成都:2021年智能优化应用实践学术研讨会

由西南交通大学机械工程学院承办,数据魔术师协办的2021年智能优化应用实践学术研讨会拟于2021年7月26日至28日在四川成都举行。

2021年智能优化应用实践学术研讨会

(2021年7月,四川,成都)

由西南交通大学机械工程学院承办,数据魔术师协办的2021年智能优化应用实践学术研讨会拟于2021年7月26日至28日在四川成都举行。此次会议线下举行。

本次学术研讨会聚焦智能优化算法及其在智能制造,产线运行,复杂物流系统等方面的应用实践落地,直击优化技术实施细节与应用实践创新,旨在搭建青年学者开放的学术交流平台,促进智能优化研究领域学术繁荣和科技创新,同时推动科技合作和业内应用。

(报名方式见文末,持续更新中……)

主旨报告嘉宾

(一)报告题目Branch-and-price-and-cut algorithms for home health care routing and scheduling
报告摘要:This talk focuses on home health care routing and scheduling problem (HHCRSP), a field that has received much research attention in recent years. Briefly stated, HHCRSP is that of designing a set of routes used by caregivers to perform various services at patients’ homes. Hence, care activities, i.e., patient visits, must be planned to reduce costs and to guarantee service quality while respecting several constraints. This talk provides an overview of recent OR models developed for the HHCRSP by identifying the most relevant features considered in the HHCRSP models, and introducing our two works on this topic, i.e., HHCRSP with start-time dependent routing and flexible time windows, and multi-skilled HHCRSP with synchronized services, where we develop efficient branch-and-price-and-cut algorithms to solve them.

殷允强,电子科技大学经济与管理学院教授、博士生导师,国家万人计划青年拔尖人才,四川省杰青,四川省海内外引进高层次人才。主要从事智能决策与优化,生产与物流运作管理,数据挖掘等方面的研究。以第一或通讯作者在NRL、EJOR、TRE、Omega、IEEE Transactions on Cybernetics、IEEE Transactions on SMC等国际主流期刊发表SCI论文60余篇。主持国家自然科学基金项目4项,国家社科重大项目1项(课题负责人),四川省杰出青年科技人才项目、国家留学基金委中法蔡元培国际合作项目等省部级项目多项。2014-2020连续7年入选Elsevier 中国高被引学者榜单。现任Complex & Intelligent Systems、International Journal of Production Research等多本SCI期刊的副主编、首席客邀编辑,中国管理科学与工程学会理事,中国优选法统筹法与经济数学研究会智能决策与博弈分会秘书长兼常务理事以及中国运筹学会排序专业委员会等多个分会的理事。

(二)报告题目A two-stage robust approach to integrated station location and rebalancing  vehicle service design in bike-sharing systems
报告摘要:A bike-sharing system is a shared mobility mechanism that provides an alternative transportation mode for short trips with almost no added travel speed loss. However, this model’s low usage ratio and high depreciation rate pose a risk to the sustainable development of the bike-sharing industry. Our study proposes a new integrated station location and rebalancing vehicle service design model. This model aims to maximize daily revenue under a given total investment for station locations and bike acquisition. To address demand ambiguity due to possible bias and loss of data, we present a two-stage robust optimization model with a demand-related uncertainty set. The first stage of our model determines the station locations, initial bike inventory, and service areas of rebalancing vehicles. In contrast to the literature, which either simplifies the rebalancing process as an inventory transshipment problem or formulates it as a complex dynamic bike rebalancing problem, we assign each rebalancing vehicle to a service area composed of several specified stations. An approximate maximum travel distance for each rebalancing vehicle is also designed and constrained to ensure that the rebalancing operation can be performed within each period. In the second stage, our model optimizes the daily fleet operation and maximizes the total revenue minus the rebalancing cost. To solve our model, we design a customized row generation approach. Our numerical studies demonstrate that our algorithm can efficiently obtain exact solutions in small instances. For a real-size problem, the nearly optimal solutions of our model also reveal a high-quality worst-case performance with a small loss in mean performance, particularly when the value of the budget ratio (that is, the average number of bikes per station) is at a medium level. Moreover, the distribution of service areas depends on the bike supply and demand level at each station. The optimal fleet rebalancing operation does not have to be confined to one geographical area. Furthermore, our robust model can achieve larger mean and worst-case revenues and a higher revenue stability than a stochastic model with a small data set.

