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Optimization and Operations Research in Mitigation of a Pandemic Cai-Hua Chen, Yu-Hang Du, Dong-Dong Ge, Lin Lei, Yin-Yu Ye Journal of the Operations Research Society of China    2022, 10 (2): 289-304.   DOI: 10.1007/s40305-022-00391-y Abstract （1561）      PDF       Knowledge map    Save The pandemic of COVID-19 initiated in 2019 and spread all over the world in 2020 has caused significant damages to the human society, making troubles to all aspects of our daily life. Facing the serious outbreak of the virus, we consider possible solutions from the perspectives of both governments and enterprises. Particularly, this paper discusses several applications of supply chain management, public resource allocation, and pandemic prevention using optimization and machine learning methods. Some useful insights in mitigating the pandemic and economy reopening are provided at the end of this paper. These insights might help governments to reduce the severity of the current pandemic and prevent the next round of outbreak. They may also improve companies' reactions to the increasing uncertainties appearing in the business operations. Although the coronavirus imposes challenges to the entire society at the moment, we are confident to develop new techniques to prevent and eradicate the disease. Reference | Related Articles | Metrics | Comments（0）

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Certifying the Global Optimality of Quartic Minimization over the Sphere Sheng-Long Hu Journal of the Operations Research Society of China    2022, 10 (2): 241-287.   DOI: 10.1007/s40305-021-00347-8 Abstract （1532）      PDF       Knowledge map    Save The quartic minimization over the sphere is an NP-hard problem in the general case. There exist various methods for computing an approximate solution for any given instance. In practice, it is quite often that a global optimal solution was found but without a certification. We will present in this article two classes of methods which are able to certify the global optimality, i.e., algebraic methods and semidefinite program (SDP) relaxation methods. Several advances on these topics are summarized, accompanied with some emerged new results. We want to emphasize that for mediumor large-scaled instances, the problem is still a challenging one, due to an apparent limitation on the current force for solving SDP problems and the intrinsic one on the approximation techniques for the problem. Reference | Related Articles | Metrics | Comments（0）

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Augmented Lagrangian Methods for Convex Matrix Optimization Problems Ying Cui, Chao Ding, Xu-Dong Li, Xin-Yuan Zhao Journal of the Operations Research Society of China    2022, 10 (2): 305-342.   DOI: 10.1007/s40305-021-00346-9 Abstract （1571）      PDF       Knowledge map    Save In this paper, we provide some gentle introductions to the recent advance in augmented Lagrangian methods for solving large-scale convex matrix optimization problems (cMOP). Specifically, we reviewed two types of sufficient conditions for ensuring the quadratic growth conditions of a class of constrained convex matrix optimization problems regularized by nonsmooth spectral functions. Under a mild quadratic growth condition on the dual of cMOP, we further discussed the R-superlinear convergence of the Karush-Kuhn-Tucker (KKT) residuals of the sequence generated by the augmented Lagrangian methods (ALM) for solving convex matrix optimization problems. Implementation details of the ALM for solving core convex matrix optimization problems are also provided. Reference | Related Articles | Metrics | Comments（0）

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Operations Research in the Blockchain Technology Xu Wang, Ling-Yun Wu Journal of the Operations Research Society of China    2022, 10 (2): 401-422.   DOI: 10.1007/s40305-021-00348-7 Abstract （1552）      PDF       Knowledge map    Save In the past decade, as a decentralized distributed database technology blockchain has developed rapidly at an unprecedented speed and been applied to a wide range of scenarios far beyond cryptocurrencies, for example, insurance, energy, risk management, and Internet of things (IoT). The blockchain technology combines the achievements from cryptography, computer science, economics, and operations research and has increasingly attracted attention from both academia and industry. Though the operations research has been widely adopted in the blockchain technology, there is a lack of comprehensive survey on the operations research in blockchain-related issues. In order to fill the gap, we analyze the blockchain technology through the perspective of operations research and present a comprehensive review of the operations research problems from the aspects of security and stability, efficiency and performance, and resource allocation. This paper aimed to help the relevant readers in the field of operations research find their own points of interest and conduct in-depth research on the blockchain technology, hoping to promote the rapid development and wider application of the blockchain technology in the near future. Reference | Related Articles | Metrics | Comments（0）

