Most Download

Published in last 1 year | In last 2 years| In last 3 years| All| Most Downloaded in Recent Month | Most Downloaded in Recent Year|

Most Downloaded in Recent Year

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

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 （1622）      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）

Select

A Stochastic Adaptive Radial Basis Function Algorithm for Costly Black-Box Optimization Zhe Zhou, Fu-Sheng Bai Journal of the Operations Research Society of China    2018, 6 (4): 587-610.   DOI: https://doi.org/10.1007/s40305-018-0204-8 Abstract （150）      PDF       Knowledge map    Save In this paper, we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box functions. The exploration radii in local searches are generated adaptively. Each iteration point is selected from some randomly generated trial points according to certain criteria. A restarting strategy is adopted to build the restarting version of the algorithm. The performance of the presented algorithm and its restarting version are tested on 13 standard numerical examples. The numerical results suggest that the algorithm and its restarting version are very effective. Related Articles | Metrics | Comments（0）

Select

Unbounded Serial-Batching Scheduling on Hierarchical Optimization Cheng He, Hao Lin, Li Li Journal of the Operations Research Society of China    2021, 9 (4): 909-914.   DOI: 10.1007/s40305-020-00334-5 Abstract （2494）      PDF       Knowledge map    Save The paper considers a serial-batching scheduling problem on hierarchical optimization with two regular maximum costs, where hierarchical optimization means the primary objective function is minimized, and keeping the minimum value of the primary objective function, the secondary objective function is also minimized. In serial-batching machine environment, the machine processes the jobs in batch, and the jobs in the identical batch are processed by entering into the machine together and leaving the machine together. The time taken to process a batch amounts to the total processing time of the jobs in the batch. Moreover, a fixed switching time s is inserted when a machine begins to process a new batch. We only study the unbounded model, i.e., the batch capacity is unbounded. We give an algorithm that can solve the hierarchical optimization problem in $O\left(n^{4}\right)$ time, where n denotes the number of jobs. Reference | Related Articles | Metrics | Comments（0）

Select

On the l_1-Norm Invariant Convex k-Sparse Decomposition of Signals Guang-Wu Xu · Zhi-Qiang Xu Journal of the Operations Research Society of China    2013, 1 (4): 537-541.   DOI: 10.1007/s40305-013-0030-y Abstract （211）      PDF       Knowledge map    Save Inspired by an interesting idea of Cai and Zhang, we formulate and prove the convex k-sparse decomposition of vectors that is invariant with respect to the l_1 norm. This result fits well in discussing compressed sensing problems under the Restricted Isometry property, but we believe it also has independent interest. As an application, a simple derivation of the RIP recovery condition δk + θk,k < 1 is presented. Related Articles | Metrics | Comments（0）

Select

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 （1610）      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）

Select

Preface–Special Issue on Mathematical Optimization: Past, Present and Future Zai-Wen Wen, Ya-Xiang Yuan Journal of the Operations Research Society of China    2020, 8 (2): 197-198.   DOI: 10.1007/s40305-020-00310-z Abstract （618）      PDF       Knowledge map    Save Related Articles | Metrics | Comments（0）

Select

A Counter-Example to a Conjecture of Ben-Tal, Nemirovski and Roos Ya-Xiang Yuan Journal of the Operations Research Society of China    2013, 1 (1): 155-.   Abstract （223）      PDF       Knowledge map    Save In this short note, we present a counter-example to a conjecture made by Ben-Tal et al. in SIAM J. Optim. 13: 535–560, 2002. Related Articles | Metrics | Comments（0） Cited: Baidu(2)

Select

The Park-and-Ride Behavior in a Cumulative Prospect Theory-Based Model Li-Jun Tian · Cheng-Rui Lyu ·Yong-Xiang Zhao Journal of the Operations Research Society of China    2017, 5 (3): 363-376.   DOI: 10.1007/s40305-017-0153-7 Abstract （3040）      PDF       Knowledge map    Save As an effective travel demand management means, park-and-ride (P&R) mode is an important part of urban traffic. In a traffic corridor with P&R service, suppose that the travel time on highway is uncertain, a cumulative prospect theory (CPT)-based travel decision-making model is established with two travel modes of driving all the way and (P&R). With this setting, the effect of various factors such as the transit fare rate, the parking fee and the total travel demand on the CPT-based and expected utility theory (EUT)-based equilibrium results are compared. In addition, the sensitivity analysis focus on CPT-related parameters are also performed. The numerical results in our case show that the equilibrium flow on P&R mode is always underestimated in an EUT-based model, especially for a low total travel demand. Also, it is found that reducing the transit fare rate or parking fee for P&R station and raising the parking fee for CBD has the same effect on promoting more commuters transfer to P&R mode, whatever CPT-based or EUT-based model is employed.Furthermore, commuter’s reference dependence characteristic is also observed in a CPT-based model, and it is especially noticeable when the road uncertainty is large. Related Articles | Metrics | Comments（0） Cited: Baidu(1)

