变路网情况下多库房应急物资调度模型及算法

考虑一类大规模自然灾害应急救援情景,基于实际应用条件和需要,建立了最优变路网情况下多库房应急物资调度模型。探讨了车辆所依托的路网结构可变和多库房对调度算法的双重影响,设计了一种求解问题的动态加速自适应遗传算法。作为结论的直接应用,给出的仿真算例验证了问题模型及其求解算法的合理性和有效性。     

应急物资配送车辆调度模型与优化综述

抢险救灾行动的有效规划和调度在挽救人民群众生命和减轻财产损失中起着重要作用,利用数学建模方法和计算机仿真技术,辅助决策者完成应急物资配送车辆调度已成为学术界的共识。围绕模型和优化两个关键点,分析了近年来应急物资配送车辆调度的研究现状,归纳出了应急物资配送车辆调度模型的主要优化目标和影响因素,对比分析了多种优化算法的应用效果,提出了现有研究中存在的问题,最后研讨了应急物资配送车辆调度研究的发展趋势。     

Logistical support scheduling under stochastic travel times given an emergency repair work schedule

Stochastic factors during the operational stage could have a significant influence on the planning results of logistical support scheduling for emergency roadway repair work. An optimal plan might therefore lose its optimality when applied in real world operations where stochastic disturbances occur. In this study we employ network flow techniques to construct a logistical support scheduling model under stochastic travel times. The concept of time inconsistency is also proposed for precisely estimating the impact of stochastic disturbances arising from variations in vehicle trip travel times during the planning stage. The objective of the model is to minimize the total operating cost with an unanticipated penalty cost for logistical support under stochastic traveling times in short term operations, based on an emergency repair work schedule, subject to related operating constraints. This model is formulated as a mixed-integer multiple-commodity network flow problem and is characterized as NP-hard. To solve the problem efficiently, a heuristic algorithm, based on problem decomposition and variable fixing techniques, is proposed. A simulation-based evaluation method is also presented to evaluate the schedules obtained using the manual method, the deterministic model and the stochastic model in the operation stage. Computational tests are performed using data from Taiwan’s 1999 Chi-Chi earthquake. The preliminary test results demonstrate the potential usefulness of the proposed stochastic model and solution algorithm in actual practice.     

Optimal joint routing and link scheduling for real-time traffic in TDMA Wireless Mesh Networks

We investigate the problem of joint routing and link scheduling in Time-Division Multiple Access (TDMA) Wireless Mesh Networks (WMNs) carrying real-time traffic. We propose a framework that always computes a feasible solution (i.e. a set of paths and link activations) if there exists one, by optimally solving a mixed integer-nonlinear problem. Such solution can be computed in minutes or tens thereof for e.g. grids of up to 4 × 4 nodes. We also propose heuristics based on Lagrangian decomposition to compute suboptimal solutions considerably faster and/or for larger WMNs, up to about 50 nodes. We show that the heuristic solutions are near-optimal, and we exploit them to gain insight on the schedulability in WMN, i.e. to investigate the optimal placement of one or more gateways from a delay bound perspective, and to investigate how the schedulability is affected by the transmission range.     

A large neighbourhood search heuristic for ship routing and scheduling with split loads

The purpose of this paper is to present and solve a new, important planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies are committed to carrying some contract cargoes and will try to derive additional revenue from optional spot cargoes. In most of the literature on ship routing and scheduling problems a cargo cannot be transported by more than one ship. By introducing split loads this restriction is removed and each cargo can be transported by several ships. In this paper we propose a large neighbourhood search heuristic for the ship routing and scheduling problem with split loads. Computational results show that the heuristic provides good solutions to real-life instances within reasonable time. It is also shown that introducing split loads can yield significant improvements.     

基于资源-时延转换的应急资源配送模型

考虑灾后抢修失效路段对路网连通性的影响,以最大化应急资源配送效益为目标,基于资源-时延转换的思路,引入时延的随机性,构建了路网、路径联合优化模型。基于模型整体框架,设计了改进的遗传算法用于求解问题。算例分析展现了模型的实际应用过程和求解算法的有效性。     

Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.     

Optimal search path for service in the presence of disruptions

We consider the following basic search path problem: a customer residing at a node of a network needs to obtain service from one of the facilities; facility locations are known and fixed. Facilities may become inoperational with certain probability; the state of the facility only becomes known when the facility is visited. Customer travel stops when the first operational facility is found. The objective is to minimize the expected total travel distance.     

Optimal feedback scheduling of model predictive controllers

Model predictive control （MPC） could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling （FS-CBS） of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server （CBS）, whose reserved processor time is adjusted dynamically. The constraints in the FS- CBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.     

Optimal booking and scheduling in outpatient procedure centers

Patient appointment booking, sequencing, and scheduling decisions are challenging for outpatient procedure centers due to uncertainty in procedure times and patient attendance. We extend a previously developed appointment scheduling model to formulate a model based on a two-stage stochastic mixed integer program for optimizing booking and appointment times in the presence of uncertainty. The objective is to maximize expected profit. Analytical insights are reported for special cases and experimental results show that they provide useful rules of thumb for more general problems. Three solution methods are described which take advantage of the underlying structure of the stochastic program, and a series of experiments are performed to determine the best method. A case study based on an endoscopy suite at a large medical center is used to draw a number of useful managerial insights for procedure center managers.     

