new route to other routes while providing route reliability and throughput guarantee. An on-demand route discovery algorithm is proposed to find reliable candidate paths, while a joint link and channel assignment routing algorithm with sequentially-connected-link coordination is proposed to choose the near-optimal route for improving the route reliability and minimizing negative influence. Simulation results demonstrate that the proposed algorithm achieves considerable improvement over existing schemes in both route reliability and throughput.

关键词： Cognitive radio networks joint link and channel assignment routing route reliability interference-impact ratio integer nonlinear programming

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Ad hoc heuristic for the cover printing problem

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DISCRETE OPTIMIZATION 2012年第1期9卷 17-28页

作者： Romero, David Alonso-Pecina, Federico Univ Nacl Autonoma Mexico Inst Matemat Cuernavaca 62210 Morelos Mexico

We address an NP-hard combinatorial optimization problem arising in a printing shop. An impression grid is composed by a set of plates. The cover printing problem consists in designing the composition of impression grids, and determining the number of times each grid is to be printed in order to fulfill the demand of different book covers at minimum total printing cost;the latter comes from three fixed costs: for printing one sheet, for producing one plate, and for composing one impression grid. For each cover an unlimited number of plates can be made. To deal with this challenging problem we present an ad hoc heuristic that outperforms all previously proposed approaches, including genetic algorithms, GRASP, and simulated annealing. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization integer nonlinear programming Cover printing Label printing Heuristic Job splitting

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Robust Route and Channel Selection in Cognitive Radio Networks

Robust Route and Channel Selection in Cognitive Radio Networ...

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14th IEEE International Conference on Communication Technology (ICCT)

作者： Zhang, Jianzhao Yao, Fuqiang Liu, Yongxiang Cao, Long PLA Univ Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Telecommun Technol Inst Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467321013;9781467321006

In this paper, the joint route and channel selection problem with reliability guarantee in cognitive radio network (CRN) is addressed. Since the interference to licensed spectrum owners needs to be strictly controlled, cognitive radio enabled users (CUs) should vacate the using channel immediately on detecting licensed users and the end to end performance may be seriously degraded. Furthermore, the route constructed for a session may impose great negative influence on other current or latish sessions in the network. To account for these challenges, the route reliability is formulated based on the link valid probability and a metric named interference impact is proposed to value the influence of a route to the adjacent links. With the objective of minimizing the interference impact while providing throughput and reliability guarantee, the joint route and channel selection problem is formulated and shown to be in the form of integer nonlinear programming (INLP). Based on the analysis of the problem-specific feature, an on-demand route discovery algorithm is proposed to find reliable candidate routes, and a heuristic cross-layer optimization algorithm is proposed to choose the near-optimal route. Simulation results demonstrate that the proposed algorithm can guarantee the current route requirement while improving whole network throughput.

关键词： cognitive radio networks path stability interference impact integer nonlinear programming

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Solving fuzzy queueing decision problems via a parametric mixed integer nonlinear programming method

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第1期177卷 445-457页

作者： Chen, Shih-Pin Natl Chung Cheng Univ Dept Business Adm Chiayi 621 Taiwan

This paper proposes a mathematical programming method to construct the membership functions of the fuzzy objective value of the cost-based queueing decision problem with the cost coefficients and the arrival rate being fuzzy numbers. On the basis of Zadeh's extension principle, three pairs of mixed integer nonlinear programs (MINLP) parameterized by the possibility level alpha are formulated to calculate the lower and upper bounds of the minimal expected total cost per unit time at alpha, through which the membership function of the minimal expected total cost per unit time of the fuzzy objective value is constructed. To provide a suitable optimal service rate for designing queueing systems, the Yager's ranking index method is adopted. Two numerical examples are solved successfully to demonstrate the validity of the proposed method. Since the objective value is completely expressed by a membership function rather than by a crisp value, it conserves the fuzziness of the input information, thus more information is provided for designing queueing systems. The successful extension of queueing decision models to fuzzy environments permits queueing decision models to have wider applications in practice. (c) 2006 Elsevier B.V. All rights reserved.

关键词： queueing decision model fuzzy sets integer nonlinear programming

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