Pub Date : 2016-06-02DOI: 10.1080/0740817X.2015.1078524
Haengju Lee, Y. Eun
Abstract This paper discusses the estimation of primary demand (i.e., the true demand before the stockout-based substitution effect occurs) for a heterogeneous-groups product category that is sold in the department store setting, based on historical sales data, product availability, and market share information. For such products, a hierarchical consumer choice model can better represent purchasing behavior. This means that choice occurs on multiple levels: A consumer might choose a particular product group on the first level and purchase a product within that chosen group on the second level. Hence, in the present study, we used the nested multinomial logit (NMNL) choice model for the hierarchical choice and combined it with non-homogeneous Poisson arrivals over multiple periods. The expectation-maximization (EM) algorithm was applied to estimate the primary demand while treating the observed sales data as an incomplete observation of that demand. We considered the estimation problem as an optimization problem in terms of the inter-product-group heterogeneity, and this approach relieves the revenue management system of the computational burden of using a nonlinear optimization package. We subsequently tested the procedure with simulated data sets. The results confirmed that our algorithm estimates the demand parameters effectively for data sets with a high level of inter-product-group heterogeneity.
{"title":"Estimating Primary Demand for a Heterogeneous-Groups Product Category under Hierarchical Consumer Choice Model","authors":"Haengju Lee, Y. Eun","doi":"10.1080/0740817X.2015.1078524","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1078524","url":null,"abstract":"Abstract This paper discusses the estimation of primary demand (i.e., the true demand before the stockout-based substitution effect occurs) for a heterogeneous-groups product category that is sold in the department store setting, based on historical sales data, product availability, and market share information. For such products, a hierarchical consumer choice model can better represent purchasing behavior. This means that choice occurs on multiple levels: A consumer might choose a particular product group on the first level and purchase a product within that chosen group on the second level. Hence, in the present study, we used the nested multinomial logit (NMNL) choice model for the hierarchical choice and combined it with non-homogeneous Poisson arrivals over multiple periods. The expectation-maximization (EM) algorithm was applied to estimate the primary demand while treating the observed sales data as an incomplete observation of that demand. We considered the estimation problem as an optimization problem in terms of the inter-product-group heterogeneity, and this approach relieves the revenue management system of the computational burden of using a nonlinear optimization package. We subsequently tested the procedure with simulated data sets. The results confirmed that our algorithm estimates the demand parameters effectively for data sets with a high level of inter-product-group heterogeneity.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"541 - 554"},"PeriodicalIF":0.0,"publicationDate":"2016-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1078524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59752092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-26DOI: 10.1080/0740817X.2016.1189632
A. Khaleghei, V. Makis
ABSTRACT A new competing risk model is proposed to calculate the Conditional Mean Residual Life (CMRL) and Conditional Reliability Function (CRF) of a system subject to two dependent failure modes, namely, degradation failure and catastrophic failure. The degradation process can be represented by a three-state continuous-time stochastic process having a healthy state, a warning state, and a failure state. The system is subject to condition monitoring at regular sampling times that provides partial information about the system is working state and only the failure state is observable. To model the dependency between two failure modes, it is assumed that the joint distribution of the time to catastrophic failure and sojourn time in the healthy state follow Marshal–Olkin bivariate exponential distributions. The Expectation–Maximization algorithm is developed to estimate the model's parameters and the explicit formulas for the CRF and CMRL are derived in terms of the posterior probability that the system is in the warning state. A comparison with a previously published model is provided to illustrate the effectiveness of the proposed model using real data.
