Pub Date : 2016-03-23DOI: 10.1080/0740817X.2015.1110652
A. Sinha, A. Krishnamurthy
ABSTRACT We analyze tradeoffs related to production and subcontracting decisions in an assemble-to-order system with capacity constraints and stochastic lead times. We assume that component replenishment is carried out by orders to a subcontractor and component stock levels at the manufacturer are determined by dual index-based policies. Furthermore, customer demands for the final product are immediately satisfied if all of the required components are in stock; otherwise, they are back-ordered. In order to maintain high service levels, the manufacturer reserves the option to produce components internally. Using queuing models, we analyze the tradeoffs related to internal manufacturing versus subcontracting under different types of dual index policies. We use Matrix-Geometric methods to conduct an exact analysis for an assemble-to-order system with two components and develop a decomposition-based algorithm to analyze the performance of systems with more than two products. Numerical studies provide useful insights on the performance of the various dual index policies under study.
{"title":"Dual index production and subcontracting policies for assemble-to-order systems","authors":"A. Sinha, A. Krishnamurthy","doi":"10.1080/0740817X.2015.1110652","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1110652","url":null,"abstract":"ABSTRACT We analyze tradeoffs related to production and subcontracting decisions in an assemble-to-order system with capacity constraints and stochastic lead times. We assume that component replenishment is carried out by orders to a subcontractor and component stock levels at the manufacturer are determined by dual index-based policies. Furthermore, customer demands for the final product are immediately satisfied if all of the required components are in stock; otherwise, they are back-ordered. In order to maintain high service levels, the manufacturer reserves the option to produce components internally. Using queuing models, we analyze the tradeoffs related to internal manufacturing versus subcontracting under different types of dual index policies. We use Matrix-Geometric methods to conduct an exact analysis for an assemble-to-order system with two components and develop a decomposition-based algorithm to analyze the performance of systems with more than two products. Numerical studies provide useful insights on the performance of the various dual index policies under study.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"415 - 427"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1110652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59753435","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 : 2016-03-22DOI: 10.1080/0740817X.2015.1110270
David J. Eckman, L. Maillart, A. Schaefer
ABSTRACT We consider a search for an immobile object that can only be detected if the searcher is within a given range of the object during one of a finite number of instantaneous detection opportunities; i.e., “pings.” More specifically, motivated by naval searches for battery-powered flight data recorders of missing aircraft, we consider the trade-off between the frequency of pings for an underwater locator beacon and the duration of the search. First, assuming that the search speed is known, we formulate a mathematical model to determine the pinging period that maximizes the probability that the searcher detects the beacon before it stops pinging. Next, we consider generalizations to discrete search speed distributions under a uniform beacon location distribution. Lastly, we present a case study based on the search for Malaysia Airlines Flight 370 that suggests that the industry-standard beacon pinging period—roughly 1 second between pings—is too short.
{"title":"Optimal pinging frequencies in the search for an immobile beacon","authors":"David J. Eckman, L. Maillart, A. Schaefer","doi":"10.1080/0740817X.2015.1110270","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1110270","url":null,"abstract":"ABSTRACT We consider a search for an immobile object that can only be detected if the searcher is within a given range of the object during one of a finite number of instantaneous detection opportunities; i.e., “pings.” More specifically, motivated by naval searches for battery-powered flight data recorders of missing aircraft, we consider the trade-off between the frequency of pings for an underwater locator beacon and the duration of the search. First, assuming that the search speed is known, we formulate a mathematical model to determine the pinging period that maximizes the probability that the searcher detects the beacon before it stops pinging. Next, we consider generalizations to discrete search speed distributions under a uniform beacon location distribution. Lastly, we present a case study based on the search for Malaysia Airlines Flight 370 that suggests that the industry-standard beacon pinging period—roughly 1 second between pings—is too short.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"57 1","pages":"489 - 500"},"PeriodicalIF":0.0,"publicationDate":"2016-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1110270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59753145","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 : 2016-03-22DOI: 10.1080/0740817X.2015.1110269
Yang Zhao, A. Shrivastava, K. Tsui
ABSTRACT Approaches to solve the imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of the hidden structure within the majority class. The purpose of this article is to first highlight the effect of sub-clusters within the majority class on the detection of the minority instances and then handle the imbalanced classification problem by learning the structure in the data. We propose a decomposition-based approach to a two-class imbalanced classification problem. This approach works by first learning the hidden structure of the majority class using an unsupervised learning algorithm and thus transforming the classification problem into several classification sub-problems. The base classifier is constructed on each sub-problem. The ensemble is tuned to increase its sensitivity toward the minority class. We also provide a metric for selecting the clustering algorithm by comparing estimates of the stability of the decomposition, which appears necessary for good classifier performance. We demonstrate the performance of the proposed approach through various real data sets.