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Survey on Multi-period Mean-Variance Portfolio Selection Model Xiang-Yu Cui, Jian-Jun Gao, Xun Li, Yun Shi Journal of the Operations Research Society of China    2022, 10 (3): 599-622.   DOI: 10.1007/s40305-022-00397-6 Abstract （1189）      PDF       Knowledge map    Save Due to the non-separability of the variance term,the dynamic mean-variance (MV) portfolio optimization problem is inherently difficult to solve by dynamic programming.Li and Ng (Math Finance 10(3):387-406,2000) and Zhou and Li (Appl Math Optim 42(1):19-33,2000) develop the pre-committed optimal policy for such a problem using the embedding method.Following this line of research,researchers have extensively studied the MV portfolio selection model through the inclusion of more practical investment constraints,realistic market assumptions and various financial applications.As the principle of optimality no longer holds,the pre-committed policy suffers from the time-inconsistent issue,i.e.,the optimal policy computed at the intermediate time t is not consistent with the optimal policy calculated at any time before time t.The time inconsistency of the dynamic MV model has become an important yet challenging research topic.This paper mainly focuses on the multi-period mean-variance (MMV) portfolio optimization problem,reviews the essential extensions and highlights the critical development of time-consistent policies. Reference | Related Articles | Metrics | Comments（0）

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A Survey of Truck–Drone Routing Problem: Literature Review and Research Prospects Yi-Jing Liang, Zhi-Xing Luo Journal of the Operations Research Society of China    2022, 10 (2): 343-377.   DOI: 10.1007/s40305-021-00383-4 Abstract （1644）      PDF       Knowledge map    Save The vehicle routing problem (VRP) has been an important research topic in operations research for decades. The major applications of the VRP arise in transportation, especially the last-mile delivery. In recent years, a growing number of logistic companies introduce drones or unmanned aerial vehicles in the delivery operations. Therefore, the truck-drone routing problem (TDRP), where trucks and drones are scheduled and coordinated to serve customers, vitalizes a new research stream in the literature. In this paper, we provide a comprehensive review on the TDRP. First, two basic models for the traveling salesman problem with drones and vehicle routing problem with drones are presented. Second, researches devoted to the TDRP are classified according to their addressed constraints and features. Third, prevalent algorithms that have been widely used in the existing literature are reviewed and described. Last, potential research opportunities are identified for future study. Reference | Related Articles | Metrics | Comments（0）

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How is Systemic Risk Amplified by Three Typical Financial Networks Jia-Li Ma, Shu-Shang Zhu, Xiao-Chuan Pang Journal of the Operations Research Society of China    2022, 10 (3): 579-598.   DOI: 10.1007/s40305-021-00389-y Abstract （1160）      PDF       Knowledge map    Save Financial institutions are typically tied together via inter-liability,portfolio overlapping and share cross-holding.These connections among financial institutions constitute the three most common financial networks,which may lead to financial risk contagion and even systemic risk when some institutions suffer shock.In this paper,firstly,for a given shock,we prove the existence of the equilibrium clearing vector of the financial system characterized by these three typical financial networks.Then,we mathematically derive an analytical form to show how these three contagion channels jointly affect and amplify the loss of the non-default institutions,and explain how the lack of liquidity of external investment assets exacerbates the loss caused by portfolio overlapping.Finally,the influence of the characteristics of financial network on risk contagion is verified by numerical simulation.These results provide basis for understanding the financial systemic risk contagion in the real world. Reference | Related Articles | Metrics | Comments（0）

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The Developments of Proximal Point Algorithms Xing-Ju Cai, Ke Guo, Fan Jiang, Kai Wang, Zhong-Ming Wu, De-Ren Han Journal of the Operations Research Society of China    2022, 10 (2): 197-239.   DOI: 10.1007/s40305-021-00352-x Abstract （1620）      PDF       Knowledge map    Save The problem of finding a zero point of a maximal monotone operator plays a central role in modeling many application problems arising from various fields, and the proximal point algorithm (PPA) is among the fundamental algorithms for solving the zero-finding problem. PPA not only provides a very general framework of analyzing convergence and rate of convergence of many algorithms, but also can be very efficient in solving some structured problems. In this paper, we give a survey on the developments of PPA and its variants, including the recent results with linear proximal term, with the nonlinear proximal term, as well as the inexact forms with various approximate criteria. Reference | Related Articles | Metrics | Comments（0）

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Price Competition in the Random Coefficient Attraction Choice Models with Linear Cost Xiao-Yi Feng, Yang-Yang Xie, Hou-Min Yan Journal of the Operations Research Society of China    2022, 10 (3): 623-658.   DOI: 10.1007/s40305-021-00366-5 Abstract （1261）      PDF       Knowledge map    Save We study the pricing game between competing retailers under various random coefficient attraction choice models.We characterize existence conditions and structure properties of the equilibrium.Moreover,we explore how the randomness and cost parameters affect the equilibrium prices and profits under multinomial logit (MNL),multiplicative competitive interaction (MCI) and linear attraction choice models.Specifically,with bounded randomness,for the MCI and linear attraction models,the randomness always reduces the retailer's profit.However,for the MNL model,the effect of randomness depends on the product's value gap.For high-end products (i.e.,whose value gap is higher than a threshold),the randomness reduces the equilibrium profit,and vice versa.The results suggest high-end retailers in MNL markets exert more effort in disclosing their exact product performance to consumers.We also reveal the effects of randomness on retailers'pricing decisions.These results help retailers in making product performance disclosure and pricing decisions. Reference | Related Articles | Metrics | Comments（0）