{"title":"Reliability estimation of a system subject to condition monitoring with two dependent failure modes","authors":"A. Khaleghei, V. Makis","doi":"10.1080/0740817X.2016.1189632","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1189632","url":null,"abstract":"ABSTRACT A new competing risk model is proposed to calculate the Conditional Mean Residual Life (CMRL) and Conditional Reliability Function (CRF) of a system subject to two dependent failure modes, namely, degradation failure and catastrophic failure. The degradation process can be represented by a three-state continuous-time stochastic process having a healthy state, a warning state, and a failure state. The system is subject to condition monitoring at regular sampling times that provides partial information about the system is working state and only the failure state is observable. To model the dependency between two failure modes, it is assumed that the joint distribution of the time to catastrophic failure and sojourn time in the healthy state follow Marshal–Olkin bivariate exponential distributions. The Expectation–Maximization algorithm is developed to estimate the model's parameters and the explicit formulas for the CRF and CMRL are derived in terms of the posterior probability that the system is in the warning state. A comparison with a previously published model is provided to illustrate the effectiveness of the proposed model using real data.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1058 - 1071"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1189632","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-26DOI: 10.1080/0740817X.2016.1189631
Mostafa Abouei Ardakan, M. Sima, Ali Zeinal Hamadani, D. Coit
ABSTRACT This article presents a new interpretation and formulation of the Reliability–Redundancy Allocation Problem (RRAP) and demonstrates that solutions to this new problem provide distinct advantages compared with traditional approaches. Using redundant components is a common method to increase the reliability of a system. In order to add the redundant components to a system or a subsystem, there are two traditional types of strategies called active and standby redundancy. Recently a new redundancy strategy, called the “mixed” strategy, has been introduced. It has been proved that in the Redundancy Allocation Problem (RAP), this new strategy has a better performance compared with active and standby strategies alone. In this article, the recently introduced mixed strategy is implemented in the RRAP, which is more complicated than the RAP, and the results of using the mixed strategy are compared with the active and standby strategies. To analyze the performance of the new approach, some benchmark problems on the RRAP are selected and the mixed strategy is used to optimize the system reliability in these situations. Finally, the reliability of benchmark problems with the mixed strategy is compared with the best results of the systems when active or standby strategies are considered. The final results show that the mixed strategy results in an improvement in the reliability of all the benchmark problems and the new strategy outperforms the active and standby strategies in RRAP.
{"title":"A novel strategy for redundant components in reliability--redundancy allocation problems","authors":"Mostafa Abouei Ardakan, M. Sima, Ali Zeinal Hamadani, D. Coit","doi":"10.1080/0740817X.2016.1189631","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1189631","url":null,"abstract":"ABSTRACT This article presents a new interpretation and formulation of the Reliability–Redundancy Allocation Problem (RRAP) and demonstrates that solutions to this new problem provide distinct advantages compared with traditional approaches. Using redundant components is a common method to increase the reliability of a system. In order to add the redundant components to a system or a subsystem, there are two traditional types of strategies called active and standby redundancy. Recently a new redundancy strategy, called the “mixed” strategy, has been introduced. It has been proved that in the Redundancy Allocation Problem (RAP), this new strategy has a better performance compared with active and standby strategies alone. In this article, the recently introduced mixed strategy is implemented in the RRAP, which is more complicated than the RAP, and the results of using the mixed strategy are compared with the active and standby strategies. To analyze the performance of the new approach, some benchmark problems on the RRAP are selected and the mixed strategy is used to optimize the system reliability in these situations. Finally, the reliability of benchmark problems with the mixed strategy is compared with the best results of the systems when active or standby strategies are considered. The final results show that the mixed strategy results in an improvement in the reliability of all the benchmark problems and the new strategy outperforms the active and standby strategies in RRAP.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1043 - 1057"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1189631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-26DOI: 10.1080/0740817X.2016.1189630
James Cao, K. C. So
ABSTRACT This article examines the value of demand forecast updates in an assembly system where a single assembler must order components from independent suppliers with different lead times. By staggering each ordering time, the assembler can utilize the latest market information, as it is developed, to form a better forecast over time. The updated forecast can subsequently be used to decide the following procurement decision. The objective of this research is to understand the specific operating environment under which demand forecast updates are most beneficial. Using a uniform demand adjustment model, we are able to derive analytical results that allow us to quantify the impact of demand forecast updates. We show that forecast updates can drastically improve profitability by reducing the mismatch cost caused by demand uncertainty.
