Relevant bibliographies by topics / Resource and time constrained multi-project scheduling problem

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Academic literature on the topic 'Resource and time constrained multi-project scheduling problem'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Resource and time constrained multi-project scheduling problem"

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Suresh, M., Pankaj Dutta, and Karuna Jain. "Resource Constrained Multi-Project Scheduling Problem with Resource Transfer Times." Asia-Pacific Journal of Operational Research 32, no. 06 (2015): 1550048. http://dx.doi.org/10.1142/s0217595915500487.

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Scheduling multi-project is a complex decision making process. It involves the effective and timely allocation of resources to different projects. In the case of multi-project, resources are often transferred between the projects. It consumes both time and cost, when projects are situated in different geographic locations. As a result, the net present value (NPV) of multi-projects is significantly impacted by the resource transfer time. In this paper, a new genetic algorithm (GA) approach to the multi-project scheduling problem with resource transfer times is presented, where the NPV of all projects is maximized subject to renewable resource constraints. The paper also presents a heuristic approach using two phase priority rules for the same problem. We conduct a comprehensive analysis of 60 two-phase priority rules. The proposed GA approach is compared to the heuristic approach using the well-known priority rules. An extensive computational experiment is reported.
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Altintas, Cansu, and Meral Azizoglu. "A Resource Constrained Project Scheduling Problem With Multi-Modes." International Journal of Information Technology Project Management 11, no. 1 (2020): 55–70. http://dx.doi.org/10.4018/ijitpm.2020010104.

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In this study, the authors consider a project scheduling problem with a single non-renewable resource. The authors assume that the resource is released at scheduled times and specified quantities and the resource is consumed at activity completion. The activities can be processed at different modes where a mode is defined by a processing time and a resource requirement amount. The problem is to select the modes and timings of the activities so as to minimize the project completion time. The authors give a mixed integer linear programming model and discuss some variable elimination mechanisms to enhance its efficiency.
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Kannimuthu, Marimuthu, Benny Raphael, Palaneeswaran Ekambaram, and Ananthanarayanan Kuppuswamy. "Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem." Engineering, Construction and Architectural Management 27, no. 4 (2019): 893–916. http://dx.doi.org/10.1108/ecam-03-2019-0156.

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Purpose Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint). Design/methodology/approach A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India. Findings Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution. Research limitations/implications It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified. Originality/value An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.
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Wang, Ru, and Jing Lian. "Research on Construction Schedule Optimization of Assembly Building Based on NSGA-II." E3S Web of Conferences 165 (2020): 06055. http://dx.doi.org/10.1051/e3sconf/202016506055.

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Considering multiple possible scenarios in the process of project construction, the prefabricated project scheduling problem is studied in combination with the theory of multi-mode resource-constrained project scheduling problem. Multi-objective multi-mode resource-constrained project scheduling model with time/robustness trad-offs was constructed. Next, the adjusted non-dominated genetic algorithm (NSGA-II) was designed to solve the model. Finally, the proposed model and algorithm were implicated to a real project, in order that research achievement can guide managers to make decisions and invest resources scientifically and reasonably.
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Kong, Feng, and Dong Dou. "Resource-Constrained Project Scheduling Problem under Multiple Time Constraints." Journal of Construction Engineering and Management 147, no. 2 (2021): 04020170. http://dx.doi.org/10.1061/(asce)co.1943-7862.0001990.

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Ghamginzadeh, Arman, Amir Abbas Najafi, and Mohammad Khalilzadeh. "Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Problem Under Time Uncertainty." International Journal of Fuzzy Systems 23, no. 2 (2021): 518–34. http://dx.doi.org/10.1007/s40815-020-00984-w.

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Dang Quoc, Huu, Loc Nguyen The, Cuong Nguyen Doan, and Toan Phan Thanh. "A NEW ALGORITHM FOR MULTI-SKILL RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM BASED ON CUCKOO SEARCH STRATEGY." Journal of Science Natural Science 65, no. 6 (2020): 98–109. http://dx.doi.org/10.18173/2354-1059.2020-0034.

