A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

The Multimode Resource Constrained Project Scheduling Problem for Repetitive Activities in Construction Projects

[EN] In construction projects, resource availability might limit the implementation of ideal schedules. Especially, when repetitive activities are involved, traditional resource¿constrained project scheduling problem (RCPSP) models fail to allocate the resource consumption in an efficient manner. Besides, actual models only provide local optimal solutions and do not incorporate activity acceleration routines. To fulfill this gap, partially, a mathematical optimization model, the multimode RCPSP for repetitive activities in construction projects, is proposed and solved to optimality; it takes into account acceleration routines under real construction scenarios using spreadsheets. The article shows a complete computational experimentation over a real construction project, considering several scenarios of resource availabilities and continuity conditions. The model allows analyzing the resources efficiency indexes comparing them to resource consumptions, continuity of activities, and objective functions that reveal that fragmented activities do not provide better resource efficiency outcomes.This research was partially supported by the FAPA program of Universidad de Los Andes, Colombia (code P14.246922.005/01). The authors would also like to thank the research group of Construction Engineering and Management (INgeco) at Universidad de los Andes.García-Nieves, J.; Ponz-Tienda, JL.; Salcedo-Bernal, A.; Pellicer Armiñana, E. (2018). The Multimode Resource Constrained Project Scheduling Problem for Repetitive Activities in Construction Projects. Computer-Aided Civil and Infrastructure Engineering. 33(8):655-671. https://doi.org/10.1111/mice.12356S65567133

Balancing labor requirements in a manufacturing environment

“This research examines construction environments within manufacturing facilities, specifically semiconductor manufacturing facilities, and develops a new optimization method that is scalable for large construction projects with multiple execution modes and resource constraints. The model is developed to represent real-world conditions in which project activities do not have a fixed, prespecified duration but rather a total amount of work that is directly impacted by the level of resources assigned. To expand on the concept of resource driven project durations, this research aims to mimic manufacturing construction environments by allowing a non-continuous resource allocation to project tasks. This concept allows for resources to shift between projects in order to achieve the optimal result for the project manager. Our model generates a novel multi-objective resource constrained project scheduling problem. Specifically, two objectives are studied; the minimization of the total direct labor cost and the minimization of the resource leveling. This research will utilize multiple techniques to achieve resource leveling and discuss the advantage each one provides to the project team, as well as a comparison of the Pareto Fronts between the given resource leveling and cost minimization objective functions. Finally, a heuristic is developed utilizing partial linear relaxation to scale the optimization model for large scale projects. The computation results from multiple randomly generated case studies show that the new heuristic method is capable of generating high quality solutions at significantly less computational time”--Abstract, page iv