{"title":"The value of demand forecast updates in managing component procurement for assembly systems","authors":"James Cao, K. C. So","doi":"10.1080/0740817X.2016.1189630","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1189630","url":null,"abstract":"ABSTRACT This article examines the value of demand forecast updates in an assembly system where a single assembler must order components from independent suppliers with different lead times. By staggering each ordering time, the assembler can utilize the latest market information, as it is developed, to form a better forecast over time. The updated forecast can subsequently be used to decide the following procurement decision. The objective of this research is to understand the specific operating environment under which demand forecast updates are most beneficial. Using a uniform demand adjustment model, we are able to derive analytical results that allow us to quantify the impact of demand forecast updates. We show that forecast updates can drastically improve profitability by reducing the mismatch cost caused by demand uncertainty.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1198 - 1216"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1189630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-21DOI: 10.1080/0740817X.2016.1189628
Z. M. Teksan, J. Geunes
ABSTRACT We consider a production planning problem in which a producer procures an input component for production by offering a price to suppliers. The available supply quantity for the production input depends on the price the producer offers, and this supply level constrains production output. The producer seeks to meet a set of demands over a finite horizon at a minimum cost, including component procurement costs. We model the problem as a discrete-time production and component supply–pricing planning problem with nonstationary costs, demands, and component supply levels. This leads to a two-level lot-sizing problem with an objective function that is neither concave nor convex. Although the most general version of the problem is -hard, we provide polynomial-time algorithms for two special cases of the model under particular assumptions on the cost structure. We then apply the resulting algorithms heuristically to the more general problem version and provide computational results that demonstrate the high performance quality of the resulting heuristic solution methods.
{"title":"Production planning with price-dependent supply capacity","authors":"Z. M. Teksan, J. Geunes","doi":"10.1080/0740817X.2016.1189628","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1189628","url":null,"abstract":"ABSTRACT We consider a production planning problem in which a producer procures an input component for production by offering a price to suppliers. The available supply quantity for the production input depends on the price the producer offers, and this supply level constrains production output. The producer seeks to meet a set of demands over a finite horizon at a minimum cost, including component procurement costs. We model the problem as a discrete-time production and component supply–pricing planning problem with nonstationary costs, demands, and component supply levels. This leads to a two-level lot-sizing problem with an objective function that is neither concave nor convex. Although the most general version of the problem is -hard, we provide polynomial-time algorithms for two special cases of the model under particular assumptions on the cost structure. We then apply the resulting algorithms heuristically to the more general problem version and provide computational results that demonstrate the high performance quality of the resulting heuristic solution methods.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"938 - 954"},"PeriodicalIF":0.0,"publicationDate":"2016-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1189628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-03DOI: 10.1080/0740817X.2015.1063791
Lixin Tang, Defeng Sun, Jiyin Liu
ABSTRACT This study is motivated by the practices of large iron and steel companies that have steady and heavy demands for bulk raw materials, such as iron ore, coal, limestone, etc. These materials are usually transported to a bulk cargo terminal by ships (or to a station by trains). Once unloaded, they are moved to and stored in a bulk material stockyard, waiting for retrieval for use in production. Efficient storage space allocation and ship scheduling are critical to achieving high space utilization, low material loss, and low transportation costs. In this article, we study the integrated storage space allocation and ship scheduling problem in the bulk cargo terminal. Our problem is different from other associated problems due to the special way that the materials are transported and stored. A novel mixed-integer programming model is developed and then solved using a Benders decomposition algorithm, which is enhanced by the use of various valid inequalities, combinatorial Benders cuts, variable reduction tests, and an iterative heuristic procedure. Computational results indicate that the proposed solution method is much more efficient than the standard solution software CPLEX.
