C语言常见排序算法分析和探讨

随着计算机技术不断的发展，计算机编程也在不断的变化和发展。C语言作为常见计算机编程程序，在计算计发展中有着重要作用。C语言不仅能实现不同数据类型的数据查找，同时也能实现不同而类型数据的排序，能使计算机更好的运行。在这种情况下，有必要对C语言排序算法进行分析，以解决计算编程中出现的问题。本文主要从排序算法概况、对C语言排序方法进行分析等方面出发，对C语言常见排序算法进行相应分析。     

一种成组单元作业排序的新方法

本文提出一种可以编排交叉作业,平行作业情况下的作业进度的算法并将它与分枝定界算法结合起来解决成组生产单元中的作业排序问题。这种组合式排序方法不仅实现了决策判据能随进度及生产系统状态等因素的不同而发生变化,从而使排序结果更加合理,也可以方便地增减或组合决策原则,以便优化不同的目标函数。同时,该方法也是对分枝定界算法应用于解决生产实际问题所作的一次有益的尝试。     

利用AHP确定决策方案排序的一种改进方法

针对在用AHP法求解权重排序向量时，用不同的计算方法导致排序方案不一致的矛盾现象，提出了一种在单准则下综合各个排序方案信息的方法。使AHP法更趋于完善。     

RTU的实时多任务监控程序设计

本文研究了ＲＴＵ微机终端在程序设计时所遇到的问题，并提出一种通用的考虑多任务下实时监控程序设计，着重讨论了程序设计中任务的启动，转换及排序，硬件互斥问题以及抗干扰措施等方法。     

排序滤波在工业射线图像系统中的应用

本文通过对工业射线成像系统的分析，比较了射线噪声图像的两种处理方法－线性滤波法和排序滤波法，实验表明，排序滤波地能够在减少白噪声的同时保存图像细节，是射线检测中一种有效的降噪增强处理方法。     

寻求平面上线段集凸壳的扫描算法

首先证明寻求平面上线段集凸壳问题的下界是O(nlogn)，其方法是将平面上线段集凸壳问题与排序问题联系起来，由排序问题的下界推得平面上线段集凸壳问题的下界。然后提出一个算法，计算平面上线段集凸壳问题，其基本思想是将不交线段集中的线段按其端点的x，y坐标排序，并重排线段序。然后用平面扫描方法分段完成凸壳的构造。该算法的时间复杂性是O(nlogn)。     

多目标决策中基于遗传算法的方案排序分析

分析了常规多目标决策的一般过程，提出在进行方案排序结合时，应充分利用已得到的信息，据此建立了非线性规划模型，并引入遗传算法对规划模型进行求解。通过对实例进行分析，得出这种方法能充分利用集结后的信息，得到较为可信方案排序的结论。     

计算机辅助编制成组作业计划

成组作业计划是成组生产管理中的重要组成部分。本文着重讨论成组作业计划的彼得洛夫—哈姆算法及其二次优化问题,编制了相应的算法程序,此程序不仅能完成零件组内的排序优化问题,还能完成零件组间的排序优化问题,并可显示优化排序结果的甘特图、工件的流动情况以及各机床的利用率等。     

基于候选方案排序的进化决策方法

由于无法得到准确的期望效用函数,在信息不完全和结果不确定的环境下作出决策是困难的,提出基于候选方案排序的进化决策方法,通常通过分析得出一组与候选方案期望效用相关的指标,设计决策规则归结为寻找二之间的相关关系,如果将所有候方案按其对效用有影响的指标分为n类,并利用进化算法在n!空间中索全部方案的期望效用排序,则根据此排序作出最佳决策,提出针对排序问题的遗传算法,该方法较少依赖专家知识,无须显式地构造期望效用函数,能有效处理非数值或非量化指标以及指标冲突和指标相关等问题,在带随机噪声环境下仍能获得稳健解,在仿真机器人控制器设计中的应用表明了该方法的有效性。     

成组单元的作业排序

多品种小批量生产中为提高生产率和经济效益采用成组技术。作业排序是成组技术管理工作中的一个重要问题。本文从多工序成组作业排序的基本方法中着重介绍了一种方法,并结合生产实际应用分析如何进行修正的方法。文中还结合分析对比其他几种方法,指出了本文介绍方法的优点。     

Flexible job-shop scheduling problem with resource recovery constraints

In this work, we introduce a Flexible Job-shop Scheduling Problem with Resource Recovery Constraints (FRRC). In the FRRC, besides the constraints of the classical Flexible Job-shop Scheduling Problem (FJSP), operations may require resources to be processed. The resources are available in batches and a recovery time is required between each batch. This problem is inspired by a real situation faced by a brewing company where different yeasts are available in a limited quantity and are recovered only once they have been completely used. The objective is to schedule the operations such that the makespan is minimised. A mathematical model and a metaheuristic based on a General Variable Neighborhood Search is proposed for the solution of the FRRC. Computational results over a large set of instances, adapted from the FJSP literature, are presented.     

Kapazitätsanpassung bei simultaner Losgrößen- und Reihenfolgeplanung

The paper considers the effect of capacity adaptations for the Economic Lot Scheduling Problem. A heuristic is presented which shows how to compute simultaneously lot sizes, schedules and capacity requirements for medium term planning. The results are evaluated with respect to the optimal solution of the Continuous Setup Lotsizing Problem.     

