Multi-directional local search for a bi-objective dial-a-ride problem in patient transportation

This paper considers a generalization of a bi-objective dial-a-ride problem, incorporating real-life characteristics of patient transportation. It studies the impact of combination restrictions, preventing particular user combinations and limiting the set of drivers to which particular users can be assigned. The academic literature currently lacks insights into the effect of these restrictions on the cost structure of a service provider. A multi-directional local search algorithm is developed to solve this problem, taking into account the fundamental tradeoff between operational efficiency and service quality. Local search is integrated into a variable neighborhood descent framework that applies an intelligent candidate list principle to reduce computation time. Moreover, a new scheduling procedure is proposed, constructing time schedules that minimize total user ride time. It proves to be faster and more efficient than existing scheduling procedures. Overall, computational experiments on existing benchmark data extended with combination restrictions reveal a general pattern in the effect of the combination restrictions. Such insights are essential for service providers in order to support policy choices, e.g. related to service quality or medical education of drivers.     

基于遗传算法的多技能需求现场产品服务调度

多技能需求的现场产品服务调度结合了多旅行商问题与多技能项目调度问题,需综合考虑路径优化与技能匹配。针对该问题,考虑时间窗因素,以最短旅途时间和最少客户等待时间为目标建立数学模型,基于分段染色体编码的遗传算法并采用成组分段交叉算子进行求解。实例结果证明,该算法的解能避免过早收敛,有较高的搜索效率。     

Bi-objective vibration damping optimization for congested location–pricing problem

This paper presents a bi-objective mathematical programming model for the restricted facility location problem, under a congestion and pricing policy. Motivated by various applications such as locating server on internet mirror sites and communication networks, this research investigates congested systems with immobile servers and stochastic demand as M/M/m/k queues. For this problem, we consider two simultaneous perspectives; (1) customers who desire to limit waiting time for service and (2) service providers who intend to increase profits. We formulate a bi-objective facility location problem with two objective functions: (i) maximizing total profit of the whole system and (ii) minimizing the sum of waiting time in queues; the model type is mixed-integer nonlinear. Then, a multi-objective optimization algorithm based on vibration theory (so-called multi-objective vibration damping optimization (MOVDO)), is developed to solve the model. Moreover, the Taguchi method is also implemented, using a response metric to tune the parameters. The results are analyzed and compared with a non-dominated sorting genetic algorithm (NSGA-II) as a well-developed multi-objective evolutionary optimization algorithm. Computational results demonstrate the efficiency of the proposed MOVDO to solve large-scale problems.     

基于离散人工蜂群算法的云任务调度优化

针对现今云计算任务调度只考虑单目标和云计算应用对虚拟资源的服务的质量要求高等问题,综合考虑了用户最短等待时间、资源负载均衡和经济原则,提出一种离散人工蜂群(ABC)算法的云任务调度优化策略。首先,从理论上建立了云任务调度的多目标数学模型;然后,结合偏好满意度策略并引入局部搜索算子和改变侦察蜂搜索方式,提出多目标离散型人工蜂群(MDABC)算法的优化策略。通过不同的云任务调度仿真实验,显示了改进离散人工蜂群算法相对于基础离散人工蜂群算法、遗传算法以及经典贪心算法,能够得到较高的综合满意度,表明了改进离散人工蜂群算法能够更好地改善虚拟资源中云任务调度系统的性能,具有一定的普适性。     

Scheduling prioritized patients in emergency department laboratories

This research focuses on scheduling patients in emergency department laboratories according to the priority of patients’ treatments, determined by the triage factor. The objective is to minimize the total waiting time of patients in the emergency department laboratories with emphasis on patients with severe conditions. The problem is formulated as a flexible open shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem. Then, the response surface methodology is applied for tuning the GA parameters. The algorithm is tested on a set of real data from an emergency department. Simulation results show that the proposed algorithm can significantly improve the efficiency of the emergency department by reducing the total waiting time of prioritized patients.     

Robust berth scheduling at marine container terminals via hierarchical optimization

In this paper, we present a mathematical model and a solution approach for the discrete berth scheduling problem, where vessel arrival and handling times are not known with certainty. The proposed model provides a robust berth schedule by minimizing the average and the range of the total service times required for serving all vessels at a marine container terminal. Particularly, a bi-objective optimization problem is formulated such that each of the two objective functions contains another optimization problem in its definition. A heuristic algorithm is proposed to solve the resulting robust berth scheduling problem. Simulation is utilized to evaluate the proposed berth scheduling policy as well as to compare it to three vessel service policies usually adopted in practice for scheduling under uncertainty.     

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.      