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Game Theory and the Evolution of Cooperation Bo-Yu Zhang, Shan Pei Journal of the Operations Research Society of China    2022, 10 (2): 379-399.   DOI: 10.1007/s40305-021-00350-z Abstract （1583）      PDF       Knowledge map    Save Evolution is based on the competition between individuals and therefore rewards only selfish behavior. How cooperation or altruism behavior could prevail in social dilemma then becomes a problematic issue. Game theory offers a powerful mathematical approach for studying social behavior. It has been widely used to explain the evolution of cooperation. In this paper, we first introduce related static and dynamic game methods. Then we review two types of mechanisms that can promote cooperation in groups of genetically unrelated individuals, (i) direct reciprocity in repeated games, and (ii) incentive mechanisms such as reward and punishment. Reference | Related Articles | Metrics | Comments（0）

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Preface: Special Issue on New Developments in Mathematical Programming and Operations Research Yu-Hong Dai, De-Ren Han, Ling-Yun Wu, Yan Xu Journal of the Operations Research Society of China    2022, 10 (2): 193-195.   DOI: 10.1007/s40305-022-00392-x Abstract （1701）      PDF       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Inventory Policy and Heuristic for Long-Term Multi-product Perishable Inventory Routing Problem with Static Demand Xi-Yi Chen, Jian-Bo Yang, Dong-Ling Xu Journal of the Operations Research Society of China    2022, 10 (3): 659-683.   DOI: 10.1007/s40305-021-00390-5 Abstract （1237）      PDF       Knowledge map    Save This work considers a long-term Perishable Inventory Routing Problem with multiple products,static demand,and single vehicle,in the setting of Vendor Managed Inventory.By analyzing the optimal solutions of long-term cases that can be solved in Python+Gurobi within 2 h,we capture some patterns of optimal solutions.Utilizing these patterns,experiments show that under certain conditions,the mathematical models of multi-product problems could be simplified to single-product problems,which have the same optimal solutions while taking less time to solve.Managerial insights were generated that for products with static demand in the long term,delivery should be arranged at the store level rather than at the product level.Products in the same store should have the same delivery pattern,no matter how different the unit holding costs are.By further analyzing the optimal solutions of the simplified models,we find that optimal value will stabilize in the long term,and the optimal solution is very close to the solution point where total inventory holding cost and transportation cost are close.Based on these findings,we have developed a heuristic that always provides optimal or close-to-optimal solutions with far less computational time,compared with Python+Gurobi. Reference | Related Articles | Metrics | Comments（0）

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Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems Nan Meng, Yun-Bin Zhao Journal of the Operations Research Society of China    2022, 10 (3): 447-469.   DOI: 10.1007/s40305-021-00370-9 Abstract （1202）      PDF       Knowledge map    Save Sparse signals can be possibly reconstructed by an algorithm which merges a traditional nonlinear optimization method and a certain thresholding technique.Different from existing thresholding methods,a novel thresholding technique referred to as the optimal k-thresholding was recently proposed by Zhao (SIAM J Optim 30(1):31-55,2020).This technique simultaneously performs the minimization of an error metric for the problem and thresholding of the iterates generated by the classic gradient method.In this paper,we propose the so-called Newton-type optimal k-thresholding (NTOT) algorithm which is motivated by the appreciable performance of both Newton-type methods and the optimal k-thresholding technique for signal recovery.The guaranteed performance (including convergence) of the proposed algorithms is shown in terms of suitable choices of the algorithmic parameters and the restricted isometry property (RIP) of the sensing matrix which has been widely used in the analysis of compressive sensing algorithms.The simulation results based on synthetic signals indicate that the proposed algorithms are stable and efficient for signal recovery. Reference | Related Articles | Metrics | Comments（0）

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Preface: Special Issue on Modeling, Simulation, and Optimization in Operational Research Gerhard-Wilhelm Weber, J. Joshua Thomas, José Antonio Marmolejo Saucedo, Ugo Fiore, Igor Litvinchev, Pandian Vasant Journal of the Operations Research Society of China    2022, 10 (4): 685-688.   DOI: 10.1007/s40305-021-00363-8 Abstract （2078）      PDF       Knowledge map    Save Related Articles | Metrics | Comments（0）