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The purpose of this paper is to consider the project scheduling problem under such limited constraint, called Multi-Skill Resource-Constrained Project Scheduling Problem or MS-RCPSP. The algorithm proposed in this paper is to find the optimal schedule, determine the start time for each task so that the execution time (also called makespan) taken is minimal. At the same time, our scheduling algorithm ensures that the given priority relationships and constraints are not violated. Our scheduling algorithm is built based on the Cuckoo Search strategy. In order to evaluate the proposed algorithm, experiments were conducted by using the iMOPSE dataset. The experimental results proved that the proposed algorithm found better solutions than the previous algorithm.
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Chen, James C., Wun Hao Jaong, Cheng Ju Sun, Hung Yu Lee, Jenn Sheng Wu, and Chung Chao Ku. "Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems." Key Engineering Materials 419-420 (October 2009): 633–36. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.633.

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Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.
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Tosselli, Laura, Verónica Bogado, and Ernesto Martínez. "Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling." Journal of Computer Science and Technology 18, no. 02 (2018): e14. http://dx.doi.org/10.24215/16666038.18.e14.

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In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.
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Colak, Selcuk, Anurag Agarwal, and Selcuk Erenguc. "Multi-Mode Resource-Constrained Project-Scheduling Problem With Renewable Resources: New Solution Approaches." Journal of Business & Economics Research (JBER) 11, no. 11 (2013): 455. http://dx.doi.org/10.19030/jber.v11i11.8193.

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We consider the multi-mode resource-constrained project scheduling problem (MRCPSP) with renewable resources. In MRCPSP, an activity can be executed in one of many possible modes; each mode having different resource requirements and accordingly different activity durations. We assume that all resources are renewable from period to period, such as labor and machines. A solution to this problem basically involves two decisions (i) The start time for each activity and (ii) the mode for each activity. Given the NP-Hard nature of the problem, heuristics and metaheuristics are used to solve larger instances of this problem. A heuristic for this type of problem involves a combination of two priority rules - one for each of the two decisions. Heuristics generally tend to be greedy in nature. In this study we propose two non-greedy heuristics for mode selection which perform better than greedy heuristics. In addition, we study the effect of double justification and backward/forward scheduling for the MRCPS. We also study the effect of serial vs. parallel scheduling. We found that all these elements improved the solution quality. Finally we propose an adaptive metaheuristic procedure based on neural networks which further improves the solution quality. The effectiveness of these proposed approaches, compared to existing approaches in the literature, is demonstrated through empirical testing on two well-known sets of benchmark problems.
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Dissertations / Theses on the topic "Resource and time constrained multi-project scheduling problem"

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Gholizadeh, Tayyar Shadan. "An optimization-based framework for concurrent planning of multiple projects and supply chain : application on building thermal renovation projects." Thesis, Ecole nationale des Mines d'Albi-Carmaux, 2017. http://www.theses.fr/2017EMAC0006/document.

