| 1. |
- Agrell, Per J., et al.
(författare)
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Impacts on efficiency of merging the Swedish district courts
- 2020
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Ingår i: Annals of Operations Research. - : Springer. - 0254-5330 .- 1572-9338. ; 288:2, s. 653-679
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Tidskriftsartikel (refereegranskat)abstract
- Judicial courts form a stringent example of public services using partially sticky inputs and outputs with heterogeneous quality. Notwithstanding, governments internationally are striving to improve the efficiency of and diminish the budget spent on court systems. Frontier methods such as data envelopment analysis are sometimes used in investigations of structural changes in the form of mergers. This essay reviews the methods used to evaluate the ex post efficiency of horizontal mergers. Identification of impacts is difficult. Therefore, three analytical frameworks are applied: (1) a technical efficiency comparison over time, (2) a metafrontier approach among mergers and non-mergers, and (3) a conditional difference-in-differences approach where non-merged twins of the actual mergers are identified by matching. In addition, both time heterogeneity and sources of efficiency change are examined ex post. The method is applied to evaluate the impact on efficiency of merging the Swedish district courts from 95 to 48 between 2000 and 2009. Whereas the stated ambition for the mergers was to improve efficiency, no structured ex post analysis has been done. Swedish courts are shown to improve efficiency from merging. In addition to the particular application, this work may inform a more general discussion on public service efficiency measurement under structural changes, and their limits and potential.
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| 2. |
- Akyildirim, Erdinc, et al.
(författare)
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Forecasting mid-price movement of Bitcoin futures using machine learning
- 2021
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Ingår i: Annals of Operations Research. - : SPRINGER. - 0254-5330 .- 1572-9338.
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Tidskriftsartikel (refereegranskat)abstract
- In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.
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| 3. |
- Baltas, Konstantinos, et al.
(författare)
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The role of resource orchestration in humanitarian operations : a COVID-19 case in the US healthcare
- 2022
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Ingår i: Annals of Operations Research. - : Springer. - 0254-5330 .- 1572-9338.
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates the role of resource allocation in alleviating the impact on from disruptions in healthcare operations. We draw on resource orchestration theory and analyse data stemming from US healthcare to discuss how the US healthcare system structured, bundled and reconfigured resources (i.e. number of hospital beds, and vaccines) during the COVID-19 pandemic. Following a comprehensive and robust econometric analysis of two key resources (i.e. hospital beds and vaccines), we discuss its effect on the outcomes of the pandemic measured in terms of confirmed cases and deaths, and draw insights on how the learning curve effect and other factors might influence in the efficient and effective control of the pandemic outcomes through the resource usage. Our contribution lies in revealing how different resources are orchestrated (structured, bundled, and leveraged) to help planning responses to and dealing with the disruptions to create resilient humanitarian operations. Managerial implications, limitations and future research directions are also discussed.
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| 4. |
- Hamdan, Sadeque, et al.
(författare)
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On the binary formulation of air traffic flow management problems
- 2023
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Ingår i: Annals of Operations Research. - : Springer. - 0254-5330 .- 1572-9338. ; 321, s. 267-279
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Tidskriftsartikel (refereegranskat)abstract
- We discuss a widely used air traffic flow management formulation. We show that this formulation can lead to a solution where air delays are assigned to flights during their take-off which is prohibited in practice. Although air delay is more expensive than ground delay, the model may assign air delay to a few flights during their take-off to save more on not having as much ground delay. We present a modified formulation and verify its functionality in avoiding incorrect solutions.
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| 5. |
- Hasan, Md Bokhtiar, et al.
(författare)
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Do commodity assets hedge uncertainties? What we learn from the recent turbulence period?
- 2022
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Ingår i: Annals of Operations Research. - : Springer. - 0254-5330 .- 1572-9338.
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Tidskriftsartikel (refereegranskat)abstract
- This study analyses the impact of different uncertainties on commodity markets to assess commodity markets hedging or safe-haven properties. Using time-varying dynamic conditional correlation and wavelet-based Quantile-on-Quantile regression models, our findings show that, both before and during the COVID-19 crisis, soybeans and clean energy stocks offer strong safe-haven opportunities against cryptocurrency price uncertainty and geopolitical risks (GPR). Soybean markets weakly hedge cryptocurrency policy uncertainty, US economic policy uncertainty, and crude oil volatility. In addition, GSCI commodity and crude oil also offer a weak safe-haven property against cryptocurrency uncertainties and GPR. Consistent with earlier studies, our findings indicate that safe-haven traits can alter across frequencies and quantiles. Our findings have significant implications for investors and regulators in hedging and making proper decisions, respectively, under diverse uncertain circumstances.
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| 6. |
- Horn, Matthias, et al.
