Subsea Maintenance Service Delivery:Mapping Factors Influencing Scheduled Service Duration

The oil and gas （O＆G） industry on the Norwegian continental shelf （NCS） leads the world in terms of the number of subsea O＆G installations. Ensuring the dependability of these assets is critical. Non-intrusive inspection, maintenance and repair （IMR） services are therefore needed to reduce risks. These services are planned and executed using a mono-hull offshore vessel complete with remotely operated vehicles （ROVs）, a module handling system and an active heave compensated crane. Vessel time is shared between competing jobs, using a prioritized forward-looking schedule. Extension in planned job duration may have an impact on O＆G production, service costs and health, safety, and environmental （HSE） risks. This paper maps factors influencing the job schedule efficiency. The influence factors are identified through reviews of literature as well as interviews with experts in one of the large IMR subsea service providers active on the Norwegian Continental Shelf. The findings show that the most obvious factors are weather disruption and water depth. Other factors include job complexity, job uncertainty, IMR equipment availability, as well as the mix of job complexity.

A bi-population immune algorithm for weapon transportation support scheduling problem with pickup and delivery on aircraft carrier deck

The weapon transportation support scheduling problem on aircraft carrier deck is the key to restricting the sortie rate and combat capability of carrier-based aircraft.This paper studies the problem and presents a novel solution architecture.Taking the interference of the carrier-based aircraft deck layout on the weapon transportation route and precedence constraint into consideration,a mixed integer formulation is established to minimize the total objective,which is constituted of makespan,load variance and accumulative transfer time of support unit.Solution approach is developed for the model.Firstly,based on modeling the carrier aircraft parked on deck as convex obstacles,the path library of weapon transportation is constructed through visibility graph and Warshall-Floyd methods.We then propose a bi-population immune algorithm in which a population-based forward/backward scheduling technique,local search schemes and a chaotic catastrophe operator are embedded.Besides,the randomkey solution representation and serial scheduling generation scheme are adopted to conveniently obtain a better solution.The Taguchi method is additionally employed to determine key parameters of the algorithm.Finally,on a set of generated realistic instances,we demonstrate that the proposed algorithm outperforms all compared algorithms designed for similar optimization problems and can significantly improve the efficiency,and that the established model and the bi-population immune algorithm can effectively respond to the weapon support requirements of carrier-based aircraft under different sortie missions.

Rolling horizon scheduling algorithm for dynamic vehicle scheduling system

Dynamic exclusive pickup and delivery problem with time windows (DE-PDPTW), aspecial dynamic vehicle scheduling problem, is proposed. Its mathematical description is given andits static properties are analyzed, and then the problem is simplified asthe asymmetrical travelingsalesman problem with time windows. The rolling horizon scheduling algorithm (RHSA) to solve thisdynamic problem is proposed. By the rolling of time horizon, the RHSA can adapt to the problem'sdynamic change and reduce the computation time by dealing with only part of the customers in eachrolling time horizon. Then, its three factors, the current customer window, the scheduling of thecurrent customer window and the rolling strategy, are analyzed. The test results demonstrate theeffectiveness of the RHSA to solve the dynamic vehicle scheduling problem.

Integrated schedule of order picking and delivery for instant delivery

In this study,we introduce an integrated schedule of order picking and delivery for instant delivery.Order picking,including order batching and picking sequencing,is scheduled online under real-time order arrival,which integrates order delivery by depicting order location dispersion in an online order picking strategy.Order delivery,including delivery person assignment and route planning,is modeled to minimize the total duration of order fulfillment by considering the influence of the order picking completion time.A rule-based online order picking strategy is established,and a customized ant colony optimization(ACO)algorithm is proposed to optimize order delivery.Experiments on 16 simulated instances of different scales demonstrate that our online order picking schedule considering order delivery outperforms existing approaches and that the customized ACO algorithm for order delivery is effective.

Heuristic algorithms for scheduling on uniform parallel machines with heads and tails

This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time.For this NP-hard problem,the largest sum of release date,processing time and delivery time first rule is designed to determine a certain machine for each job,and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine,and then a novel algorithm for the scheduling problem is built.To evaluate the performance of the proposed algorithm,a lower bound for the problem is proposed.The accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 jobs.The computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%,therefore the solutions obtained by the proposed algorithm are very accurate.

