Table of Content

30 December 2020, Volume 8 Issue 4

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Preface–Special Issue on Recent Developments in Operations Research: Theory and Applications

Wuyi Yue, Hsing Luh, Duan Li

2020, 8(4):  533-535.  doi:10.1007/s40305-020-00329-2

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Looking for and Aiming for an Asian OR Applicable to the Public Sector

Tatsuo Oyama

2020, 8(4):  537-559.  doi:10.1007/s40305-019-00291-8

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Operations research (OR) is a scientific approach for solving various types of societal problems and making decisions to deal with these problems appropriately. First, we briefly describe the history of OR, focusing on applying OR to public sectors, and then provide its characterization as a scientific method for decision making. Then, OR activities in Japan are introduced, emphasizing three major roles: (i) quantitative data analysis, (ii) mathematical modeling analysis, and (iii) theory building analysis. We provide an example for each of these three types of major roles. Based upon the analyses, we seek an Asian OR applicable to policy making in the public sector. Finally, we provide a summary and discuss future perspectives for OR.

Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Environment

Xiushuang Wang, Jing Zhu, Shunfu Jin, Wuyi Yue, Yutaka Takahashi

2020, 8(4):  561-580.  doi:10.1007/S40305-019-00272-x

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Achieving greener cloud computing is non-negligible for the open-source cloud platform. In this paper, we propose a novel virtual machine allocation scheme with a sleep-delay and establish a corresponding mathematical model. Taking into account the number of tasks and the state of the physical machine, we construct a two-dimensional Markov chain and derive the average latency of tasks and the energy-saving degree of the system in the steady state. Moreover, we provide numerical experiments to show the effectiveness of the proposed scheme. Furthermore, we study the Nash equilibrium behavior and the socially optimal behavior of tasks and carry out an improved adaptive genetic algorithm to obtain the socially optimal arrival rate of tasks. Finally, we present a pricing policy for tasks to maximize the social profit when managing the network resource within the cloud environment.

The Time-Scaling Transformation Technique for Optimal Control Problems with Time-Varying Time-Delay Switched Systems

Ning Zhang, Chang-Jun Yu, Fu-Sheng Xie

2020, 8(4):  581-600.  doi:10.1007/s40305-020-00299-5

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In this paper, we consider a class of optimal control problems where the dynamical systems are time-delay switched systems with the delay being a function of time. By applying the control parameterization method, the control heights and switching times become decision variables that need to be optimized. It is well-known that, for this type problem, the variable switching times cannot be optimized directly. To work around this problem, we introduce a time-scaling transformation technique so that the original system is transformed an equivalent system, which is defined on a new time horizonwithfixedswitchingtimes.Basedontherelationshipbetweentheoriginaltime scale and the new time scale, we derive the gradients of the objective and constraint functions with respect to the control heights and durations. Then, the new problem can be solved by gradient-based optimization approach. To demonstrate the effectiveness of the time-scaling transformation technique, two example problems are solved.

Proposal of Japanese Vocabulary Difficulty Level Dictionaries for Automated Essay Scoring Support System Using Rubric

Megumi Yamamoto, Nobuo Umemura, Hiroyuki Kawano

2020, 8(4):  601-617.  doi:10.1007/s40305-019-00270-z

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We are developing a Moodle plug-in, which is an AES (automated essay scoring) support system for the basic education of university students. Our system evaluates essays based on rubric, which has five evaluation viewpoints “Contents, Structure, Evidence, Style, and Skill”. Vocabulary level is one of the scoring items of Skill. It is calculated using Japanese Language Learners’ Dictionaries constructed by Sunakawa et al. Since this does not fully cover the words used in the student-level essays, we found that there is a problem with the accuracy of the vocabulary level scoring. In this paper, we propose to construct comprehensive Japanese vocabulary difficulty level dictionaries using Japanese Wikipedia as the corpus. We apply Latent Dirichlet Allocation (LDA) to the Wikipedia corpus and find the word appearance probability as oneoftheindexesofworddifficulty.WeusetheTF-IDFvalueinsteadoftheLDAvalue of the words, which rarely appears. As a result, we constructed highly comprehensive Japanese vocabulary difficulty level dictionaries. We confirmed that the vocabulary levelcanbescoredforallwordsinthetestdatasetbyusingtheconstructeddictionaries.

