A variable neighbourhood search algorithm for the constrained task allocation problem

A variable neighbourhood search algorithm that employs new neighbourhoods is proposed for solving a task allocation problem whose main characteristics are: (i) each task requires a certain amount of resources and each processor has a capacity constraint which limits the total resource of the tasks that are assigned to it; (ii) the cost of solution includes fixed costs when using processors, task assignment costs, and communication costs between tasks assigned to different processors. A computational study shows that the algorithm performs well in terms of time and solution quality relative to other local search procedures that have been proposed.     

A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar

A phased array radar (PAR) is used to detect new targets and update the information of those detected targets. Generally, a large number of tasks need to be performed by a single PAR in a finite time horizon. In order to utilize the limited time and the energy resources, it is necessary to provide an efficient task scheduling algorithm. However, the existing radar task scheduling algorithms can't be utilized to release the full potential of the PAR, because of those disadvantages such as full PAR task structure ignored, only good performance in one aspect considered and just heuristic or the meta-heuristic method utilized. Aiming at above issues, an optimization model for the PAR task scheduling and a hybrid adaptively genetic (HAGA) algorithm are proposed. The model considers the full PAR task structure and integrates multiple principles of task scheduling, so that multi-aspect performance can be guaranteed. The HAGA incorporates the improved GA to explore better solutions while using the heuristic task interleaving algorithm to utilize wait intervals to interleave subtasks and calculate fitness values of individuals in efficient manners. Furthermore, the efficiency and the effectiveness of the HAGA are both improved by adopting chaotic sequences for the population initialization, the elite reservation and the mixed ranking selection, as well as designing the adaptive crossover and the adaptive mutation operators. The simulation results demonstrate that the HAGA possesses merits of global exploration, faster convergence, and robustness compared with three state-of-art algorithms—adaptive GA, hybrid GA and highest priority and earliest deadline first heuristic (HPEDF) algorithm.     

Is It Better to Forget? Stimulus-Response, Prediction, and the Weight of Past Experience in a Fast-Paced Bargaining Task

Decision makers in dynamic environments such as air traffic control, firefighting, and call center operations adapt in real-time using outcome feedback. Understanding this adaptation is important for influencing and improving the decisions made. Recently, stimulus-response (S-R) learning models have been proposed as explanations for decision makers' adaptation. S-R models hypothesize that decision makers choose an action option based on their anticipation of its success. Decision makers learn by accumulating evidence over action options and combining that evidence with prior expectations. This study examines a standard S-R model and a simple variation of this model, in which past experience may receive an extremely low weight, as explanations for decision makers' adaptation in an evolving Internet-based bargaining environment. In Experiment 1, decision makers are taught to predict behavior in a bargaining task that follows rules that may be the opposite of, congruent to, or unrelated to a second task in which they must choose the deal terms they will offer. Both models provide a good account of the prediction task. However, only the second model, in which decision makers heavily discount all but the most recent past experience, provides a good account of subsequent behavior in the second task. To test whether Experiment 1 artificially related choice behavior and prediction, a second experiment examines both models' predictions concerning the effects of bargaining experience on subsequent prediction. In this study, decision models where long-term experience plays a dominating role do not appear to provide adequate explanations of decision makers' adaptation to their opponent's changing response behavior.     

Joint employee weekly timetabling and daily rostering: A decision-support tool for a logistics platform

To deal with their highly variable workload, logistics companies make their task force flexible using multi-skilled employees, flexible working hours or short-term contracts. Together with the legal constraints and the handling equipments’ capacities, these possibilities make personnel scheduling a complex task. This paper describes a model to support their chain of decisions from the weekly timetabling to the daily rostering (detailed task allocation).     

Robust competence assessment for job assignment

Allocating the right person to a task or job is a key issue for improving quality and performance of achievements, usually addressed using the concept of “competences”. Nevertheless, providing an accurate assessment of the competences of an individual may be in practice a difficult task. We suggest in this paper to model the uncertainty on the competences possessed by a person using a possibility distribution, and the imprecision on the competences required for a task using a fuzzy constraint, taking into account the possible interactions between competences using a Choquet integral. As a difference with comparable approaches, we then suggest to perform the allocation of persons to jobs using a robust optimisation approach, allowing to minimise the risk taken by the decision maker. We first apply this framework to the problem of selecting a candidate within n for a job, then extend the method to the problem of selecting c candidates for j jobs (c ? j) using the leximin criterion.     

A heuristic methodology for sizing a large-scale system of constrained, reusable resources

This paper proposes a methodology for sizing certain large-scale systems of reusable, capacity-constrained resources engaged in tasks of varying duration. A heuristic program schedules resources throughout a finite planning horizon using two decision variables: varying resource capacity for meeting demand and varying task duration. A model of the problem and heuristic scheduling program are presented. A sequential, iterative sizing procedure determines the number of system resources to meet demand at each stage of the problem. Results compare the methodology with heuristics used in practice to schedule resources and size a real-world, large-scale training system.     