Flexible Personnel Scheduling in the Parallel Environment

In the view of staff shortages and the huge inventory of products in the current market, we put forward a personnel scheduling model in the target of closing to the delivery date considering the parallelism. Then we designed a scheduling algorithm based on genetic algorithm and proposed a flexible parallel decoding method which take full use of the personal capacity. Case study results indicate that the flexible personnel scheduling considering the order-shop scheduling, machine automatic capabilities and personnel flexible in the target of closing to the delivery date optimize the allocation of human resources, then maximize the efficiency.

基于离散混合蛙跳算法的地震应急物资调度

建立震后应急物资调度数学模型。该模型根据各灾区的受灾情况评估其救援紧急程度,并设计一种需求拆分供应的运输机制,提高车辆的利用效率。为求解该模型,提出一种多源信息学习的离散混合蛙跳算法。所提算法引入多种信息源以扩展算法的搜索方向,降低种群的同化速度。同时,让子组最差个体学习种群中的有效信息,提高算法的收敛精度。实验结果表明,所提算法能够搜索到精度更优的调度方案,对问题规模具有良好的可扩展性。

基于配送服务模式及路径策略的物流无人机调度模型

针对远距离城市空间物流无人机配送问题,提出基于两级多中心场景的无人机(Unmanned Aerial Vehicle, UAV)配送服务模式.综合考虑客户时间、无人机充电和配送路径策略等内容,建立以最小配送总成本为目标的两级多中心物流无人机城市配送调度模型,通过比较标准算例下Cplex优化软件与鲸鱼优化算法(Whale Optimization Algorithm, WOA)、遗传算法(Genetic Algorithm,GA)和灰狼优化算法(Grey Wolf Optimization, GWO)3种启发式算法的实验结果,确定求解算法以设计两阶段算法进行求解.以天津市中心城区为例进行模型求解,并从需求点规模、机型和时间窗3方面进行敏感度分析.研究结果表明:与闭合式路径相比,半开放式路径策略分别节省约11.09%、60.45%的配送总成本和时间窗惩罚成本,成本节省随配送规模增大而增加;提出的固定路径班次模式相比现有按需模式,其可变成本和总成本降幅均高于18%,使用无人机数量减少近28%,生成的班次时刻表和飞行路径时空图可显现不同模式优劣.本文提出的模型对优化远距离城市物流无人机调度方案及配送模式有一定实用价值,可为企业在物流无人机的配送规划方面提供参考.

一种针对无人机配送网络的能量自维持调度方案

近年来,快递行业需求快速增长,物流配送行业压力剧增。无人机(UAV, unmanned aerial vehicle)配送凭借其人力成本低、灵活方便等特性成为车辆配送的有益补充。然而,无人机配送受续航能力和负载能力等因素的制约,需要低成本且能量自维持的配送和充电调度方案来支持多无人机的协同配送。提出了两阶段的能量自维持的多无人机协同配送及充电调度方案。第一阶段在满足无人机能量和载重容量约束的前提下,最小化能完成区域内所有配送任务所需的无人机数量,并给出对应配送路线。提出了无人机配送调度算法(UDSA, UAV delivery scheduling algorithm),并从理论上证明了UDSA的近似度。第二阶段对具有不同到达时间的无人机进行充电调度,最小化所有无人机的最大充电完成时间。提出了一种具有近似度的无人机充电调度算法(UCSA, UAV charging scheduling algorithm)来求解该问题。仿真实验结果表明,与基准算法相比,UDSA最多可以减少44.17%的无人机数量;UCSA最多可以缩短18.87%的最大充电完成时间。

泵站-水库原水调水系统经济运行方式优化

为提高泵站-水库原水调水系统中泵站的运行效益,最大限度降低输水系统的运行成本.通过分析该类输水系统特性以及供水方式,基于遗传算法构建泵站机组流量分配模型,优化分配泵站运行流量.在此基础上,考虑泵站时间维度上的运行方式,结合亳州加压泵站的工程状况和区域分时电价,以单位提水费用最小为优化目标,构建经济运行模型.将该方法应用于亳州供水工程,结果表明模型效果显著,并对历史运行区间内周期输水量进行离散计算,拟合得出周期内单位提水费用最低工况,进而对拟合结果进行分析.结果表明:所构建经济运行模型具有较好的适用性,可降低工程运行费用,为工程优化调度提供指导;受到泵站运行效率和分时电价的影响,泵站单位提水费用随调水量增加先降低后升高.

