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Solving the resource constrained project scheduling problem with quantum annealing

SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Armas, Luis Fernando Perez Creemers, Stefan Deleplanque, Samuel Univ Lille IESEG Sch Management CNRS UMR 9221 LEM Lille Econ Management 3 Rue Digue F-59000 Lille Nord France Univ Lille CNRS Cent Lille Univ Polytech Hauts FranceJuniaUMR 8520IEMN 41 Bd Vauban F-59000 Lille France ORSTAT KU Leuven Naamsestr 69 B-3000 Leuven Belgium Catholic Univ Louvain Ctr Operat Res & Econometr CORE Voie Roman Pays 34 B-1348 Louvain La Neuve Belgium

Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations research.

关键词： resource constrained project scheduling problem Quantum optimization Quantum annealing

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A hybrid metaheuristic method for solving resource constrained project scheduling problem

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EVOLUTIONARY INTELLIGENCE 2023年第2期16卷 519-537页

作者： Shuvo, Ohiduzzaman Golder, Swajan Islam, Md Rafiqul Khulna Univ Comp Sci & Engn Discipline Khulna 9208 Bangladesh

resource constrained project scheduling problem (RCPSP) is a renowned variant of the scheduling problem. RCPSP is very important in production and management but computationally hard. It is widely used in many fields like job shop scheduling, flow shop scheduling, transactional planning, wireless communication etc. The objective of solving RCPSP is to obtain minimum makespan maintaining all constraints. There are some exact, approximate, heuristic and metaheuristic algorithms which were proposed to solve this problem. RCPSP is an NP-hard problem. Chemical reaction optimization (CRO) is a population based metaheuristic method to solve such problems and it shows better performance comparing with some other existing algorithms. CRO explores the large search space both locally and globally using its four operators. Genetic algorithm (GA) is also a nature inspired algorithm which is used to solve various optimization problems. In this paper, we are proposing a hybrid metaheuristic approach that integrates chemical reaction optimization (CRO) and genetic algorithm (GA) named CRO-GA to solve RCPSP. We have redesigned the basic operators of CRO and GA to find out the solutions. An additional operator called priority based selection operator is used in CRO to adjust with GA. Our proposed method is compared with other related approaches such as adaptive particle swarm optimization (A-PSO), multi agent optimization algorithm (MAOA), artificial bee colony (ABC), genetic algorithm (GA) which are state of the art for the RCPSP. The experimental results show that our proposed methodology gives better results than other existing algorithms to solve RCPSP with less computational time.

关键词： Chemical reaction optimization NP-hard resource constrained project scheduling problem Makespan

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

Particle swarm optimization for multi resource constrained p...

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International Conference on Sustainable Materials, Manufacturing and Renewable Technologies (I-SMaRT)

作者： Joy, Jiby Rajeev, Srijith Abraham, Eldhose C. Fed Inst Sci & Technol Dept Mech Engn Ernakulam 683577 Kerala India Baselios Paulose II Catholicos Coll Dept Elect Illikkamukkada 686664 Kerala 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. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the International Conference on Sustainable materials, Manufacturing and Renewable Technologies 2021.

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

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An Immune Genetic Algorithm for resource constrained project scheduling problem with Discounted Cash Flows

An Immune Genetic Algorithm for Resource Constrained Project...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Asadujjaman, Md Rahman, Humyun Fuad Chakrabortty, Ripon Kumar Ryan, Michael J. Univ New South Wales Capabil Syst Ctr Sch Engn & Informat Technol Canberra ACT Australia Rajshahi Univ Engn & Technol Dept Ind & Prod Engn Rajshahi Bangladesh

ISBN: (纸本)9781538672204

The resource constrained project scheduling problem with discounted cash flows (RCPSPDC) is a well-known complex optimization problem. In this paper, an immune genetic algorithm (IGA) is proposed to solve the RCPSPDC with the objective of maximizing the net present value (NPV) to ensure project profitability. Hybridizing a genetic algorithm with immune algorithm enhances the overall performance of the IGA. The performance of the proposed algorithm has been evaluated by solving 2,160 problem instances from the standard benchmark dataset and the results have been compared with existing state-of-the-art algorithms. Extensive experiments demonstrate that the overall performance of the IGA is quite satisfactory for 50 activities dataset.

关键词： resource constrained project scheduling problem Discounted Cash Flows Net Present Value Immune Genetic Algorithm

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New Cuckoo Search Algorithm for the resource constrained project scheduling problem

New Cuckoo Search Algorithm for the Resource Constrained Pro...

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RIVF International Conference on Computing and Communication Technologies (RIVF)

作者： Huu Dang Quoc Loc Nguyen The Cuong Nguyen Doan Toan Phan Thanh Thuong Mai Univ Hanoi Vietnam Ha Noi Natl Univ Educ Hanoi Vietnam Mil Inst Sci & Technol Hanoi Vietnam

ISBN: (数字)9781728153773

ISBN: (纸本)9781728153773

The aim of this paper is to suggest novel Cuckoo Search algorithm to solving Multi-Skill resource constrained project scheduling problem (MS-RCPSP). Our proposed evolutionary algorithm, which called CSM, inspired by Cuckoo Search algorithm. CSM fast converge to global extremum, moreover our algorithm also avoid getting trapped in local extremum. Experimental results and comparisons with conventional optimization algorithms show that the proposed Cuckoo Search algorithm can effectively enhance the searching efficiently.

关键词： Evolutionary Computing resource constrained project scheduling problem Cuckoo Search Algorithm Optimization Methods

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

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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

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

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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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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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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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