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Le contexte d’application de cette recherche a été le projet CRIBA. CRIBA vise à industrialiser une solution intégrée de rénovation et d’isolation de grands bâtiments. De ce fait, une part importante de la valeur ajoutée est transférée des chantiers de rénovation vers des usines de fabrications devant être synchronisées avec les chantiers. La planification est l'une des étapes importantes de la gestion de projets. S’adaptant à une organisation, elle vise une réalisation optimale en considérant les facteurs de temps, coût, qualité ainsi que l’affectation efficace des ressources. Cette affectation est d’autant plus complexe lorsqu’un ensemble de projets se partagent les ressources, renouvelables ou non renouvelables. L'objectif global de notre étude est de développer un outil d’aide à la décision pour un décideur visant à planifier plusieurs projets en intégrant l'allocation des ressources renouvelables, et la planification des flux de ressources non-renouvelables vers ces projets. Dans ce cadre, les ressources non renouvelables telles que les machines et la main-d'œuvre ont une disponibilité initiale limitée sur les chantiers. Cependant, nous supposons que des quantités limitées supplémentaires peuvent être achetées. En outre, nous prenons en compte la volonté des coordinateurs des projets pour l’approvisionnement des chantiers en juste à temps (just in time), en particulier pour les ressources peu demandées, encombrantes et à forte valeur. Ceci oblige à étendre le cadre du modèle de la planification des projets en incluant la planification de la chaîne logistique qui approvisionne les ressources non renouvelables des chantiers. Enfin, pour répondre au besoin d’outils décisionnels responsables sur le plan environnemental, le modèle prévoit le transport et le recyclage des déchets des chantiers dans les centres appropriés. Un modèle linéaire mixte du problème est ainsi posé. Puisqu’il rentre dans la classe des modèles d'optimisation NP-durs, une double résolution est proposée. D’abord à l’aide d’un solveur puis une méta-heuristique basée sur un algorithme génétique. De plus, pour faciliter l'utilisation du modèle par des utilisateurs peu familiers avec la recherche opérationnelle, un système d'aide à la décision basé sur une application web a été développé. L’ensemble de ces contributions ont été évaluées sur des jeux de test issus du projet CRIBA<br>The application context of the current study is on a CRIBA project. The CRIBA aims to industrialize an integrated solution for the insulation and thermal renovation of building complexes in France. As a result, a significant part of the added value is transferred from the renovation sites to the manufacturing centers, making both synchronized. Planning is one of the important steps in project management. Depending on the different viewpoints of organizations, successful planning for projects can be achieved by performing to optimality within the time, cost, quality factors as well as the efficient assignment of resources. Planning for the allocation of resources becomes more complex when a set of projects is sharing renewable and non-renewable resources. The global objective of the study is to develop a decision-making tool for decision-makers to plan multiple projects by integrating the allocation of the renewable resources and planning the flow of non-renewable resources to the project worksites. In this context, non-renewable resources such as equipment and labor have a limited initial availability at the construction sites. Nevertheless, we assume that additional limited amounts can be added to the projects. In addition, we take into account the interest of the project coordinators in supplying the non-renewable resources in a just-in-time manner to the projects, especially for low-demand resources with a high price. This requires extending the framework of the project planning by including the planning of the supply chain which is responsible. Finally, in order to meet the requirements for environmentally responsible decision-making, the model envisages the transportation and recycling of waste from project sites to appropriate centers. A mixed integer linear model of the problem is proposed. Since it falls within the class of NP-hard optimization models, a double resolution is targeted: first, using a solver and then a metaheuristic based on the genetic algorithm. In addition, in order to facilitate the use of the model by users unfamiliar with operational research, a web-based decision-making support system has been developed. All the contributions are evaluated in a set of case studies from the CRIBA project
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Bettemir, Onder Halis. "Optimization Of Time-cost-resource Trade-off Problems In Project Scheduling Using Meta-heuristic Algorithms." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611971/index.pdf.

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In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood search, particle swarm optimization, ant colony optimization and electromagnetic scatter search meta-heuristic algorithms are implemented for time cost trade-off problems with unlimited resources. In this thesis, three new meta-heuristic algorithms are developed by embedding meta-heuristic algorithms in each other. Hybrid genetic algorithm with simulated annealing presents the best results for time cost trade-off. Resource leveling problem is analyzed by five genetic algorithm based meta-heuristic algorithms. Apart from simple genetic algorithm, four meta-heuristic algorithms obtained same schedules obtained in the literature. In addition to this, in one of the test problems the solution is improved by the four meta-heuristic algorithms. For the resource constrained scheduling problems<br>genetic algorithm, genetic algorithm with simulated annealing, hybrid genetic algorithm with simulated annealing and particle swarm optimization meta-heuristic algorithms are implemented. The algorithms are tested by using the project sets of Kolisch and Sprecher (1996). Genetic algorithm with simulated annealing and hybrid genetic algorithm simulated annealing algorithm obtained very successful results when compared with the previous state of the art algorithms. 120-activity multi-mode problem set is produced by using the single mode problem set of Kolisch and Sprecher (1996) for the analysis of resource constrained time cost trade-off problem. Genetic algorithm with simulated annealing presented the least total project cost.
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Meng-FangWu and 吳孟舫. "Multi-mode resources-constrained project scheduling problem with activity waiting time." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/99662373039126789705.