Select

Inexact Operator Splitting Method for Monotone Inclusion Problems Yuan-Yuan Huang, Chang-He Liu, You-Lin Shang Journal of the Operations Research Society of China    2021, 9 (2): 274-306.   DOI: 10.1007/s40305-020-00296-8 Abstract （2149）      PDF       Knowledge map    Save The Douglas–Peaceman–Rachford–Varga operator splitting methods are a class of efficient methods for finding a zero of the sum of two maximal monotone operators in a real Hilbert space; however, they are sometimes difficult or even impossible to solve the subproblems exactly. In this paper, we suggest an inexact version in which some relative error criterion is discussed. The corresponding convergence properties are established, and some preliminary numerical experiments are reported to illustrate its efficiency. Reference | Related Articles | Metrics | Comments（0）

Select

Robust Valuation, Arbitrage Ambiguity and Profit & Loss Analysis Yu-Hong Xu Journal of the Operations Research Society of China    2018, 6 (1): 59-83.   DOI: https://doi.org/10.1007/s40305-017-0181-3 Abstract （272）      PDF       Knowledge map    Save Model uncertainty is a type of inevitable financial risk. Mistakes on the choice of pricing model may cause great financial losses. In this paper we investigate financial markets with mean-volatility uncertainty. Models for stock market and option market with uncertain prior distributions are established by Peng’s G-stochastic calculus. On the hedging market, the upper price of an (exotic) option is derived following the Black–Scholes–Barenblatt equation. It is interesting that the corresponding Barenblatt equation does not depend on mean uncertainty of the underlying stocks.Appropriate definitions of arbitrage for super- and sub-hedging strategies are presented such that the super- and sub-hedging prices are reasonable. In particular, the condition of arbitrage for sub-hedging strategy fills the gap of the theory of arbitrage under model uncertainty. Finally we show that the term K of finite variance arising in the superhedging strategy is interpreted as the max Profit & Loss (P&L) of shorting a delta-hedged option. The ask-bid spread is in fact an accumulation of the superhedging P&L and the sub-hedging P&L. Related Articles | Metrics | Comments（0）

Select

SMS Encryption and Decryption Using Modified Vigenere Cipher Algorithm B. Bazeer Ahamed, Murugan Krishnamoorthy Journal of the Operations Research Society of China    2022, 10 (4): 835-848.   DOI: 10.1007/s40305-020-00320-x Abstract （2241）      PDF       Knowledge map    Save Despite many online messaging services, short message service (SMS) is still used because of its simplicity. However, such services can be easily cracked, making SMS more vulnerable to send confidential information. Therefore, appropriate cryptographic algorithms must be applied to ensure security. One of the most common cryptographic techniques used for encrypting and decrypting messages is the Vigenere cipher. However, Vigenere cipher can be cracked easily because the previous approach involved 26×10 matrixes, and the key comprised of alphabets. If the key length is small, then it becomes easier to crack the plain text by permutation. So here in this work, we have proposed a modified Vigenere cipher technique to enhance its security standards. Existing Vigenere cipher attack is not more secured, and thus we developed a method that improves the security in Vigenere cipher using Rivest-Shamir-Adleman (RSA). RSA is an asymmetric algorithm that has public and private keys. The advantage of using this algorithm is that finding factors for the large composite numbers is difficult. This method is secured but time consuming. In this proposed method, we combined RSA with Vigenere cipher so that the proposed algorithm is secured and consumes less time than RSA algorithm. This approach is widely used to encrypt SMS. Reference | Related Articles | Metrics | Comments（0）

