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In the resource levelling problem (RLP) under uncertainty, existing studies focus on obtaining an open-loop activity list that is not updated during project execution. In project management practice, it is also necessary to address more situations, such as activity overlaps and resource breakdowns. In this paper, we extend the uncertain RLP by proposing a resource levelling problem with multiple uncertainties (RLP-MU) that simultaneously considers uncertainties in activity durations, activity overlaps and resource availabilities. We formulate the RLP-MU as a Markov decision process model. Aimed at levelling resource usage by dynamically scheduling activities at each decision point based on the observed information, we develop a hybrid open–closed-loop approximate dynamic programming algorithm (HOC-ADP). In the HOC-ADP, we devise a closed-loop rollout policy to approximate the cost-to-go function and use the concept of the average project to avoid time-consuming simulation. A greedy-decoding-based estimation of distributed algorithm is also devised to construct an open-loop policy that is embedded in the HOC-ADP to further improve it. We additionally develop a simulation algorithm to evaluate the resource levelling performance of the HOC-ADP. Computational experiments on a benchmark dataset consisting of 540 problem instances are conducted to analyze the performance of the HOC-ADP, and the impact of various factors on resource levelling are investigated. The comparison experimental results indicate that our HOC-ADP outperforms the state-of-the-art meta-heuristics. [ABSTRACT FROM AUTHOR]

Project managers often face some challenges resulting from the scarcity of resources in construction management. Levelling the used resources in multiple projects is a frequently encountered problem in construction areas and manufacturing sectors. In this regard, the current study proposes a robust Forensic-Based Investigation (FBI) algorithm for resource leveling in multiple projects, considering diferent objective functions of the resource graphs. To this end, Fuzzy C-Means (FCM) clustering approach was fused into the main operation of the FBI to enhance the convergence rate using the population information. The proposed scheduling examines diferent objective functions to effciently optimize the resource Profile selection. Two case studies were taken into account in this research to elaborate on the performance of the improved optimization algorithm while dealing with the resource-leveling problem in multiple projects. The experimental findings and statistical comparisons revealed that the developed Fuzzy clustering Forensic-Based Investigation (FFBI) could acquire solutions of high quality and outperform the compared optimization algorithms [ABSTRACT FROM AUTHOR]

Despite the significance of resource levelling, project managers lack various ways to smooth resource usage fluctuation of a repetitive construction project besides changing resource usage. Tolerating interruptions is an effective way to provide flexibility for a schedule but is ignored when solving resource levelling problems. Therefore, this paper investigates the impacts of interruptions on resource usage fluctuation and develops an integrated approach that simultaneously integrates two scheduling adjusting processes: changing resource usage and tolerating interruptions. In this paper, two interruption conditions are proposed to identify which activities are suitable to be interrupted for smoothing resource usage fluctuation. The traditional resource levelling model is modified to a new scheduling model by incorporating interruptions. A two-stage GA-based scheduling algorithm is developed by integrating changing resource usage and tolerating interruptions. A commonly used pipeline project is adopted to illustrate the steps of the proposed approach and demonstrate its effectiveness and superiority through comparison with previous studies. A large-scale project further verifies the usability of the proposed approach. The results confirmed the feasibility to smooth resource usage fluctuation by interruptions, and the integrated approach can achieve a more competitive resource levelling result. [ABSTRACT FROM AUTHOR]

Resource levelling aims to obtain a feasible schedule to minimise the resource usage fluctuations during project execution. It is of crucial importance in project scheduling to ensure the effective use of scarce and expensive renewable resources, and has been successfully applied to production environments, such as make-to-order and engineering-to-order systems. In real-life projects, general temporal relationships are often needed to model complex time-dependencies among activities. We develop a novel genetic algorithm (GA) for the resource levelling problem with generalised precedence relations. Our design and implementation of GA features an efficient schedule generation scheme, built upon a new encoding mechanism that combines the random key representation and the shift vector representation. A two-pass local search-based improvement procedure is devised and integrated into the GA to enhance the algorithmic performance. Our GA is able to obtain near optimal solutions with less than 2% optimality gap for small instances in fractions of a second. It outperforms or is competitive with the state-of-the-art algorithms for large benchmark instances with size up to 1000 activities. [ABSTRACT FROM AUTHOR]

Resources play vital role in construction projects. The performance of construction industry depends chiefly on how best the resources are managed. Optimisation play pivotal role in resource management, but task is highly haphazard and chaotic under the influence of complexities and vastness. Management always looks for optimum utility of resources available with them. Hence, the project management has got important place especially in resource allocation and smooth functioning with allocated budget. To achieve these goals and to exercise enhance optimisation certain tools are used for resource allocation optimally. Present work illustrates resource optimisation exercises on two ongoing projects in Dubai, UAE. Resource demands of project A & B are individually levelled and observed cumulative requirement is 17475. In other option demands of projects A & B are aggregated and then together levelled, the necessary resource observed is 16490. Comparison of individually levelled and then combined option with aggregated and then levelled clearly indicates reduction in demand of resources by 5.65% in later option, which could be best considered for economy.