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碩士<br>國立成功大學<br>工業與資訊管理學系碩博士班<br>100<br>To make the project scheduling problem close to realistic situation, multi-mode resources-constrained project scheduling problem (MRCPSP) is extend from the resources constrained project scheduling problem (RCPSP). During the implementation of projects, there are some waiting time limitations between activities. Although the waiting time won’t increase activity processes time directly and consume any renewable resources as well as nonrenewable resources, it affects the starting time of the direct follow-up activity and the total project makespan. This study use a genetic algorithm to solve the problems of MRCPSP with waiting time limitations, and to minimize total project makespan. The dataset are selected from project scheduling problem library (PSPLIB). This study considered two limitations: First, randomly selected activities would be considered with the waiting time limitations. Second, there was different waiting time corresponding to each mode. Furthermore, this study try to explore the effects of schedules by examining different sizes project problems with activity waiting time. First of all, we use the proposed algorithm to find the mutation probability for different sizes of project problems. Then, we find the feasible solution by using different crossover methods, and compare the feasible solution. The results showed that the two-point crossover can get a better feasible solution, and one-point crossover can get a feasible solution in a shorter time.
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Wei, Ru-san, and 魏汝珊. "The Research of Multi-mode Resource Constrained Project Scheduling Problem in Stochastic Working Time." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/72145782188548163404.

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碩士<br>國立中央大學<br>土木工程研究所<br>100<br>Due to the fast development of global economy, project scheduling is more and more important. Because the project size grows rapidly nowadays, the project scheduling problem is much more complex than before. Traditionally, the critical path method (CPM) and the program evaluation and review technique (PERT) were used to formulate the project scheduling problems. The past time-controlled process for CPM and PERT is neither efficient nor effective from a system perspective, especially due to the short of environment resources recently. Considering of the resource finite and cash flow, resource constrained project scheduling problem (RCPSP), multi-mode resource constrained project scheduling problem (MRCPSP) and multi-mode resource constrained project scheduling problem with discounted cash flow (MRCPSPDCF) are researched in many years. Not only sources and money but stochastic disturbances arising from variations in working time in actual operations should be noticed. The past researches on the project scheduling is mainly based on the average working time, which do not consider the stochastic working time. Therefore, when actual project scheduling is affected by stochastic working time, the already planned project scheduling will be disturbed and lose its system optimization. Dealing with the multi-mode resource constrained project scheduling problem with discounted cash flow (MRCPSPDCF), this research adopts the time-precedence network technique to formulate a stochastic project scheduling model which considers the cash flow value of time and related operating and resource constraints. The model is formulated as an integer network flow problem with side constraints, which is characterized as NP-hard in terms of optimization. We employ the CPLEX mathematical programming solver to solve the problem. Otherwise we do the numerical tests to evaluate the performance of the proposed model, and the data comes from Project Scheduling Problem Library (PSPLIB).Performing sensitive and scenario analysis for different parameters, and the test results show the model to be good and that the solution method could be useful in practice. At last, conclusions and suggestions are given.
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Nudtasomboon, Nudtapon. "Methodology for the multi-objective, resource-constrained project scheduling problem." Thesis, 1993. http://hdl.handle.net/1957/35939.

