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Multi-mode resource constrained project scheduling problem along with contractor selection

INFOR: Information Systems and Operational Research 2021年第2期59卷 212-231页

作者： Reza Nemati-Lafmejani Hamed Davari-Ardakani Department of Industrial Engineering Faculty of Engineering Kharazmi University Tehran Iran

In real-world environments, selecting the right contractor is an important issue which considerably influences completion time, total cost and quality of performing the project. This paper deals with the multi-mode resource constrained project scheduling problem (MRCPSP) and contractor selection (CS) in an integrated manner. In fact, each activity is assigned to a contractor, an execution mode is selected for each activity, and the start/finish times of activities are determined. This paper presents a bi-objective optimization model to deal with MRCPSP-CS, aiming to minimize the total cost and completion time of the project, simultaneously. Then, four multi-objective decision making (MODM) techniques are used to solve the proposed model. Since none of MODM techniques dominates other ones, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to assess the performance of MODM techniques, confirming that MCGP-U outranks other ones. Finally, the augmentedε-Constraint method is used to solve some test problems, and perform sensitivity analysis on the number of contractors. Sensitivity analyses show that by increasing the number of available contractors, the Pareto front is significantly improved, and the Number of Pareto-optimal Solutions (NPS) increases. This helps decision maker(s) make appropriate decisions in a more flexible manner.

关键词： resource constrained project scheduling problem multiple modes contractor selection multi-objective decision making augmented ε -constraint method

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Particle swarm optimization for multi resource constrained project scheduling problem with varying resource levels

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Materials Today: Proceedings 2021年 47卷 5125-5129页

作者： Jiby Joy Srijith Rajeev Eldhose C. Abraham Department of Mechanical Engineering Federal Institute of Science and Technology Angamaly Ernakulam Kerala 683577 India Department of Electronics Baselios Paulose II Catholicos College Kerala 686664 India

This paper deals with particle swarm optimization for multi resource constrained project scheduling problem with varying resource levels (MRCPSPVRL), and it is an updated version of particle swarm optimization for resource constrained project scheduling problem with varying resource levels (RCPSPVRL). In a project scheduling problem, there are many activities with certain durations and resource requests, which are connected by successor and predecessor relations. RCPSPVRL uses only one type of resource. However, in realistic project scheduling problems, different types of resources are used to solve the task. MRCPSPVRL uses various types of resources with limited availability by varying resource levels in a predetermined fashion. In MRCPSPVRL, maximum time required for completing the task is divided into different time periods, and resources are varied within these different time periods. The objective of this system is to schedule all the activities effectively by utilizing all types of resources. MRCPSPVRL is tested using PSO (Particle Swarm Optimization) on MATLAB R2020b.

关键词： Particle swarm optimisation resource constrained project scheduling problem resource constrained project scheduling problem with varying resource levels

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Discrete Cuckoo Search for resource constrained project scheduling problem

Discrete Cuckoo Search for Resource Constrained Project Sche...

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IEEE International Conference on Computational Science and Engineering

作者： Kirils Bibiks Fun Hu Jian-Ping Li Aleister Smith Faculty of Engineering and Informatics University of Bradford Bradford United Kingdom Thales Research and Technology Thales Avionics Ltd Crawley United Kingdom

ISBN: (纸本)9781467382984

In this paper, a novel population-based meta-heuristic algorithm, Discrete Cuckoo Search (DCS), is applied to solve resource constrained project scheduling problem (RCPSP). The DCS is a modification of the existing Cuckoo Search which was adapted for solving combinatorial optimization problems. Application of the DCS for solving RCPSP was achieved by changing some of the algorithm's core elements, such as nest, egg, objective function, and movement through the search space. The proposed DCS is then tested on a set of benchmark instances and its performances are compared against other existing meta-heuristic algorithms. The numerical results have shown that the proposed algorithm is very efficient and outperforms several other popular meta-heuristic algorithms, both in terms of quality of the results and execution time.

