Scheduling Rules Based on Gene Expression Programming for Resource-Constrained Project Scheduling Problem

In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances.

Memetic algorithm for multi-mode resource-constrained project scheduling problems

A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.

An Evolutionary Algorithm with Multi-Local Search for the Resource-Constrained Project Scheduling Problem

This paper introduces a hybrid evolutionary algorithm for the resource-constrained project scheduling problem (RCPSP). Given an RCPSP instance, the algorithm identifies the problem structure and selects a suitable decoding scheme. Then a multi-pass biased sampling method followed up by a multi-local search is used to generate a diverse and good quality initial population. The population then evolves through modified order-based recombination and mutation operators to perform exploration for promising solutions within the entire region. Mutation is performed only if the current population has converged or the produced offspring by recombination operator is too similar to one of his parents. Finally the algorithm performs an intensified local search on the best solution found in the evolutionary stage. Computational experiments using standard instances indicate that the proposed algorithm works well in both computational time and solution quality.

Optimization of Multi-Execution Modes and Multi-Resource-Constrained Offshore Equipment Project Scheduling Based on a Hybrid Genetic Algorithm

Offshore engineering construction projects are large and complex,having the characteristics of multiple execution modes andmultiple resource constraints.Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems(RCPSPs).To solve RCPSP problems in offshore engineering construction more rapidly,a hybrid genetic algorithmwas established.To solve the defects of genetic algorithms,which easily fall into the local optimal solution,a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation.Then,an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions,reduce the computation time and avoid premature convergence.A calibrated function method was used to cater to the roulette rules,and appropriate rules for encoding,decoding and crossover/mutation were designed.Finally,a simple network was designed and validated using the case study of a real offshore project.The performance of the genetic algorithmand a simulated annealing algorithmwas compared to validate the feasibility and effectiveness of the approach.

Fuzzy Resource-Constrained Project Scheduling Problem for Software Development

This paper presents a new method to solve the resource-constrained project scheduling problem for software development. In this method,activity duration times are described as fuzzy variables and resource-constrained software project scheduling problems are described as fuzzy programming models. First,how to model the software project scheduling problem under the fuzzy environment conditions is proposed. Second,in order to satisfy the different requirements of decision-making,two novel fuzzy project scheduling models,expected cost model and credibility maximization model,are suggested. Third,a hybrid intelligent algorithm integrated by genetic algorithm and fuzzy simulation is designed to solve the above two fuzzy programming models. Numerical experiments illustrate the effectiveness of the hybrid intelligent algorithm.

SOLVING RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEMS WITH BI-CRITERIA HEURISTIC SEARCH TECHNIQUES

In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm forsolving multiple resource-constrained project scheduling problems. The heuristic solves problems intwo phases. In the pre-processing phase, the algorithm estimates distance between a state and the goalstate and measures complexity of problem instances. In the search phase, the algorithm uses estimatesof the pre-processing phase to further estimate distances to the goal state. The search continues in astepwise generation of a series of intermediate states through search path evaluation process withbacktracking. Developments of intermediate states are exclusively based on a bi-criteria new stateselection technique where we consider resource utilization and duration estimate to the goal state. Wealso propose a variable weighting technique based on initial problem complexity measures.Introducing this technique allows the algorithm to efficiently solve complex project schedulingproblems. A numerical example illustra

Solving resource availability cost problem in project scheduling by pseudo particle swarm optimization

This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish al activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of sol-ving the resource availability cost problem （RACP） by using start time of each activity to code the schedule. Then, a novel heuris-tic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimiza-tion （PPSO） combined with PSO and path relinking procedure is presented to solve the RACP. Final y, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP.

A New Algorithm for Resource Constraint Project Scheduling Problem Based on Multi-Agent Systems

The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.

Project Scheduling Problem with Uncertain Variables

Project scheduling problem is mainly to determine the schedule of allocating resources in order to balance the total cost and the completion time. This paper chiefly uses chance theory to introduce project scheduling problem with uncertain variables. First, two types of single-objective programming models with uncertain variables as uncertain chance-constrained model and uncertain maximization chance-constrained model are established to meet different management requirements, then they are extended to multi-objective programming model with uncertain variables.