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This study is concerned with the problem of resource-constrained project scheduling which includes splittable and nonsplittable jobs, renewable and nonrenewable resources, variation in resource availabi1ity, time-resource tradeoff, time-cost tradeoff, and multiple objectives. The problem is formulated as a zero-one integer programming model. A specialized solution technique is developed for the preemptive goal programming, resource-constrained project. scheduling problem for time, cost, and resource leveling objectives. In addition, single objective algorithms are also provided for the time, cost, and resource leveling objectives. These algorithms are based on the idea of the implicit enumeration process, and use the special structures of the problem to expedite the search process. Computer-generated problems are used to test each of the single objective algorithms. The results show that the algorithms give optimal solutions to tested problems with time and cost objectives using a reasonable computation time; however, heuristic solutions are more feasible for problems with resource leveling objective. The multiple objective algorithm is illustrated through application to a warehouse project problem.<br>Graduation date: 1993
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Chen, Tai-Lin, and 陳泰霖. "Floating Time Consumption Impacts On Multi-Project Resource Constrained Scheduling." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/40143395770738407012.

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碩士<br>國立屏東科技大學<br>工業管理系所<br>100<br>Float time is job’s buffer for project scheduling. If using float is hardly, schedule completion time can delay and cost can loss hard. Most project schedule research focuses on minimize completion time or minimize total cost, the few studies on floating consume affect the project schedule. So this study develops a new Critical Path Method(CPM) for calculating the correct float time, to avoid using errors float time, and reducing impact for completion time. This study applied Ant Colony Optimization (ACO) to establish the best combination and develop resource constraints scheduling model to achieve the resource allocation optimization and the shortest finishing time of a project under resource constraints and the sequence for project activities. Finally, the case of empirical scheduling model efficiency, and calculate the correct float information, reducing the error of the float.
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Song, Jin-Ru, and 宋瑾茹. "A Study on the Robust Resource-Constrained Multi-Project Scheduling Problem." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/64929523114990587107.

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碩士<br>國立屏東科技大學<br>工業管理系所<br>96<br>The main purpose of this study is to investigate a bi-objective resource-constrained multi-project scheduling problem. Two objectives are considered: net present value (NPV) maximization and robustness maximization. A mixed integer nonlinear programming (MINLP) model and a particle swarm optimization (PSO) algorithm are presented to solve the bi-objective resource-constrained multi-project scheduling problem. The effectiveness of the proposed PSO algorithm will be demonstrated by comparing it with the MINLP model and some existing rules. The results indicate that the solution obtained by the PSO algorithm is very close to the optimal solution, and that the proposed PSO algorithm does not consume much computational time. In addition, the results indicate that the PSO algorithm is superior to the existing heuristic rules under the performance criteria of the project NPV and the schedule robustness. The approaches presented in this study could provide project managers with useful tools for making better decisions.
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Buddhakulsomsiri, Jirachai. "Multi-mode resource-constrained project scheduling problem with resource vacations and task splitting." Thesis, 2003. http://hdl.handle.net/1957/31495.

