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Mohamed R. Salama; Ronald G. McGarvey. Resilient supply chain to a global pandemic. International Journal of Production Research 2021, 1 -31.
AMA StyleMohamed R. Salama, Ronald G. McGarvey. Resilient supply chain to a global pandemic. International Journal of Production Research. 2021; ():1-31.
Chicago/Turabian StyleMohamed R. Salama; Ronald G. McGarvey. 2021. "Resilient supply chain to a global pandemic." International Journal of Production Research , no. : 1-31.
Classical facility location models can generate solutions that do not maintain consistency in the set of utilized facilities as the number of utilized facilities is varied. We introduce the concept of nested facility locations, in which the solution utilizing p facilities is a subset of the solution utilizing q facilities, for all \(i \le p < q \le j\), given some lower limit i and upper limit j on r, the number of facilities that will be utilized in the future. This approach is demonstrated with application to the p-median model, with computational testing showing these new models achieve reductions in both average regret and worst-case regret when \(r \ne p\) facilities are actually utilized.
Ronald G. McGarvey; Andreas Thorsen. Nested-solution facility location models. Optimization Letters 2021, 1 -18.
AMA StyleRonald G. McGarvey, Andreas Thorsen. Nested-solution facility location models. Optimization Letters. 2021; ():1-18.
Chicago/Turabian StyleRonald G. McGarvey; Andreas Thorsen. 2021. "Nested-solution facility location models." Optimization Letters , no. : 1-18.
Woody biomass provides an opportunity to reduce carbon emissions from existing power plants via co-firing with coal. However, increased demand for woody biomass in electricity generation could potentially increase the price for biomass procurement. This paper presents an econometric demand response model for biomass procurement prices, which is then integrated into a robust mixed-integer nonlinear programming (MINLP) model. We utilize a two-stage approach to efficiently solve this MINLP. The model is then applied to demonstrate the demand-price relationship and to identify efficient frontiers for optimal state partnerships that achieve aggregated CO\(_2\) emission rate targets at minimum aggregate cost, subject to a constraint on the probability of satisfying the desired emission targets.
Bayram Dundar; Ronald G. McGarvey; Francisco X. Aguilar. Optimal multi-state partnerships for woody biomass co-firing incorporating a demand-response function for biomass procurement. Optimization Letters 2021, 1 -24.
AMA StyleBayram Dundar, Ronald G. McGarvey, Francisco X. Aguilar. Optimal multi-state partnerships for woody biomass co-firing incorporating a demand-response function for biomass procurement. Optimization Letters. 2021; ():1-24.
Chicago/Turabian StyleBayram Dundar; Ronald G. McGarvey; Francisco X. Aguilar. 2021. "Optimal multi-state partnerships for woody biomass co-firing incorporating a demand-response function for biomass procurement." Optimization Letters , no. : 1-24.
The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.
Muthana Al-Amidie; Ahmed Al-Asadi; Amjad Humaidi; Ayad Al-Dujaili; Laith Alzubaidi; Laith Farhan; Mohammed Fadhel; Ronald McGarvey; Naz Islam. Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain. Electronics 2021, 10, 529 .
AMA StyleMuthana Al-Amidie, Ahmed Al-Asadi, Amjad Humaidi, Ayad Al-Dujaili, Laith Alzubaidi, Laith Farhan, Mohammed Fadhel, Ronald McGarvey, Naz Islam. Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain. Electronics. 2021; 10 (5):529.
Chicago/Turabian StyleMuthana Al-Amidie; Ahmed Al-Asadi; Amjad Humaidi; Ayad Al-Dujaili; Laith Alzubaidi; Laith Farhan; Mohammed Fadhel; Ronald McGarvey; Naz Islam. 2021. "Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain." Electronics 10, no. 5: 529.
Achieving high quality of service (QOS) at the end-users while maintaining the low interference power at the primary users is the main goal in underlay cognitive radio networks. This goal becomes a more difficult task in the designing of beamforming vectors where all channels state information (CSIs) in the secondary network, as well as the interference CSIs, are uncertain. This task is addressed in this study using an iterative optimization technique. In this technique, the original CSI problem, which is difficult to solve as a single optimization problem, is instead separated into two sub-problems. The first subproblem is the interference power problem, which can be solved either sub-optimally or optimally using Lagrange duality. The second sub-problem is the QOS problem, which can be solved either sub-optimally or robustly using non-monotone spectral projected gradient method. The two sub-problem solutions are then recombined into a single problem to extract beamforming vectors. Two methods are invoked to extract the beamforming vectors: either the successive convex approximation (SCA) method or the bisection search method. Theoretical analysis and simulation results indicate that the two methods can offer a tradeoff between better QOS (using bisection search method) or less computational complexity (using SCA method)
Ahmed Al‐Asadi; Muthana Al‐Amidie; Saddam K. Alwane; Hayder M. Albehadili; Ronald G. McGarvey; And Naz E. Islam. Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario. IET Communications 2020, 14, 3934 -3943.
