大数据与人工智能时代下复杂系统管理研究的若干关键科学问题

当前,世界正在进入大数据与人工智能时代,复杂系统已呈现出人—机—网跨尺度耦合与互融互通的新形态。新形态下的复杂系统管理,既要对传统管理理论和方法进行突破创新,更要充分借助先进的技术手段对其进行赋能。本文面向大数据和人工智能时代下复杂系统管理的国家重大需求,阐述了当前复杂系统管理研究存在的重大机遇与挑战,分析了当前复杂系统管理研究的研究现状并指出了未来的发展趋势,在此基础上,进一步凝练了未来5~10年该领域的发展目标、拟开展的重大关键科学问题及其资助重点。

Editorial Special Issue on Simulation and AI

Simulation is an important area in Operations Research,Management Science and many disciplines,and it has many advantages in analyzing the performance of complex stochastic models.Over the last 15 years,AI has been gaining steam and fundamentally reshaped a number of current and emerging areas.Simulation plays a central role in deep learning and reinforcement learning,which are the foundation of AI,and it can continue improving the AI techniques,particularly in addressing some of its bottlenecks.

Solving Inventory Management Problems through Deep Reinforcement Learning

Inventory management(e.g.lost sales)is a central problem in supply chain management.Lost sales inventory systems with lead times and complex cost function are notoriously hard to optimize.Deep reinforcement learning(DRL)methods can learn optimal decisions based on trails and errors from the environment due to its powerful complex function representation capability and has recently shown remarkable successes in solving challenging sequential decision-making problems.This paper studies typical lost sales and multi-echelon inventory systems.We first formulate inventory management problem as a Markov Decision Process by taking into account ordering cost,holding cost,fixed cost and lost-sales cost and then develop a solution framework DDLS based on Double deep Q-networks(DQN).In the lost-sales scenario,numerical experiments demonstrate that increasing fixed ordering cost distorts the ordering behavior,while our DQN solutions with improved state space are flexible in the face of different cost parameter settings,which traditional heuristics find challenging to handle.We then study the effectiveness of our approach in multi-echelon scenarios.Empirical results demonstrate that parameter sharing can significantly improve the performance of DRL.As a form of information sharing,parameter sharing among multi-echelon suppliers promotes the collaboration of agents and improves the decisionmaking efficiency.Our research further demonstrates the potential of DRL in solving complex inventory management problems.

Efficient learning for decomposing and optimizing random networks

In this study,we consider the problem of node ranking in a random network.A Markov chain is defined for the network,and its transition probability matrix is unknown but can be learned by sampling random interactions among nodes.Our objective is to decompose the Markov chain into several ergodic classes and select the best node in each ergodic class.We propose a dynamic sampling procedure,which gives a probability guarantee on correct decomposition and maximizes a weighted probability of correct selection of the best node in each ergodic class.Numerical experiment results demonstrate the efficiency of the proposed sampling procedure.

Variance reduction for generalized likelihood ratio method by conditional Monte Carlo and randomized Quasi-Monte Carlo methods

The generalized likelihood ratio(GLR)method is a recently introduced gradient estimation method for handling discontinuities in a wide range of sample performances.We put the GLR methods from previous work into a single framework,simplify regularity conditions to justify the unbiasedness of GLR,and relax some of those conditions that are difficult to verify in practice.Moreover,we combine GLR with conditional Monte Carlo methods and randomized quasi-Monte Carlo methods to reduce the variance.Numerical experiments show that variance reduction could be significant in various applications.