Annals of Operations Research

Afsar, Bekir; Silvennoinen, Johanna; Ruiz, Francisco; Ruiz, Ana B.; Misitano, Giovanni; Miettinen, Kaisa

doi: 10.1007/s10479-024-05941-6pmid: N/A

In multiobjective optimization problems, Pareto optimal solutions representing different tradeoffs cannot be ordered without incorporating preference information of a decision maker (DM). In interactive methods, the DM takes an active part in the solution process and provides preference information iteratively. Between iterations, the DM can learn how achievable the preferences are, learn about the tradeoffs, and adjust the preferences. Different interactive methods have been proposed in the literature, but the question of how to select the best-suited method for a problem to be solved remains partly open. We propose an experimental design for evaluating interactive methods according to several desirable properties related to the cognitive load experienced by the DM, the method’s ability to capture preferences and its responsiveness to changes in the preferences, the DM’s satisfaction in the overall solution process, and their confidence in the final solution. In the questionnaire designed, we connect each questionnaire item to be asked with a relevant research question characterizing these desirable properties of interactive methods. We also conduct a between-subjects experiment to compare three interactive methods and report interesting findings. In particular, we find out that trade-off-free methods may be more suitable for exploring the whole set of Pareto optimal solutions, while classification-based methods seem to work better for fine-tuning the preferences to find the final solution.

Babou, Nadia; Rebaine, Djamal; Boudhar, Mourad

doi: 10.1007/s10479-024-06097-zpmid: N/A

We address in this paper the two-machine open shop problem with a single server to prepare jobs before going through the processing so as to minimize the makespan. The server is only needed during the preparation phase before becoming available again, leaving the prepared job to complete its processing. We present three lower bounds with respect to the makespan. In addition, we show the NP\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\mathcal{N}\mathcal{P}$$\end{document}-completeness of two restricted cases. Then, we present a well solvable case. Finally, we develop two mixed integer linear programming (MILP) models for the general problem along with an experimental study we conducted to analyze their performance.

Corbet, Shaen; Hou, Yang; Hu, Yang; Oxley, Les

doi: 10.1007/s10479-024-06001-9pmid: N/A

Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets.

Gui, Lin; Li, Xinyu; Zhang, Qingfu; Gao, Liang

doi: 10.1007/s10479-024-06039-9pmid: N/A

Uniform sampling in the permutation space is very important for solving permutation problems with NP-hard nature. However, due to the complexity of this space, there is no uniform sampling method for it up to now. In this paper, the description of permutation space and a review of uniform sampling in other space are given. After that, the limitation of the random method for uniform sampling is analyzed, and a k-means clustering algorithm with an improved Borda's method is introduced for sampling based on the above analysis. An extended Latin matrix is defined, and a sampling method based on this matrix that can only solve for a fixed number of sampling is presented. The properties of this method are explored and demonstrated. A uniform sampling method is then proposed for an arbitrary number of sampling points. Experiments are implemented under different sizes of permutation spaces and the results show that the method proposed in this paper has superior performance, which is more than 100 times better than the random method.

Katsafados, Apostolos G.; Anastasiou, Dimitris

doi: 10.1007/s10479-024-06048-8pmid: N/A

Motivated by the successful usage of machine learning around computer science and its wide acceptance from the finance literature, we utilize monthly data spanning the period 2008–2018 for the Euro area peripheral countries, in order to embark on a two-fold mission. First, to construct short-term prediction models for bank deposit flows in the Euro area peripheral countries, employing machine learning techniques. Second, to examine whether textual features enhance the predictive ability of our models. From the variety of models tested, we find that Random Forest models including both textual features and macroeconomic variables outperform models including only macro factors or textual features. Monetary policy authorities or macroprudential regulators could adopt our approach to timely predict potential excessive bank deposit outflows and assess the resilience of the whole banking sector in the Euro area peripheral countries.