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The research presented in this dissertation addresses the Multi-Mode Resource-Constrained Project Scheduling Problem (MMRCPSP) in the presence of resource unavailability. This research is motivated by the scheduling of engineering design tasks in automotive product development to minimize the project completion time, but addresses a general scheduling situation that is applicable in many contexts. The current body of MMRCPSP research typically assumes that, 1) individual resource units are available at all times when assigning tasks to resources and, 2) before assigning tasks to resources, there must be enough resource availability over time to complete the task without interruption. In many situations such as assigning engineering design tasks to designers, resources are not available over the entire project-planning horizon. In the case of engineering designers and other human resources, unavailability may be due to several reasons such as vacation, training, or being scheduled to do other tasks outside the project. In addition, when tasks are scheduled they are often split to accommodate unavailable resources and are not completed in one continuous time segment. The objectives of this research are to obtain insight into the types of project scheduling situations where task splitting may result in significant makespan improvements, and to develop a fast and effective scheduling heuristic for such situations. A designed computational experiment was used to gain insight into when task splitting may provide significant makespan improvements. Problem instances were randomly generated using a modification of a standard problem generator, and optimally solved with and without task splitting using a branch and bound algorithm. In total 3,880 problem instances were solved with and without task splitting. Statistical analysis of the experimental data reveals that high resource utilization is the most important factor affecting the improvements obtained by task splitting. The analysis also shows that splitting is more helpful when resource unavailability occurs in multiple periods of short duration versus fewer periods of long duration. Another conclusion from the analysis indicates that the project precedence structure and the number (not amount) of resources used by tasks do not significantly affect the improvements due to task splitting. Using the insights from the computational testing, a new heuristic is developed that can be applied to large problems. The heuristic is an implementation of a simple priority rule-based heuristic with a new parameter used to control the number of task splits. It is desirable to obtain the majority of task splitting benefits with the smallest number of split tasks. Computational experiments are conducted to evaluate its performance against known optimal solutions for small sized problems. A deterministic version of the heuristic found optimal solutions for 33% of the problems and a stochastic version found optimal solutions for over 70%. The average percent increase in makespan compared to optimal was 7.58% for the deterministic heuristic and less than 2% for the stochastic versions demonstrating acceptable performance.<br>Graduation date: 2003
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Lo, Chiao-Yu, and 駱巧瑜. "Solving the Multi-mode Resource-constrained Multi-project Scheduling Problem by Bi-level Programming." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/42309930614264632884.

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碩士<br>淡江大學<br>資訊管理學系碩士班<br>102<br>In practice, project management is often performed in a multi-project context, where individual projects compete for source resources. Moreover, the activities in a project could be accomplished in one out of several execution modes, in which, each execution mode represents an alternative combination of resource requirement of the activity and its duration. This study aims to deal with such a multi-project, multi-mode, and resource-constrained project scheduling problem. Previous studies on multi-project scheduling problems generally assumed that resources can be shared among projects, and thus, the multiple projects can be combined into a single project, and solved by available algorithms that are formulated for single project scheduling. The present study considers a different case where resources cannot be shared among projects and hence the resources need to be allocated to individual projects; after the resources are allocated to each project, the project manager of each project faces a typical multi-mode resource constrained project scheduling problem. Owing to the above hierarchical decision-making structure, this study suggests using the bi-level decentralized programming to model the problem. The resources used in a project in fact is a combination of various resources. Thus, it is ideal to allocate resources to projects in a combinatorial manner. Combinatorial auction is suitable for dealing with such a problem. In the combinatorial action mechanism considered in this study, upper-level decision-maker is the auctioneer and the project managers at the low-level are bidders. Project managers submit bids, which are in the form of resource combination and are obtained by solving a least-cost multi-mode resource-constrained projects scheduling problem, to the upper-level decision-maker. After receiving all bids from project managers, the upper-level decision-maker solves a winner determination problem to determine the winning bids which represent the result of the resource allocation decision. In addition to the regular combinatorial auction model, this study proposes a fuzzy combinatorial auction model to deal with the situation where the resources are not enough to complete projects by their due dates. The solution of the fuzzy combinatorial auction model shows the trade-off between resource expansion and tardiness improvement. The proposed solution procedure is programmed by JAVA with CPLEX library, and uses problem instances of Besikci et al. (2013) to evaluate the performance of the proposed approach. The results show the solutions of our approach not only able to compete with that of literature, and outperform the literature in computation times.
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Chien, Ming Tu, and 錢明淦. "A Genetic Algorithms for the Resource Constrained Project Scheduling Problem with Multi-Mode." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/14949490672869325225.