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On Convexification for a Class of Global Optimization Problems Qian Yan, Xin-Min Yang, Zhi-You Wu Journal of the Operations Research Society of China    2022, 10 (3): 427-446.   DOI: 10.1007/s40305-021-00379-0 Abstract （1202）      PDF       Knowledge map    Save In this paper,firstly,we give a counterexample to point out there exist deficiencies in our previous works (Wu et al.in J Glob Optim 31:45-60,2005).In addition,we improve the corresponding results.Finally,an example is presented to illustrate how a monotone non-convex optimization problem can be transformed into an equivalent convex minimization problem. Reference | Related Articles | Metrics | Comments（0）

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Preface:Special Issue on Optimization,Financial Engineering,Risk and Operations Management David Yao, Shu-Zhong Zhang, Xun-Yu Zhou Journal of the Operations Research Society of China    2022, 10 (3): 423-425.   DOI: 10.1007/s40305-022-00426-4 Abstract （1284）      PDF       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Marriage Market with Indifferences: A Linear Programming Approach Noelia Juarez, Pablo A. Neme, Jorge Oviedo Journal of the Operations Research Society of China    2023, 11 (1): 219-242.   DOI: 10.1007/s40305-021-00360-x Abstract （1622）      PDF       Knowledge map    Save We study stable and strongly stable matchings in the marriage market with indifference in their preferences. We characterize the stable matchings as integer extreme points of a convex polytope. We give an alternative proof for the integrity of the strongly stable matching polytope. Also, we compute men-optimal (women-optimal) stable and strongly stable matchings using linear programming. When preferences are strict, we find the men-optimal (women-optimal) stable matching. Reference | Related Articles | Metrics | Comments（0）

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Optimal Consumption, Leisure and Job Choice under Inflationary Environment Yu-Song Zhang, Chen Fei, Hai-Feng Pan, Jian Huang Journal of the Operations Research Society of China    2023, 11 (1): 83-108.   DOI: 10.1007/s40305-021-00369-2 Abstract （1492）      PDF       Knowledge map    Save The optimal job choice, consumption and portfolio decision-making of economic agents under inflationary environment for a continuous infinite time are studied. The agent's preference is characterized by the Cobb-Douglas utility function with two variables of consumption and leisure. The economic agent invests in three kinds of assets:risk-free bonds, inflation index bonds and risky assets. The agent has two kinds of working conditions:One is the work with high income and little leisure time, and the other is the work with low income and much leisure time. Firstly, the real wealth process after inflation discount is derived by using Itô formula. Then, based on the expected utility maximization standard under any working state, martingale method is adopted to obtain the closed form solution of optimal job choice, consumption and portfolio decision-making. Finally, the effects of wealth and inflation volatility on the optimal consumption and portfolio strategies are quantitatively analyzed by numerical simulation with given parameters. Reference | Related Articles | Metrics | Comments（0）

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A Polynomial-Time Algorithm with Tight Error Bounds for Single-Period Unit Commitment Problem Ruo-Tian Gao, Shu-Cherng Fang, Cheng Lu, Wen-Xun Xing Journal of the Operations Research Society of China    2023, 11 (1): 1-28.   DOI: 10.1007/s40305-021-00376-3 Abstract （1520）      PDF       Knowledge map    Save This paper proposes a Lagrangian dual-based polynomial-time approximation algorithm for solving the single-period unit commitment problem, which can be formulated as a mixed-integer quadratic programming problem and proven to be NP-hard. Tight theoretical bounds for the absolute errors and relative errors of the approximate solutions generated by the proposed algorithm are provided. Computational results support the effectiveness and efficiency of the proposed algorithm for solving large-scale problems. Reference | Related Articles | Metrics | Comments（0）

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On Equilibrium in a Constant Retrial Queuing System with Reserved Time and Vacations Lin-Lin Wang, Li-Wei Liu, Xu-Dong Chai, Zhen Wang Journal of the Operations Research Society of China    2022, 10 (4): 785-800.   DOI: 10.1007/s40305-019-00290-9 Abstract （1830）      PDF       Knowledge map    Save This paper investigates an M/M/1 constant retrial queue with reserved time and vacations. A new arriving customer will take up the server and accept service immediately if the server is idle. Otherwise, if the server is busy or on vacation, customers have to join a retrial orbit and wait for retry. Once a service is completed, the server will reserve a random time to seek a customer from the orbit at a constant retrial rate. If there is no arrivals (from the orbit or outside) during the idle period, to save energy, the server will take a vacation. This paper studies the fully unobservable case. First, the steady-state condition of the system is analyzed by using the Foster’s criterion, and the customers’ expected waiting time is obtained based on the generating function technique. And then, by introducing an appropriate revenue structure, the equilibrium strategies of customers and the socially optimal strategy are all derived. Furthermore, a comparison between them is made and the effect of some main system parameters is studied. Reference | Related Articles | Metrics | Comments（0）