AMA StyleAhmed Al‐Asadi, Muthana Al‐Amidie, Saddam K. Alwane, Hayder M. Albehadili, Ronald G. McGarvey, And Naz E. Islam. Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario. IET Communications. 2020; 14 (21):3934-3943.
Chicago/Turabian StyleAhmed Al‐Asadi; Muthana Al‐Amidie; Saddam K. Alwane; Hayder M. Albehadili; Ronald G. McGarvey; And Naz E. Islam. 2020. "Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario." IET Communications 14, no. 21: 3934-3943.
For small farmers wishing to sell their products under the “local agriculture” marketing concept, connecting with consumers can be challenging. One approach to mitigating this disconnect between where production occurs and where consumers reside is through a network of regional consolidation points. In this study, we utilize optimization models to assist the Missouri Coalition of Environment (MCE) in helping farmers from Missouri and Illinois route products from their farms to a central hub in St. Louis. The aim of this study was to minimize the ton-miles traveled by farmers and MCE vehicles in delivering agricultural products from farms to regional hubs to the central hub. Given historical data about variability of plant and animal production in the Greater Plains region, a robust optimization approach was incorporated to increase the likelihood that the network can accommodate uncertainty in agricultural production. GAMS/CPLEX was used to solve the model under different configurations and identify potential locations for regional hubs. Computational testing determined that the capacity of hubs plays a key role in the optimal assignments: given the assumed model constraint that farmers can travel only to their nearest regional hub, solutions may sacrifice a better objective function value in order to accommodate farmers’ travel requirements.
Ashish Kambli; Ronald G. McGarvey. Network design for local agriculture using robust optimization. Information Processing in Agriculture 2020, 1 .
AMA StyleAshish Kambli, Ronald G. McGarvey. Network design for local agriculture using robust optimization. Information Processing in Agriculture. 2020; ():1.
Chicago/Turabian StyleAshish Kambli; Ronald G. McGarvey. 2020. "Network design for local agriculture using robust optimization." Information Processing in Agriculture , no. : 1.
A major source of primary health care for millions of Americans, community health centers (CHCs) act as a key point of access for diabetes care. The ability of a CHC to deliver high quality care, that supports patients’ management of their diabetes, may be impacted by the unique set of resources and constraints it faces, both in terms of characteristics of its patient population and aspects of operations. This study examines how patient and regional characteristics, staffing patterns, and efficiency were associated with diabetes management at CHCs (percentage of patients with uncontrolled diabetes, HbA1C > 9%). Data on a sample of 1229 CHCs came from multiple sources. CHC-level information was obtained from the Uniform Data System and regional-level information from the Behavioral Risk Factor Surveillance System and the US Census American Community Survey. A clustering methodology, latent class analysis, identified seven underlying staffing patterns at CHCs. Data envelopment analysis was performed to evaluate the efficiency of CHCs, relative to centers with similar staffing patterns. Finally, generalized linear models were used to examine the association between staffing patterns, efficiency, and patient and regional-level characteristics. Findings from this study have sociological, practical, and methodological implications. Findings highlight that the intersection of patient racial composition with regional racial composition is significant; diabetes control appears to be worse at CHCs serving racial minorities living in predominantly White areas. Findings suggest that CHCs that incorporate more behavioral health care into their staffing mix have lower rates of uncontrolled diabetes among their patients. Finally, greater efficiency in CHC operations is associated with better diabetes control among patients. By identifying sociodemographic and operational characteristics associated with better hemoglobin control among diabetes patients, the current study contributes to our understanding of both health care operations and health inequalities.
Maggie Thorsen; Ronald McGarvey; Andreas Thorsen. Diabetes management at community health centers: Examining associations with patient and regional characteristics, efficiency, and staffing patterns. Social Science & Medicine 2020, 255, 113017 .