Kongo, Takumi

doi: 10.1007/s10479-024-06057-7pmid: N/A

In cooperative games with transferable utilities defined on a variable set of players, we characterize the family of values satisfying efficiency, linearity, the equal treatment property, and the weak null player out property. The last property weakens the usual null player out property, and together with efficiency, it is interpreted as considering equal support from others for null players. Together with the fact that efficiency, linearity, and the equal treatment property characterize the Shapley value along with the usual null player out property, our result reveals how weakening the null player out property can expand the possibilities of solutions. The characterized family contains well-known values in the literature, such as the Shapley value, equal division value, equal surplus division value, and the egalitarian non-separable contributions value, etc. In addition, each value in the characterized family is determined by an infinite sequence of real numbers. Furthermore, the equal treatment property in our characterization can be replaced by the balanced contributions property for symmetric players. Comparing this result with the existing one also shows that how weakening the balanced contributions property more can expand the possibilities of solutions.

Lyu, Wenrong; Shan, Erfang; Cui, Zeguang

doi: 10.1007/s10479-024-06073-7pmid: N/A

Consistency is widely adopted in designing allocation rules for cooperative games. It imposes that the allocation rules give players the same payoff in the reduced game as in the initial cooperative game. The reduced game is obtained from the initial cooperative game by removing one or more players. In this paper, by extending the concept of consistency to cooperative games with coalition and graph structures, we establish a new axiomatization of the Owen graph value. Moreover, we give a comparison between the axiomatizations of the Owen, Banzhaf–Owen and symmetric coalitional Banzhaf graph values.

Paul, Alice; Martonosi, Susan E.

doi: 10.1007/s10479-024-06022-4pmid: N/A

Traditional network interdiction problems focus on removing vertices or edges from a network so as to disconnect or lengthen paths in the network; network diversion problems seek to remove vertices or edges to reroute flow through a designated critical vertex or edge. We introduce the all-pairs vitality maximization problem (VIMAX), in which vertex deletion attempts to maximize the amount of flow passing through a critical vertex, measured as the all-pairs vitality of the vertex. The assumption in this problem is that in a network for which the structure is known but the physical locations of vertices may not be known (e.g., a social network), locating a person or asset of interest might require the ability to detect a sufficient amount of flow (e.g., communications or financial transactions) passing through the corresponding vertex in the network. We formulate VIMAX as a mixed integer program, and show that it is NP-Hard. We compare the performance of the MIP and a simulated annealing heuristic on both real and simulated data sets and highlight the potential increase in vitality of key vertices that can be attained by subset removal. We also present graph theoretic results that can be used to narrow the set of vertices to consider for removal.

Qin, Shanshan; Tan, Zhenni; Wu, Yuehua

doi: 10.1007/s10479-024-05989-4pmid: N/A

Regime-switching models provide an efficient framework for capturing the dynamic behavior of data observed over time and are widely used in economic or financial time series analysis. In this paper, we propose a novel and robust hidden semi-Markovian regime-switching (rHSMS) method. This method uses a general ρ\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\rho $$\end{document}-based distribution to correct for data problems that contain atypical values, such as outliers, heavy-tailed or mixture distributions. Notably, the rHSMS method enhances not only the scalability of the distribution assumptions for all regimes, but also the scalability to accommodate arbitrary sojourn types. Furthermore, we develop a likelihood-based estimation procedure coupled with the use of the EM algorithm to facilitate practical implementation. To demonstrate the robust performance of the proposed rHSMS method, we conduct extensive simulations under different sojourns and scenarios involving atypical values. Finally, we validate the effectiveness of the rHSMS method using monthly returns of the S &P500 Index and the Hang Seng Index. These empirical applications demonstrate the utility of the rHSMS approach in capturing and understanding the complexity of financial market dynamics.

Rödder, Wilhelm; Dellnitz, Andreas; Reucher, Elmar

doi: 10.1007/s10479-023-05193-wpmid: N/A

Power in human societies is a central phenomenon. Even though, it took ages to understand it and – even more – to measure it. Only in the last decades attempts were made to model power relations and to assign respective power indices to actors in a network. The present work goes a step further. It measures power of actors and groups of actors in networks by means of conditional relations. In a probabilistic framework, such relations are specified as conditionals: Which actor receives power given that the adjacent actor has it, and which actor looses power given that the neighbour dominates. This pattern of power relations allows for an exact calculation of an actor’s and groups of actors’ power index. The new decision analytics tool for this is maximizing entropy for the whole net and evaluating each actor’s influence therein. The new concept is applied to a middle size Kronecker net of clans and subclans operating in a today’s society.