(författare)
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A* Search for Prize-Collecting Job Sequencing with One Common and Multiple Secondary Resources
- 2021
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Ingår i: Annals of Operations Research. - : SPRINGER. - 0254-5330 .- 1572-9338. ; 302, s. 477-505
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Tidskriftsartikel (refereegranskat)abstract
- We consider a sequencing problem with time windows, in which a subset of a given set of jobs shall be scheduled. A scheduled job has to execute without preemption and during this time, the job needs both a common resource for a part of the execution as well as a secondary resource for the whole execution time. The common resource is shared by all jobs while a secondary resource is shared only by a subset of the jobs. Each job has one or more time windows and due to these, it is not possible to schedule all jobs. Instead, each job is associated with a prize and the task is to select a subset of jobs which yields a feasible schedule with a maximum sum of prizes. First, we argue that the problem is NP-hard. Then, we present an exact A* algorithm and derive different upper bounds for the total prize; these bounds are based on constraint and Lagrangian relaxations of a linear programming relaxation of a multidimensional knapsack problem. For comparison, a compact mixed integer programming (MIP) model and a constraint programming model are also presented. An extensive experimental evaluation on three types of problem instances shows that the A* algorithm outperforms the other approaches and is able to solve small to medium size instances with up to about 40 jobs to proven optimality. In cases where A* does not prove that an optimal solution is found, the obtained upper bounds are stronger than those of the MIP model.
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| 7. |
- Ingebretsen Carlson, Jim
(författare)
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A speedy auction using approximated bidders' preferences
- 2020
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Ingår i: Annals of Operations Research. - : Springer Science and Business Media LLC. - 0254-5330 .- 1572-9338. ; 288:1, s. 65-93
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a combinatorial auction, which is of particular interest when short completion times are of importance. It is based on a method for approximating the bidders' preferences over two types of item when complementarity between the two may exist. The resulting approximated preference relation is shown to be complete and transitive at any given price vector. It is shown that an approximated Walrasian equilibrium always exists if all bidders either view the items as substitutes or complements. If the approximated preferences of the bidders comply with the gross substitutes condition, then the set of approximated Walrasian equilibrium prices forms a complete lattice. A process is proposed that is shown to always reach the smallest approximated Walrasian price vector. Simulation results suggest that the approximation procedure works well as the difference between the approximated and true minimal Walrasian prices is small.
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| 8. |
- Jana, Rabin K., et al.
(författare)
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COVID-19 news and the US equity market interactions: An inspection through econometric and machine learning lens
- 2022
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Ingår i: Annals of Operations Research. - : SPRINGER. - 0254-5330 .- 1572-9338.
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Tidskriftsartikel (refereegranskat)abstract
- This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets.
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| 9. |
- Kalaivaani, P. C. D., et al.
(författare)
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Advanced lightweight feature interaction in deep neural networks for improving the prediction in click through rate
- 2021
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Ingår i: Annals of Operations Research. - : Springer. - 0254-5330 .- 1572-9338.
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Tidskriftsartikel (refereegranskat)abstract
- Online advertising has expanded to a hundred-dollar billion industry in recent years, with sales growing at faster rate in every year. Prediction of the click-through rate (CTR) is an important role in recommended systems and online ads. Click through rating (CTR) is the newest evolution in the advertising and marketing digital world. It is essential for any online advertising company in real time to display the appropriate ads to the right users in the correct context. A huge amount of research work proposed considers each ad separately and does not takes in the relationship with other ads that may have an impact on Click Through Rate. A Factorization machine, a more generalized predictor like support vector machines (SVM) is not able to estimate reliable parameters under sparsity. The main drawback is that the primary features and existing algorithms considers the large weighted parameters. KGCN (Knowledge graph-based convolution network) overcomes the drawback and works on alternating graphs which creates additional clustering and node comparison with high latency and performance. A new framework DeepLight Weight is proposed to resolve the high server latency and high usage of memory issues in online advertising. This work presents a framework to improve the CTR predictions with an objective to accelerate the model inference, prune redundant parameters and the dense embedding vectors. Field Weighed Factorization machine helps to organize the data features with high structure to improve the accuracy. For clearing latency issues, structural pruning makes the algorithm work with dense matrices by combining and executing the individual matrix values or neural nodes.
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| 10. |
- Karlsson, Emil, et al.
(författare)
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A matheuristic approach to large-scale avionic scheduling
- 2021
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Ingår i: Annals of Operations Research. - : SPRINGER. - 0254-5330 .- 1572-9338. ; 302, s. 425-459
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Tidskriftsartikel (refereegranskat)abstract
- Pre-runtime scheduling of avionic systems is used to ensure that the systems provide the desired functionality at the correct time. This paper considers scheduling of an integrated modular avionic system which from a more general perspective can be seen as a multiprocessor scheduling problem that includes a communication network. The addressed system is practically relevant and the computational evaluations are made on large-scale instances developed together with the industrial partner Saab. A subset of the instances is made publicly available. Our contribution is a matheuristic for solving these large-scale instances and it is obtained by improving the model formulations used in a previously suggested constraint generation procedure and by including an adaptive large neighbourhood search to extend it into a matheuristic. Characteristics of our adaptive large neighbourhood search are that it is made over both discrete and continuous variables and that it needs to balance the search for feasibility and profitable objective value. The repair operation is to apply a mixed-integer programming solver on a model where most of the constraints are treated as soft and a violation of them is instead penalised in the objective function. The largest solved instance, with respect to the number of tasks, has 54,731 tasks and 2530 communication messages.
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