AMA StyleMaggie Thorsen, Ronald McGarvey, Andreas Thorsen. Diabetes management at community health centers: Examining associations with patient and regional characteristics, efficiency, and staffing patterns. Social Science & Medicine. 2020; 255 ():113017.
Chicago/Turabian StyleMaggie Thorsen; Ronald McGarvey; Andreas Thorsen. 2020. "Diabetes management at community health centers: Examining associations with patient and regional characteristics, efficiency, and staffing patterns." Social Science & Medicine 255, no. : 113017.
Traditionally, Network Function Virtualization uses Service Function Chaining (SFC) to place service functions and chain them with corresponding flows allocation. With the advent of Edge computing and IoT, a reliable orchestration of latency- sensitive SFCs is needed to compose and maintain them in geo- distributed cloud infrastructures. However, the optimal SFC composition in this case becomes the NP-hard integer multi- commodity-chain flow (MCCF) problem that has no known approximation guarantees. In this paper, we first outline our novel practical and near optimal SFC composition scheme that is based on our novel metapath composite variable approach, admits end-to-end network QoS constraints (e.g., latency) and reaches 99% optimality on average in seconds for practically sized geo-distributed cloud infrastructures. We then propose a novel metapath-based SFC maintenance algorithm that guarantees a distributed control plane consistency without use of expensive consensus protocols. Using trace-driven simulations comprising of challenging disaster-incident conditions, we show that our solution composes twice as many SFCs and uses ≈10x less control messages than state-of-the-art methods. Finally, experimental evaluations of our SFC orchestration prototype deployed on a realistic cloud/edge computing testbed show significant speed-ups (up to 4x) for our case-study geo-distributed latency-sensitive object tracking pipeline w.r.t. its IP-based cloud computing alternative.
Dmitrii Chemodanov; Prasad Calyam; Flavio Esposito; Ronald McGarvey; Kannappan Palaniappan; Antonio Pescape'. A Near Optimal Reliable Orchestration Approach for Geo-Distributed Latency-Sensitive SFCs. IEEE Transactions on Network Science and Engineering 2020, 7, 2730 -2745.
AMA StyleDmitrii Chemodanov, Prasad Calyam, Flavio Esposito, Ronald McGarvey, Kannappan Palaniappan, Antonio Pescape'. A Near Optimal Reliable Orchestration Approach for Geo-Distributed Latency-Sensitive SFCs. IEEE Transactions on Network Science and Engineering. 2020; 7 (4):2730-2745.
Chicago/Turabian StyleDmitrii Chemodanov; Prasad Calyam; Flavio Esposito; Ronald McGarvey; Kannappan Palaniappan; Antonio Pescape'. 2020. "A Near Optimal Reliable Orchestration Approach for Geo-Distributed Latency-Sensitive SFCs." IEEE Transactions on Network Science and Engineering 7, no. 4: 2730-2745.
The design problem associated with robust downlink beamforming in multicast, multigroup, multicell wireless systems is addressed. The channel state information (CSI) of users is assumed to be imperfect and the uncertainty of CSI is modelled using the Frobenius norm. The objective is to optimise the signal-to-interference-plus-noise ratio over all users with a constraint on the maximum total transmitted power. This was achieved through a robust solution using the successive convex approximation (SCA) method. The beamforming problem is treated as a bi-convex problem, which is solved using the iterate-alternative convex technique. Here, the CSI uncertainty is addressed using a convex package through the non-monotone spectral projected gradient method and the beamforming vector is extracted using the SCA method. Also, the authors offer the required condition to extract the beamform vector using the SCA method through a suboptimal solution that always addressed before using different beamforming methods. Their simulation results examine all proposed system parameters in order to show convergence and feasibility of the solution. They also compare the solution with a suboptimal solution and the quality of service method for imperfect CSI in downlink beamforming. Numerical results show that the robust solution achieves the best power efficiency for practical solutions.
Ahmed Al‐Asadi; Muthana Al‐Amidie; Athanasios C. Micheas; Ronald G. McGarvey; Naz E. Islam. Worst case fair beamforming for multiple multicast groups in multicell networks. IET Communications 2019, 13, 664 -671.
AMA StyleAhmed Al‐Asadi, Muthana Al‐Amidie, Athanasios C. Micheas, Ronald G. McGarvey, Naz E. Islam. Worst case fair beamforming for multiple multicast groups in multicell networks. IET Communications. 2019; 13 (6):664-671.