朱宁,天津大学管理与经济学部副教授,主要研究方向是交通系统运营管理与优化。使用混合整数优化、随机优化、鲁棒优化、随机过程等技术工具对交通与物流系统中的选址、车辆路径优化、资源分配等问题进行研究。具体研究问题包括:交通检测器优化选址、公共交通系统优化与建模、无人机线路优化、公共自行车系统选址与再平衡问题、灾难救援中的线路优化等问题。以一作/通讯作者发表论文19篇,包括IJOC、TS、TR-Part B/C、EJOR、Journal of Scheduling、系统工程学报等期刊,主持国家自然科学基金项目三项。

(三)报告题目Feeder Vessel Routing and Transshipment Coordination in a Hub-and-Spoke Maritime Shipping Network: A Branch-&-Price Algorithm

报告摘要:With increasing container-shipping traffic, congestion at transshipment hub ports happens from time to time incurring longer-than-expected waiting time for vessels and loss of transshipment connections. This situation is even worse for feeder companies, due to their relatively lower berthing priority. It is essential to design the feeder vessel routes and schedules in response to hub port congestion while ensuring efficient transshipment connection. In this paper, we study, for a feeder liner company, the vessel routing and transshipment coordination problem with limited choices of berthing time slots. We propose a set covering model with the objective of minimizing total operational cost and transshipment connection cost. A branch-&-price exact algorithm is developed, which can solve instances with up to 30 ports to optimality within one hour. Computational results based on a real-world shipping network demonstrate that, by integrating the hub port visiting time decision with feeder vessel routing, transshipment coordination can be significantly enhanced even under hub port congestion situations.

金建钢,博士,上海交通大学交通运输工程系副教授。分别于清华大学和新加坡国立大学获得学士和博士学位。专注于大规模组合优化、整数规划和网络优化等运筹优化方法在交通和物流系统中的应用研究。在Transportation Science, Transportation Research Part A/B/C/E等期刊上发表30余篇SCI/SSCI期刊论文。主持3项国家自然科学基金项目(中加国际合作研究项目、面上、青年)。入选交通部交通运输青年科技英才、上海市启明星计划、上海市晨光计划。获INFORMS Railway Application Section铁路优化竞赛一等奖、新加坡国立大学校长奖。担任国际期刊Computers & Industrial Engineering领域编辑。

(四)报告题目Contractor Selection in Project Outsourcing via Request-for-Quote

报告摘要:Inspired by the widespread adoption of request-for-quote (RFQ) in project outsourcing, in this paper, we consider two RFQ structures (fixed-term and upfront-fee) with two contract forms (cost-sharing and time-incentive) to select cost-efficient contractors and maximize the project client’s payoff. By capturing the fundamental time-cost trade-off in project management, our RFQ design problem features both adverse selection arising from the imperfect information on contractors’ cost efficiency and moral hazard as the contractor’s effort to complete the project is not observable nor verifiable. Practical restrictions on time incentives, such as free disposal constraints, further complicate our problem and render moral hazard in effect. We find that with the contractor’s direct cost (e.g., labor, equipment) to be shared, the cost-sharing contract can induce a higher effort from contractors, shorter expected completion time, and higher client payoffs than the time-incentive contract.
Further, we develop a closed-form analytical bound on the relative decrease in the client’s payoff by offering a fixed-term RFQ instead of its upfront-fee counterpart.

邓天虎,清华大学工业工程系副教授。2008年毕业于清华大学工业工程系,获学士学位;2013年毕业于美国加州大学伯克利分校,获博士学位。主要研究智慧供应链的方法论框架和企业解决方案。负责执行的中石油天然气管网优化项目入围INFORMS设立的管理科学应用界最高奖项弗兰茨·厄德曼奖 (Franz Edelman Award)2018年决赛。目前研究成果已于Operations Research、Manufacturing & Service Operations Management, Informs Journal on Computing以及Interfaces等国际学术期刊上获得发表。

 (五)报告题目A Two-phase Approach for Data-Driven Vehicle Routing with Time Windows

 报告摘要:On-time delivery is of utmost importance in today’s urban logistics. However, travel times are uncertain and classical deterministic routing solutions often fail to ensure timely delivery. Moreover, the classical probability measure only focuses on the probability of lateness but ignores the corresponding magnitude. In the paper, we propose a general risk index which covers the previously studied riskiness index, essential risk index, and service fulfillment risk index, and induces a new one. Leveraging its good properties, we develop an exact two-phase framework, which incorporates the column generation, column enumeration, and branch-and-price. The extensive experiments demonstrate that the newly proposed method enables to solve some instances up to 100 customers and illustrate the interesting performance of different measures.

张真真,同济大学经济与管理学院副教授。曾担任新加坡国立大学工业工程系助理教授,于香港城市大学管理科学系获得博士学位,厦门大学计算机科学系获得硕士和学士学位。主要研究涉及大规模整数规划和鲁棒优化理论及在车辆路径和调度问题上的应用。所做的研究多从实际问题出发,例如飞利浦的全球采购问题、香港公立医院的非紧急救护车调度问题、快时尚品牌CHARLES & KEITH的商品转配问题、和新加坡物流公司的车辆调度问题等。目前已发表SCI/SSCI期刊论文19篇,包括1篇Operations Research,3篇Transportation Science, 2篇Transportation Research Part B等。同时,长期担任20多个国际知名期刊的审稿人,包括Operations Research, Transportation Research Part B/D/E, European Journal of Operational Research及IEEE Transactions on Evolutionary Computation等。

(六)报告题目Approximation algorithm for integrated production and transportation scheduling problem

报告摘要:This talk focuses on an integrated production and transportation problem for a commit-to-delivery businesses mode of a make-to-order manufacturing company, which uses third party logistics service providers to deliver products to customers on or before certain committed delivery dates. The third party logistics service providers often provide various quantity discounts, as well as modes with different guaranteed shipping times. To our best knowledge, this paper is a first attempt to solve the integrated production and transportation problem for the commit-to-delivery mode with such general shipping costs. Interestingly, we find that when the unit inventory holding cost is sufficiently small, there exists an FPTAS for the two-day problem, the development of which hinges on a newly discovered property for the optimization of general piecewise linear functions.