An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

Généralisations du problème d'ordonnancement de projet à ressources limitées

Un problème d'ordonnancement de projet à ressources limitées (POPRL) consiste en l'ordonnancement d'un ensemble de tâches, nécessitant un ou plusieurs types de ressources, renouvelables ou non renouvelables, en quantités limitées. La résolution d'un POPRL a pour but la détermination des dates d'exécution des tâches en tenant compte des contraintes de préséance et de disponibilité des ressources et ayant comme objectif la minimisation de la durée totale du projet. Le POPRL est un problème d'optimisation combinatoire de complexité NP-dur (Blazewicz et al. 1983). Une revue de littérature du (POPRL) est présentée au chapitre 2. Plus de 125 articles scientifiques sont analysés. Les contributions relatives à ce problème portent sur les méthodes exactes de résolution, la détermination de bornes inférieures sur la durée du projet et les méthodes heuristiques (approchées) de résolution. L'aspect pratique de ce problème dans des contextes industriels divers a conduit à de nombreuses généralisations du problème classique. On constate que malgré les efforts déployés pour définir des POPRL plus généraux, les contraintes de transfert des ressources continuent à être ignorées, nous constatons aussi que l'optimisation du problème en considérant les coûts a été très peu traitée dans la littérature. Ce qui forcent les gestionnaires dans la plus part des cas à se baser uniquement sur leur expérience pour réaliser ou ajuster manuellement les ordonnancements produits par des heuristiques conçues pour résoudre des versions simplifiées du problème. Cette thèse tente de combler partiellement ces lacunes. Le chapitre 3 traite le problème d'ordonnancement de projet à ressources limitées POPRLTT avec des temps de transfert des ressources. Un temps de transfert est le temps nécessaire pour transférer une ressource du lieu d'execution d'une activité vers un autre. Ainsi, le temps de transfert d'une ressource dépend des lieux des activités à exécuter, ainsi que des caractéristiques des ressources à transférer. L'objectif dans un POPRLTT est la détermination des dates d'exécution des tâches en tenant compte des contraintes de préséance et de disponibilité des ressources et les temps de transfert des ressources. L'objectif est de minimiser la durée totale du projet. Nous proposons un nouvel algorithme génétique basé sur un opérateur de croisement de deux positions. L'étude expérimentale menée sur un grand nombre de problèmes test prouve que l'algorithme proposé est meilleur que les deux méthodes déjà existantes dans la littérature. Une généralisation du problème d'ordonnancement de projet à ressources limitées et des temps de transfert des ressources au contexte multi mode (POPRL=PMETT) est présentée au chapitre 4. Dans ce problème, nous supposons que la préemption est non autorisée, et les ressources utilisées sont renouvelables et non renouvelables, chaque activité a plusieurs modes d'exécution, et les relations de préséance sont de type dit début-fin sans décalage. L'objectif est de choisir un temps de début (ou de fin) et un mode d'exécution pour chaque tâche du projet, pour que la durée du projet soit minimisée tout en respectant les contraintes de préséance, de disponibilité de ressources et les temps de transfert. Au meilleur de notre connaissance, cette version du problème n'a jamais été abordée auparavant. Nous proposons une formulation mathématique de ce problème, ensuite nous présentons un algorithme génétique, que nous avons conçu pour résoudre les instances de grandes tailles. Pour tester les méthodes proposées nous développons des nouveaux ensembles de problèmes-tests pour le POPRL=PMETT, qui pourront être utilisés dans l'avenir pour mener des recherches dans ce domaine. Dans le chapitre 5, nous définissons une nouvelle généralisation du problème d'ordonnancement de projet à ressources limitées en considérant l'objectif de minimiser le coût total d'exécution du projet. Celui-ci est composé de deux éléments principaux: le coût direct des ressources à utiliser et les frais généraux qui ne dépendent pas de la quantité de ressources allouées, mais qui sont proportionnels à la durée du projet. Ce problème, que nous appelons Problème général d'allocation et de nivellement des ressources d'un projet (PGANRP) est très commun en pratique, mais très peu de recherche est consacrée à ce problème. Dans un PGANRP, nous devons simultanément déterminer les quantités des ressources à allouer au projet au cours de son exécution et réduire la variabilité de l'utilisation des ressources au minimum tout en essayant de terminer le projet à une date de fin acceptable. Les quantités des ressources à allouer au projet devraient permettre l'accomplissement du projet à cette date et devient une limite sur la disponibilité de ces ressources durant toute l'exécution du projet. Nous proposons, une formulation mathématique du problème et deux approches de recherche dans le voisinage pour les instances de grandes tailles.The resource-constrained project scheduling problem (RCPSP) consists of scheduling a set of activities or tasks using one or more resource types available in limited quantity. In the standard version of this problem, pre-emption is not allowed, precedence relations are of the no-lag, finish-to-start type, and the used resources are renewable meaning that the same resources quantity are available each time period. Solving this NP-hard optimization problem requires the determination of tasks execution date such that the project duration is minimized without using more than the available resource quantities. In the first chapter of this thesis, the research problem and research objectives are presented while chapter 2 reviews the literature and contributions to the RCPSP and some of its extended versions. More than 125 published papers are reviewed. These contributions are divided into 4 groups of contributions. Those proposing optimal solution methods, those developing lower bounds on the project duration, those proposing heuristic and approximate solution methods, and those extending the standard version of the problem in order to make it closer to the real-life problem. This literature review revealed that very few contributions explicitly take into consideration the time required to transfer resources between execution sites of the project. Only three such contributions are published and none of these three publication deal with the case where tasks have more than one execution mode. This review also revealed that the large majority of the published research deals with the problem where the objective is to minimize the duration of the project. However, in almost all real-life situations, the objective is to minimise the total cost of the project. That is why this thesis is dedicated to solve these neglected extensions of the RCPSP. Chapter 3 deals with the resource-constrained project scheduling problem with transfer times (RCPSPTT). Thus the goal in this case is to determine execution dates that allows for resources to be transferred between execution sites while respecting the precedence relations between these tasks as well as resources availability. A new genetic algorithm (GA) is developed to solve the RCPSPTT. This algorithm uses a new and efficient crossover operator. The chapter also study the performance of the proposed genetic algorithm and shows that it produces better results than the two previously published solution heuristics. It is to notice that the proposed GA considers renewable resource types and assume that tasks have only one execution mode. Chapter 4 deals with the multi-mode resource-constrained project scheduling problem with transfer times (MRCPSPTT). Thus, it extends the problem studied in the previous chapter to the multi-mode case under the assumptions of no pre-emption while using renewable and non-renewable resources. This problem has never been the subject of any published research before. An integer linear mathematical formulation of the problem is given as well as new genetic algorithm is developed to solve it. An extensive empirical analysis is then presented and shows that the proposed GA is able to produce the optimal solution for 529 test instances with 10, 20 and 30 activities. Chapter 5 introduces the generalized resource allocation and leveling problem (GRALP). This problem can be stated as follows. Given a set of project tasks to execute, their possible execution modes and precedence relations, an upper bound on the amount of resources that can be made available to the project, a project due date, the cost of resource utilization and the overhead cost; determine the execution date and mode for each task and the amount of resources to allocate to the project. The objective is to minimize the total project execution cost while respecting precedence constraints, project due date and not using more than the amount of resources that we decided to allocate to the project. Again we notice that this problem has never been the subject of any published research work. Chapter 5 presents an integer linear formulation of the problem, a neighborhood search solution heuristic, a genetic algorithm to solve it and an empirical experiment to evaluate the proposed heuristics showing the superiority of the proposed GA. Finally, the conclusions of the thesis and some propositions for future research are given