Employing different loss functions for the classification of images via supervised learning

Supervised learning methods are powerful techniques to learn a function from a given set of labeled data, the so-called training data. In this paper the support vector machines approach is applied to an image classification task. Starting with the corresponding Tikhonov regularization problem, reformulated as a convex optimization problem, we introduce a conjugate dual problem to it and prove that, whenever strong duality holds, the function to be learned can be expressed via the dual optimal solutions. Corresponding dual problems are then derived for different loss functions. The theoretical results are applied by numerically solving a classification task using high dimensional real-world data in order to obtain optimal classifiers. The results demonstrate the excellent performance of support vector classification for this particular problem.     

A learning trajectory for university students regarding the concept of vector

This paper presents and evaluates a hypothetical learning trajectory by which students bridge the transition from elementary to university-level instruction regarding the concept of vector. The trajectory consists of an instructional sequence of five tasks and begins with a problem in context. Each task is carried out with the support of a Virtual Interactive Didactic Scenario, accompanied by exploration and guided learning sheets, in which the problem is introduced through the simulation of the movement of a robotic arm. This proposal was implemented at the beginning of the SARS-CoV-2 pandemic using various digital media. Two teaching experiments were carried out with engineering students at a Mexican public university. We present the hypothetical learning trajectory that should be followed toward solving the task, and contrast it in each case with the students’ actual learning trajectory. The results show that more than 70 % of the students successfully transitioned from the geometrical vector representation of elementary physics to the algebraic one.     

任务重要性、产出关联性与创业投资家的激励机制研究

针对我国创业投资基金运作中独特的制度安排,对创业投资家在多任务环境下的激励问题进行了研究。Holmstrom和Milgrom的经典理论认为:若代理人在多任务中的努力投入可替代,则不同任务间的激励强度必须互补。然而本文的理论分析表明:1)若任务间的重要性存在差异,则最优的激励合约要求委托人增加重要任务的激励强度,同时降低其它任务的激励强度,即任务间的激励必然是对抗性的;2)若产出的分布受共同冲击的影响(正相关),则当任务间努力投入的替代系数较小时,应该对代理人实施对抗性激励,反之应对代理人实施互补性激励。对抗性激励机制的存在为委托人在某些环境下对高风险但重要任务实施强激励提供了理论依据。     

多星测控调度问题任务优先级研究

在对多星测控调度问题工程需求进行分析的基础上,提出了多星测控问题中影响测控任务优先级的10个影响因素,并对各项因素对测控任务优先级的影响方式与影响程度进行了分析.利用功效系数法给出了设定优先级的算法,最后给出了一个算例.     

Probability Density Functions of Task-processing Times for Deterministic,Time-varying Processor Efficiency

We consider a task being performed in an environment in which the work-rate (or efficiency of the processor varies with time in a deterministic manner. Given the probability density function (p.d.f.) of the total work-content of the task (i.e. processing time at 100% efficiency), we develop a closed-form expression for the p.d.f. of the task-processing time. Numerical examples are given.     

A new method for managing the uncertainties in evaluating multi-person multi-criteria location choices,using a perceptual computer

The international logistics centers choice problem is a very important issue in International logistics. The location choice problem usually involves numbers and words in which all of the criteria are weighted using words and the performance evaluations for all sub-criteria are either numbers or words. How to aggregate all of these data without losing information is a very daunting task using a type-1 fuzzy set (T1 FS) approach. This paper applies a new methodology—Perceptual Computer (Per-C)—to help solve this hierarchical multi-person multi-criteria decision making problem. The Per-C has three components: encoder, computing with words (CWW) engine and decoder. First, the interval approach (IA) is used to obtain interval type-2 fuzzy set (IT2 FS) word models for the words in a pre-specified vocabulary. Second, a linguistic weighted average (LWA) is used to aggregate all the data including numbers and words modeled by IT2 FSs. Finally, a centroid-based ranking method is used to rank the location choices, and a similarity measure is used to obtain similarities of the location choices. The decision-maker decides the winning location choice as the one with the highest ranking and least similarity to other locations.     

一类带时间窗车辆分配问题的贪婪算法

本文对一类带时间窗的车辆分配问题进行了分析,引入了车辆任务的概念,并将问题转化为车辆与车辆任务的匹配问题,同时制订了运输任务选择和车辆选择的贪婪策略,并在此基础上设计了车辆分配问题的贪婪算法,最后通过实例验证了算法的有效性。     

突发事件应急救援人员的分组方法研究

针对突发事件应急救援人员分组问题,给出了一种基于优化模型的分组方法。在本文中,首先描述了突发事件的应急救援人员分组问题;然后依据救援人员特征表现评价信息,计算救援人员完成不同任务的综合表现评价值;进一步地,以完成各救援任务的"效果"最好为目标,考虑每个救援人员只能被分到一个组里且每组中的人员数量要满足救援任务的需要,构建了救援人员分组的多目标优化模型,并通过求解优化模型来得到应急救援人员的分组方案。最后,通过一个算例分析说明了本文提出方法的可行性和有效性。     