Simultaneous supply and production planning

This paper considers a novel production and logistics planning problem called the Economic Lot and Supply Scheduling Problem. The problem combines the Economic Lot-Sizing Problem and the Vehicle Routing Problem into a simultaneous problem-solving approach. The considered problem is observable in several industrial areas, e.g. in retailing and in the automotive industry. Assuming a certain delivery policy the paper presents a complete mathematical model and a ?-exact solution procedure. Analytical results for a broad range of test instances are calculated which demonstrate new insights into the considered planning problem. The results emphasise the economic advantage of the proposed simultaneous modeling and solution approach, especially for certain parameter settings.     

The Distributed Assembly Permutation Flowshop Scheduling Problem

Nowadays, improving the management of complex supply chains is a key to become competitive in the twenty-first century global market. Supply chains are composed of multi-plant facilities that must be coordinated and synchronised to cut waste and lead times. This paper proposes a Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP) with two stages to model and study complex supply chains. This problem is a generalisation of the Distributed Permutation Flowshop Scheduling Problem (DPFSP). The first stage of the DAPFSP is composed of f identical production factories. Each one is a flowshop that produces jobs to be assembled into final products in a second assembly stage. The objective is to minimise the makespan. We present first a Mixed Integer Linear Programming model (MILP). Three constructive algorithms are proposed. Finally, a Variable Neighbourhood Descent (VND) algorithm has been designed and tested by a comprehensive ANOVA statistical analysis. The results show that the VND algorithm offers good performance to solve this scheduling problem.     

An Extension to the Class of Easy Economic Lot Scheduling Problems

We extend known conditions under which a rotation schedule is optimal in the Economic Lot Scheduling Problem by using a tighter lower bound that explicitly considers machine capacity. We also show how these results can be used when backorders are allowed.     

Metaheuristics for the Stochastic Hoist Scheduling Problem (SHSP)

Experts look for solutions to increase production delivery, but industrial systems are generally disturbed by random events. In many cases, there is a lack of information about random events consequences on the production time and quality of the manufactured products. Surface treatment lines are robotic cells with time window constraints and so random events can have important consequences on production management. Products could be damaged by constraint violations and rejected by the control quality department. Moreover, owing to the structure of these systems, variations in transportation times can induce unrecoverable situations (deadlock occurrences, for example). Therefore, experts look for schedules to prevent random events consequences. One of the most difficult operational problems in surface treatment lines is the proper coordination of the material-handling resources management and job input scheduling. Previous related research works concern the makespan minimization problem and do not take into account the random events consequences: this problem is denoted by the Hoist Scheduling Problem (HSP). This paper focuses on the Stochastic Hoist Scheduling Problem (SHSP), which is the HSP with random events implying variations in transportation times. It appears that no previous work can be reported concerning such stochastic problems but only deterministic ones. A method is proposed here based on stochastic metaheuristics used to determine the production schedules for which the random events consequences are low. An industrial study that highlights the efficiency of the method is proposed. A company that cannot be named for confidential reasons has provided the test data.     

Fuzzy uncertainty modelling for project planning: application to helicopter maintenance

Maintenance is an activity of growing interest, especially for critical systems. In particular, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance centre is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans in the short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multi-resource, multi-project planning (respectively Rough Cut Capacity Planning and the Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on a tactical level considering uncertainties in macro-task work content, and modelling the continuous workload on the operational level considering uncertainties in task duration. We model uncertainties using a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy Rough Cut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster that aims at building a centre for civil helicopter maintenance.     

The Application of Marginal Analysis to the Economic Lot Scheduling Problem

Application of marginal analysis to the Economic Lot Scheduling Problem reduces the solution search to a simple systematic calculation and feasibility check. Compared with mathematical programming methods and heuristic approaches advocated previously, marginal analysis results in lower optimum cost solution with shorter optimum basic period lengths. For comparative purposes, data are included which allow evaluation of marginal analysis results with dynamic programming results of previous researchers.     

Electric vehicle scheduling and optimal charging problem: complexity,exact and heuristic approaches

This paper deals with the Electric Vehicle (EV) Scheduling and Optimal Charging Problem. More precisely, given a fleet of EVs and Combustion Engine Vehicles (CVs), a set of tours to be processed by vehicles and a charging infrastructure, the problem aims to optimise the assignment of vehicles to tours and minimise the charging cost of EVs while considering several operational constraints mainly related to chargers, electricity grid and EVs driving range. We prove that the Electric Vehicle Scheduling and Charging Problem (EVSCP) is NP-hard in the ordinary sense. We provide a mixed-integer linear programming formulation to model the EVSCP and use CPLEX to solve small and medium instances. To solve large instances, we propose two heuristics: a Sequential Heuristic (SH) and a Global Heuristic (GH). The SH considers the EVs sequentially. To each EV, it assigns a set of tours and guarantees the feasibility of a charging schedule. Then, it generates an optimal charging schedule for this EV. However, the GH computes, in the first step, a feasible assignment of tours to all EVs. In the second step, it applies a global Min-Cost-Flow-based charging algorithm to minimise the charging cost of the EVs fleet. To evaluate the efficiency of our solving approaches, computational results on a large set of real and randomly generated test instances are reported and compared.     

When Is The Economic Lot Scheduling Problem Easy?

The Economic Lot Scheduling Problem, common in industry and the literature as well, is NP-hard and remains unsolved in general. We show 'that the very simple Rotation Cycle scheduling rule produces optimal or nearly optimal schedules in very many realistic situations and we give upper bounds for the maximum percentage deviation of the Rotation Cycle's schedule fro m optimality. An implication for Group Technology is discussed.