Project Scheduling问题和Job-Shop问题的神经网络解

Project Scheduling问题和Job-Shop问题是著名的NP难题。本文用神经网络方法去解这两个问题,软件模拟结果是令人满意的。这种方法也为解一大类组合优化问题提供了一个新的途径。

Solving Resource-constrained Multiple Project Scheduling Problem Using Timed Colored Petri Nets

To solve the resource-constrained multiple project scheduling problem(RCMPSP) more effectively,a method based on timed colored Petri net(TCPN) was proposed.In this methodology,firstly a novel mapping mechanism between traditional network diagram such as CPM(critical path method)/PERT(program evaluation and review technique) and TCPN was presented.Then a primary TCPN(PTCPN) for solving RCMPSP was modeled based on the proposed mapping mechanism.Meanwhile,the object PTCPN was used to simulate the multiple projects scheduling and to find the approximately optimal value of RCMPSP.Finally,the performance of the proposed approach for solving RCMPSP was validated by executing a mould manufacturing example.

Economic optimization of resource-constrained project scheduling:a two-phase metaheuristic approach

This paper deals with the problem of project scheduling subject to multiple execution modes with non-renewable resources, and a model that handles some of monetary issues in real world applications.The objective is to schedule the activities to maximize the expected net present value(NPV) of the project, taking into account the activity costs, the activity durations, and the cash flows generated by successfully completing an activity.Owing to the combinatorial nature of this problem, the current study develops a hybrid of branch-and-bound procedure and memetic algorithm to enhance both mode assignment and activity scheduling.Modifications for the makespan minimization problem have been made through a set of benchmark problem instances.Algorithmic performance is rated on the maximization of the project NPV and computational results show that the two-phase hybrid metaheuristic performs competitively for all instances of different problem sizes.

面向资源约束的电动公交车充电调度策略

为减少公交运营成本、合理制定插入式充电模式下公交总站的电动公交车充电调度方案,本文基于帝国竞争算法提出了一种面向资源约束的公交车充电调度策略(RCO-CSS)。基于电动公交车运营的时空特点和充放电特性,应用多技能资源约束多项目调度问题(MSRC-MPSP)运筹规划思想对电动公交车充电问题进行抽象建模,以车队规模与充电桩数量为主要资源参数,以最小化充电成本和日均设备购置成本为目标,构建资源约束充电调度模型,进而设计一种二阶段演化帝国竞争算法(TSE-ICA)对模型进行求解,输出最佳的充电调度方案及匹配的行车运营计划。采用4个分别包含5、10、20和36条线路的公交运行实例对RCO-CSS进行了性能评估与有效性验证。在实例探讨中,首先运用Taguich法对资源参数进行了敏感性分析,发现资源越宽裕,模型输出的日充电费用越小,但车辆与充放电设备平摊至每日的购置成本越大;其次,将TSE-ICA与其他4种先进的元启发式算法进行实验数值对比,验证了所提算法的寻优性能;最后,通过与无序充电调度策略和常规有序充电调度策略进行比较,证明了RCO-CSS能够更好地降低用电成本、设备购置成本和电池充放电次数。基于MSRC-MPSP和TSE-ICA的RCO-CSS为公交运营商制定充电调度方案和行车运营计划提供了一种可行且敏捷高效的新思路。

基于改进多种群遗传算法的多目标资源受限项目调度问题研究

多目标资源受限项目调度问题普遍存在于生产经营活动中,具有较高的实用价值。本文提出了一种改进的多种群遗传算法以解决多目标资源受限项目调度问题,为改变现有算法计算步骤理解困难、不易推广的特点,巧妙地通过平移工作解决此类问题,算法首先对工作的优先级进行随机编码,然后根据工序约束进行解码,并根据解码结果安排工作调度,同时为防止无效迭代,融合了禁忌搜索的思想。最后,通过PSPLIB标准算例库中算例进行实验,实验结果表明该算法在具备较高精度的同时,兼顾了快捷性和简洁性。

基于改进NSGA-II算法的装配式建筑施工调度优化

针对以往装配式建筑调度研究主要基于每项活动只有确定的活动时间和一种执行模式,而实际调度过程中存在不确定的活动时间和多种执行模式,建立多目标多模式资源约束下的模糊工期调度模型,提出一种改进的非支配排序遗传算法(INSGA-II)来求解(时间−成本)双目标优化模型。该算法根据活动的优先级关系进行种群初始化和交叉操作,同时提出新的包含活动列表、模式列表和资源列表的3段编码。最后,通过装配式建筑施工现场实际案例分析和算法性能对比,证明本文构建的调度模型和算法设计能有效地解决多模式资源约束下的模糊工期调度模型,为施工调度计划的设计提供科学的思路和方法。

