Table of Content

25 May 2020, Volume 29 Issue 5

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Theory Analysis and Methodology Study

Optimization Model and Algorithm for Rescue Plan of Islands in Shipping-air Coordination

LIN Wan-ni, WANG Nuo, SHEN Ming-qi, SONG Yun-ting

2020, 29(5):  1-8.  DOI: 10.12005/orms.2020.0112

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The supply of materials for the remote islands may be disturbed by emergency crisis, which needs emergency rescue. Under this circumstance, this paper establishes a shipping-air combined scheduling model of islands. The model takes the central island as a rescue origin, the selection of rescue routes and distribution of materials batch as an optimization content, and the shortest time from central island to the surrounding islands as an objective. According to the characteristics of the model, on the basic of the partition based on autonomous genetic algorithm (PB-GA), a genetic algorithm of double-layer search capable of considering two modes and multiple batches of transportation is proposed. Finally, an optimization analysis is carried out with the emergency rescue of the South China Sea Islands as an example and comparison of algorithms. The results show that the proposed algorithm is better in optimization results and computation time, which verifies the rationality and validity of the model and algorithm proposed in this paper. This paper provides an analytical method for making shipping-air coordinated rescue plan of islands.

Optimal Pricing and Releasing Order in a Two-stage Freemium Model

CHEN Xiao-yan, GENG Wei

2020, 29(5):  9-16.  DOI: 10.12005/orms.2020.0113

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In business practices where firms adopt the freemium model, the free versions are often released prior to the premium versions to take the advantage of consumer learning. We develop a two-stage model to solve the optimal pricing problem for the firms. Given all other parameters fixed, the optimal price of the premium version is obtained analytically. Conditions to guarantee a higher profit for firms applying such two-stage model compared to the conventional single-stage model with simultaneous releasing are then constructed. Following numerical analysis we make a sensitivity analysis of the optimal profit against the strength of the learning effect, and it indicates that the aforementioned conditions could be partially relaxed after optimizing the strength.

Appointment Scheduling of Medical Examination Based on MDP and Dynamic programming

LIANG Feng, XU Ping

2020, 29(5):  17-25.  DOI: 10.12005/orms.2020.0114

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Medical examination plays an important role in the diagnosis of disease. In view of the appointment scheduling of medical examination equipment, this paper considers two sets of medical equipment and three types of patients (outpatients, inpatients, emergency patients) with different check time. With the goal of maximizing the revenue in medical examination system, a finite horizon Markov decision process (MDP) model is proposed. Then, combined with dynamic programming model and iteration, the optimal appointment scheduling strategy can be obtained. Using matlab to do the programming, this paper simulates the arrival of different patients. Based on the setting of related parameters, the numerical examples show that the scheduling strategy in this paper is superior to the traditional appointment strategy. Finally, sensitivity analysis is carried out to explore the applicability of this appointment scheduling strategy. The conclusion is that Markov decision process (MDP) is suitable for the appointment scheduling of medical examination equipment. When the system capacity is scarce, or the arrival rate of inpatients increases, this decision model will be superior to traditional strategy.

Appointment Scheduling of Heterogeneous Outpatients under Random Service Time

ZHANG Wen-si, LI Jin-lin, RAN Lun, WANG Wei

2020, 29(5):  26-36.  DOI: 10.12005/orms.2020.0115

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Given variety and complexity of diseases, along with uncertainty of patient behaviors, this article focuses on heterogeneous outpatients characterized by different service time and behaviors, and develops model to design optimal appointment scheduling rules. A stochastic mixed integer programming has been proposed to solve the appointment scheduling problem under the assumptions of patient heterogeneity in stochastic service duration distribution and patient no-shows. A heuristic algorithm has been presented based on a two-patients appointment scheduling system to solve the optimal arrival time of each patient. The numerical results suggest that when the service time for each patient are i.i.d. variables, the service time allocation exhibits a dome shape, i.e., job allowances initially increase and then decrease. When the service time distributions are different from each other, the efficiency of algorithm can be verified by comparing with sample average approximation method.

