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Linear formulations and valid inequalities for a classic location problem with congestion: a robust optimization application

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OPTIMIZATION LETTERS 2020年第5期14卷 1265-1285页

作者： Ahmadi-Javid, Amir Ramshe, Nasrin Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

This study investigates a classic stochastic location problem in a service system with immobile servers that are represented by M/G/1 queues. It shows that the basic integer nonlinear optimization model of the problem can be reformulated as different mixed-integer linear models. It also proposes effective valid inequalities to enhance the linear reformulations. The models are applied to tackle a robust counterpart of the problem when demand rates are uncertain. Our results can generally be used for similar decision problems with congestion.

关键词： Stochastic facility location models Service systems M G 1 M M 1 and M D 1 queues integer nonlinear programming Robust optimization

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Residential Load Disaggregation Considering State Transitions

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第2期16卷 743-753页

作者： Zeinal-Kheiri, Sevda Shotorbani, Amin Mohammadpour Mohammadi-Ivatloo, Behnam Univ Tabriz Fac Elect & Comp Engn Tabriz 5166 Iran Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada

Information about power consumption patterns of devices, helps the residential consumers manage their energy usage. Nonintrusive load monitoring is an effective tool to extract the consumption patterns from the measured aggregated data at the meter. In this paper, an optimization-based method is proposed to disaggregate the total load, using low frequency data. The proposed algorithm is enhanced by enforcing the power profiles of appliances to be piecewise constant over specific time durations. Moreover, the state transitions of the appliances are determined and then employed as the optimization constraints to improve the estimation results. The proposed method is evaluated using almanac of minutely power data set (AMPds) and reference energy disaggregation data set (REDD) datasets by several performance metrics. Results indicate that the designed algorithm is able to recognize the frequently varying appliances in spite of piecewise constancy presumption. Furthermore, breaking down the optimization problem to the smaller parts enhances the ability of the algorithm to be operated in real time.

关键词： integer nonlinear programming load disaggregation optimization piecewise constancy state transition

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Balancing producer and consumer risks in optimal attribute testing: A unified Bayesian/Frequentist design

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期286卷 576-587页

作者： Fernandez, Arturo J. Correa-Alvarez, Cristian D. Pericchi, Luis R. Univ La Laguna Dept Matemat Estadist & Invest Operat Tenerife 38071 Canary Islands Spain Inst Tecnol Metropolitano ITM Dept Calidad & Prod Medellin Antioquia Colombia Univ Puerto Rico Rio Piedras Dept Math San Juan PR 00931 USA Univ Puerto Rico Rio Piedras Ctr Biostat & Bioinformat San Juan PR 00931 USA

Unified Bayesian/frequentist approaches for the design of attributes sampling plans are introduced by minimizing and limiting a weighted-average of the classical or expected producer and consumer risks adopted by the decision maker. This change of paradigm in the construction of test plans for lot acceptance provides optimal inspection schemes from both frequentist and Bayesian perspectives. Classes of admissible test plans minimizing the weighted-average risks are first defined. Constrained optimization problems are then solved in order to determine sample sizes with smallest classical or expected weighted-average risks, as well as acceptance test plans with minimal inspection effort and controlled risks. The proposed methodology to determine optimal sample sizes and decision criteria for lot sentencing constitutes a plausible consensus between Frequentists and Bayesians. Moreover, the use of prior information on the fraction defective allows the practitioners to appreciably reduce the required sample size for lot screening. Optimal lot inspection schemes can also be easily updated using past performance of the sampling plans. Besides the advantages the suggested approach has for hypothesis testing, namely large sample consistency and reproducible results, it permits to make a trade-off between the opposite goals of customers and manufacturers. Some applications in reliability demonstration testing and industrial quality control are presented for illustrative purposes. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Quality control Constrained optimization Lot sampling inspection integer nonlinear programming Reliability demonstration testing

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Optimal lot sentencing based on defective counts and prior acceptability

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2021年 388卷 113317-113317页

作者： Fernandez, Arturo J. Perez-Gonzalez, Carlos J. Suarez-Rancel, M. Mercedes Univ La Laguna Dept Matemat Estadist & Invest Operat Tenerife 38071 Canary Islands Spain