Project Scheduling Using Hybrid Genetic Algorithm with Fuzzy Logic Controller in SCM Environment

In supply chain management (SCM) environment, we consider a resource-constrained project scheduling problem (rcPSP) model as one of advanced scheduling problems considered by a constraint programming technique. We develop a hybrid genetic algorithm (hGA) with a fuzzy logic controller (FLC) to solve the rcPSP which is the well known NP-hard problem. This new approach is based on the design of genetic operators with FLC through initializing the serial method which is superior for a large rcPSP scale. For solving these rcPSP problems, we first demonstrate that our hGA with FLC (flc-hGA) yields better results than several heuristic procedures presented in the literature. We have revealed a fact that flc-hGA has the evolutionary behaviors of average fitness better than hGA without FLC.

Hybrid genetic algorithm for bi-objective resourceconstrained project scheduling

In this study,we considered a bi-objective,multi-project,multi-mode resource-constrained project scheduling problem.We adopted three objective pairs as combinations of the net present value(NPV)as a financial performance measure with one of the time-based performance measures,namely,makespan(Cmax),mean completion time(MCT),and mean flow time(MFT)(i.e.,minCmax/maxA^PF,minA/Cr/max7VPF,and min MFTI mdixNPV).We developed a hybrid non-dominated sorting genetic algorithm Ⅱ(hybrid-NSGA-Ⅱ)as a solution method by introducing a backward-forward pass(BFP)procedure and an injection procedure into NSGA-Ⅱ.The BFP was proposed for new population generation and post-processing.Then,an injection procedure was introduced to increase diversity.The BFP and injection procedures led to improved objective functional values.The injection procedure generated a significantly high number of non-dominated solutions,thereby resulting in great diversity.An extensive computational study was performed.Results showed that hybrid-NSGA-Ⅱ surpassed NSGA-Ⅱ in terms of the performance metrics hypervolume,maximum spread,and the number of nondominated solutions.Solutions were obtained for the objective pairs using hybrid-NSGA-Ⅱ and three different test problem sets with specific properties.Further analysis was performed by employing cash balance,which was another financial performance measure of practical importance.Several managerial insights and extensions for further research were presented.

甲供非商品化资源约束下可中断项目群调度模型

不可更新资源的约束会导致项目群中各合同项目开工延误甚至施工中断。针对不可更新资源受限理论研究存在的局限性,开展了多种甲供非商品化资源(NCRPE)约束下可中断项目群调度问题的研究。首先,由于资源分配多样性会给项目群进度造成不确定性影响,围绕项目群工期—延误成本双目标优化模型展开研究,采用改进编码方式的多目标粒子群算法(MOPSO)进行求解,获取项目群的资源分配方案,利用串行调度生成机制(SSGS)得到项目群进度计划;其次,将MOPSO算法和NSGA-II算法进行对比,通过IGD和HV两个指标来衡量MOPSO算法求解该问题的有效性;最后,结合实际案例,对模型进行求解分析。在甲供非商品化资源约束和项目可中断情况下,构建的项目群调度模型能够有效地实现项目群工期和合同项目延误成本的双目标优化。研究成果为业主合理分配NCRPE以及进行项目群调度提供理论依据。

带转移时间的资源受限项目鲁棒调度优化

为使项目在面临活动工期扰动时尽可能按照基准调度计划稳定执行,针对带转移时间的资源受限项目调度问题提出鲁棒调度与资源分配集成优化方法。分别从资源转移关系、活动时差、随机活动工期三个不同角度设计三种解鲁棒性代理指标,构建了两个混合整数规划模型(MinEA,MaxPF)和一个随机规划模型(MinTPC),并分别采用精确方法和禁忌搜索启发式算法求解;在此基础上提出MinTPC+MaxPF混合优化策略。仿真实验结果表明,MinTPC+MaxPF方法在项目解鲁棒性和质鲁棒性两方面均能获得最优绩效;MinEA方法的解鲁棒性优于MinTPC,而后者的质鲁棒性优于前者。所研究的模型和方法,可以为项目决策者在工期不确定环境下协调活动进度安排和优化资源转移提供指导,提升项目计划鲁棒性,提高企业的经济效益。