A weighted causal theory for acquiring and utilizing open knowledge

Motivated by enabling intelligent robots/agents to take advantage of open-source knowledge resources to solve open-ended tasks, a weighted causal theory is introduced as the formal basis for the development of these robots/agents. The action model of a robot/agent is specified as a causal theory following McCain and Turner's nonmonotonic causal theories. New knowledge is needed when the robot/agent is given a user task that cannot be accomplished only with the action model. This problem is cast as a variant of abduction, that is, to find the most suitable set of causal rules from open-source knowledge resources, so that a plan for accomplishing the task can be computed using the action model together with the acquired knowledge. The core part of our theory is constructed based on credulous reasoning and the complexity of corresponding abductive reasoning is analyzed. The entire theory is established by adding weights to hypothetical causal rules and using them to compare competing explanations which induce causal models satisfying the task. Moreover, we sketch a model theoretic semantics for the weighted causal theory and present an algorithm for computing a weighted-abductive explanation. An application of the techniques proposed in this paper is illustrated in an example on our service robot, KeJia, in which the robot tries to acquire proper knowledge from OMICS, a large-scale open-source knowledge resource, and solve new tasks with the knowledge.     

A reference length approach for the 3D strip packing problem

In the three-dimensional strip packing problem (3DSP), we are given a container with an open dimension and a set of rectangular cuboids (boxes) and the task is to orthogonally pack all the boxes into the container such that the magnitude of the open dimension is minimized. We propose a block building heuristic based on extreme points for this problem that uses a reference length to guide its solution. Our 3DSP approach employs this heuristic in a one-step lookahead tree search algorithm using an iterative construction strategy. We tested our approach on standard 3DSP benchmark test data; the results show that our approach produces better solutions on average than all other approaches in literature for the majority of these data sets using comparable computation time.     

无人机协同任务的优化方案

研究的是多架无人机协同完成侦察、中继等作战任务的方案优化问题.对于侦查任务,首先建立了以总行进路径最短为目标的非线性0-1规划模型,求得单架加载S-1无人机无法完成任务,故又建立了以两组行进路径之和最短和组间路程差最小为目标函数的目标群分组的非线性0-1规划模型,得到最优分组方案.然后,由于S-2的扫描范围广,无需行遍所有目标点即可完成任务,故建立以扫描所有目标点为约束条件,遍历点数最少为目标的非线性0-1规划模型,求得仅需经过18个点即可完全扫描所有68个目标点.接下来,在满足S-1和S-2扫描每个目标点的时间差小于4h的前提下,得到了最优的飞行方案.对于中继任务,通过调整无人机的起飞时间,减少通过雷区的任意两架无人机的位置差异,得到仅需1架中继无人机的最优飞行方案.     

A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs

A common assumption in the literature on mixed-model assembly line balancing is that a task that is common to multiple models must be assigned to a single station. In this paper, we relax this restriction, and allow a common task to be assigned to different stations for different models. We seek to minimize the sum of costs of the stations and the task duplication. We develop an optimal solution procedure based on a backtracking branch-and-bound algorithm and evaluate its performance via a large set of experiments. A branch-and-bound based heuristic is then developed for solving large-scale problems. The heuristic solutions are compared with a lower bound and experiments show that the heuristic provides much better solutions than those obtained by traditional approaches.     

Algorithms for finding optimum trees: Description,use and evaluation

This work is devoted to the problem of finding an optimum spanning tree in an undirected graph. Both min-sum and min-max trees are sought. The five algorithms considered are among the most well-known proposed in the literature. They are described in sect. 1 as thoroughly as possible, using a simplified Pascal language; all min-sum algorithms are derived from a unique prototype formulation. In sect. 2, the algorithms are implemented in PFORT to enhance their portability and ad hoc data structures are utilized in order to obtain subroutines as efficient as possible. Finally, in sect. 3, the programs are evaluated, comparing their performances in handling several classes of randomly generated graphs. Various observations are reported, and some indications for choosing the most suitable algorithm in each case are provided.Sponsored by the CNR finalized project on Informatics (subproject P1, task SOFMAT), Italy.     

制造型企业基层管理者工作满意度对工作倦怠的影响实证分析

本文旨在探讨制造型企业基层管理者工作满意度对工作倦怠的影响。先运用2次预调查的数据分别对MBI-GS和工作满意度量表进行了探索性和验证性因素分析,然后利用验证的量表对21家制造型企业进行了调查。相关分析表明,工作满意度及其4个维度工作任务、领导行为、薪酬与工作环境、晋升与发展同情绪衰竭和消极怠慢都呈显著负相关,同职业效能感都呈显著正相关。分层回归分析表明,工作满意度的4个维度对消极怠慢都有显著的负向影响,对职业效能感都有显著的正向影响;领导行为、工作任务、薪酬与工作环境对情绪衰竭都有显著的负向影响。在工作满意度4个维度中,领导行为对情绪衰竭和消极怠慢的影响最大,工作任务对职业效能感的影响最大。