Optimal inspection schemes for lot sentencing based on defective counts and prior lot acceptability, which provide appropriate protections to manufacturers and customers, are derived by minimizing the sampling inspection effort. An efficient computational procedure to determine the best test plan is presented. Moreover, an explicit approximation of the optimal scheme is deduced and its accuracy is analyzed. The achieved results practically solve the underlying constrained optimization problem in closed-form. The developed methodology is applied to the design of reliability demonstration test plans using failure count data and posterior odds ratios. Some illustrative examples are also presented. In most practical cases, the suggested approach is quite robust to small deviations in the prior acceptability, and allows the practitioners to substantially reduce the required sample size for screening lots of incoming and outgoing goods, as well as to appreciably improve the evaluations of the actual producer and consumer risks. Furthermore, it provides straightforward ways to combine multiple expert opinions and to update the optimal inspection scheme using the current estimate of the prior lot acceptability. In addition, the proposed test plans usually outperform the noninformative/frequentist schemes in terms of sampling cost and are quasi-optimal when the previous knowledge is slightly modified. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Quality control Constrained optimization Lot sampling inspection integer nonlinear programming Reliability demonstration testing

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Mining the preferences of patients for ubiquitous clinic recommendation

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HEALTH CARE MANAGEMENT SCIENCE 2020年第2期23卷 173-184页

作者： Chen, Tin-Chih Toly Chiu, Min-Chi Natl Chiao Tung Univ Dept Ind Engn & Management 1001 Univ Rd Hsinchu Taiwan Natl Chin Yi Univ Technol Dept Ind Engn & Management 57Sec 2Zhongshan Rd Taichung 411 Taiwan

A challenge facing all ubiquitous clinic recommendation systems is that patients often have difficulty articulating their requirements. To overcome this problem, a ubiquitous clinic recommendation mechanism was designed in this study by mining the clinic preferences of patients. Their preferences were defined using the weights in the ubiquitous clinic recommendation mechanism. An integer nonlinear programming problem was solved to tune the values of the weights on a rolling basis. In addition, since it may take a long time to adjust the values of weights to their asymptotic values, the back propagation network (BPN)-response surface method (RSM) method is applied to estimate the asymptotic values of weights. The proposed methodology was tested in a regional study. Experimental results indicated that the ubiquitous clinic recommendation system outperformed several existing methods in improving the successful recommendation rate.

关键词： Ubiquitous recommendation Clinic integer nonlinear programming Data mining

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A classifying ubiquitous clinic recommendation approach for forming patient groups and recommending suitable clinics

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年第0期133卷 165-174页

Existing ubiquitous clinic recommendation systems assume that patients' preferences are alike and thus apply the same recommendation mechanism to all mobile patients. However, this assumption may be unreasonable and must be relaxed. Accordingly, this study proposes a classifying ubiquitous recommendation approach. The classifying ubiquitous recommendation approach divides patients into multiple groups by mining their unknown preferences before recommending them suitable clinics. To tune the recommendation mechanism of each patient group, an integer nonlinear programming problem is solved in a rolling manner. When a new patient accesses the ubiquitous clinic recommendation system, the recommendation mechanisms of all groups are applied to recommend the suitable clinic to the new patient. The results of a regional experiment indicated that the classifying ubiquitous recommendation approach improved the successful recommendation rate by up to 52%. Therefore, patients' unknown preferences are different and affect their behaviors in choosing clinics, which should be considered in grouping patients and in tailoring the clinic recommendation mechanism.

关键词： Clinic Ubiquitous recommendation Classifying integer nonlinear programming

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Optimal truncated repetitive lot inspection with defect rates

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APPLIED MATHEMATICAL MODELLING 2019年 75卷 223-235页

作者： Perez-Gonzalez, Carlos J. Fernandez, Arturo J. Kohansal, Akram Asgharzadeh, Akbar Univ La Laguna Dept Matemat Estadist & Invest Operat San Cristobal la Laguna 38271 Spain Imam Khomeini Int Univ Dept Stat Qazvin Iran Univ Mazandaran Fac Math Sci Dept Stat Babol Sar Iran