{"title":"Integrated storage space allocation and ship scheduling problem in bulk cargo terminals","authors":"Lixin Tang, Defeng Sun, Jiyin Liu","doi":"10.1080/0740817X.2015.1063791","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1063791","url":null,"abstract":"ABSTRACT This study is motivated by the practices of large iron and steel companies that have steady and heavy demands for bulk raw materials, such as iron ore, coal, limestone, etc. These materials are usually transported to a bulk cargo terminal by ships (or to a station by trains). Once unloaded, they are moved to and stored in a bulk material stockyard, waiting for retrieval for use in production. Efficient storage space allocation and ship scheduling are critical to achieving high space utilization, low material loss, and low transportation costs. In this article, we study the integrated storage space allocation and ship scheduling problem in the bulk cargo terminal. Our problem is different from other associated problems due to the special way that the materials are transported and stored. A novel mixed-integer programming model is developed and then solved using a Benders decomposition algorithm, which is enhanced by the use of various valid inequalities, combinatorial Benders cuts, variable reduction tests, and an iterative heuristic procedure. Computational results indicate that the proposed solution method is much more efficient than the standard solution software CPLEX.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"428 - 439"},"PeriodicalIF":0.0,"publicationDate":"2016-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1063791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59751450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-03DOI: 10.1080/0740817X.2015.1056392
Y. Bukchin, E. Wexler
Abstract The effect of workers’ learning curves on the production rate in manual assembly lines is significant when producing relatively small batches of different products. This research studies this effect and suggests applying a work-sharing mechanism among the workers to improve the makespan (time to complete the batch). The proposed mechanism suggests that adjacent cross-trained workers will help each other in order to reduce idle times caused by blockage and starvation. The effect of work sharing and buffers on the makespan is studied and compared with a baseline situation, where the line does not contain any buffers and work sharing is not applied. Several linear programming and mixed-integer linear programming formulations for makespan minimization are presented. These formulations provide optimal work allocations to stations and optimal parameters of the work-sharing mechanism. A numerical study is conducted to examine the effect of buffers and work sharing on the makespan reduction in different environment settings. Numerical results are given along with some recommendations regarding the system design and operation.
{"title":"The effect of buffers and work sharing on makespan improvement of small batches in assembly lines under learning effects","authors":"Y. Bukchin, E. Wexler","doi":"10.1080/0740817X.2015.1056392","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1056392","url":null,"abstract":"Abstract The effect of workers’ learning curves on the production rate in manual assembly lines is significant when producing relatively small batches of different products. This research studies this effect and suggests applying a work-sharing mechanism among the workers to improve the makespan (time to complete the batch). The proposed mechanism suggests that adjacent cross-trained workers will help each other in order to reduce idle times caused by blockage and starvation. The effect of work sharing and buffers on the makespan is studied and compared with a baseline situation, where the line does not contain any buffers and work sharing is not applied. Several linear programming and mixed-integer linear programming formulations for makespan minimization are presented. These formulations provide optimal work allocations to stations and optimal parameters of the work-sharing mechanism. A numerical study is conducted to examine the effect of buffers and work sharing on the makespan reduction in different environment settings. Numerical results are given along with some recommendations regarding the system design and operation.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"403 - 414"},"PeriodicalIF":0.0,"publicationDate":"2016-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1056392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59750909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-04-25DOI: 10.1080/0740817X.2015.1133940
Yanting Li, L. Shu, F. Tsung
ABSTRACT The spatial scan statistic is one of the main tools for testing the presence of clusters in a geographical region. The recently proposed Fast Subset Scan (FSS) method represents an important extension, as it is computationally efficient and enables detection of clusters with arbitrary shapes. Aimed at automatically and simultaneously detecting multiple clusters of any shapes, this article explores the False Discovery (FD) approach originated from multiple hypothesis testing. We show that the FD approach can provide a higher detection power and better identification capability than the standard scan and FSS methods, on average.