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碩士<br>元智大學<br>工業工程研究所<br>87<br>The purport of this article is to develop the heuristic algorithm, which is applicable in the multi-mode and the project scheduling problem of resource constrained, so as to shorten the construction time of the project as much as possible. The developed heuristic algorithm is based on genetic algorithms. We submitted two special encoding technologies to match with appropriate project scheduling so that the problem of illegal offspring could be avoided. Meanwhile, we introduced the idea of using the immigration operator and dynamic and adaptive strategies for varying the control parameters to strengthen the searching ability of algorithms. In addition, we also attempted several ways in crossover and mutation. Regarding setting of the parameters in algorithms, different parameters in this article was designed according to the Taguchi’s experiment in order to find out the most stable and suitable parameters set. In this article, Turbo C 2.0 was used to write programs. In order to compare with the effectiveness of algorithms designed by other international researchers, this program was tested by sample test in the PSPLIB. The result was perfect and superior to other researches’, such as average error, standard average error or the numbers of the best solution. In addition, we set up a website concerning the problem of case managing. Those who are interested in this project can down load the important references. This website also provides linkage to different sample tests. The address of this website is http://project.engineer.com.tw.
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Books on the topic "Resource and time constrained multi-project scheduling problem"

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Franck, Birger. Resource-constrained project scheduling problem with time windows: Structural questions and priority-rule methods. Universität Karlsruhe, 1998.

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Nudtasomboon, Nudtapon. Methodology for the multi-objective, resource-constrained project scheduling problem. 1993.

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A Decomposition Approach for the Multi-Modal, Resource-Constrained, Multi-Project Scheduling Problem with Generalized Precedence and Expediting Resources. Storming Media, 2001.

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Book chapters on the topic "Resource and time constrained multi-project scheduling problem"

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Jędrzejowicz, Piotr, and Aleksander Skakovski. "A Cross-Entropy Based Population Learning Algorithm for Multi-mode Resource-Constrained Project Scheduling Problem with Minimum and Maximum Time Lags." In Computational Collective Intelligence. Technologies and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16693-8_40.

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Saeidi, Arman, Kamran Rezaie, Alireza Nazari, and Amir Hossein Ordibazar. "Proposing a Pre-emptive Resource Constrained Project Scheduling Problem (PRCPSP) Model to Optimize Manpower and Project Delivery Time (A Case Study)." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62784-3_40.

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Coelho, José, and Mario Vanhoucke. "The Multi-Mode Resource-Constrained Project Scheduling Problem." In Handbook on Project Management and Scheduling Vol.1. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05443-8_22.

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Yaghoubi, Saeed, Siamak Noori, and Amir Azaron. "The Markovian Multi-Criteria Multi-Project Resource-Constrained Project Scheduling Problem." In Handbook on Project Management and Scheduling Vol. 2. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05915-0_8.

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Jedrzejowicz, Piotr, and Ewa Ratajczak-Ropel. "A-Team Solving Distributed Resource-Constrained Multi-project Scheduling Problem." In Computational Collective Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98446-9_23.

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Myszkowski, Paweł B., and Jȩdrzej J. Siemieński. "GRASP Applied to Multi–Skill Resource–Constrained Project Scheduling Problem." In Computational Collective Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45243-2_37.

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Zhou, Rong, Chun-ming Ye, and Hui-min Ma. "Model Research of Multi-Objective and Resource-Constrained Project Scheduling Problem." In The 19th International Conference on Industrial Engineering and Engineering Management. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38427-1_104.

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Pamay, M. Berke, Kerem Bülbül, and Gündüz Ulusoy. "Dynamic Resource Constrained Multi-Project Scheduling Problem with Weighted Earliness/Tardiness Costs." In International Series in Operations Research & Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-9056-2_10.

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Serafini, P., and M. G. Speranza. "A Multi-Stage Decomposition Approach for a Resource Constrained Project Scheduling Problem." In Methodology, Implementation and Applications of Decision Support Systems. Springer Vienna, 1991. http://dx.doi.org/10.1007/978-3-7091-2606-6_9.

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Stürck, Christian, and Patrick Gerhards. "Providing Lower Bounds for the Multi-Mode Resource-Constrained Project Scheduling Problem." In Operations Research Proceedings 2016. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55702-1_73.

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Conference papers on the topic "Resource and time constrained multi-project scheduling problem"

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Liu, Wenjian, and Jinghua Li. "Development of Hybrid Genetic Algorithms for the Resource Constrained Multi-Project Scheduling Problem." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85721.