A heuristic optimization approach for multi-vehicle and one-cargo green transportation scheduling in shipbuilding

To actively respond to the call for green shipbuilding, block cooperative transportation has been particularly concerned in reducing carbon emission in the shipyard, and hence a “multi-vehicle and one-cargo” (MVOC) green transportation scheduling problem emerges. Aiming to solve this problem effectively and improve transportation efficiency and reduce energy consumption, a bi-objective mathematical model combined routing model with synchronization constraints is proposed to simultaneously minimize non-value-added transportation time cost and total CO₂ emission. A Pareto-based multi-objective Tabu Search (MOTS) algorithm is then designed to solve the model, in which local improvements are developed to generate promising neighboring individuals. Experimental results show that the proposed MOTS algorithm can effectively solve the problem even on a large scale and outperform the classic algorithm of nondominated sorting genetic algorithm-II (NSGA-Ⅱ). It is hoped that this work enables an operation mode with high efficiency and low energy consumption and provides useful insights for flatcar transportation scheduling operators in the shipyard.     

基于CMBBO的医院管理资源调度

手术室资源调度关系到医院资源配置的合理性,对医院竞争力具有至关重要的影响.针对BBO算法收敛速度效率低、易陷入局部最优的缺陷,提出了云模型BBO算法,并结合所建立的医院手术室资源调度模型,提出了一种基于CMBBO的医院手术资源调度模型.将该模型应用于某大型三甲医院,结果表明手术资源使用率和手术服务能力得到提升、手术等待...     

零等待约束下多产品间歇过程的多目标调度

针对零等待约束下多产品间歇过程的总流程时间和完工时间最小化问题,提出一种多目标离散组搜索算法求解.在采用启发式规则产生初始解的基础上,通过发现者、追随者和巡逻者的操作设计,算法不断更新Pareto前沿,同时,混合了基于插入邻域的多目标局部搜索方法.大量计算实验表明,所提出的算法获得的非支配解集在IGD和Set Coverage指标上优于非支配排序遗传算法和模拟退火算法,可为多目标决策者提供更好的决策依据,利于间歇生产过程的优化运行.     

面向在线客服系统的调度算法研究

Different from traditional customer service systems, online customer service systems offer business services for multiple customers simultaneously, which makes the adaptation and scheduling between service providers and customers a big challenge. Based on the characteristics of online customer service, this paper proposes a scheduling model for online customer service systems. The scheduling model is composed of three constituents: a multi-priority customer queue, the states of the scheduling system and the transition relations between them, and the correspondence between scheduling strategies and states of the system. Its scheduling algorithm is designed. Experiments verify the rationality of the scheduling model and the effectiveness of the scheduling algorithm. In comparison to the operating customer service system, the algorithm can not only considerably reduce the average waiting time of customers, but also achieve load balancing among service providers, when guaranteeing high quality of services.     

Solving a bi-objective flowshop scheduling problem by a Multi-objective Immune System and comparing with SPEA2+ and SPGA

This paper presents a bi-objective flowshop scheduling problem with sequence-dependent setup times. The objective functions are to minimize the total completion time and the total earliness/tardiness for all jobs. An integer programming model is developed for the given problem that belongs to an NP-hard class. Thus, an algorithm based on a Multi-objective Immune System (MOIS) is proposed to find a locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed MOIS, different test problems are solved. Based on some comparison metrics, the computational results of the proposed MOIS is compared with the results obtained using two well-established multi-objective genetic algorithms, namely SPEA2+ and SPGA. The related results show that the proposed MOIS outperforms genetic algorithms, especially for the large-sized problems.     

异构系统中一种基于可用性的抢占式任务调度算法*

针对大多数现有的异构系统调度算法没有考虑由多类任务特别是抢占式任务所引起的可用性需求的不足,在现有基于可用性的非抢占式任务调度算法的基础上,通过计算任务的平均等待时间来确定优先级等级,对异构系统中多类抢占式任务的可用性约束的调度问题进行了探索,提出了一种基于可用性的抢占式优先调度算法P-SSAC。该算法在不增加硬件代价的前提条件下通过调度增加了系统的可用性,缩短了任务的平均等待时间,同时该算法可对抢占式的任务进行有效调度。仿真实验结果表明,该算法有效实现了异构系统可用性和任务等待时间之间的折中。     

特殊工艺约束并行多机双目标调度问题的研究

针对在特殊工艺约束下,非等同并行多机总完工时间最小和总拖后惩罚最小双目标调度问题（BOSP）,设计了一个双目标调度模型,进而构造了一个基于向量组编码的遗传算法。此算法的编码方法简单,能有效地反映实际调度方案,收敛速度快。同时为了更好地适应调度实时性和解大型此类问题的需要,在基于遗传算法自然并行性特点的基础上,实现了主从式控制网络模式下并行遗传算法。仿真结果表明,此算法是有效的,优于普通的遗传算法,具有较高的并行性,并能适用于解大型此类调度问题。     

Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints

This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs. The proposed model considers unrelated parallel machines. The jobs have non-identical due dates and ready times, and there are some precedence relations between them. Furthermore, sequence-dependent setup times, which are included in the proposed model, may be different for each machine depending on their characteristics. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. This paper proposes an efficient genetic algorithm (GA) to solve the bi-objective parallel machine scheduling problem. The performance of the presented model and the proposed GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and large-sized problems.     