{"title":"A false discovery approach for scanning spatial disease clusters with arbitrary shapes","authors":"Yanting Li, L. Shu, F. Tsung","doi":"10.1080/0740817X.2015.1133940","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1133940","url":null,"abstract":"ABSTRACT The spatial scan statistic is one of the main tools for testing the presence of clusters in a geographical region. The recently proposed Fast Subset Scan (FSS) method represents an important extension, as it is computationally efficient and enables detection of clusters with arbitrary shapes. Aimed at automatically and simultaneously detecting multiple clusters of any shapes, this article explores the False Discovery (FD) approach originated from multiple hypothesis testing. We show that the FD approach can provide a higher detection power and better identification capability than the standard scan and FSS methods, on average.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"684 - 698"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1133940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59754314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-04-13DOI: 10.1080/0740817X.2016.1172743
Y. Shu, Q. Feng, E. P. Kao, Hao Liu
ABSTRACT We use Lévy subordinators and non-Gaussian Ornstein–Uhlenbeck processes to model the evolution of degradation with random jumps. The superiority of our models stems from the flexibility of such processes in the modeling of stylized features of degradation data series such as jumps, linearity/nonlinearity, symmetry/asymmetry, and light/heavy tails. Based on corresponding Fokker–Planck equations, we derive explicit results for the reliability function and lifetime moments in terms of Laplace transforms, represented by Lévy measures. Numerical experiments are used to demonstrate that our general models perform well and are applicable for analyzing a large number of degradation phenomena. More important, they provide us with a new methodology to deal with multi-degradation processes under dynamicenvironments.
{"title":"Lévy-driven non-Gaussian Ornstein–Uhlenbeck processes for degradation-based reliability analysis","authors":"Y. Shu, Q. Feng, E. P. Kao, Hao Liu","doi":"10.1080/0740817X.2016.1172743","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1172743","url":null,"abstract":"ABSTRACT We use Lévy subordinators and non-Gaussian Ornstein–Uhlenbeck processes to model the evolution of degradation with random jumps. The superiority of our models stems from the flexibility of such processes in the modeling of stylized features of degradation data series such as jumps, linearity/nonlinearity, symmetry/asymmetry, and light/heavy tails. Based on corresponding Fokker–Planck equations, we derive explicit results for the reliability function and lifetime moments in terms of Laplace transforms, represented by Lévy measures. Numerical experiments are used to demonstrate that our general models perform well and are applicable for analyzing a large number of degradation phenomena. More important, they provide us with a new methodology to deal with multi-degradation processes under dynamicenvironments.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"1003 - 993"},"PeriodicalIF":0.0,"publicationDate":"2016-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1172743","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59755430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-04-06DOI: 10.1080/0740817X.2016.1156788
Siyang Gao, Weiwei Chen
ABSTRACT In this article, the problem of selecting an optimal subset from a finite set of simulated designs is considered. Given the total simulation budget constraint, the selection problem aims to maximize the Probability of Correct Selection (PCS) of the top m designs. To simplify the complexity of the PCS, an approximated probability measure is developed and an asymptotically optimal solution of the resulting problem is derived. A subset selection procedure, which is easy to implement in practice, is then designed. More important, we provide some useful insights on characterizing an efficient subset selection rule and how it can be achieved by adjusting the simulation budgets allocated to all of the designs.
{"title":"A new budget allocation framework for selecting top simulated designs","authors":"Siyang Gao, Weiwei Chen","doi":"10.1080/0740817X.2016.1156788","DOIUrl":"https://doi.org/10.1080/0740817X.2016.1156788","url":null,"abstract":"ABSTRACT In this article, the problem of selecting an optimal subset from a finite set of simulated designs is considered. Given the total simulation budget constraint, the selection problem aims to maximize the Probability of Correct Selection (PCS) of the top m designs. To simplify the complexity of the PCS, an approximated probability measure is developed and an asymptotically optimal solution of the resulting problem is derived. A subset selection procedure, which is easy to implement in practice, is then designed. More important, we provide some useful insights on characterizing an efficient subset selection rule and how it can be achieved by adjusting the simulation budgets allocated to all of the designs.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"855 - 863"},"PeriodicalIF":0.0,"publicationDate":"2016-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2016.1156788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59754942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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