Optimal Routing Problem in Dynamic Stochastic Networks Based on Robust Optimization Approach

SUN Shi-chao

2020, 29(5):  37-42.  DOI: 10.12005/orms.2020.0116

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Accidents, bad weather and traffic congestion contribute to the uncertainties of travel times in real-life transportation networks, which greatly impact the quality of individual's life and the reliability of transportation systems. In this context, this paper proposes a robust optimization approach which addresses the optimal routing problem in transportation networks with dynamic stochastic link travel times. Specifically, taking the reliability of travel times into consideration, the indicator of robust schedule delay is used as the criterion of optimality to evaluate the candidate paths. The objective function is defined as minimizing the largest cost between the actual arriving time and the desired arrival time in such a road environment. Then, under the stochastic consistent condition, a mathematical proof is given to simplify the model, and it is converted into solving an optimal path problem in a deterministic dynamic network. In the end, a modified Dijkstra's algorithm is applied to solving the problem in a sampled network. The computation complexity of the algorithm is polynomial-time, and the proposed approach is not probability-based. Thus, it has an potential of an application to a large-scale network.

Optimization of AGV Dispatching and Configuration Considering Path Conflict

FAN Hou-ming, YUE Li-jun, LI Dang, MA Meng-zhi

2020, 29(5):  43-51.  DOI: 10.12005/orms.2020.0117

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Reasonable scheduling for limited terminal resources to meet the laytime of ship is one of the important objectives of automated container terminals. Aiming at the problem of automatic guided vehicle (AGV) configuration and scheduling, considering the ship discharging and loading time requirements and the path conflict in the AGV transportation process, a new staged scheduling strategy is proposed. The container handling operation is divided into three stages: discharging stage, discharging and loading synchronization stage and loading stage. In each stage, a scheduling optimization model is established to minimize the maximum completion time and minimize the AGV no-load and waiting time. An improved heuristic algorithm based on NSGA-II is used to solve the model. According to the actual completion time of this phase, the configuration and scheduling scheme of the next phase AGV is selected from the optimal solution set. Finally, compared with other scheduling schemes, the scheduling scheme of this paper can meet the requirements of laytime, and the utilization rate of AGV is improved, which is more in line with the actual operational requirements of the terminal.

Research on Path Optimization Modeling and Algorithm of WorkshopHandling Robotwith Time Window

REN Jian-feng, YE Chun-ming, YANG Feng

2020, 29(5):  52-60.  DOI: 10.12005/orms.2020.0118

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This paper takes the workshop handling robot as a research object, and solves the path optimization problem of pickup and delivery with the time window. This paper proposes a method of reinforcement learning genetic-ant colony hybrid algorithm(RLGA). Firstly, the number of initial handling robots is solved by scanning method, and the geometric center of sub-path nodes is set as virtual node. The ant colony algorithm embedded with genetic operator is used to solve the optimal connection virtual node. Secondly, the optimal sub-path is solved by using the algorithm of reinforcement learning.Finally, the weighted sum of the basic cost, transportation cost and time penalty cost is taken as the target solution, and the optimal solution satisfying the constraint condition is obtained. The superiority of the reinforcement learning genetic-ant colony hybrid algorithm is verified by comparing with the results of the benchmark problem.

Approximation Algorithm for the Robust Dynamic Facility Location Problem

WU Chen-chen, WANG Li, XU Chun-ming, XU Da-chuan

2020, 29(5):  61-66.  DOI: 10.12005/orms.2020.0119

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The facility location problem is one of the important problems in combinatorial optimization. The dynamic facility location problem is a generalization of the classic facility location problem in which the open cost of the facilities and the demand of the clients change over time. Moreover, the classic facility location problem always assumes that all clients need to be served. A shortcoming of the model is that it does not consider a few very distant clients. To serve these clients, some facilities need to be open to serve them only. This is not good for using the facilities resource. Thus, in the setting of the model, it is allowable that a constant number of clients can be not served (which is called the facility location problem with outliers). On the other hand, it is also allowable to pay penalty cost for not serving some clients (which is called the facility location problem with penalties). In this paper, we combine the above robust setting to consider the facility location problem with penalties and outliers for which we propose a 3-approximation algorithm.