Select

Optimizing Distribution Route of Convenience Vegetable Stores Considering Transit Nodes Pu Li, Hong-Jie Lan, You-Hua Chen Journal of the Operations Research Society of China    2020, 8 (3): 515-531.   DOI: 10.1007/s40305-020-00319-4 Abstract （485）      PDF       Knowledge map    Save Convenience vegetable stores are distributed in various districts of the city, which is difficult to distribute, mainly reflected in the high cost of distribution and transportation. In order to solve this problem, combining with the characteristics of convenience vegetable stores and urban transportation, an optimization model of distribution route considering transit nodes is established. The model takes into account both soft time window and hard time window. Soft time window is used to calculate the cost increase caused by an urban traffic jam. Hard time window is the unified service time of convenience vegetable stores, and the cost of transit damage is considered to make the model more realistic. The genetic algorithm is used to solve the model with a onestage solution method. The effectiveness and feasibility of the model and algorithm are verified by an example. The effectiveness of the proposed algorithm is verified by comparing the optimal solutions in the case of setting up transit and not setting up transit. The result shows that when the number of convenience vegetable stores is large, the establishment of transit can reduce the overall transportation cost. Quantitative analysis shows that with the further increase in convenience vegetable stores in cities, the distribution of convenience vegetable stores can be improved by planning scientific path and by increasing transit nodes. Reference | Related Articles | Metrics | Comments（0）

Select

A Mini-Batch Proximal Stochastic Recursive Gradient Algorithm with Diagonal Barzilai–Borwein Stepsize Teng-Teng Yu, Xin-Wei Liu, Yu-Hong Dai, Jie Sun Journal of the Operations Research Society of China    2023, 11 (2): 277-307.   DOI: 10.1007/s40305-022-00436-2 Abstract （1028）      PDF       Knowledge map    Save Many machine learning problems can be formulated as minimizing the sum of a function and a non-smooth regularization term. Proximal stochastic gradient methods are popular for solving such composite optimization problems. We propose a minibatch proximal stochastic recursive gradient algorithm SRG-DBB, which incorporates the diagonal Barzilai–Borwein (DBB) stepsize strategy to capture the local geometry of the problem. The linear convergence and complexity of SRG-DBB are analyzed for strongly convex functions. We further establish the linear convergence of SRGDBB under the non-strong convexity condition. Moreover, it is proved that SRG-DBB converges sublinearly in the convex case. Numerical experiments on standard data sets indicate that the performance of SRG-DBB is better than or comparable to the proximal stochastic recursive gradient algorithm with best-tuned scalar stepsizes or BB stepsizes. Furthermore, SRG-DBB is superior to some advanced mini-batch proximal stochastic gradient methods. Reference | Related Articles | Metrics | Comments（0）

Select

Inexact Proximal Point Methods for Quasiconvex Minimization on Hadamard Manifolds Nancy Baygorrea· Erik Alex Papa Quiroz ·Nelson Maculan Journal of the Operations Research Society of China    2016, 4 (4): 397-.   DOI: 10.1007/s40305-016-0133-3 Abstract （523）      PDF       Knowledge map    Save In this paper we present two inexact proximal point algorithms to solve minimization problems for quasiconvex objective functions on Hadamard manifolds. We prove that under natural assumptions the sequence generated by the algorithms are well defined and converge to critical points of the problem. We also present an application of the method to demand theory in economy. Related Articles | Metrics | Comments（0） Cited: Baidu(4)

Select

The Multi-visits Drone-Vehicle Routing Problem with Simultaneous Pickup and Delivery Service Si Zhang, Lu Li Journal of the Operations Research Society of China    2024, 12 (4): 965-995.   DOI: 10.1007/s40305-023-00471-7 Abstract （333）      PDF       Knowledge map    Save The development of convergent technology makes the drone expected to become a commercial delivery method for terminal logistics distribution. Although the industry has begun to experiment with the coordinated transportation of drones and vehicles, some limited assumptions have been made to reduce the complexity of synchronization. This paper studies the mathematical formulations and efficient solution methodologies for the multi-visits drone-vehicle routing problem with simultaneous pickup and delivery service (MDVRPSPD), whose objective is to minimize the distance traveled by drones and vehicles and the total number of drones used. To solve the MDVRPSPD problem, a three-stage solution method is designed. Firstly, the scalable K-means++ algorithm is used to determine the vehicle’s parking location, and we optimize the vehicle’s driving route by traveling salesman problem (TSP) modeling; then, the classic tabu search algorithm is extended to arrange the schedule of drones which consider multi-visits and simultaneous pickup and delivery service. Meanwhile, a set of extensive computational experiments are conducted to demonstrate the efficiency of developed heuristics and the superiority of drone-vehicle delivery system on delivery issues. Reference | Related Articles | Metrics | Comments（0）

Select

Smoothing Approximations for Some Piecewise Smooth Functions Hao Wu · Peng Zhang · Gui-Hua Lin Journal of the Operations Research Society of China    2015, 3 (3): 317-.   DOI: 10.1007/s40305-015-0091-1 Abstract （321）      PDF       Knowledge map    Save In this paper, we study smoothing approximations for some piecewise smooth functions.We first present two approaches for one-dimensional case: a global approach is to construct smoothing approximations over the whole domain and a local approach is to construct smoothing approximations within appropriate neighborhoods of the nonsmooth points.We obtain some error estimate results for both approaches and discuss whether the smoothing approximations can inherit the convexity of the original functions. Furthermore, we extend the global approach to some multiple dimensional cases. Related Articles | Metrics | Comments（0） Cited: Baidu(1)