{"title":"Imbalanced classification by learning hidden data structure","authors":"Yang Zhao, A. Shrivastava, K. Tsui","doi":"10.1080/0740817X.2015.1110269","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1110269","url":null,"abstract":"ABSTRACT Approaches to solve the imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of the hidden structure within the majority class. The purpose of this article is to first highlight the effect of sub-clusters within the majority class on the detection of the minority instances and then handle the imbalanced classification problem by learning the structure in the data. We propose a decomposition-based approach to a two-class imbalanced classification problem. This approach works by first learning the hidden structure of the majority class using an unsupervised learning algorithm and thus transforming the classification problem into several classification sub-problems. The base classifier is constructed on each sub-problem. The ensemble is tuned to increase its sensitivity toward the minority class. We also provide a metric for selecting the clustering algorithm by comparing estimates of the stability of the decomposition, which appears necessary for good classifier performance. We demonstrate the performance of the proposed approach through various real data sets.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"614 - 628"},"PeriodicalIF":0.0,"publicationDate":"2016-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1110269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59752925","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 : 2016-03-16DOI: 10.1080/0740817X.2015.1110649
Jen-Yi Chen, M. Dada, Qiaohai Hu
ABSTRACT We consider three types of purchase contracts a manufacturer could offer in order to maximize its profit when supplying a retailer that uses responsive pricing to sell in an uncertain market: buy-now before the selling season starts, reserve stock for possible future purchase, and wait-and-see the market before making purchases. The existing literature has shown that adding a recourse purchase—i.e., the wait-and-see alternative—is always beneficial for the retailer who faces an uncertain demand. We find that this is not necessarily the case for the manufacturer who supplies the retailer, as its optimal contract mix depends on the market uncertainty as well as its production characteristics. The manufacturer should offer only the buy-now alternative if its recourse production is much more costly than advance production. As the recourse production cost decreases, the manufacturer should add a second contract to the portfolio: initially the reserve contract and then the wait-and-see contract. However, when the recourse production is cheaper than advance production, the manufacturer should drop the buy-now contract from the mix. As such, it is only in a small region, which shrinks with decreasing uncertainty in demand, that the manufacturer finds it optimal to offer all three purchasing alternatives.
{"title":"Designing supply contracts: buy-now, reserve, and wait-and-see","authors":"Jen-Yi Chen, M. Dada, Qiaohai Hu","doi":"10.1080/0740817X.2015.1110649","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1110649","url":null,"abstract":"ABSTRACT We consider three types of purchase contracts a manufacturer could offer in order to maximize its profit when supplying a retailer that uses responsive pricing to sell in an uncertain market: buy-now before the selling season starts, reserve stock for possible future purchase, and wait-and-see the market before making purchases. The existing literature has shown that adding a recourse purchase—i.e., the wait-and-see alternative—is always beneficial for the retailer who faces an uncertain demand. We find that this is not necessarily the case for the manufacturer who supplies the retailer, as its optimal contract mix depends on the market uncertainty as well as its production characteristics. The manufacturer should offer only the buy-now alternative if its recourse production is much more costly than advance production. As the recourse production cost decreases, the manufacturer should add a second contract to the portfolio: initially the reserve contract and then the wait-and-see contract. However, when the recourse production is cheaper than advance production, the manufacturer should drop the buy-now contract from the mix. As such, it is only in a small region, which shrinks with decreasing uncertainty in demand, that the manufacturer finds it optimal to offer all three purchasing alternatives.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"881 - 900"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1110649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59753302","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 : 2016-03-16DOI: 10.1080/0740817X.2015.1110267
F. Meisel, W. Rei, M. Gendreau, C. Bierwirth
ABSTRACT We consider the problem of designing supply networks for producing and distributing goods under restricted customer order lead times. Companies apply various instruments for fulfilling orders within preset lead times, such as locating facilities close to markets, producing products to stock, choosing fast modes of transportation, or delivering products directly from plants to customers without the use of distribution centers. We provide two alternative models that consider these options to a different extent, when designing multi-layer, multi-product facility networks that guarantee meeting restricted lead times. A computational evaluation compares both models with respect to solvability and the quality of the obtained networks. We find that formulating the problem as a time–space network flow model considerably helps to design high-quality networks. Furthermore, the lead times quoted to customers affect the design of all layers in the supply network. In turn, this shows that when service requirements are applied, the strategic planning of the network should be adapted accordingly. Concerning the instruments considered for meeting quoted lead times, the choice between make-to-order and make-to-stock production is found to be of utmost importance, whereas transportation decisions have a minor impact.