Study of Site Selection Model for Interval Programming of Emergency Facilities in Chemical Industry Park

GUO Huan-huan, WANG Fei-yue, PEI Jia-kun, YANG Chen-yu

2020, 29(5):  67-73.  DOI: 10.12005/orms.2020.0120

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The location of emergency facilities is affected by the demand for emergency materials. To optimize the location layout of emergency facilities and improve emergency response capabilities, the emergency response of chemical industry park is studied in this paper. Considering the randomness and complexity of emergency incidents in chemical industry park, and the uncertainty of emergency demand, a mathematical model for interval programming of emergency facility location in chemical industry park is established on the basis of the traditional deterministic emergency facility location. The model aims to maximize the safety, minimize the cost and optimize service efficiency of emergency facility location in the same time, and it assumes that the demand for emergency materials is an interval value. Then, the model is solved by introducing the interval programming theory and the fuzzy theory, which not only avoids the volatility of the random probability distribution of the uncertain parameters, but also reduces the uncertainty in the model solution. Finally, a case analysis is carried out based on the potential accidents of various enterprises in the park. And the reasonable layout plan of the emergency facilities in the park is obtained. The result shows that the model works well for the solution and provides a reference for the decision-making of the emergency facilities in the park.

A Multi-objective Robust Optimization Model for Emergency Logistics Center Location

LAI Zhi-zhu, WANG Zheng, GE Dong-mei, CHEN Yu-long

2020, 29(5):  74-83.  DOI: 10.12005/orms.2020.0121

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To solve the location of emergency logistics centers and the transportation of emergency materials after major emergencies, the multi-objective deterministic model and the multi-objective robust optimization model for emergency logistics centers location problem are developed, taking into consideration of the following characteristics: the uncertainty about demand of emergency materials and transportation cost/time in the transport process, tow objectives of emergency rescue cost and emergency response time. We describe the uncertainties of the problem by using scenarios and develop special method to transform the multi-objective problem into a single target problem that uses the optimal result of single-objective model (cost target and time target, respectively) and linear weighting method by cost preference weight. A universal shuffled frog leading algorithm is designed which uses facility points providing the rescue material as coding schemes. To verify the validity of the model and algorithm, a multi-scenario calculation example is designed. The results show that the models and the algorithm have high feasibility and effectiveness, and robust optimization model can maintain good anti-interference ability for various uncertain types. The influence of cost preference weight and robust constraint coefficient is discussed, and the results shows that we can distinguish various emergency rescue phases by the range of cost preference weight. In addition, we give some suggestions on cost preference weight and robust constraint coefficient.

Continuous Facility Location: Models, Methods and Applications

ZHANG Su, WU Chen-chen, JIANG Jian-lin, LV Yi-bing

2020, 29(5):  84-95.  DOI: 10.12005/orms.2020.0122

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Given a metric space and some customers whose locations are known, facility location is to locate new facilities so that some target determined by new facilities and customers achieves the optimality. Continuous facility location is a type of important problem in location, where new facilities are located in some continuous area of the metric space. This paper focuses on reviewing the research work of models, methods and applications in continuous facility location field. Firstly, the paper discusses some important elements in continuous facility location including the number of new facilities, distance measuring function and objective function. Then several classical models and extended models of continuous facility location are introduced. This paper also briefly summaries common optimization methods and techniques for continuous facility location, including conjugate duality, global optimization, optimization under uncertainty, variational inequality and Voronoi diagrams. At last, the paper gives a few important applications and proposes some future research directions of continuous facility location.