关键词： Cuckoo Search Discrete Cuckoo Search Combinatorial optimization resource constrained project scheduling problem

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A New resource constrained project scheduling problem

A New Resource Constrained Project Scheduling Problem

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2009 Asia-Pacific Conference on Information Processing

作者： Xiaoxiang Liu,Weigang Jiang,Jianwen Xie Department of Computer Science of Zhuhai College Jinan University Zhuhai,China Yitian Jia Department of Accounting of Zhuhai College Jinan University Zhuhai,China

The optimal scheduling of resource and program has became the key to the success of enterprise development *** conventional resource constrained project scheduling problem seeks a solution which satisfies the time and resource constraints on purpose of a minimal construction *** the development of the society,the realistic problems trend to be more and more complicated,and the RCPSP model is thereupon *** paper proposes a new project scheduling model with success rate and it is suitable for large-scale project scheduling problems,then,an improved particle swarm optimization called CPSO based on PSO and GA is used to solve the *** application of solving examples with CPSO demonstrates that this algorithm is effective.

关键词： resource constrained project scheduling problem particle swarm algorithm with crossover genetic algorithm

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Solving resource-constrained project scheduling problem Using Metaheuristic Algorithm 5

Solving Resource-Constrained Project Scheduling Problem Usin...

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5th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Munlin, Mudarmeen Mahanakorn Univ Technol Fac Informat Sci & Technol Bangkok Thailand

ISBN: (纸本)9781538663929

We propose the metaheuristic algorithm to solve the The resource-constrained project scheduling problem (RCPSP). The approach method extends the Particle Swarm Optimization (PSO) by regrouping the agent particles within the appropriate radius of the circle. It initializes the group of particles, calculates the fitness function, and finds the best particle in that group. Then, it incorporates the adaptive mutation and forward-backward improvement to hybridize local search algorithm for constructing the feasible project scheduling with the minimal make-span. The efficiency of the proposed method is tested against the well-known benchmarks. The results show that the proposed method gives better optimum rate and standard deviation than some existing procedures.

关键词： resource constrained project scheduling problem particle swarm optimization adaptive mutation forward-backward improvement metaheuristic algorithm

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An Enhanced Invasive Weed Optimization in resource-constrained project scheduling problem 11

An Enhanced Invasive Weed Optimization in Resource-Constrain...

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11th International Conference on Awareness Science and Technology (iCAST)

作者： Cai, Wei Chen, Haojie Zhang, Jian Southwest Jiaotong Univ Inst Adv Design & Mamnufacturing Sch Ofillechan Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728191195

In this research, an enhanced invasive weed optimization (EIWO) has been proposed to solve resource-constrained project scheduling problem (RCPSP) which subjects to the makespan minimization. Firstly, a hybrid population initialization method is illustrated to improve the quality of initial solutions. Secondly, to enhance the local exploitation ability, a local search approach is embedded in the spatial dispersal process. Thirdly, an improved competitive exclusion based on acceptance probability is proposed. At the end of this article, EIWO is tested and verified by standard benchmark problems from PSPLIB. Compared with the existing algorithms through computer numerical experiments, the new EIWO algorithm is more effective and efficient in solving RCPSP.

关键词： resource constrained project scheduling problem Invasive weed optimization Priority rules Local search Competitive exclusion

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A graphical processing unit-based parallel hybrid genetic algorithm for resource-constrained multi-project scheduling problem

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第16期33卷 e6266-e6266页

作者： Uysal, Furkan Sonmez, Rifat Isleyen, Selcuk Kursat Ankara Social Sci Univ Grad Sch Social Sci TR-06100 Ankara Turkey Middle East Tech Univ Dept Civil Engn Ankara Turkey Gazi Univ Dept Ind Engn Ankara Turkey

In this article, we present a parallel graphical processing unit (GPU)-based genetic algorithm (GA) for solving the resource-constrained multi-project scheduling problem (RCMPSP). We assumed that activity pre-emption is not allowed. problem is modeled in a portfolio of projects where precedence and resource constraints affect the portfolio duration. We also assume that the durations, availability of resources are deterministic and portfolio has a static nature. The objective in this article is to find a start time for each activity of the project so that the portfolio duration is minimized, while satisfying precedence relations and resource availabilities within a reasonable amount of time for small and large problem instances. In order to compare the efficiency of the proposed parallel GPU-based GA, problem is solved together with a CPU and a GPU. The results showed that GPU-based parallel GA has high potential for improving the performance of GAs for the RCMPSP particularly, for large-scale problems.