Chicago/Turabian StyleAhmed Al‐Asadi; Muthana Al‐Amidie; Athanasios C. Micheas; Ronald G. McGarvey; Naz E. Islam. 2019. "Worst case fair beamforming for multiple multicast groups in multicell networks." IET Communications 13, no. 6: 664-671.
Community health centers (CHCs) provide comprehensive medical services to medically under-served Americans, helping to reduce health disparities. This study aimed to identify the unique compositions and contexts of CHCs to better understand variation in access to early prenatal care and rates of low birth weights (LBW). Data include CHC-level data from the Uniform Data System, and regional-level data from the US Census American Community Survey and Behavioral Risk Factor Surveillance System. First, latent class analysis was conducted to identify unobserved subgroups of CHCs. Second, data envelopment analysis was performed to evaluate the operational efficiency of CHCs. Third, we used generalized linear models to examine the associations between the CHC subgroups, efficiency, and perinatal outcomes. Seven classes of CHCs were identified, including two rural classes, one suburban, one with large centers serving poor minorities in low poverty areas, and three urban classes. Many of these classes were characterized by the racial compositions of their patients. Findings indicate that CHCs serving white patients in rural areas have greater access to early prenatal care. Health centers with greater efficiency have lower rates of LBW, as do those who serve largely white patient populations in rural areas. CHCs serving poor racial minorities living in low-poverty areas had particularly low levels of access to early prenatal care and high rates of LBW. Findings highlight that significant diversity exists in the sociodemographic composition and regional context of US health centers, in ways that are associated with their operations, delivery of care, and health outcomes. Results from this study highlight that while the provision of early prenatal care and the efficiency with which a health center operates may improve the health of the women served by CHCs and their babies, the underlying social and economic conditions facing patients ultimately have a larger association with their health.
Maggie L. Thorsen; Andreas Thorsen; Ronald McGarvey. Operational efficiency, patient composition and regional context of U.S. health centers: Associations with access to early prenatal care and low birth weight. Social Science & Medicine 2019, 226, 143 -152.
AMA StyleMaggie L. Thorsen, Andreas Thorsen, Ronald McGarvey. Operational efficiency, patient composition and regional context of U.S. health centers: Associations with access to early prenatal care and low birth weight. Social Science & Medicine. 2019; 226 ():143-152.
Chicago/Turabian StyleMaggie L. Thorsen; Andreas Thorsen; Ronald McGarvey. 2019. "Operational efficiency, patient composition and regional context of U.S. health centers: Associations with access to early prenatal care and low birth weight." Social Science & Medicine 226, no. : 143-152.
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult. Furthermore, each pixel containing target is likely to be a mixture of both target and non-target signatures (i.e., sub-pixel targets), making extracting a pure prototype signature for the target class from the data extremely difficult. The proposed approach addresses these problems by introducing a data mixing model and optimizing the response of the hybrid sub-pixel detector within a multiple instance learning framework. The proposed approach iterates between estimating a set of discriminative target and non-target signatures and solving a sparse unmixing problem. After learning target signatures, a signature based detector can then be applied on test data. Both simulated and real hyperspectral target detection experiments show the proposed algorithm is effective at learning discriminative target signatures and achieves superior performance over state-of-the-art comparison algorithms.
Changzhe Jiao; Chao Chen; Ronald McGarvey; Stephanie Bohlman; Licheng Jiao; Alina Zare. Multiple instance hybrid estimator for hyperspectral target characterization and sub-pixel target detection. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 146, 235 -250.
AMA StyleChangzhe Jiao, Chao Chen, Ronald McGarvey, Stephanie Bohlman, Licheng Jiao, Alina Zare. Multiple instance hybrid estimator for hyperspectral target characterization and sub-pixel target detection. ISPRS Journal of Photogrammetry and Remote Sensing. 2018; 146 ():235-250.
Chicago/Turabian StyleChangzhe Jiao; Chao Chen; Ronald McGarvey; Stephanie Bohlman; Licheng Jiao; Alina Zare. 2018. "Multiple instance hybrid estimator for hyperspectral target characterization and sub-pixel target detection." ISPRS Journal of Photogrammetry and Remote Sensing 146, no. : 235-250.