Dynamic Optimization and Coordination on Joint Green Innovation in a Supply Chain ConsideringDisappointment Aversion

GUAN Zhi-min, QU You, ZHAO Ying

2020, 29(5):  96-107.  DOI: 10.12005/orms.2020.0123

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Green innovation has become an important method for enterprises to obtain advantage against the background of green development. Considering supply chain members who possess disappointment aversion and the green products which are characteristic of dynamic change, based on disappointment theory, a dynamic optimization and coordination model for joint green innovation is established. Using the feedback method, we obtain the optimal equilibrium strategies under different decision-making scenarios, and investigate the impacts of supply chain members' disappointment aversion on product's green level as well as the supply chain's performance. Furthermore, a two-way cost sharing contract is proposed to coordinate the supply chain. The results show that: whether the supply chainmembers conduct green innovation jointly as well as the green degree of product are particularly relevant for the level of supply chain members' disappointment aversion; under the decentralized scenario, the cost sharing proportion decreases with manufacturer's disappointment aversion degree and increases with supplier's disappointment aversion degree in the situation which manufacturer would like to share the green innovation costs of supplier; by comparison, the product's green degree and supply chain's performance under centralized setting have more advantage than that under decentralized setting; besides, under certain conditions, a two-way costs sharing contract adoption can realize supply chain coordination.

A Comparative Study of Green Efforts in Supply Chain Based on Different Government Subsidy Strategies

CAO Yu, XUN Jing-ya, LI Qing-song

2020, 29(5):  108-118.  DOI: 10.12005/orms.2020.0124

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This paper considers a two-stage supply chain consisting of one manufacturer and one retailer. The paper explores the impact of government subsidy strategies on supply chain green efforts and pricing decisions considering the green efforts of manufacturers and retailers. This paper compares and analyzes the differences in the influence of three strategies including government non-subsidy strategies, government-to-manufacturer subsidies strategies and government-to-consumer subsidies strategies on decision-making, and considers the influence of retailer's risk aversion characteristics on optimal decision-making. It is shown that government subsidies can encourage manufacturers and retailers to improve their green efforts, and government subsidies are always beneficial to the green development of the supply chain. When government subsidies manufacturers, the green quality level is higher than that of government-to-consumer subsidies strategies, but the profit of supply chain is not optimal. When the government subsidizes consumers, the supply chain can get the highest profit although the green quality level of the product is not optimal. The study also finds that the risk aversion feature does not change the influence of parameters on the optimal decision and the optimal profit, but it will reduce the retailer's green efforts and lead to a decline in its profits.

Study on the Investment of Container Port along the Maritime Silk Road in the Contest of Industry Transfer

YANG Zhong-zhen, CHEN Dong-xu, GONG Zhi-guang

2020, 29(5):  119-124.  DOI: 10.12005/orms.2020.0125

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Port investment and port pricing along the maritime silk road (MSR) where plants may be relocated due to economic globalization are studied. First, dummy links are added to the physical transport network to form a super-network to describe the processes of industry transfer, products manufacturing and transportation. Then, a model for optimizing the port investment along the MSR is built with the variables of transport impedance, land rent and wage describing the equilibrium among port investment, industry transfer and regional economic growth. The model determines the port investment amount and the price level for the sites where the plants are relocated with the objective of maximizing the port profits. At last, taking port of Colombo as an example, we calculate the interactions between the port demand and supply and the upper bound to offer theoretical base for port investment along the MSR.

Research on Layout Decision Making of Electric Vehicle Fast Charging Station Based on Road Network

HE Ya-wei, DONG Pei-wu, CHEN Xiang

2020, 29(5):  125-134.  DOI: 10.12005/orms.2020.0126

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Due to the requirement of transportation energy transformation and environmental pollution control, the development and promotion of Battery Electric Vehicles(BEV)has become the common choice of many countries around the world. However, the limited all-electric-range(AER)of BEV and the lack of public fast charging facilities restrict driver's use and long-distance travel. To solve the problem of improving the construction of public fast charging network in China, a distributed decision model is established in this research for the scientific distribution problem of fast charging stations serving the mid-way recharging demand, based on Chinese main highway network. The empirical studies show that for BEV with an AER less than 200 kilometers, the coverage of recharging demand can be increased from 50% to over 90% by adding the number of fast charging stations distributed with best strategy from 50 to 250. For BEV with an AER more than 250 kilometers, 150 fast charging stations distributed by the optimal strategy can satisfy at least 96.49% of the whole mid-way recharging demand. Throughanalyzing 30 cases under different constraints of AER and number of fast charging stations, this research can not only provide the optimal scheme of location and quantity combination for the fast charging station distribution problem in different scenarios, but also provide strong theoretical support and policy recommendations for the improvement of Chinese charging infrastructure and the sustainable development of the electric vehicle industry.