李锋,华中科技大学管理学院副教授、博士生导师。2008年、2010年、2016年分别在东北大学信息与计算科学、系统工程获得学士、硕士、博士学位,2013年-2015年在美国马里兰大学联合培养,2016-2017年在香港理工大学做博士后研究。主要研究方向为生产调度、物流优化、组合优化等。研究成果主要发表在INFORMS Journal on Computing ( 3篇,UTD 24期刊)、Naval Research Logistics(3篇)、European Journal of Operational Research、Transportation Research Part E、Computers and Operations Research等国际著名期刊上。2019年入选湖北省“楚天学子”人才项目。主持一项国家自然科学基金青年项目。

(七)报告题目考虑载重成本的时间窗约束下分割载货取送货问题

报告摘要:基于物流配送中的载重成本因素、集中式仓储和预约服务需求,分别构建了考虑载重成本的目标函数、发货点分割载货约束条件和时间窗约束条件,建立了考虑载重成本的时间窗约束下分割载货取送货问题的混合整数模型。基于拓展后的Set-partitioning 模型设计了分枝定价切割(Branch-and-price-and-cut)算法。通过分析车辆路径可行域的结构特征,证明了分枝定价算法中定价问题的最优解是路径可行域的极点,验证了算法完备性。在label-setting算法中,基于发货点分割载货条件约束下的路径结构特征,设计了分段函数结构的标号设定方法和标号淘汰准则,并且应用双向标号搜索提高了标号设定算法效率。基于问题可行解的结构特征,应用 Arc-flow 不等式约束分枝下界。实验结果表明在发货点储货量足够大的情况下分枝定价切割算法可以得到大部分算例的最优解,增加发货点储货量具有降低运输成本的作用。

薛力,西北工业大学管理学院管理科学与工程系助理教授。2011年本科毕业于西安交通大学电气工程及自动化专业,同年保送进入西安交通大学管理学院学习,2013年参加西安交通大学与香港城市大学双博士联合培养项目,2018年获得西安交通大学管理学院工商管理专业博士学位和香港城市大学商学院博士学位。以第一作者身份在OMEGA,EJOR等国际期刊发表多篇学术论文,目前主持国家自然科学基金青年项目1项,陕西省自然科学基金青年项目1项。

(八)报告题目不确定环境下装配调度的超启发式优化技术

报告摘要:在以人工为主的实际装配生产环节中,不确定性频率较高,同时考虑工时不确定性和新产品插单两种动态因素,构建调度优化模型并快速求解意义重大。受迭代时间和优化随机性的影响,元启发式的应用受到限制且鲁棒性严重恶化,促使启发式算法更加广泛应用于工程实际,同时如何形成更优优先级规则成为研究焦点。在分析20种不同优先级规则在时间维度和方案鲁棒性等方面的调度性能特性基础上,按照超启发式思想设计出一种改进遗传规划算法以实现更优优先级规则的生成,并设计局部搜索、遗传算子、相同/相似功能解消除等改进方法以提升算法的搜索能力;再按照集成学习的思想构造一种集成遗传规划算法进化出一组优先级规则进行协同决策,以提升稳定求解能力;报告最后介绍该方法在复杂产品装配过程中的应用。本次会议拟于2021年7月26日报到,7月27日全天大会报告,7月28日安排企业参观。参会人数拟限制在150人左右,先到先得。

张剑,西南交通大学机械工程学院制造工程系副系主任,中国机械工程学会工业大数据与智能系统分会委员,教授,工学博士,从事复杂机电产品设计与优化、智能制造、数字化车间、生产调度等方面的研究工作。先后主持或者参研国家科技支撑计划项目、国家科技重大专项、863计划、四川省科技厅项目、智能制造专项等国家及省部级纵向项目和其他多项横向项目,发表文章40多篇高质量论文,其中高被引SCI论文1篇,出版专著1部,授权发明专利13项,另申请发明专利20多项,软件著作权6项,获得四川省科技进步一等奖1项。

会议安排

本次研讨会筹备流程如下(待进一步更新):

发布会议预通知

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参会方式

欲参会人员请添加下方二维码联系会议助手(左图)或者西南交通大学郭鹏副教授(右图),然后加入研讨会微信群。

成都:2021年智能优化应用实践学术研讨会
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网址引用: 数据魔术师. 成都:2021年智能优化应用实践学术研讨会. 思谋网. https://www.scmor.com/view/6719.

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