Over 1300 federally-qualified health centers (FQHCs) in the US provide care to vulnerable populations in different contexts, addressing diverse patient health and socioeconomic characteristics. In this study, we use data envelopment analysis (DEA) to measure FQHC performance, applying several techniques to account for both quality of outputs and heterogeneity among FQHC operating environments. To address quality, we examine two formulations, the Two-Model DEA approach of Shimshak and Lenard (denoted S/L), and a variant of the Quality-Adjusted DEA approach of Sherman and Zhou (denoted S/Z). To mitigate the aforementioned heterogeneities, a data science approach utilizing latent class analysis (LCA) is conducted on a set of metrics not included in the DEA, to identify latent typologies of FQHCs. Each DEA quality approach is applied in both an aggregated (including all FQHCs in a single DEA model) and a partitioned case (solving a DEA model for each latent class, such that an FQHC is compared only to its peer group). We find that the efficient frontier for the aggregated S/L approach disproportionately included smaller FQHCs, whereas the aggregated S/Z approach’s reference set included many larger FQHCs. The partitioned cases found that both the S/L and S/Z aggregated models disproportionately disfavored (different) members of certain classes with respect to efficiency scores. Based on these results, we provide general insights into the trade-offs of using these two models in conjunction with a clustering approach such as LCA.
Ronald G. McGarvey; Andreas Thorsen; Maggie L. Thorsen; Rohith Madhi Reddy. Measuring efficiency of community health centers: a multi-model approach considering quality of care and heterogeneous operating environments. Health Care Management Science 2018, 22, 489 -511.
AMA StyleRonald G. McGarvey, Andreas Thorsen, Maggie L. Thorsen, Rohith Madhi Reddy. Measuring efficiency of community health centers: a multi-model approach considering quality of care and heterogeneous operating environments. Health Care Management Science. 2018; 22 (3):489-511.
Chicago/Turabian StyleRonald G. McGarvey; Andreas Thorsen; Maggie L. Thorsen; Rohith Madhi Reddy. 2018. "Measuring efficiency of community health centers: a multi-model approach considering quality of care and heterogeneous operating environments." Health Care Management Science 22, no. 3: 489-511.
Andreas Thorsen; Ronald G. McGarvey. Efficient frontiers in a frontier state: Viability of mobile dentistry services in rural areas. European Journal of Operational Research 2018, 268, 1062 -1076.
AMA StyleAndreas Thorsen, Ronald G. McGarvey. Efficient frontiers in a frontier state: Viability of mobile dentistry services in rural areas. European Journal of Operational Research. 2018; 268 (3):1062-1076.
Chicago/Turabian StyleAndreas Thorsen; Ronald G. McGarvey. 2018. "Efficient frontiers in a frontier state: Viability of mobile dentistry services in rural areas." European Journal of Operational Research 268, no. 3: 1062-1076.
The identification of critical network components is of interest to both interdictors wishing to degrade the network’s performance, and to defenders aiming to preserve network performance in the face of disruption. In this study, novel formulations for the defender’s problem, based on the dual to the multi-commodity flow problem, are developed to solve the critical node problem (CNP), in which the nodes can be disabled, for a variety of commonly-studied objectives, including minimum connectivity, cardinality-constraint CNP, and β-disruptor problem. These objectives have applications in many types of networks, including transportation, communications, public health, and terrorism. Extensive computational experiments are presented, demonstrating that the proposed models dramatically reduce the computational time needed to solve such problems when compared to the best-performing models in the current literature. The proposed CNP models perform particularly well for networks that are originally disconnected (before interdiction) and for networks with a large number of two-degree nodes.
Gokhan Karakose; Ronald G. McGarvey. Optimal Detection of Critical Nodes: Improvements to Model Structure and Performance. Networks and Spatial Economics 2018, 19, 1 -26.
AMA StyleGokhan Karakose, Ronald G. McGarvey. Optimal Detection of Critical Nodes: Improvements to Model Structure and Performance. Networks and Spatial Economics. 2018; 19 (1):1-26.
Chicago/Turabian StyleGokhan Karakose; Ronald G. McGarvey. 2018. "Optimal Detection of Critical Nodes: Improvements to Model Structure and Performance." Networks and Spatial Economics 19, no. 1: 1-26.
This paper examines a network disruption model from the perspective of a network attacker, who wishes to identify the subset of \( K \) nodes to disable on a node-capacitated directed flow network (e.g., air transport) such that the disrupted flow is maximized. Such models can also be used by a network defender who wishes to identify the nodes whose disruption would have the greatest impact on network performance. In this problem, the system flow is constrained not only by the disruption to nodes, but also by the limited capacity on nodes that are not disrupted. This paper presents multiple mixed-integer linear programming formulations for this problem, including a novel path-based formulation, and novel multi-commodity flow-based formulations. Computational testing is provided, demonstrating that the multi-commodity based formulation is able to solve relatively large-sized network instances, with the best performance obtained by a two-step procedure that utilizes an approximation to obtain an initial solution for the provably-optimal multi-commodity flow based formulation.