{"title":"Designing supply networks under maximum customer order lead times","authors":"F. Meisel, W. Rei, M. Gendreau, C. Bierwirth","doi":"10.1080/0740817X.2015.1110267","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1110267","url":null,"abstract":"ABSTRACT We consider the problem of designing supply networks for producing and distributing goods under restricted customer order lead times. Companies apply various instruments for fulfilling orders within preset lead times, such as locating facilities close to markets, producing products to stock, choosing fast modes of transportation, or delivering products directly from plants to customers without the use of distribution centers. We provide two alternative models that consider these options to a different extent, when designing multi-layer, multi-product facility networks that guarantee meeting restricted lead times. A computational evaluation compares both models with respect to solvability and the quality of the obtained networks. We find that formulating the problem as a time–space network flow model considerably helps to design high-quality networks. Furthermore, the lead times quoted to customers affect the design of all layers in the supply network. In turn, this shows that when service requirements are applied, the strategic planning of the network should be adapted accordingly. Concerning the instruments considered for meeting quoted lead times, the choice between make-to-order and make-to-stock production is found to be of utmost importance, whereas transportation decisions have a minor impact.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"921 - 937"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1110267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59753133","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 : 2016-03-16DOI: 10.1080/0740817X.2015.1110268
Jianguo Wu, Yong Chen, Shiyu Zhou
ABSTRACT The detection of steady-state operation is critical in system/process performance assessment, optimization, fault detection, and process automation and control. In this article, we propose a new robust and computationally efficient online steady-state detection method using multiple change-point models and exact Bayesian inference. An average run length approximation is derived that can provide insight and guidance in the application of the proposed algorithm. An extensive numerical analysis shows that the proposed method is much more accurate and robust than currently available methods.
{"title":"Online detection of steady-state operation using a multiple-change-point model and exact Bayesian inference","authors":"Jianguo Wu, Yong Chen, Shiyu Zhou","doi":"10.1080/0740817X.2015.1110268","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1110268","url":null,"abstract":"ABSTRACT The detection of steady-state operation is critical in system/process performance assessment, optimization, fault detection, and process automation and control. In this article, we propose a new robust and computationally efficient online steady-state detection method using multiple change-point models and exact Bayesian inference. An average run length approximation is derived that can provide insight and guidance in the application of the proposed algorithm. An extensive numerical analysis shows that the proposed method is much more accurate and robust than currently available methods.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"599 - 613"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1110268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59753255","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 : 2016-03-03DOI: 10.1080/0740817X.2015.1055390
E. Beier, Saravanan Venkatachalam, V. Leon, Lewis Ntaimo
ABSTRACT We present a nodal decomposition–coordination method for stochastic programs with private data (information) restrictions. We consider coordinated systems where a single optimal or close-to-optimal solution is desired. However, because of competitive issues, confidentiality requirements, incompatible database issues, or other complicating factors, no global view of the system is possible. In our iterative methodology, each entity in the cooperation forms its own nodal deterministic or stochastic program. We use Lagrangian relaxation and subgradient optimization techniques to facilitate negotiation between the nodal decisions in the system without any one entity gaining access to the private information from other nodes. We perform a computational study on supply chain inventory coordination problem instances. The results demonstrate that the new methodology can obtain solution values that are close to the optimal within a stipulated time without violating private information restrictions. The results also show that the stochastic solutions outperform the corresponding expected value solutions.