The υ-position Value Measure on Centrality of Hypernetworks

SHAN Er-fang, CAI Lei, ZENG Han, PENG Chao-jing

2020, 29(5):  135-142.  DOI: 10.12005/orms.2020.0127

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In political, economic and social networks, using the allocation rules in cooperative game theory, such as Shapley value and Banzhaf value, to measure the centrality or importance of nodes is an important method to identify key nodes in networks. By considering the different roles of the hyperlinks representing various organizations in hypernetworks, we first generalize the position value to general allocation rule, called υ-position value, in hypernetwork games. The υ-position value can measure the centrality and relative importance of players in hypernetworks, which can be regarded as a generalization of the degree measure in networks. Secondly, we show that the like-Shapley-position value in hypernetwork games can be uniquely characterized by the component hyperlink power and the local balance hyperlink contribution. Finally, we give an example to illustrate application of the υ-position value in the centrality of hypernetworks.

Evolutionary Game Research on Cooperative Innovation of New Energy Vehicle Industry under Market Mechanism and Government Regulation

XU Jian-zhong, SUN Ying

2020, 29(5):  143-151.  DOI: 10.12005/orms.2020.0128

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Energy-saving and new energy vehicles are the key measures to solve energy, climate and environmental problems, which attract more and more attention of governments.Based on evolutionary game theory, this paper analyses the cooperative behavior of Government-Industry-University-Research(GIUR)new energy vehicles from two aspects of market mechanism and government regulation. First, a game model is established to observe the cooperation behaviors of multiple stakeholders. By analyzing the replication dynamic equation and evolutionary stability strategy, we can get the influencing factors of cooperative behavior. Finally, numerical simulation is carried out to verify the theoretical research. The results show that under the market mechanism, reasonable government subsidies, liquidated damages and income distribution coefficient can encourage GIUR to actively carry out cooperative innovation in the new energy automobile industry.Rational tax rate and administrative penalty under government supervision are conducive to promoting the stability of cooperative innovation of new energy vehicles.In addition, in the process of new energy automobile cooperation, additional social benefits will be obtained, which will increase the enthusiasm of the government to participate in the cooperative innovation activities of new energy automobiles.The results can guide GIUR's better decision in the future.

Collaboration of Haze Control: Based on the Evolutionary Game

GAO Ming, LIAO Meng-ling

2020, 29(5):  152-160.  DOI: 10.12005/orms.2020.0129

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Local governments, pollution enterprises and the public are all major participants in the haze control . It is of great significance for haze control to study the interests and influencing factors systematically between the three participants. From the perspective of evolutionary game, this paper establishes a tripartite-game model on the basis of bounded rationality, and explores the conditions of collaborative governance. The results of equilibrium point analysis and numerical simulation can be obtained: The three participants of evolutionary game have different strategies under different stable conditions. The system can get rid of the bad state, and then form a tripartite co-governance model by increasing the amount of direct sewage penalty, potential benefits of local government, potential losses of polluting enterprises,public long-term benefits and reducing the cost of public. The evolution speed of any participants will be affected by the proportion of its own strategy and the other two subject strategies. However, no matter how the proportion of the participants changes, the final game result will not be changed.These findings have reference effect for haze control.

Evolutionary Equilibrium Analysis of Mass Emergency Derived from Environmental Pollution Based on Prospect Theory

TAN De-qing, XU Hao

2020, 29(5):  161-170.  DOI: 10.12005/orms.2020.0130

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The mass emergency derived from environmentalpollution as a hotpot issue affecting the stability and development of society become the important challenge faced by governments at all levels. It is of great significance to prevent and deal with such emergency by analyzing the complex interests and behavioral mechanism of participants and governments. This paper introduces the prospect value function and constructs the perceived values of gains and losses faced by environmental pollution enterprise and surrounding people, and then makes a comparative static analysis by evolutionary game model which describes the cognition and decision law of the participants closer to the reality. The conclusion of the study shows that the environmental pollution enterprise and surrounding people have the characteristics of earning perception and loss aversion, and under these characters, the probability of mass emergency has the obvious correlation with cost sharing by the government for environmental pollution enterprise, subsidies for enterprise and surrounding people when they become reconciled with the perceived value of loss for enterprise and the perceived values of gains and participation costs for surrounding people and so on. Finally, some suggestions are given according to the numerical analysis and comparison with the situation without considering the prospective value, which provide the theoretical support for government and relevant departments to control such mass emergencies.