Project scheduling is an important part of project planning in many management companies. Resource lev­eling problem describes the process of reducing the fluctuations in resource usage over the project duration. The goal of resource leveling is to minimize the incremental demands that cause fluctuations of resources, and thus avoid unde­sirable cyclic hiring and firing during project execution. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource leveling (FCDE-RL), is introduced. Fuzzy Cluster­ing Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the optimization algorithm from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-par­ent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental results revealed that the new optimization model is a promising alternative to assist project managers in dealing with construction project resource leveling. First published online: 13 Jul 2016

Project scheduling is an important part of project planning in many management companies. Resource leveling problem describes the process of reducing the fluctuations in resource usage over the project duration. The goal of resource leveling is to minimize the incremental demands that cause fluctuations of resources, and thus avoid undesirable cyclic hiring and firing during project execution. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource leveling (FCDE-RL), is introduced. Fuzzy Clustering Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the optimization algorithm from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-parent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental results revealed that the new optimization model is a promising alternative to assist project managers in dealing with construction project resource leveling. [ABSTRACT FROM AUTHOR]

This paper proposes an evolutionary computation based approach for solving resource leveling optimization problems in project management. In modern management engineering, problems of this kind refer to the optimal handling of available resources in a candidate project and have emerged, as the result of the even increasing needs of project managers in facing project complexity, controlling related budgeting and finances and managing the construction production line. Standard approaches, such as exhaustive or greedy search methodologies, fail to provide near-optimum solutions in feasible time even for small scale problems, whereas intelligent approaches manage to quickly reach high quality near-optimal solutions. In this work, a new genetic algorithm is proposed which investigates the start time of the non-critical activities of a project, in order to optimally allocate its resources. The innovation of the proposed approach is related to certain genetic operations applied like crossover for the improvement of the solution quality from generation to generation. The presentation and performance comparison of all multi-objective functions for resource leveling that are available in literature is another interesting part of this work. Detailed experiments with small and medium size benchmark problems taken from publicly available project data resources produce highly accurate resource profiles. As shown in the experimental results, the proposed methodology proves capable of coping even with large size project management problems without the need to divide the original problem to sub-problems due to complexity. [ABSTRACT FROM AUTHOR]

We study the problem of levelling resources in a project with generalized precedence relationships, given a deadline for the completion of all the activities and variable execution intensities and flexible durations of the activities. Variable execution intensities have been taken into account firstly by Kis (Math Program 103(3):515-539, ) applied to a real world scenario in which, due to the physical characteristics of some manufacturing processes, the effort associated with a certain activity for its execution may vary over time. Generalized precedence relationships and variable intensity execution and duration have not been dealt with together to the best of our knowledge. For this novel problem we propose a mixed-integer linear programming formulation, a lower bound based on Lagrangian relaxation, and a branch and bound algorithm. Computational results on known benchmarks are provided. [ABSTRACT FROM AUTHOR]

A review of the recent literature on the models that focus on resource leveling in Critical Path Method networks shows that different objective functions have been used to optimize resource utilization. The main objective of this study is to investigate the impacts of using different objective functions on resource utilization histograms in Critical Path Method networks. For this purpose, nine different resource leveling objective functions were optimized via a genetic algorithm-based model. The model was developed using actual data obtained from a steel framed industrial building construction project. It was found that each of these objective functions generates different resource utilization histograms. In order to determine the improvement levels achieved by resource leveling using nine different objective functions, the improvement percentage in each parameter and the average improvement percentage for each objective function were calculated. Even though the objective function that involves the minimization of the sum of the square of the deviations in daily resource usage provided the best average improvement percentage in the studied case, another objective function(s) may provide better average improvement percentage in different projects. The contractor should consider all objective functions for resource leveling and select the one(s) that provides the best average improvement percentage.

The resource levelling problem (RLP) arises in project scheduling where the intention is to reduce the fluctuation of the resource demand. The solutions of the RLP are achieved through the rearrangement of the project activities under a constraint of preselected precedence relationships. To relieve the constraint, a new concept of the RLP with relationship options is initiated. An activity of the project network can have one or more alternative types of relationship with other activities as appropriate. An alternative relationship provides more float time and allows new possibilities for the arrangement of efficient patterns of the project resource demand. The scheduling problem model was formulated by using mathematical equations on spreadsheet software and solved by using the genetic algorithm based optimization. The prototype was tested in two different project instances. The test results demonstrated that this new model could calculate and arrange the project schedules for all selected alternative types of relationships. The model with relationship options provided consistent results for efficient resource utilization schedules, which were better than the one without relationship options. These schedules could decrease the resource demand fluctuation and the maximum resource demand level. This new model of the RLP with relationship options not only provides the additional flexibility to level the resource demands but also determines suitable types of relationships for the project activities. [ABSTRACT FROM PUBLISHER]

