Commercial Project Management Software Research Articles

An Empirical Analysis of a Set of Hybrid Heuristics for the Solution of the Resource Leveling Problem

Consideration is given to the heuristic solution of the resource leveling problem (RLP) in project scheduling with limited resources. The objective is to minimize the changes in the level of resource usage from period to period over the planning horizon of the project while keeping the project duration fixed. First, we present two novel greedy schedule algorithms for the RLP solution. The performance of the proposed algorithms is investigated as low-level hybrids in the context of three famous population-based heuristics, namely differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). Then, we additionally present two new high-level hybridization schemes (HS), referred to herein as parallel and serial HS, respectively, which combine DE, GA, and PSO in a single hybrid solution algorithm. Detailed experimentation over known complex datasets measures the efficiency of the new hybrids. Statistical analysis employed rank the hybrids according to their solution efficiency. Moreover, comparisons between the developed best hybrid and commercial project management software show a substantial higher performance for the former over real-world construction projects.

A Memetic Algorithm for the Solution of the Resource Leveling Problem

In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the proposed algorithm is to integrate complementary strengths of different optimization methods and incorporate the individual learning as a separate process for achieving a successful optimization method for the RLP. The performance of the MA is compared with the state-of-the-art leveling methods. For small instances up to 30 activities, mixed-integer linear models are presented for two leveling metrics to provide a basis for performance evaluation. The computational results indicate that the new integrated framework of the MA outperforms the state-of-the-art leveling heuristics and meta-heuristics and provides a successful method for the RLP. The limitations of popular commercial project management software are also illustrated along with the improvements achieved by the MA to reveal potential contributions of the proposed integrated framework in practice.

Genetic algorithm based optimization for resource leveling problem with precedence constrained scheduling

Resource Leveling Problem (RLP) in construction projects mainly deals with reducing the peaks and valleys of daily resource requirements. RLP is an interesting field of research and several researchers have tried to solve it over the past few decades. Numerical methods and heuristics do not provide good results for solving Non-deterministic polynomial-hard (NP-hard) problems. So that, a metaheuristic approach is highly required to predict the solution with greater efficiency. This research work aims to solve RLP using Genetic Algorithm as the meta-heuristic approach, developed in a programming platform. Modified critical path scheduling method, was adopted to develop a schedule that determines the partial critical nature of activity with a start to start precedence relationship. Activities from a real-time construction project were considered to evaluate the proposed idea. The result obtained was significantly better than the results provided by existing commercial project management software and helps to solve RLP with the advantage of considering real-time floats.

The preemptive stochastic resource-constrained project scheduling problem

Preemption (or the splitting of activities) is a common practice in many project environments, and has been a standard feature of commercial project management software packages for years. Despite its prevalence in daily practice, preemption has received little attention in the project scheduling literature. A possible explanation for this lack of research interest is the common assumption that preemption only has a limited impact on the optimal makespan of a project. In this article, however, we show that the benefit of preemption can be significant, and that it increases with the size and the complexity of the project network. In addition, we also investigate how activity duration variability impacts the benefits of preemption. To this end, we study the preemptive stochastic resource-constrained project scheduling problem (PSRCPSP), and present an exact solution procedure. Even though the deterministic preemptive resource-constrained project scheduling problem (PRCPSP) has received some attention in the literature, we are the first to study the PSRCPSP. We use hypoexponential distributions to model the activity durations, and define a new continuous-time Markov chain (CTMC) that drastically reduces memory requirements when compared to the well-known CTMC of Kulkarni and Adlakha (1986) (Operations Research, 34(5), 769–781). In addition, we also propose a new and efficient approach to structure the state space of the CTMC.

Simultaneous multi-criteria optimization for scheduling linear infrastructure projects

This paper presents a simultaneous multi-criteria optimization approach for scheduling linear infrastructure projects. The proposed model provides planners with sets of non-dominated alternatives and their corresponding tradeoffs. The associated research methodology includes: (1) developing a resource driven scheduling module; (2) applying a multi-criteria optimization technique to optimize the multi-objective scheduling problem; (3) integrating the proposed model with a commercial project management software; and (4) applying the developed model on two literature-drawn case studies. The developed multi-criteria optimization approach utilizes Genetic Algorithms and Pareto Front sorting. The resulting sets of schedules are based on the multiple inter-conflicting objectives of simultaneously minimizing project duration, minimizing cost, minimizing interruptions, and minimizing unit delivery delays. The results indicate that the proposed approach can explore a greater range of solutions compared to existing models. The developed multi-criteria optimization approach can aid planners with proposing optimal set of schedules.

A review of analytical models, approaches and decision support tools in project monitoring and control

This paper reviews the problems, approaches and analytical models on project control systems and discusses the possible research extensions. We focused on literature in Earned Value Analysis (EVA), optimization tools, and the design of decision support systems (DSS) that will contribute to helping project managers in planning and controlling under uncertain project environments. The review reveals that further research is essential to develop analytical models using EVA metrics to forecast project performance. It also suggests that DSS should be model driven, function as early warning systems and should be integrated to commercial project management software.