On Semi-infinite Mathematical Programming Problems with Equilibrium Constraints Using Generalized Convexity

Bhuwan Chandra Joshi, Shashi Kant Mishra, Pankaj Kumar

2020, 8(4):  619-636.  doi:10.1007/s40305-019-00263-y

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In this paper, we consider semi-infinite mathematical programming problems with equilibrium constraints (SIMPPEC). By using the notion of convexificators, we establish sufficient optimality conditions for the SIMPPEC. We formulate Wolfe and Mond–Weir-type dual models for the SIMPPEC under the invexity and generalized invexity assumptions. Weak and strong duality theorems are established to relate the SIMPPEC and two dual programs in the framework of convexificators.

Optimal Contract for the Principal-Agent Under Knightian Uncertainty

Kun-Lun Wang, Chen Fei, Wei-Yin Fei

2020, 8(4):  637-654.  doi:10.1007/s40305-020-00316-7

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Under the Knightian uncertainty, this paper constructs the optimal principal (he)-agent (she) contract model based on the principal’s expected profit and the agent’s expected utility function by using the sublinear expectation theory. The output process in the model is provided by the agent’s continuous efforts and the principal cannot directly observe the agent’s efforts. In the process of work, risk-averse agent will have the opportunity to make external choices. In order to promote the agent’s continuous efforts, the principal will continuously provide the agents with consumption according to the observable output process after the probation period. In this paper, the Hamilton– Jacobi–Bellman equation is deduced by using the optimality principle under sublinear expectation while the smoothness viscosity condition of the principal-agent optimal contract is given. Moreover, the continuation value of the agent is taken as the state variable to characterize the optimal expected profit of the principal, the agent’s effort and the consumption level under different degrees of Knightian uncertainty. Finally, the behavioral economics is used to analyze the simulation results. The research findings are that the increasing Knightian uncertainty incurs the decline of the principal’s maximum profit; within the probation period, the increasing Knightian uncertainty leads to the shortening of probation period and makes the agent give higher effort when she faces the outside option; what’s more, after the smooth completion of the probation period for the agent, the agent’s consumption level will rise and her effort level will drop as Knightian uncertainty increasing.

Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique

Yuji Matsuo, Tatsuo Oyama

2020, 8(4):  655-667.  doi:10.1007/s40305-019-00282-9

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In this paper, we propose a hybrid forecasting model (HFM) for the short-term electric loadforecastingusingartificialneuralnetwork(ANN),discreteFouriertransformation (DFT) and principal component analysis (PCA) techniques in order to attain higher prediction accuracy. Firstly, we estimate Fourier coefficients by the DFT for predicting the next-day load curve with an ANN and obtain approximate load curves by applying the inverse discrete Fourier transformation. Approximate curves, together with other input variables, are given to the ANN to predict the next-day hourly load curves. Furthermore, we predict PCA scores to obtain approximate load curves in the first step, which are then given to the ANN again in the second step. Both DFT and PCA models use input variables such as calendrical and meteorological data as well as past electric loads. Applying those models for forecasting hourly electric load in the metropolitan area of Japan for January and May in 2018, we train our models using historical data since January 2008. The forecast results show that the HFM consisting of “ANN with DFT” and “ANN with PCA” predicts next-day hourly loads more accurately than the conventional three-layered ANN approach. Their corresponding mean average absolute errors show 2.7% for ANN with DFT, 2.6% for ANN with PCA and 3.0% for the conventional ANN approach. We also find that in May, when electric demand is smaller with smaller fluctuations, forecasting errors are much smaller than January for all the models. Thus, we can conclude that the HFM would contribute to attaining significantly higher forecasting accuracy.

Network-Based Multiple UAVs Search Planning for Disaster Relief

Hozumi Morohosi

2020, 8(4):  669-679.  doi:10.1007/s40305-019-00283-8

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This paper studies the use of multiple unmanned aerial vehicles (UAVs) for searching a victim of disaster. We propose a network-based optimization model for planning the search path of multiple UAVs in a disaster-stricken area and estimating the number of UAVs necessary for search. A heuristic algorithm is devised to solve the optimization model and applied to the problem instances taken from potential hazard areas in Japan. Our computational result shows relatively small number of UAVs is enough to cover the area in most of cases. We also give an investigation on the relation between search area and the number of UAVs necessary for search via regression methods.