The paper deals with a class of problems often met in modern project management under the term 'resource leveling optimization problems'. The problems of this kind refer to the optimal allocation of available resources in a candidate project and have emerged, as the result of the even increasing needs of project managers in facing project complexity, controlling related budgeting and finances and managing the construction production line. For the effective resolution of resource leveling optimization problems, the use of nature inspired intelligent methodologies is proposed. Traditional approaches, such as exhaustive or greedy search methodologies, often fail to provide near-optimum solutions in a short amount of time, whereas the proposed intelligent approaches manage to timely achieve high quality near-optimal solutions. In the paper, extensive experimental results are presented, based on available data collections existing in literature for a number of known benchmark project management problems. The comparative analysis of three different intelligent metaheuristics, shows that a hybrid nature inspired intelligent approach, combining ant colony optimization and genetic algorithms, proves to be the most effective approach in the majority of benchmark problems and special decision making settings tested. [ABSTRACT FROM AUTHOR]

A review of the recent literature on the models that focus on resource leveling in Critical Path Method networks shows that different objective functions have been used to optimize resource utilization. The main objective of this study is to investigate the impacts of using different objective functions on resource utilization histograms in Critical Path Method networks. For this purpose, nine different resource leveling objective functions were optimized via a genetic algorithm-based model. The model was developed using actual data obtained from a steel framed industrial building construction project. It was found that each of these objective functions generates different resource utilization histograms. In order to determine the improvement levels achieved by resource leveling using nine different objective functions, the improvement percentage in each parameter and the average improvement percentage for each objective function were calculated. Even though the objective function that involves the minimization of the sum of the square of the deviations in daily resource usage provided the best average improvement percentage in the studied case, another objective function(s) may provide better average improvement percentage in different projects. The contractor should consider all objective functions for resource leveling and select the one(s) that provides the best average improvement percentage. [ABSTRACT FROM AUTHOR]

We present a project scheduling problem subject to general temporal constraints where the utilization of a set of renewable resources has to be smoothed over time. For solving the NP-hard optimization problem, we point out some important structural properties and introduce a new enumeration scheme. Combining this enumeration scheme with some branch-and-bound techniques, we propose an appropriate solution procedure for the project scheduling problem at hand. To outline the practical importance of resource levelling, we sketch exemplary the optimization of mid-term planning schedules with regard to the resource requirements of IT projects. Finally, we present results from a comprehensive computational study on problem instances of the well-known rlp_ j10 and rlp_ j20 test sets devised by Kolisch et al. (Benchmark Instances for Project Scheduling Problems, Kluwer, Boston, ). [ABSTRACT FROM AUTHOR]

Resource levelling techniques aim to minimize the fluctuation from one time period to another in resource usage. Except for small-sized problems, though, computational optimization procedures are inefficient when solving construction resource levelling problems. Consequently, heuristic and metaheuristic approaches are used to get an acceptable, but not necessarily optimal, solution. A simulated annealing hyperheuristic to generate better-levelled resource profiles is proposed. Unlike traditional heuristic algorithms, a hyperheuristic operates in the 'low level' heuristics domain rather than in the solutions domain. A low level heuristic, on the other hand, works in the current solution neighbourhood. The algorithm has been programmed within a commercial project management software system to improve its performance. The low level heuristics operate on the priority levels that the software uses for resource levelling. An illustrative example and the computational analysis demonstrate the potential of the procedure in solving complex scheduling problems. [ABSTRACT FROM AUTHOR]

In some urban transportation companies driving periods are short when compared with the total duty time, leading to long non-driving periods that can be used as cover time. This paper presents the Crew Timetabling Problem, an extension of the Crew Scheduling Problem in which crew timetables are obtained by levelling the cover crew resources. An objective function for this problem is proposed in order to balance the number of driving and cover crews. A Lisbon Underground case study is used to illustrate the Crew Timetabling Problem. The problem is represented in a multigraph and solved by a tabu search-based heuristic. [ABSTRACT FROM AUTHOR]

In the field of resource constrained scheduling, the papers in the literature are mainly focused on minimizing the maximum completion time of a set of tasks to he carried out, paying attention to respecting the maximum simultaneous availability of each resource type in the system. This work, instead, considers the issues of balancing the resource usage and minimizing the peak of the resources allocated each time in the schedule, while keeping the makespan low. To this aim we propose a local search algorithm which acts as a multi start greedy heuristic. Extensive experiments on various randomly generated test instances are provided. Furthermore, we present a comparison with lower bounds and known heuristics. [ABSTRACT FROM AUTHOR]