Horizontal Competition Game and Price-Service Strategies Considering Manufacturers Opening Showrooms

ZHANG Qin-yi, LIU Yong-mei, LI Xue-lan

2020, 29(5):  171-180.  DOI: 10.12005/orms.2020.0131

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This paper divides consumers into two types, high and low experience cost consumers based on different experience cost, and then establishes the utility function for each consumer. Subsequently, it analyzes the Bertrand game between two manufacturers about whether to open the showrooms and their corresponding price-service strategies after opening the showroom. Finally, it analyzes the equilibrium of opening showroom. The results show that both manufactures would open showrooms when the percentage of low experience cost consumers and the likelihood of a product match are high or the percentage of low experience cost consumers is low, but both manufacturers would get the least profits and trap into Prisoner's Dilemma. Under the condition of high percentage of low experience cost and low likelihood of product match, both manufacturers will choose not to open showroom and both parties achieve Pareto Optimality.

Participants' Behavior Game Model and Simulation Analysis in Crowdsourcing Considering Reputation Effect

LIU Wei, Ding Kai-wen

2020, 29(5):  181-188.  DOI: 10.12005/orms.2020.0132

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In order to prevent the risk in crowdsourcing, this paper focuses on participants, including employers and service providers in crowdsourcing, and constructs non-cooperative and cooperation game model of the participants' behavior in crowdsourcing based on differential game theory. Then we explore different game strategies of employers and service providers under different scenarios. The result shows that when reputation effect gets threshold condition, the return of employers and service providers and the total return of crowdsourcing system in the case of cooperative game are greater than those of non-cooperative game. Similarly, the effort controlling default behavior of the employers and service providers in the case of cooperative game are is also greater than that of non-cooperative game. When the penalty imposed by platform gets a certain threshold, the effort controlling default behavior of employers and service providers increases gradually to achieve Pareto improvement. The crowdsourcing platform can incorporate credit rating or reputation into the crowdsourcing transaction pricing by establishing an effective credit assessment mechanism to guide participants in restricting their behavior and controlling risks in crowdsourcing transaction. In addition, we set the model parameter based on Zhubajie crowdsourcing platform as case, and make a numerical analysis to validate the theoretical model.

Measurement and Empirical Research of Synergy in Complex Systems Based on Kolmogorov Entropy

SONG Yan-qiu, LI Hui-jia, WANG Qian, LI Gui-jun

2020, 29(5):  189-197.  DOI: 10.12005/orms.2020.0133

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It is a great challenge to measure the degree of synergy in complex systems. By investigating some models that focus on analyzing the degree of coordination, it has been found that most of them suffer strict constraints in the real applications. Based on synergetic effect ideology, in this article, we first explore and verify the relationship between Kolmogorov entropy and system synergy, and then propose a new model to quantify the degree of synergy in complex systems. Based on the trend of order parameter at some point in the system, the proposed model can be used to measure the degree of order in complex systems efficiently. The proposed model is on the base of the classical synergy theory, which opens a new research area and makes up the deficiency of existing research. By analyzing the synergetic degree of Science and Technology Financial System in each province of China from 2004 to 2015, we find that the proposed model can be used to find the ordering parameter of subsystem, calculating the system synergy, and describing the reflection of system development on important events, efficiently.