Select

The Rate of Convergence of Augmented Lagrangian Method for Minimax Optimization Problems with Equality Constraints Yu-Hong Dai, Li-Wei Zhang Journal of the Operations Research Society of China    2024, 12 (2): 265-297.   DOI: 10.1007/s40305-022-00439-z Abstract （127）      PDF       Knowledge map    Save The augmented Lagrangian function and the corresponding augmented Lagrangian method are constructed for solving a class of minimax optimization problems with equality constraints. We prove that, under the linear independence constraint qualification and the second-order sufficiency optimality condition for the lower level problem and the second-order sufficiency optimality condition for the minimax problem, for a given multiplier vector μ, the rate of convergence of the augmented Lagrangian method is linear with respect to ||μ - μ*|| and the ratio constant is proportional to 1/c when the ratio ||μ - μ*/c is small enough, where c is the penalty parameter that exceeds a threshold c* > 0 and μ* is the multiplier corresponding to a local minimizer. Moreover, we prove that the sequence of multiplier vectors generated by the augmented Lagrangian method has at least Q-linear convergence if the sequence of penalty parameters {ck} is bounded and the convergence rate is superlinear if {ck} is increasing to infinity. Finally, we use a direct way to establish the rate of convergence of the augmented Lagrangian method for the minimax problem with a quadratic objective function and linear equality constraints. Reference | Related Articles | Metrics | Comments（0）

Select

Optimal Algorithms for Integer Inverse Undesirable p-Median Location Problems on Weighted Extended Star Networks Esmaeil Afrashteh, Behrooz Alizadeh, Fahimeh Baroughi Journal of the Operations Research Society of China    2021, 9 (1): 99-117.   DOI: 10.1007/s40305-018-0229-z Abstract （1286）      PDF       Knowledge map    Save This paper is concerned with the problem of modifying the edge lengths of a weighted extended star network with n vertices by integer amounts at the minimum total cost subject to be given modification bounds so that a set of p prespecified vertices becomes an undesirable p-median location on the perturbed network. We call this problem as the integer inverse undesirable p-median location model. Exact combinatorial algorithms with $\mathcal{O}({p^2}\;n\;\log \;n)$ and $\mathcal{O}({p^2}(n\;\log \;n + n\;\log \;\eta {}_{\max }))$ running times are proposed for solving the problem under the weighted rectilinear and weighted Chebyshev norms, respectively. Furthermore, it is shown that the problem under the weighted sum-type Hamming distance with uniform modification bounds can be solved in $\mathcal{O}({p^2}\;n\;\log \;n)$ time. Reference | Related Articles | Metrics | Comments（0）

Select

The Generic Uniqueness and Well-Posedness of Nash Equilibria for Stable Population Games Wen-Sheng Jia, Xiao-Ling Qiu, Ding-Tao Peng Journal of the Operations Research Society of China    2021, 9 (2): 455-464.   DOI: 10.1007/s40305-019-00281-w Abstract （2260）      PDF       Knowledge map    Save This paper aims at studying a new kind of stable population games introduced by J. Hofbauer and H. Sandholm in 2009. We first construct a complete distance space M consisting of stable population games and show that most of stable population games have unique Nash equilibrium point that according to Baire’s category theorem. It implies that every stable population game that possesses more than one Nash equilibrium can be approached arbitrarily by a sequence of the stable population game each of which has a unique Nash equilibrium. Then, we construct a bounded rationality function and deduce some results on the generic well-posedness implying Tikhonov well-posedness and Hadamard well-posedness for stable population games. Reference | Related Articles | Metrics | Comments（0）

Select

On the Convergence Rate of an Inexact Proximal Point Algorithm for Quasiconvex Minimization on Hadamard Manifolds Nancy Baygorrea, Erik Alex Papa Quiroz, Nelson Maculan Journal of the Operations Research Society of China    2017, 5 (4): 457-467.   DOI: 10.1007/s40305-016-0129-z Abstract （516）      PDF       Knowledge map    Save In this paper, we present an analysis about the rate of convergence of an inexact proximal point algorithm to solve minimization problems for quasiconvex objective functions on Hadamard manifolds.We prove that under natural assumptions the sequence generated by the algorithm converges linearly or superlinearly to a critical point of the problem. Related Articles | Metrics | Comments（0）