Gokhan Karakose; Ronald G. McGarvey. Optimal K-node disruption on a node-capacitated network. Optimization Letters 2018, 13, 695 -715.
AMA StyleGokhan Karakose, Ronald G. McGarvey. Optimal K-node disruption on a node-capacitated network. Optimization Letters. 2018; 13 (4):695-715.
Chicago/Turabian StyleGokhan Karakose; Ronald G. McGarvey. 2018. "Optimal K-node disruption on a node-capacitated network." Optimization Letters 13, no. 4: 695-715.
Ronald G. McGarvey. When to call on an advantageous restart option. Journal of Sports Analytics 2018, 4, 133 -143.
AMA StyleRonald G. McGarvey. When to call on an advantageous restart option. Journal of Sports Analytics. 2018; 4 (2):133-143.
Chicago/Turabian StyleRonald G. McGarvey. 2018. "When to call on an advantageous restart option." Journal of Sports Analytics 4, no. 2: 133-143.
Co-firing woody biomass with coal is one approach electricity providers can take to achieve emissions reductions without significantly modifying their existing infrastructure. An important aspect of EPA recommendations for reducing carbon emissions from coal-fired power plants is an allowance for states to enter into multi-state compliance partnerships. This study presents mixed integer linear programming models to identify min-cost approaches for reducing carbon emissions via biomass co-firing subject to spatially-explicit biomass availability constraints, utilising a robust optimisation approach to address uncertainties in costs and emission rates. We apply these models to a set of 18 states in the Northern US, to demonstrate how one state could identify efficient sets of multi-state collaborators.
Bayram Dundar; Ronald G. McGarvey; Francisco Aguilar. A robust optimisation approach for identifying multi-state collaborations to reduce CO2 emissions. Journal of the Operational Research Society 2018, 70, 601 -619.
AMA StyleBayram Dundar, Ronald G. McGarvey, Francisco Aguilar. A robust optimisation approach for identifying multi-state collaborations to reduce CO2 emissions. Journal of the Operational Research Society. 2018; 70 (4):601-619.
Chicago/Turabian StyleBayram Dundar; Ronald G. McGarvey; Francisco Aguilar. 2018. "A robust optimisation approach for identifying multi-state collaborations to reduce CO2 emissions." Journal of the Operational Research Society 70, no. 4: 601-619.
Gokhan Karakose; Ronald McGarvey. Capacitated path-aggregation constraint model for arc disruption in networks. Transportation Research Part E: Logistics and Transportation Review 2018, 109, 225 -238.
AMA StyleGokhan Karakose, Ronald McGarvey. Capacitated path-aggregation constraint model for arc disruption in networks. Transportation Research Part E: Logistics and Transportation Review. 2018; 109 ():225-238.
Chicago/Turabian StyleGokhan Karakose; Ronald McGarvey. 2018. "Capacitated path-aggregation constraint model for arc disruption in networks." Transportation Research Part E: Logistics and Transportation Review 109, no. : 225-238.
Gokhan Karakose; Ronald G. McGarvey. Node-securing connectivity-based model to reduce infection spread in contaminated networks. Computers & Industrial Engineering 2018, 115, 512 -519.
AMA StyleGokhan Karakose, Ronald G. McGarvey. Node-securing connectivity-based model to reduce infection spread in contaminated networks. Computers & Industrial Engineering. 2018; 115 ():512-519.
Chicago/Turabian StyleGokhan Karakose; Ronald G. McGarvey. 2018. "Node-securing connectivity-based model to reduce infection spread in contaminated networks." Computers & Industrial Engineering 115, no. : 512-519.
Esma Birisci; Ronald McGarvey. Optimal production planning utilizing leftovers for an all-you-care-to-eat food service operation. Journal of Cleaner Production 2018, 171, 984 -994.
AMA StyleEsma Birisci, Ronald McGarvey. Optimal production planning utilizing leftovers for an all-you-care-to-eat food service operation. Journal of Cleaner Production. 2018; 171 ():984-994.
Chicago/Turabian StyleEsma Birisci; Ronald McGarvey. 2018. "Optimal production planning utilizing leftovers for an all-you-care-to-eat food service operation." Journal of Cleaner Production 171, no. : 984-994.