Application Research

Research on the Design of Post-market Knowledge Sharing Incentive Mechanism Based on Hybrid Offerings

LUO Jian-qiang, HU Bing-kun

2020, 29(5):  198-206.  DOI: 10.12005/orms.2020.0134

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The blockade of manufacturing enterprises' proprietary knowledge of hybrid offerings for profit and protection purposes will create a knowledge gap between supply and demand. On the basis of clarifying the reasons for the knowledge gap, this paper constructs the model of supply and demand knowledge sharing incentive mechanism under different information conditions and different risk preferences. This result shows that the supply and demand can reach the Pareto optimal under the condition of symmetric market information, and the customers can share the knowledge of hybrid offerings by buying out intellectual property or building knowledge alliances. In the case of asymmetric market information, manufacturing enterprises often share knowledge in the form of knowledge authorization. The increase in the proportion of income distribution of the manufacturing enterprises by the customer can increase the marginal effect of the sharing of protective knowledge, but the marginal effect of the sharing of profitable knowledge remains unchanged. Considering the risk of knowledge leakage, the numerical analysis shows that the proportion of risk that the manufacturing enterprises is willing to bear is more sensitive to the shared cost coefficient of protective knowledge than the profitable knowledge.

Research on the Effect of Night Trading on the Market of Precious Metal Futures

HUI Xiao-feng, YAO Xuan, MA Ying

2020, 29(5):  207-217.  DOI: 10.12005/orms.2020.0135

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The changes in liquidity, volatility and linkage of the precious metals futures market in China before and after the launch of night trading sessions are compared, and both the long-term and short-term effects of the night trading sessions are studied in this article. The revised liquidity ratio is used to measure the market liquidity, and a dummy variable is incorporated into the regression model to further study the contribution of night trading sessions; the conventional daily rate of return is decomposed into overnight and day rate of return. The EGARCH models with three kinds of rate of return and two periods (long-term and short-term) are established. The paper also establishes VAR-BEKK-GARCH models to study the linkage between domestic and foreign futures markets from both the mean and variance levels. The study finds that the overnight trading sessions in China significantly increase the liquidity of the precious metals futures market; at the same time, the overnight trading sessions significantly reduce the volatility of the market, especially the volatility of the overnight yield, while the volatility of day rate of return has not been significantly reduced. Finally, the launch of night trading has strengthened the linkage between domestic and foreign futures markets and enhanced the information transmission efficiency of the precious metal futures market in China.

Management Science

Study on the Relationship between Endowment Ensurance and Economic Growth ——Taking Inner Mongolia Autonomous Region as an Example

HAO Ying

2020, 29(5):  218-226.  DOI: 10.12005/orms.2020.0136

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Although China's economy is developing steadily, the problem of population aging is also getting worse. How to deal with the relationship between endowment insurance and economic development is particularly important. Using the relevant data of Inner Mongolia autonomous region from 2002 to 2016, VAR model is built, and the relationship between them is analyzed in depth by using variance decomposition and impulse response, which provides theoretical reference for China to cope with the aging problem and could effectively solve the old-age care problem of the elderly. The results show that the endowment insurance and economic growth between the two influence each other and will do so in a long time, but the endowment insurance, the ginseng of income and expenditure and economic growth are the influence degree of the difference between each other.Contributors in economic growth for the influence of the contributors is greater than the impact on the economic growth, and the endowment insurance of the influence of income and expenditure on economic growth is even larger.

Overview

Research Progress and Prospects for Application of Reinforcement Learning in Operations Research

XU Xiang-bin, LI Zhi-peng

2020, 29(5):  227-239.  DOI: 10.12005/orms.2020.0137

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Reinforcement learning has become a new research hotspot in the field of artificial intelligence, and has been successfully applied in various fields. Reinforcement learning regards many problems in the community of operational optimization as sequential decision problems, modeled as Markov decision processes, and then solve them. It has great advantages in solving complex, dynamic and random operation optimization problems. This paper mainly summarizes the application of reinforcement learning in the area of operational optimization. Firstly, it introduces the basic principles of reinforcement learning and its application framework in the field of operational optimization. Then it systematically reviews and summarizes the reinforcement learning in inventory control, path optimization, packing and loading and job shop scheduling. And the latest deep reinforcement learning and the application of traditional methods in the field of operations research are compared and analyzed to highlight the superiority of deep reinforcement learning. Finally, several research directions worthy of further discussion are proposed, and it is expected to provide reference for the study of reinforcement learning in the field of operational optimization.