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In multi-project environment, multiple projects share and compete for the limited resources to achieve their own goals. Besides resource constraints, there exist precedence constraints among activities within each project. This paper presents a hybrid genetic algorithm to solve the resource-constrained multi-project scheduling problem (RCMPSP), which is well known NP-hard problem. Objectives described in this paper are to minimize total project time of multiple projects. The chromosome representation of the problem is based on activity lists. The proposed algorithm was operated in two phases. In the first phase, the feasible schedules are constructed as the initialization of the algorithm by permutation based simulation and priority rules. In the second phase, this feasible schedule was optimized by genetic algorithm, thus a better approximate solution was obtained. Finally, after comparing several different algorithms, the validity of proposed algorithm is shown by a practical example.
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Cai, Zhicheng, and Xiaoping Li. "A hybrid genetic algorithm for resource-constrained multi-project scheduling problem with resource transfer time." In 2012 IEEE International Conference on Automation Science and Engineering (CASE 2012). IEEE, 2012. http://dx.doi.org/10.1109/coase.2012.6386457.

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Gnagi, M., T. Rihm, and N. Trautmann. "A Continuous-Time MILP Formulation for the Multi-Mode Resource-Constrained Project Scheduling Problem." In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2018. http://dx.doi.org/10.1109/ieem.2018.8607285.

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Ozturk, Guler, and Adalet Oner. "Continuous Time MILP Models for Multi-Mode Resource Constrained Project Scheduling Problems." In 2020 9th International Conference on Industrial Technology and Management (ICITM). IEEE, 2020. http://dx.doi.org/10.1109/icitm48982.2020.9080355.

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Zhang, Jing-wen, and Hui-fang Shan. "Multi-Mode Double Resource-Constrained Time/Cost Trade-Offs Project Scheduling Problems." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5302886.

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Gnagi, M., and N. Trautmann. "A Continuous-Time Mixed-Binary Linear Programming Formulation for the Multi-Site Resource-Constrained Project Scheduling Problem." In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2019. http://dx.doi.org/10.1109/ieem44572.2019.8978811.

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Cai, Junqi, and Zhihong Peng. "A Heuristic Algorithm for Solving Resource Constrained Project Scheduling Problem with Transfer Time under Resource Bundle." In 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8865803.

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Bofill, Miquel, Jordi Coll, Josep Suy, and Mateu Villaret. "Compact MDDs for Pseudo-Boolean Constraints with At-Most-One Relations in Resource-Constrained Scheduling Problems." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/78.

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Pseudo-Boolean (PB) constraints are usually encoded into Boolean clauses using compact Binary Decision Diagram (BDD) representations. Although these constraints appear in many problems, they are particularly useful for representing resource constraints in scheduling problems. Sometimes, the Boolean variables in the PB constraints have implicit at-most-one relations. In this work we introduce a way to take advantage of these implicit relations to obtain a compact Multi-Decision Diagram (MDD) representation for those PB constraints. We provide empirical evidence of the usefulness of this technique for some Resource-Constrained Project Scheduling Problem (RCPSP) variants, namely the Multi-Mode RCPSP (MRCPSP) and the RCPSP with Time-Dependent Resource Capacities and Requests (RCPSP/t). The size reduction of the representation of the PB constraints lets us decrease the number of Boolean variables in the encodings by one order of magnitude. We close/certify the optimum of many instances of these problems.
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Gnagi, M., A. Zimmermann, and N. Trautmann. "A Continuous-Time Unit-Based MILP Formulation for the Resource-Constrained Project Scheduling Problem." In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2018. http://dx.doi.org/10.1109/ieem.2018.8607337.

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Rihm, Tom, and Norbert Trautmann. "An assignment-based continuous-time MILP model for the resource-constrained project scheduling problem." In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2017. http://dx.doi.org/10.1109/ieem.2017.8289846.

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