混合三维分布估计算法求解分布式加工装配和车辆配送集成调度问题

本文针对一类广泛存在的分布式加工装配和车辆配送集成调度问题(Integrated Scheduling Problem of Distributed Production Assembly and Vehicle Delivery,ISP_DPAVD),以最小化运输和延迟惩罚总成本为优化目标,提出一种混合三维分布估计算法(Hybrid three-Dimensional Estimation of Distribution Algorithm,H3DEDA)进行求解.ISP_DPAVD包含两个耦合的子问题,即加工装配阶段子问题(子问题1)和车辆配送阶段子问题(子问题2).由于每个子问题1的解(部分解1)均会确定1个具体的子问题2,故ISP_DPAVD的解空间非常庞大.根据这一特点,在H3DEDA中,先设计结合邻域变换的启发式规则来快速获取子问题2的优良解,以实现子问题间的部分解耦并明显缩减搜索空间,再设计三维EDA引导的全局搜索和变邻域驱动的局部搜索来获取ISP_DPAVD的高质量解.通过在不同规模测试问题上的仿真实验和算法比较,验证了H3DEDA求解ISP_DPAVD的有效性.

基于动态订单合并的滚动配取路径多目标优化

外卖配取过程中实时订单的不断插入具有强烈的不确定性,需持续进行滚动优化以动态更新配取路径。动态条件下,有效地合并取餐与配送作业(dynamic order combination,DOC)可显著减少冗余路径。本文将动态配取路径规划问题转化为变长开放链滚动优化问题,并构建多目标滚动配取路径规划模型对DOC与节点排序进行集成决策。考虑滚动优化框架下紧前决策对紧后决策的调度影响,模型在兼顾配取效率和客户满意度的同时,考虑了基于look-forward的滚动调度后效性。针对该模型,本文基于NSGA-Ⅲ框架开发了多目标元启发式算法进行求解,并设计了基于插入限制规则的元胞数组解编码和混合PMX&SBX交叉方式以适应模型的复杂可行域结构。通过一系列的仿真实验,本文验证了所提出的模型和算法的有效性与优越性。

An Approximation Algorithm for the Parallel-Machine Customer Order Scheduling with Delivery Time and Submodular Rejection Penalties

In this paper,we consider the parallel-machine customer order scheduling with delivery time and submodular rejection penalties.In this problem,we are given m dedicated machines in parallel and n customer orders.Each order has a delivery time and consists of m product types and each product type should be manufactured on a dedicated machine.An order is either rejected,in which case a rejection penalty has to be paid,or accepted and manufactured on the m dedicated machines.The objective is to find a solution to minimize the sum of the maximum delivery completion time of the accepted orders and the penalty of the rejected orders which is determined by a submodular function.We design an LP rounding algorithm with approximation ratio of n+1 for this problem.

南水北调中线输水调度预演系统研发及应用

南水北调中线输水调度仿真预演是精准化调度决策的基础。针对中线输水线路长、控制闸门多、运行工况复杂等调度难点,开发了数字孪生南水北调中线输水调度仿真预演平台,构建了水情数据智能清洗模型、自主学习训练模型、总干渠一维恒定流和非恒定流精准模拟模型、典型建筑物局部三维水力仿真简易模型等,实现对任意设定工况下水力调控方案效果的精准仿真,形成有效的调度预案。预演平台具备较强的灵活性和通用性,方便用户自由模拟不同时空尺度下的各类运行工况,可适应入流和出流等边界条件的变化。系统具有自主学习能力,能够随着监测数据的积累,不断自动完善计算模型参数和结构,提高计算精度。南水北调中线输水调度预演系统为安全高效输水调度提供有力的技术支撑。

A Penalty Groups-Assisted Iterated Greedy Integrating Idle Time Insertion:Solving the Hybrid Flow Shop Group Scheduling with Delivery Time Windows