A Benders decomposition for the location-allocation and scheduling model in a healthcare system regarding robust optimization

The hospital location and service allocation is one of the most important aspects of healthcare systems. Due to lack of studies on covering location-allocation and scheduling problems with respect to the uncertain budget, this paper develops a bi-objective hybrid model to locate hospitals and allocate machines and services scheduled. The costs of establishing facilities are assumed to be uncertain, while a robust counterpart model is employed to overcome the uncertainty. Covering the demand of each service is limited as well. Moreover, hospitals have a limited space to the specialized equipment like CT scan and MRI machines, while there is a cost constraint on hospitals and the specialized equipment. The aim of this paper is to find a near-optimal solution including the number of hospitals and the specialized equipment, the location of hospitals, the assignment of demand of each service and the specialized equipment to hospitals, the determination of allowable number of each service of hospitals, the determination of demand that should be transferred from one hospital to another (patient transfer), and schedule services. As the proposed model, minimizing the total costs and the completion time of demand simultaneously, is an NP-hard problem, it is impossible to solve its large-scale version with exact methods in a reasonable time. Thus, a hybrid algorithm including simulated annealing optimization and the Benders decomposition is employed to solve it. The CPLEX optimizer verifies the presented algorithm to solve the proposed model. The sensitivity analysis is performed to validate the proposed robust model against of uncertain situations while the Monte Carlo simulation is used to analyze the quality and the robustness of solutions under uncertain situations. The results show that the uncertainty used in the proposed model properly formulates real-world situations compared to the deterministic case. Finally, the contributions and the future research are presented.

     

一种考虑截止期的METCD业务组合调度方法

海洋设备检定、校准和检测（Marine Equipment Testing, Calibrate & Detection, METCD）业务规模大、紧急情况多,如何对业务进行合理的调配是海洋计量检定行业亟待解决的问题。本文提出了一种考虑截止期的任务组合METCD业务调度方法。在建立业务调度问题数学模型的基础上,采用最早截止时间优先-蚁群算法(EDF-PACO)对模型求解,在最早截止日期的约束条件下对任务组合处理的最优调度方案,达到降低任务总完成时间和减少执行空间浪费双重优化目标。为了验证方法的可行性,以国家海洋局东海标准技术中心的业务为实例,将EDF-PACO算法与传统的最早截止时间优先算法和蚁群算法进行比较,结果表明本文所提出的调度方法在满足截止期的约束条件下能高效地对海洋设备的计量检定业务进行组合调度。     

一种优先满足最小服务需求的动态网格资源调度算法

资源调度问题是网格研究必须解决的关键问题之一。目前,围绕着网格中的资源调度算法,国内外已做了大量的研究工作,先后提出了各种静态和动态调度算法。本文针对目前网格调度机制存在的问题,介绍了一种新的网格调度技术--优先满足最小服务需求的动态网格资源调度算法。该调度算法优先满足现有任务对资源的最小要求,从而减小小单个任务的等待时间。实验结果表明,该方法不但可以有效减少单个任务的延迟,而且在任务的吞吐率及CPU效率方面都比较好。     

基于TSP方法求解等待时间受限的置换流水车间调度

等待时间受限的置换流水车间调度问题要求工件在连续两个机器间的等待时间满足上限值约束.对此,分析了工件序列中相邻工件的加工持续时间及其上下界关系,并且提出一种启发式方法.首先,建立旅行商间题（TSP）以生成初始调度;然后,采用扩展插入方法优化调度解.为了衡量算法性能,给出问题下界的计算方法和相关评价指标,并通过数据实验验证了该启发式和下界计算方法的可行性和有效性.     

Markovian queue optimisation analysis with an unreliable server subject to working breakdowns and impatient customers

This study investigates an infinite capacity Markovian queue with a single unreliable service station, in which the customers may balk (do not enter) and renege (leave the queue after entering). The unreliable service station can be working breakdowns even if no customers are in the system. The matrix-analytic method is used to compute the steady-state probabilities for the number of customers, rate matrix and stability condition in the system. The single-objective model for cost and bi-objective model for cost and expected waiting time are derived in the system to fit in with practical applications. The particle swarm optimisation algorithm is implemented to find the optimal combinations of parameters in the pursuit of minimum cost. Two different approaches are used to identify the Pareto optimal set and compared: the epsilon-constraint method and non-dominate sorting genetic algorithm. Compared results allow using the traditional optimisation approach epsilon-constraint method, which is computationally faster and permits a direct sensitivity analysis of the solution under constraint or parameter perturbation. The Pareto front and non-dominated solutions set are obtained and illustrated. The decision makers can use these to improve their decision-making quality.