A Tabu Search Approach for the Resource Constrained Project Scheduling Problem

An appropriate tabu search implementation is designed to solve the resource constrained project scheduling problem. This approach uses well defined move strategies and a structured neighbourhood, defines appropriate tabu status and tenure and takes account of objective function approximation to speed up the search process. A sound understanding of the problem has helped in many ways in designing and enhancing the tabu search methodology. The method uses diversification, intensification and handles infeasibility via strategic oscillation.The above methodology is tested on existing problems from the literature and also on parametrically generated problems with encouraging results. For comparison of results, optimal solutions are used in the former and lower bounds obtained by Lagrangian heuristics are used in the latter.     

Using Swap-Based Search Trees to obtain Solutions for Resource Constrained Project Scheduling Problems

The high level of complexity in modern construction projects causes the problem of scheduling the involved tasks under precedence and resource constraints to be a great challenge. Additionally applications on real construction sites require non linear and non continuous constraints and objective functions. In order to optimize large scale construction sites in practice, this paper proposes a new optimization approach based on creating search trees from swapping and delaying tasks. The convergence of the proposed algorithm is analyzed and its complexity is compared to other available algorithms. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Resource Constrained Scheduling within Fixed Project Durations

This paper presents a practical approach for resource constrained scheduling within a given project duration. The method ensures a stable project plan when activity durations are reduced to achieve this. It handles all four categories of resource (unconstrained, renewable, non-renewable and doubly-constrained resources), and aims to shorten the project duration at minimum cost. Soft constraints and resource availabilities which vary over time are allowed in the method. Computational experience is reported.     

一种求解柔性资源约束前摄性项目调度问题的启发式算法

在不确定环境中,一个具有较高鲁棒性的进度计划可以保证项目的稳定实施。考虑到现实中资源可能具有多种技能,会对制定鲁棒性较高进度计划的过程产生影响,因此本文研究了柔性资源约束下前摄性项目调度优化问题。首先界定研究问题;然后从鲁棒性最大化的视角出发,构建了研究问题的优化模型,在对模型进行分析的基础上将其分解为经典鲁棒优化和资源技能分配两个子模型;随后设计了求解问题的基于削峰算法的启发式算法;最后用一个实际案例验证了算法有效性,并分析了关键参数对进度计划鲁棒性的影响,得到如下结论:项目进度计划鲁棒性随着项目工期的延长、资源可用量的增加或资源柔性的提高而增大。     

求解资源约束项目调度问题的启发式算法综述

本文综述了求解RCPSP的启发式算法.首先在对各种优先权规则进行归纳的基础上,概述基于优先权规则的RCPSP启发式算法研究现状;其次,综述项目进度的表述方式及常用超启发式策略,汇总求解RCPSP的超启发式算法的研究成果.此外,简要介绍除上述两大类启发式算法之外的其他几种启发式算法;最后,对全文进行总结,并指出该领域几个有希望的研究方向.     

一类资源约束排序问题

引入与研究 1| pj=fj( uj) ,∑uj U| ∑ ( wj Cj+ uj)型资源约束排序问题 .针对系统中加工顺序确定的情况 ,给出三个寻求最优资源分配的算法 ;就 fj=f和 fj=bj+ g,wj=w等情况研究系统的最优排序 .     

Four Payment Models for the Multi-Mode Resource Constrained Project Scheduling Problem with Discounted Cash Flows

In this paper, the multi-mode resource constrained project scheduling problem with discounted cash flows is considered. The objective is the maximization of the net present value of all cash flows. Time value of money is taken into consideration, and cash in- and out-flows are associated with activities and/or events. The resources can be of renewable, nonrenewable, and doubly constrained resource types. Four payment models are considered: lump sum payment at the terminal event, payments at prespecified event nodes, payments at prespecified time points and progress payments. For finding solutions to problems proposed, a genetic algorithm (GA) approach is employed, which uses a special crossover operator that can exploit the multi-component nature of the problem. The models are investigated at the hand of an example problem. Sensitivity analyses are performed over the mark up and the discount rate. A set of 93 problems from literature are solved under the four different payment models and resource type combinations with the GA approach employed resulting in satisfactory computation times. The GA approach is compared with a domain specific heuristic for the lump sum payment case with renewable resources and is shown to outperform it.     