The hybrid flow shop group scheduling problem(HFGSP)with the delivery time windows has been widely studied owing to its better flexibility and suitability for the current just-in-time production mode.However,there are several unresolved challenges in problem modeling and algorithmic design tailored for HFGSP.In our study,we place emphasis on the constraint of timeliness.Therefore,this paper first constructs a mixed integer linear programming model of HFGSP with sequence-dependent setup time and delivery time windows to minimize the total weighted earliness and tardiness(TWET).Then a penalty groups-assisted iterated greedy integrating idle time insertion(PG IG ITI)is proposed to solve the above problem.In the PG IG ITI,a double decoding strategy is proposed based on the earliest available machine rule and the idle time insertion rule to calculate the TWET value.Subsequently,to reduce the amount of computation,a skip-based destruction and reconstruction strategy is designed,and a penalty groups-assisted local search is proposed to further improve the quality of the solution by disturbing the penalized groups,i.e.,early and tardy groups.Finally,through comprehensive statistical experiments on 270 test instances,the results prove that the proposed algorithm is effective compared to four state-of-the-art algorithms.

轧钢热装效益的提升探讨

通过提高组织优化、信息化和数字化水平,提出了一系列措施实现资源配置调整、调度协同化、技能培训、引入智能调度系统、数据集成与共享以及数据驱动的决策等,有助于增加企业经济效益,并建议与制造部门密切合作,不断追求优化和改进,旨在提升永钢集团轧钢厂的热装效益。

染机助剂输配送优化调度算法研究

针对染机助剂输配送多采用时序输送方式,缺乏优化调度算法,造成助剂输送等时较长、输送效率低等问题,重点研究了一种染机助剂输配送优化调度算法,算法以车间整体染机等时最短为优化目标,建立优化调度算法模型,获取染机助剂输送优化顺序,以广东省某纺织印染企业染色车间生产为例,通过采用优化调度算法,助剂输送效率提升16.77%,为印染环节数字化、智能化改造提供了良好的思路。

响应用户随机需求下远距离物流配送调度仿真

由于远距离物流通常涉及多个节点、多个运输模式和不同的运输路径,且需要考虑不同节点之间的时间窗要求,导致调度过程中的信息传递和协调难度增加。为此,提出一种响应用户随机需求的远距离物流配送调度算法。针对用户随机需求信息和经验数据,采用汇集预测方法形成合理的虚拟用户出现概率、坐标位置和需求量信息,以远距离物流配送调度时间最短和用户满意度最高为目标,按照车辆先真实后虚拟用户配送的原则,组建响应用户随机需求的远距离物流配送调度模型。引入改进粒子群(Particle Swarm Optimization,PSO)算法对模型求解,确定最优远距离物流配送调度方案。仿真分析证明,所提算法可以快速响应用户需求,用户满意度均在96%以上,远距离物流配送调度时间最长仅为30min。

面向簇化移动机器人的网络资源调度算法

工厂自动化要求超可靠低时延通信,使控制中心能够可靠实时地向移动机器人传输控制指令。为此,面向移动机器人作业的工厂环境,提出基于深度增强学习的资源调度算法(DRL-RS)。DRL-RS算法由两个阶段构成:在第一阶段,一起作业的移动机器人形成簇群,将簇群内多个移动机器人的指令包融合成一个包,再将此包传输至簇群的领导者;在第二阶段,领导者向它簇群成员广播指令包。DRL-RS算法引用深度增强学习算法优化资源调度。领导者扮演Agent,通过向环境学习,择优选择接入点以及子信道和传输功率,进而最大化向所有机器人传输指令包的成功率。性能分析结果表明,DRL-RS算法传输指令包成功率逼近于穷搜索法。

APS在封装基板智能工厂的设计与应用

文章重点探究先进计划及排配系统在封装基板工厂的设计和应用。鉴于封装基板生产工艺复杂,对生产计划与调度精度要求甚高。APS客制化系统设计综合考虑了封装基板工厂的工艺流程、设备特性和产能等要素,能依据订单需求、库存状况及生产能力,制定详尽灵活且有助于生产最大化的计划。其应用成效斐然,加快生产速度,极大提升资源配置效率,优化生产排程,缩短生产周期,降低生产成本。方法首先可通过需求与供给的匹配提供现有工单交期及投料计划,先以无限产能计算生产速度的权重值,再以有限产能设定各工站约束条件,实现连批生产,使产能最大化,最终实现长期产能规划模拟,提前评估人力、订单、物料及设备需求的变化。同时,通过实时监控和动态调整,增强应对生产不确定性的能力,大幅提高产品交付准时率,为封装基板工厂实现精益化生产、提升市场竞争力提供关键的技术支持与保障。