关键词： parallel genetic algorithm resource constrained multi‐ project scheduling problem resource constrained project scheduling problem resource management

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Using priority rules for resource-constrained project scheduling problem in static environment

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第0期169卷

作者： Dumic, Mateja Jakobovic, Domagoj Univ Osijek Dept Math Trg Ljudevita Gaja 6 Osijek Croatia Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

The resource-constrained project scheduling problem (RCPSP) is one of the scheduling problems that belong to the class of NP-hard problems. Therefore, heuristic approaches are usually used to solve it. One of the most commonly used heuristic approaches are priority rules (PRs). PRs are easy to use, fast and able to respond to system changes, which makes them applicable in a dynamic environment. The disadvantage of PRs is that when applied in a static environment, they do not achieve results of the same quality as heuristic approaches designed for a static environment. Moreover, a new PR must be evolved separately for each optimization criterion, which is a challenging process. Therefore, recently significant effort has been put into the automatic development of PRs. Although PRs are mainly used in a dynamic environment, they are also used in a static environment in situations where speed and simplicity are more important than the quality of the obtained solution. Since PRs evolved for a dynamic environment do not use all the information available in a static environment, this paper analyzes two adaptations for evolving PRs in a static environment for the RCPSP -iterative priority rules and rollout approach. This paper shows that these approaches achieve better results than the PRs evolved and used without these adaptations. The results of the approaches presented in the paper were also compared with the results obtained with the genetic algorithm as a representative of the heuristic approaches used mainly in the static environment.

关键词： Genetic programming resource constrained project scheduling problem Priority rules Iterative priority rules Rollout Static environment

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An innovative framework for optimizing discrete berth allocation and quay crane assignment problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Xiang, Xi Chang, Xuqiang Gong, Lin Liu, Xin Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

This paper delves into the complexities linked with discrete berth allocation and quay crane assignment within automated container terminals, analyzing scenarios that involve both time-invariant and time-variant considerations. We propose a novel approach, redefining discrete berth allocation and time-invariant quay crane assignment problems as resource-constrained project scheduling problems, which are addressed with an efficient branch and cut algorithm. Furthermore, our investigation extends to the domain of time-variant specific quay crane assignment, introducing a purpose-designed two-stage algorithm for effective resolution. Numerical experiments affirm the efficacy of these algorithms ineffectively addressing the intricate yet pivotal challenges inherent in automated terminal operations.

关键词： Discrete berth allocation Quay crane assignment problems Branch and cut resource constrained project scheduling problem

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Evolving rollout-justification based heuristics for resource constrained project scheduling problems

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 50卷 100556-000页

作者： Chand, Shelvin Singh, Hemant Ray, Tapabrata Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

resource constrained project scheduling is critical in logistic and planning operations across a range of industries. An interesting heuristic for solving this problem is the Rollout-Justification (RJ) procedure. This procedure, which has conceptual similarities with dynamic programming, incrementally builds a solution by identifying the next activity to schedule based on the projections made using a guiding priority rule (heuristic) coupled with forward-backward local search. A critical component that affects the performance of RJ procedure is the guiding priority rule (or a set of rules). In this study, instead of using existing rules from literature, we aim to evolve new priority rules using genetic programming, and systematically investigate their use with the RJ procedure. Apart from evolving new rules, we also investigate new ways of integrating/utilizing the rules within RJ procedure. To this end we consider the use of both forward and backward scheduling, independent and cohesive ensemble rule approaches, limited and unlimited number of function evaluations, among others. We use data from the project scheduling library (PSPLib) to train and test the evolved rules and their integration with RJ. A comprehensive set of numerical experiments are performed to benchmark the rules evolved using the proposed approach against a range of existing rules. The results demonstrate the competence and potential of the proposed approach, both in terms of accuracy and complexity.

关键词： resource constrained project scheduling problem Hyper-heuristics Genetic programming Priority rules Rollout Justification

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