一类资源分配与排序问题

给出与研究1 rj=bj-ajuj,∑uj U-∑uj+Cm ax型资源分配与排序问题.对于系统中加工顺序确定的情况给出并证明一个寻求其最优资源分配的多项式算法;就系统参量的某些特殊情况研究系统的最优排序.     

Heuristics for Large Constrained Vehicle Routing Problems

This paper presents a heuristic for solving very large routing problems (thousands of customers and hundreds of vehicles) with side constraints such as time windows. When applied to traditional benchmarks (Solomon's), we obtain high quality results with short resolution time (a few seconds). We also introduce a LDS (Limited Discrepancy Search) variation that produces state-of-the-art results. The heart of this heuristic is a combination of a look-ahead insertion algorithm, an incremental local optimization scheme and a constraint solver for constrained traveling salesman problems. The incrementality means that instead of visiting some large neighborhood after an initial solution has been found, a limited number of moves is examined, after each insertion, on the partial solution. This incremental version is not only faster, it also yields better results than using local optimization once a full solution has been built. We also show how additional constraints can be used in order to guide the insertion process. Because of its use of separate CP (Constraint Programming) modules, this method is flexible and may be used to solve large dispatching problems that include many additional constraints such as setup times (asymmetrical distance) or skill matching.     

Search Heuristics for Box Decomposition Methods

In this paper we study search heuristics for box decomposition methods that solve problems such as global optimization, minimax optimization, or quantified constraint solving. For this we unify these methods under a branch-and-bound framework, and develop a model that is more convenient for studying heuristics for such algorithms than the traditional models from Artificial Intelligence. We use the result to prove various theorems about heuristics and apply the outcome to the box decomposition methods under consideration. We support the findings with timings for the method of quantified constraint solving developed by the author.     

基于遗传禁忌算法的双资源约束下并行生产线调度研究

并行生产线调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题。在考虑遗传算法早熟收敛特性和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传算法和禁忌搜索算法的双资源并行作业车间的调度优化问题算法,即考虑到了产品的调度受到机器、工人等资源制约的影响,对算法中种群的构造,适应度计算,遗传操作等方面进行了研究,最后给出了实例仿真和结论。     

银行授信约束多模式工期最小化项目进度问题研究

本文研究银行授信额度约束下活动具有多种执行模式的工期最小化项目进度问题。首先对所研究问题进行界定;随后采用基于事件的研究方法构建了问题的整数规划优化模型;鉴于问题的NP-hard属性,设计了双层模拟退火搜索嵌套的启发式求解算法;最后对一个算例进行了求解分析,讨论了银行授信额度对项目进度安排及完成时间的影响。结果表明:随着银行授信额度的提高,承包商安排项目进度的可用资金随之增加,使得项目可以在较短的时间内完成;然而,如果在此过程中业主对承包商的支付总量保持不变,那么承包商的项目收益会随完成时间的提前而下降。     

项目鲁棒调度的资源分配启发式算法研究

合理的资源配置是提高项目调度鲁棒性一种有效的方法。本文针对项目鲁棒调度问题,提出了Max-PRUA资源分配启发式算法,以期通过生成鲁棒性高的资源分配方案来提高调度计划的鲁棒性。本算法设计了最大化利用优先关系和不可避免弧传递资源的资源分配两项策略来传递最大资源量,以减少由额外约束传递的资源量,降低对项目调度鲁棒性的影响。为寻优最优资源分配方案,配合局部搜索算法,本算法构建了动态活动组GRA,通过对组内活动顺序重排以生成多种资源分配方案,以利于从解空间中寻优出最佳的鲁棒性方案。最后通过大量的仿真实验验证和与其它算法进行比较,结果表明本算法对于不同规模和不同因素影响的项目均有较好的适应性,生成的资源分配方案对调度计划鲁棒性影响较小,是一种有效的算法。     

A Robust Genetic Algorithm for Resource Allocation in Project Scheduling

Genetic algorithms have been applied to many different optimization problems and they are one of the most promising metaheuristics. However, there are few published studies concerning the design of efficient genetic algorithms for resource allocation in project scheduling. In this work we present a robust genetic algorithm for the single-mode resource constrained project scheduling problem. We propose a new representation for the solutions, based on the standard activity list representation and develop new crossover techniques with good performance in a wide sample of projects. Through an extensive computational experiment, using standard sets of project instances, we evaluate our genetic algorithm and demonstrate that our approach outperforms the best algorithms appearing in the literature.     