A Parallel Branch and Bound Algorithm for the Resource Leveling Problem with Minimal Lags

[EN] The efficient use of resources is a key factor to minimize the cost while meeting time deadlines and quality requirements; this is especially important in construction projects where field operations take fluctuations of resources unproductive and costly. Resource Leveling Problems (RLP) aim to sequence the construction activities that maximize the resource consumption efficiency over time, minimizing the variability. Exact algorithms for the RLP have been proposed throughout the years to offer optimal solutions; however, these problems require a vast computational capability ( combinatorial explosion ) that makes them unpractical. Therefore, alternative heuristic and metaheuristic algorithms have been suggested in the literature to find local optimal solutions, using different libraries to benchmark optimal values; for example, the Project Scheduling Problem LIBrary for minimal lags is still open to be solved to optimality for RLP. 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Rojas-Quintero, and the Department of Systems Engineering at the Universidad de Los Andes. The authors are also grateful to the anonymous reviewers for their valuable and constructive suggestions.Ponz Tienda, JL.; Salcedo-Bernal, A.; Pellicer Armiñana, E. (2017). A Parallel Branch and Bound Algorithm for the Resource Leveling Problem with Minimal Lags. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING. 32:474-498. doi:10.1111/mice.12233S47449832Adeli, H. (2000). High-Performance Computing for Large-Scale Analysis, Optimization, and Control. Journal of Aerospace Engineering, 13(1), 1-10. doi:10.1061/(asce)0893-1321(2000)13:1(1)ADELI, H., & KAMAL, O. (2008). Parallel Structural Analysis Using Threads. Computer-Aided Civil and Infrastructure Engineering, 4(2), 133-147. doi:10.1111/j.1467-8667.1989.tb00015.xAdeli, H., & Kamal, O. (1992). Concurrent analysis of large structures—II. applications. Computers & Structures, 42(3), 425-432. doi:10.1016/0045-7949(92)90038-2Adeli, H., Kamat, M. 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Adaptive Control of Resource Flow to Optimize Construction Work and Cash Flow via Online Deep Reinforcement Learning

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a deep reinforcement learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperforms the vanilla empirical method and genetic algorithm, possesses remarkable capability in diverse projects and external environments, and a hybrid agent of DRL and empirical method leads to the best result. This paper contributes to adaptive control and optimization of coupled work, resource, and cash flows, and may serve as a step stone for adopting DRL technology in construction project management

A heuristic procedure to solve the project staffing problem with discrete time/resource trade-offs and personnel scheduling constraints

Highlights • Project staffing with discrete time/resource trade-offs and calendar constraints. • An iterated local search procedure is proposed. • Different problem decomposition techniques are applied. Abstract When scheduling projects under resource constraints, assumptions are typically made with respect to the resource availability and activities are planned each with its own duration and resource requirements. In resource scheduling, important assumptions are made with respect to the staffing requirements. Both problems are typically solved in a sequential manner leading to a suboptimal outcome. We integrate these two interrelated scheduling problems to determine the optimal personnel budget that minimises the overall cost. Integrating these problems increases the scheduling flexibility, which improves the overall performance. In addition, we consider some resource demand flexibility in this research as an activity can be performed in multiple modes. In this paper, we present an iterated local search procedure for the integrated multi-mode project scheduling and personnel staffing problem. Detailed computational experiments are presented to evaluate different decomposition heuristics and comparison is made with alternative optimisation techniques

Use of genetic algorithms in multi-objective multi-project resource constrained project scheduling

Resource Constrained Project Scheduling Problem (RCPSP) has been studied extensively by researchers by considering limited renewable and non-renewable resources. Several exact and heuristic methods have been proposed. Some important extensions of RCPSP such as multi-mode RCPSP, multi-objective RCPSP and multi-project RCPSP have also been focused. In this study, we consider multi-project and multi-objective resource constrained project scheduling problem. As a solution method, non-dominated sorting genetic algorithm is adopted. By experimenting with different crossover and parent selection mechanisms, a detailed fine-tuning process is conducted, in which response surface optimization method is employed. In order to improve the solution quality, backward-forward pass procedure is proposed as both post-processing as well as for new population generation. Additionally, different divergence applications are proposed and one of them, which is based on entropy measure is studied in depth. The performance of the algorithm and CPU times are reported. In addition, a new method for generating multi-project test instances is proposed and the performance of the algorithm is evaluated through test instances generated through this method of data generation. The results show that backward-forward pass procedure is successful to improve the solution quality