港口油品配送系统调度优化的启发式方法

基于带时间窗的车辆路径问题优化理论与方法,首先给出了港口油品配送计划调度满足的数学模型,然后基于智能理论,针对配送时间、空间和配送载重量等限制条件,发展了一系列进行运输路径计划制定的启发式方法,如种子需求选择方法、路径插入可行性检测方法及移动、交换等优化路径方法,来得出最优的港口船舶柴油配送调度方案。最后通过实例验证说明了该文方法的有效性。     

应急物资混合协同配送模式研究

为了寻求生物反恐体系中高效的应急物资配送方式,首先对比分析了传统的点对点配送模式和枢纽辐射模式,指出它们各自具有的优势:然后构建一种混合协同配送模式以兼顾这两种模式的长处,进而建立了混合协同配送模式函数模型并给出了具体求解的启发式搜索算法.通过仿真表明,采用该模式能形成更加高效的应急救援网络,对实际应急救援工作具有一定...     

救灾物资多阶段分配与调度问题建模与求解

救灾物资多阶段分配与调度问题是灾害应急决策中的一个难点问题.首先,基于三角模糊数描述发放点物资需求的不确定性, 并基于路段历史行程时间函数刻画交通路网的动态性,构建应急救援物资多阶段分配与调度模型;然后,基于蚁群优化搜索各储备点到各发放点的通行时间矩阵,引入NSGA-II搜索救灾物资分配和调度方案,设计救灾物资多阶段分配与调度集成优化算法和编码调整策略;最后,通过仿真实验验证所提方法的有效性.实验结果表明,所提方法可为决策者提供多种不同偏好的方案,从而为决策者提供更多的选择空间,契合应急场景.     

基于二维免疫的应急救援物资并行分配算法

现有应急救援物资分配研究大都局限于按照各发放点的优先级串行分配救援物资,难以适应复杂的大规模应急场景。为此,构建了面向多发放点的应急救援物资并行分配模型,并设计了基于二维二进制免疫和抗体修正的多发放点多救援物资并行分配算法。实验结果表明,该算法在大规模样本下较串行分配方法更加高效,能够从全局角度同时给出多个发放点的救援物资分配方案,为解决重大灾害中的复杂应急救援物资分配问题提供了一个有益的尝试。     

A bilevel decomposition algorithm for simultaneous production scheduling and conflict-free routing for automated guided vehicles

We address a bilevel decomposition algorithm for solving the simultaneous scheduling and conflict-free routing problems for automated guided vehicles. The overall objective is to minimize the total weighted tardiness of the set of jobs related to these tasks. A mixed integer formulation is decomposed into two levels: the upper level master problem of task assignment and scheduling; and the lower level routing subproblem. The master problem is solved by using Lagrangian relaxation and a lower bound is obtained. Either the solution turns out to be feasible for the lower level or a feasible solution for the problem is constructed, and an upper bound is obtained. If the convergence is not satisfied, cuts are generated to exclude previous feasible solutions before solving the master problem again. Two types of cuts are proposed to reduce the duality gap. The effectiveness of the proposed method is investigated from computational experiments.     

Optimal and heuristic solution methods for a multiprocessor machine scheduling problem

This paper deals with the scheduling of operations on a multiprocessor machine in the context of shoe manufacturing. Multiprocessor machines are composed of several parallel processors. Unlike parallel machines, the entire machine needs to be stopped whenever a single processor needs a setup. Our industrial partner, one of the major winter-shoe manufacturers in Canada, offers a relatively large variety of models. For each of these models, all the sizes proposed by this manufacturer must be produced in various quantities. Our objective is to schedule the production of the required sizes on the machine's different processors in order to minimize the global makespan, which includes both the production time and the set up time. We first present a mathematical formulation of the problem. Then, we introduce a decomposition procedure based on the mathematical model, and we demonstrate that this procedure is very efficient for small- and medium-size instances. We also propose two construction heuristics and two improvement heuristics for larger problems, and we report the results of our extensive computational experiments to demonstrate the quality of the proposed heuristics in terms of reducing production time and computational effort.     

An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems

In this work we propose an estimation of distribution algorithm (EDA) as a new tool aiming at minimizing the total flowtime in permutation flowshop scheduling problems. A variable neighbourhood search is added to the algorithm as an improvement procedure after creating a new offspring. The experiments show that our approach outperforms all existing techniques employed for the problem and can provide new upper bounds.     

并行作业调度系统的设计与实现

在并行计算的作业调度过程中，涉及到调度系统两个方面的内容：调度策略和调度算法。文章讨论了调度策略的设计和调度算法的选择．并通过一个实际的并行处理系统加以说明。它使调度系统更好地满足了并行处理系统作业调度的需要，提高了系统资源的利用率。     

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.     

Joint routing, scheduling, and power control for multichannel wireless sensor networks with physical interference

Reliability and real-time requirements bring new challenges to the energy-constrained wireless sensor networks, especially to the industrial wireless sensor networks. Meanwhile, the capacity of wireless sensor networks can be substantially increased by operating on multiple nonoverlapping channels. In this context, new routing, scheduling, and power control algorithms are required to achieve reliable and real-time communications and to fully utilize the increased bandwidth in multichannel wireless sensor networks. In this paper, we develop a distributed and online algorithm that jointly solves multipath routing, link scheduling, and power control problem, which can adapt automatically to the changes in the network topology and offered load. We particularly focus on finding the resource allocation that realizes trade-off among energy consumption, end-to-end delay, and network throughput for multichannel networks with physical interference model. Our algorithm jointly considers 1) delay and energy-aware power control for optimal transmission radius and rate with physical interference model, 2) throughput efficient multipath routing based on the given optimal transmission rate between the given source-destination pairs, and 3) reliable-aware and throughput efficient multichannel maximal link scheduling for time slots and channels based on the designated paths, and the new physical interference model that is updated by the optimal transmission radius. By proving and simulation, we show that our algorithm is provably efficient compared with the optimal centralized and offline algorithm and other comparable algorithms.