{"title":"Nodal decomposition–coordination for stochastic programs with private information restrictions","authors":"E. Beier, Saravanan Venkatachalam, V. Leon, Lewis Ntaimo","doi":"10.1080/0740817X.2015.1055390","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1055390","url":null,"abstract":"ABSTRACT We present a nodal decomposition–coordination method for stochastic programs with private data (information) restrictions. We consider coordinated systems where a single optimal or close-to-optimal solution is desired. However, because of competitive issues, confidentiality requirements, incompatible database issues, or other complicating factors, no global view of the system is possible. In our iterative methodology, each entity in the cooperation forms its own nodal deterministic or stochastic program. We use Lagrangian relaxation and subgradient optimization techniques to facilitate negotiation between the nodal decisions in the system without any one entity gaining access to the private information from other nodes. We perform a computational study on supply chain inventory coordination problem instances. The results demonstrate that the new methodology can obtain solution values that are close to the optimal within a stipulated time without violating private information restrictions. The results also show that the stochastic solutions outperform the corresponding expected value solutions.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"283 - 297"},"PeriodicalIF":0.0,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1055390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59750439","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 : 2016-03-03DOI: 10.1080/0740817X.2015.1063793
Wenting Pan, K. C. So
Abstract In this article we analyze the interactions among the assembler and two component suppliers in their procurement decisions under a Vendor-Managed Inventory (VMI) contract. Under the VMI contract, the assembler first offers a unit price for each component and will pay component suppliers only for the amounts used to meet the actual demand. The two independent component suppliers then decide on the production quantities of their individual components before the actual demand is realized. We assume that one of the component suppliers has uncertainty in the supply process, in which the actual number of components available for assembly is equal to a random fraction of the production quantity. Under the assembly structure, both component suppliers need to take into account the underlying supply uncertainty in deciding their individual production quantities, as both components are required for the assembly of the final product. We first analyze the special case under deterministic demand and then extend our analysis to the general case under stochastic demand. We derive the optimal component prices offered by the assembler and the corresponding equilibrium production quantities of the component suppliers.
{"title":"Component procurement strategies in decentralized assembly systems under supply uncertainty","authors":"Wenting Pan, K. C. So","doi":"10.1080/0740817X.2015.1063793","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1063793","url":null,"abstract":"Abstract In this article we analyze the interactions among the assembler and two component suppliers in their procurement decisions under a Vendor-Managed Inventory (VMI) contract. Under the VMI contract, the assembler first offers a unit price for each component and will pay component suppliers only for the amounts used to meet the actual demand. The two independent component suppliers then decide on the production quantities of their individual components before the actual demand is realized. We assume that one of the component suppliers has uncertainty in the supply process, in which the actual number of components available for assembly is equal to a random fraction of the production quantity. Under the assembly structure, both component suppliers need to take into account the underlying supply uncertainty in deciding their individual production quantities, as both components are required for the assembly of the final product. We first analyze the special case under deterministic demand and then extend our analysis to the general case under stochastic demand. We derive the optimal component prices offered by the assembler and the corresponding equilibrium production quantities of the component suppliers.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"42 1","pages":"267 - 282"},"PeriodicalIF":0.0,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1063793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59751467","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 : 2016-03-03DOI: 10.1080/0740817X.2015.1063792
Irem Sengul Orgut, J. Ivy, R. Uzsoy, James R. Wilson
Abstract Mathematical models are presented and analyzed to facilitate a food bank's equitable and effective distribution of donated food among a population at risk for hunger. Typically exceeding the donated supply, demand is proportional to the poverty population within the food bank's service area. The food bank seeks to ensure a perfectly equitable distribution of food; i.e., each county in the service area should receive a food allocation that is exactly proportional to the county's demand such that no county is at a disadvantage compared to any other county. This objective often conflicts with the goal of maximizing effectiveness by minimizing the amount of undistributed food. Deterministic network-flow models are developed to minimize the amount of undistributed food while maintaining a user-specified upper bound on the absolute deviation of each county from a perfectly equitable distribution. An extension of this model identifies optimal policies for the allocation of additional receiving capacity to counties in the service area. A numerical study using data from a large North Carolina food bank illustrates the uses of the models. A probabilistic sensitivity analysis reveals the effect on the models' optimal solutions arising from uncertainty in the receiving capacities of the counties in the service area.