Single and repetitive sampling schemes are conventional methods for evaluating the quality of lots or batches of products. Truncation repetitive sampling plans are introduced in this paper in order to significantly reduce the size of samples drawn from the lot. Under this scheme type, if a decision about the acceptance or rejection of the lots cannot be made in the original inspection, they can be reinspected, at most, a prefixed number of times. The Poisson distribution is assumed for the number of defects found in the samples drawn from the lot. Best truncated repetitive sampling plans are determined by solving integer nonlinear programming problems. A simplified methodology is suggested to obtain the plans with optimal inspection effort and controlled risks by using an iterative procedure. According to the results obtained, optimal truncated plans are shown to be better than the optimal single and repetitive schemes in reducing the average sample number of the inspection. An application to the manufacturing of glass is presented for illustrative purposes. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Quality control Producer and consumer risks Average sample number Poisson distribution integer nonlinear programming

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A stochastic location model for designing primary healthcare networks integrated with workforce cross-training

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OPERATIONS RESEARCH FOR HEALTH CARE 2020年 24卷

作者： Ahmadi-Javid, Amir Ramshe, Nasrin Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

Health posts aim to elevate the level of public health by providing primary health services. The effectiveness of a health post network depends on factors such as the number and location of health posts. The service quality in such networks can be improved by controlling the amount of congestion at the facilities. An important policy for decreasing operational cost of these networks is to consider a workforce mix of flexible and dedicated servers. This paper integrates the network design with workforce cross-training in the presence of congestion, where the queuing system at each health post is modeled by a set of multi-class M/G/m queues offering multiple service types. The problem is formulated as an integer nonlinear programming model, and a linearization method is used to solve it. A hypothetical case study illustrates how the model can be used, and interesting managerial insights are presented. (C) 2019 Published by Elsevier Ltd.

关键词： Primary healthcare Service network design problem Congested facility location Flexibility and server mix integer nonlinear programming Multi-class M/G/m queues

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A maritime scheduling transportation-inventory problem with normally distributed demands and fully loaded/unloaded vessels

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APPLIED MATHEMATICAL MODELLING 2018年 53卷 540-566页

作者： Soroush, H. M. Al-Yakoob, S. M. Kuwait Univ Dept Stat & Operat Res POB 5969 Safat 13060 Kuwait Kuwait Univ Dept Math POB 5969 Safat 13060 Kuwait

Uncertainties in oil demands have significant effects on the routing and scheduling of oil tankers and inevitably influence the pertinent fleet operational expenses, shortage and excess inventory penalties, and chartering costs. In this paper, we study a stochastic maritime scheduling transportation-inventory problem to transport crude oil from a source using a set of fully loaded heterogeneous vessels to be fully unloaded at a destination over a finite time horizon. Daily demands at the destination are normally distributed random variables and different penalties are imposed on the shortages/excesses in daily storage levels at the destination. The expected total cost of such a fleet operation, consists of the total vessels' operational expenses, expected total penalties resulting from violating specified lower and upper bounds at the destination storage facility, and chartering expenses. We aim to simultaneously optimize the fleet schedules and maintain desirable daily storage levels to meet the stochastic demand requirements at the destination with acceptable reliability levels. We formulate the problem as a stochastic optimization model and then use chance constrained programming to convert it to an exact mixed integer nonlinear program with a convex objective function and linear constraints. Solutions obtained via the proposed stochastic model and its deterministic counterpart are compared and analyzed to gain insights into the impact of the variations in demands and the probabilities of meeting demands on the overall operation and cost structure. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Stochastic vessel scheduling Transportation Inventory/shortages Chance constraints integer nonlinear programming

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Two level uncapacitated facility location problem with disruptions

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 137卷 106089-000页

作者： Ramshani, Mohammad Ostrowski, Jim Zhang, Kaike Li, Xueping Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA

Uncapacitated facility location problems (UFLPs) deal with the selection of facilities and assignment of customers to them. Two level UFLPs (TUFLPs) consider an additional level of facilities between the customers and the main facilities, through which the customers are connected to the production facilities. Disruption uncertainty introduces more complexity to such problems as it accounts for the probability that facilities may not always be able to satisfy the demand of their assigned customers. In this paper, we study a TUFLP with single assignment under disruption uncertainty. We investigate a two-level distribution chain, in which the flow starts from the production unit, goes through distribution centers, and then ends at the customer level. We assume a region based single commodity distribution chain whose components, excluding the customers, are prone to breakdowns due to disasters. We develop two mathematical programming formulations for the problem and develop a Tabu search algorithm and a problem specific heuristic (route subset selector - RSS), and interpret the results.

关键词： Resilient supply chain design Uncapacitated facility location problems integer nonlinear programming Tabu search Meta-heuristic

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