具有线性减少加工时间的资源约束单机排序问题

讨论具有连续资源的单机排序问题.在这一模型中,工件的准备时间是所消耗资源的非负严格减少连续函数,工件的加工时间是开工时间的严格减少线性函数.考虑两类问题,第一类问题的目标函数是在满足最大完工时间限制条件下极小化资源消耗总量.第二类问题的目标函数是在满足资源消耗总量限制条件下极小化最大完工时间.对两类问题讨论了最优排序的某些特征.基于对问题的分析,分别给出了求解最优资源分配的方法.结果表明,加工时间为常数情况的结论对于加工时间是开工时间线性函数的情况仍然成立.     

柔性Flow shop最小和调度问题的启发式研究

本文研究有n个作业须在s个处理机中心进行加工，处理机中心i由l1个同速机组成的非抢占式柔性nowshop加权完成时间调度问题。每个作业有同样的加工路径通过每个处理机中心，但只需在处理机中心的任一台机器上加工处理，作业到达时间相同。目的是确定一个作业在每个处理机中心机器上的可行调度序列，使所有作业在最后处理机中心的加权完成时间总和最小化。在作业处理时间和权重有界、每个作业的工序处理时间为同分布的随机变量、不同作业的处理时间相互独立时，通过分组这种机器环境，我们证明该问题在作业数趋于无究时，一个基于加权最短处理时间的启发式算法是渐近最优的。     

Heuristics for Flowshop Scheduling Problems Minimizing Mean Tardiness

Several heuristics are presented for the flowshop scheduling problem with the objective of minimizing mean tardiness. We consider the cases in which job sequences on all machines are the same (permutation flowshop) and in which they may be different. For the former case, the various methods that have been devised for minimizing the makespan are modified for our objective, while the list scheduling algorithm is used for the latter case. These heuristics are tested and compared with each other on randomly-generated test problems.     

资源有限的加权总完工时间单机排序问题

本讨论资源有限的加权总工时间单机排序问题，对现在仍为OPEN问题1｜pj=bj-ajuj，∑uj≤U｜∑wjCj给出了一个有关最优解中最优资源分配的重要性质，并利用该性质分别给出了三种情况bj=b,wj=w,aj=a；bj=b,wj=w,uj=u；aj=a,wj=w,uj=u的最优算法。     

Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming

In this paper, a greedy heuristic and two local search algorithms, 1-opt local search and k-opt local search, are proposed for the unconstrained binary quadratic programming problem (BQP). These heuristics are well suited for the incorporation into meta-heuristics such as evolutionary algorithms. Their performance is compared for 115 problem instances. All methods are capable of producing high quality solutions in short time. In particular, the greedy heuristic is able to find near optimum solutions a few percent below the best-known solutions, and the local search procedures are sufficient to find the best-known solutions of all problem instances with n 100. The k-opt local searches even find the best-known solutions for all problems of size n 250 and for 11 out of 15 instances of size n = 500 in all runs. For larger problems (n = 500, 1000, 2500), the heuristics appear to be capable of finding near optimum solutions quickly. Therefore, the proposed heuristics—especially the k-opt local search—offer a great potential for the incorporation in more sophisticated meta-heuristics.     

Project Scheduling with Resource Constraints Using Branch and Bound Methods

The problem of minimising the overall duration of a project subject to constraints on the availability of resources is considered with particular reference to the use of branch and bound methods. A new resource based bound, new dominance rules and an innovatory network breakdown technique which preserves optimality are reported. Computational experience and comparisons with schedules obtained by decision rule methods are given.