{"title":"Modeling for the equitable and effective distribution of donated food under capacity constraints","authors":"Irem Sengul Orgut, J. Ivy, R. Uzsoy, James R. Wilson","doi":"10.1080/0740817X.2015.1063792","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1063792","url":null,"abstract":"Abstract Mathematical models are presented and analyzed to facilitate a food bank's equitable and effective distribution of donated food among a population at risk for hunger. Typically exceeding the donated supply, demand is proportional to the poverty population within the food bank's service area. The food bank seeks to ensure a perfectly equitable distribution of food; i.e., each county in the service area should receive a food allocation that is exactly proportional to the county's demand such that no county is at a disadvantage compared to any other county. This objective often conflicts with the goal of maximizing effectiveness by minimizing the amount of undistributed food. Deterministic network-flow models are developed to minimize the amount of undistributed food while maintaining a user-specified upper bound on the absolute deviation of each county from a perfectly equitable distribution. An extension of this model identifies optimal policies for the allocation of additional receiving capacity to counties in the service area. A numerical study using data from a large North Carolina food bank illustrates the uses of the models. A probabilistic sensitivity analysis reveals the effect on the models' optimal solutions arising from uncertainty in the receiving capacities of the counties in the service area.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"48 1","pages":"252 - 266"},"PeriodicalIF":0.0,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1063792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59751768","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 : 2016-03-03DOI: 10.1080/0740817X.2015.1056391
Rafay Ishfaq, Uzma Raja, Mark M. Clark
ABSTRACT The changing landscape of environmental regulations, discovery of new domestic sources of natural gas, and the economics of energy markets has resulted in a major shift in the choice of fuel for electric power generation. This research focuses on the relevant factors that impact a power plant's decision to switch fuel from coal to natural gas and the timing of such decisions. The factors studied in this article include capital costs of plant replacement, public policy, associated monetary penalties, availability and access to gas supply networks, and the option of plant retirement. These factors are evaluated in a case study of power plants in the Southeastern United States, using mathematical programming and logistic regression models. The results show that environmental regulations can be effective if the monetary penalties imposed by such regulations are set at an appropriate level, with respect to plant replacement costs. Although it is economic for large-size (power generation capacity > 600 MW) coal-fired power plants to switch fuel to natural gas, plant retirement is more suitable for smaller-sized plants. This article also presents a multi-logit decision model that can help identify the best time for a power plant to switch fuel and whether such a decision is useful in the context of plant replacement costs, fuel costs, electric power decommission limits, and environmental penalties.
{"title":"Fuel-switch decisions in the electric power industry under environmental regulations","authors":"Rafay Ishfaq, Uzma Raja, Mark M. Clark","doi":"10.1080/0740817X.2015.1056391","DOIUrl":"https://doi.org/10.1080/0740817X.2015.1056391","url":null,"abstract":"ABSTRACT The changing landscape of environmental regulations, discovery of new domestic sources of natural gas, and the economics of energy markets has resulted in a major shift in the choice of fuel for electric power generation. This research focuses on the relevant factors that impact a power plant's decision to switch fuel from coal to natural gas and the timing of such decisions. The factors studied in this article include capital costs of plant replacement, public policy, associated monetary penalties, availability and access to gas supply networks, and the option of plant retirement. These factors are evaluated in a case study of power plants in the Southeastern United States, using mathematical programming and logistic regression models. The results show that environmental regulations can be effective if the monetary penalties imposed by such regulations are set at an appropriate level, with respect to plant replacement costs. Although it is economic for large-size (power generation capacity > 600 MW) coal-fired power plants to switch fuel to natural gas, plant retirement is more suitable for smaller-sized plants. This article also presents a multi-logit decision model that can help identify the best time for a power plant to switch fuel and whether such a decision is useful in the context of plant replacement costs, fuel costs, electric power decommission limits, and environmental penalties.","PeriodicalId":13379,"journal":{"name":"IIE Transactions","volume":"47 1","pages":"205 - 219"},"PeriodicalIF":0.0,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0740817X.2015.1056391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59750816","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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