No-wait two-machine permutation flow shop scheduling problem with learning effect,common due date and controllable job processing times

We consider a two-machine no-wait permutation flow shop common due date assignment scheduling problem where the processing time of a job is given as a function of its position in the sequence and its amount of resource allocated to this job. The common due date (CON) assignment method means that all the jobs are given a common due date. We need to make a decision on the common due date, resource allocation and the sequence of jobs to minimise total earliness, tardiness, common due date cost and total resource cost. We show that the problem remains polynomially solvable under the proposed model.     

Single machine due-window assignment scheduling with resource-dependent processing times to minimise total resource consumption cost

This paper considers a single-machine scheduling problem involving convex resource-dependent processing times and due-window assignment simultaneously. The goal is to minimise the total resource consumption cost under the constraint that the schedule cost involving earliness, tardiness, window location, window size and makespan does not exceed a given limit for two popular due window assignment methods: the common flow allowance (slack) due window assignment method (referred to SLKW) and the common due window assignment method (referred to CONW). We show that the problem can be solved in polynomial time. Some extensions of the problem are also given.     

Convex resource allocation scheduling in the no-wait flowshop with common flow allowance and learning effect

We study the problem of two-machine no-wait flowshop scheduling with learning effect and convex resource-dependent processing times. Under the condition of the due-date assignment with common flow allowance (i.e. slack (SLK) due-date assignment), we provide a bi-criteria analysis where the first criterion is to minimise the scheduling criteria (i.e. the weighted sum of earliness, tardiness and flow allowance costs), and the second criterion is to minimise the resource consumption cost (i.e. the weighted sum of resource consumption cost). The objective is to determine the optimal job sequence, resource allocations and common (flow allowance) slack time that minimise the three different versions of the two criteria. We prove that these problems can be solved in polynomial time.     

Single-machine due-window assignment scheduling based on common flow allowance,learning effect and resource allocation

This article considers a single-machine due-window assignment scheduling problem based on a common flow allowance (i.e. all jobs have slack due window (SLKW)). We assume that the actual processing time of a job is a function of its position in a sequence (learning effect) and its continuously divisible and non-renewable resource allocation. The problem is to determine the optimal due windows, the optimal resource allocation and the processing sequence simultaneously to minimise costs for earliness, tardiness, the window location, window size, makespan and resource consumption. For a linear or a convex function of the amount of a resource allocated to the job, we provide a polynomial time algorithm, respectively. Some extensions of the problem are also shown.     

Ant colonies with cooperative process applied to the resource allocation problem

Abstract

In this paper, a novel algorithm describing ant colonies, with cooperation, is proposed to solve the resource allocation problem. The resource allocation problem is to allocate resources to activities, with the objective of optimizing the cost function. In our study, we viewed the search in ant colonies as a mechanism providing a main portion of diversity in search space. The cooperative process conducts fine‐tuning for the solution provided by ant colonies, and it has the ability to escape from poor local optima. In this paper, several examples are tested to prove the superiority of our proposed algorithm. From simulation results, the proposed algorithm indeed has remarkable performance.     

研发型企业多项目人力资源调度研究——基于蚁群优化的超启发式算法

现阶段,研发型企业的项目处于多项目环境下,为了解决多项目并行时人力资源争夺问题,本文针对该类企业多项目管理中人力资源调度进行优化研究,以考虑项目延期惩罚成本的最小总成本为目标函数,将现实问题抽象建模。基于国内外的研究提出了一种超启发式算法进行求解,该算法将人力资源调度问题分为项目活动分配和人员选择项目活动两个部分,采用蚁群优化作为高层启发式策略搜索低层启发式规则,再进一步根据规则解构造出可行解。最后本研究设计多组仿真实验与启发式规则进行对比,结果表明该算法有较好的搜索性能,为人力资源的调度问题提供了新的解决方案。     

Solving a stochastic berth allocation problem using a hybrid sequence pair-based simulated annealing algorithm

In this article, a continuous berth allocation problem is studied with stochastic ship arrival and handling times. The objective is to minimize a weighted sum of the expected waiting costs, berthing deviation costs and expected overtime costs. The sequence pair representation is utilized to project the solution space of the problem into two permutations. Then, a scenario-based method is used to capture the uncertainty. To effectively solve the problem over the sequence pair solution space, a simulated annealing is combined with two algorithms. One of the algorithms is used to determine the berthing positions and the other one is used to determine the berthing times. Computational experiments are conducted to evaluate the performance of the solution method and to verify the advantages of the proposed stochastic approach. The results indicate that the proposed methodology is both efficient and effective.     

基于资源缓冲的鲁棒性多模式项目调度优化

资源可用量的不足会影响项目的平稳实施.研究如何通过插入资源缓冲以最大化项目进度计划鲁棒性来抵抗资源可用量不足的问题.首先对研究问题进行界定;随后构建了包含三组决策变量的整数规划优化模型;针对问题的NP-hard属性,设计禁忌搜索启发式算法;最后,通过一个算例对研究进行验证说明,得到如下结论:与工期最小化进度计划相比,鲁棒性最大化进度计划具有较强的抗干扰能力;所构建的优化模型可实现资源缓冲的合理分配,进而有效提高项目进度计划的鲁棒性;项目进度计划的鲁棒性随项目截止日期的延迟而上升,但上升幅度趋于平缓.     

Research on scheduling with job-dependent learning effect and convex resource-dependent processing times

We study resource allocation scheduling with job-dependent learning effect on a single machine with or without due date assignment considerations. For a convex resource processing time function, we provide a polynomial time algorithm to find the optimal job sequence, and resource allocations that minimise the schedule criterion (the total compression cost) subject to the constraint that the total compression cost (the schedule criterion) is less than or equal to a fixed amount.     

Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm

Constrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA/H could be a competitive alternative for solving cMOPs. Finally, the characteristics of cMOEA/H are studied.     

道路施工机群智能化管理系统的设计及开发

以高等级公路施工机群管理为背景,研究和开发了智能化公路工程机械机群管理系统,描述了系统的体系结构设计,组成系统的各级中心站的设计以及功能,以及监控系统的核心功能,包括资源配置功能、监控调度功能及故障诊断功能等。本文所描述的系统是计算机智能化信息采集及控制在一个新的行业领域的应用,并针对问题的特殊性,设计并实现了实时监控及动态优化调度的功能和算法。     

柔性资源约束下的项目调度问题双目标优化

现实项目在实施过程中面临着诸多不确定因素,鲁棒项目调度是应对项目不确定性,减少进度偏差的有效手段。此外,项目中还广泛存在一类能力不均衡的多技能资源,这类资源会提高调度计划制定的难度,但同时资源之间更为灵活的替代、合作关系也为鲁棒项目调度提供了更大的优化空间。基于此,设计两阶段算法求解不确定环境下含有此类资源的项目鲁棒调度计划。第1阶段构造基于规则的启发式算法：结合活动优先规则、资源权重规则,通过0-1规划模型求解基准调度计划及资源分配方案。第2阶段设计鲁棒优化算法：通过时间缓冲的有偏随机插入与回退,以及资源分配方案的调整对基准调度计划进行鲁棒优化。仿真实验表明,不同风险水平下,算法在求解不同规模的项目算例时,所得的调度计划在鲁棒性方面均表现出了明显的优势。