Hybrid Genetic Algorithm with Simulated Annealing for Resource-Constrained Project Scheduling

AbstractResource-constrained project scheduling problem (RCPSP) is a very important optimization problem in construction project management. Despite the importance of the RCPSP in project scheduling and management, commercial project management software provides very limited capabilities for the RCPSP. In this paper, a hybrid strategy based on genetic algorithms, and simulated annealing is presented for the RCPSP. The strategy aims to integrate parallel search ability of genetic algorithms with fine tuning capabilities of the simulated annealing technique to achieve an efficient algorithm for the RCPSP. The proposed strategy was tested using benchmark test problems and best solutions of the state-of-the-art algorithms. A sole genetic algorithm, and seven heuristics of project management software were also included in the computational experiments. Computational results show that the proposed hybrid strategy improves convergence of sole genetic algorithm and provides a competitive alternative for the RCPSP...

A new tabu search-based hyper-heuristic algorithm for solving construction leveling problems with limited resource availabilities

We present a tabu search-based hyper-heuristic algorithm for solving construction resource leveling problems, i.e. resource leveling under resource constraints with the prescribed maximum project duration to be equal or greater than the initial/minimum duration, as well as the related resource availability cost problem. The algorithm operates within a commercial project management software package by altering the priorities assigned to activities. The hyper-heuristic controls a set of low-level heuristics, which modify the priorities of selected activities by performing simple moves such as “replace” and “swap”. The most promising heuristics according to their efficiency are applied first, and a tabu list is used to prohibit heuristics with recently poor performance from being applied too soon. The application of the algorithm in three project cases showed that the proposed procedure is promising for handling resource optimization problems. ► Hyperheuristics recently introduced to treat construction project scheduling problems. ► THH is the first tabu hyperheuristic applied to construction scheduling problems. ► THH is a flexible procedure that easily can be applied to different problems. ► Application of the algorithm proved it as promising for treating leveling problems.

Construction Resource Allocation and Leveling Using a Threshold Accepting–Based Hyperheuristic Algorithm

AbstractIn this study we propose a threshold accepting based hyperheuristic for solving in a single run both the resource-constrained project scheduling problem or resource allocation, and the resource leveling problem. Having their roots in the field of artificial intelligence, hyperheuristics operate in the “low-level” heuristics domain rather than in the solutions domain. The hyperheuristic has been implemented within a commercial project management software package. Low-level heuristics operate on the solution domain defined by the priority values that the software uses for resource allocation. A case example from the literature and computational experiments on randomly generated projects demonstrate that the hyperheuristic achieves good performance in a timely manner, improving the results provided by the software.

Resource-Constrained Critical Path Scheduling by a GRASP-Based Hyperheuristic

The greedy randomized adaptive search procedure (GRASP) is a well-known computational procedure for handling combinatorial optimization problems. Hyperheuristics, or heuristics to choose heuristics, are higher-level algorithms that operate on low-level heuristics, which in turn work in the solution domain. In this paper, a GRASP-inspired hyperheuristic is proposed for solving the resource-constrained project scheduling problem (RCPSP), one of the most important challenges facing construction project schedulers. The hyperheuristic is the main component of a multilevel procedure that runs within a commercial project management software system. On the basis of simple moves such as “swap” and “replace,” the low-level heuristics operate on the priority values that the software uses for scheduling activities. The efficiency of this prioritization-based algorithm has been addressed through a series of computational experiments on randomly generated projects. Moreover, the proposed procedure shows the advantages of utilizing the hyperheuristic approach to deal with optimization problems in the construction field.

A simulated annealing hyperheuristic for construction resource levelling

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.

Improved genetic algorithm for resource-constrained scheduling of large projects

The generalized model of the resource-constrained project scheduling problem (RCPSP) is valuable because it can be incorporated into the advanced computational methods of commercial project management software for practical applications. A construction schedule generated by most commercial project management programs does not guarantee its optimality when the resources are limited. This paper presents an improved elitist genetic algorithm (GA) for resource-constrained scheduling of large projects. The proposed algorithm allocates multiple renewable resources to activities of a single large-sized project to achieve the objective of minimizing the project duration. A permutation-based decoding procedure is developed using the improved parallel schedule generation scheme. A new parameter, named transformation power, is created in the transformation method of the improved algorithm to ensure that the individual selection process performs correctly. Extensive computational results using a standard set of large-sized multiple resource-constrained project scheduling problems are presented to demonstrate the performance and accuracy of the algorithm.

Towards a conceptual reference model for project management information systems

Project management information systems have changed considerably over the last decade. They no longer focus on scheduling and resource management alone. Instead, they have become comprehensive systems that support the entire life-cycle of projects, project programs, and project portfolios. In this context, project-oriented organizations are facing a new challenge: the design, implementation, and operation of project management information systems have become increasingly complex. Numerous processes have to be considered, diverse stakeholder interests taken into account, and corresponding software systems selected. The reference information model (RefMod PM) presented in this article addresses this challenge and aims to accelerate the set-up of project information systems. RefMod PM was developed with the help of 13 domain experts from German and Swiss enterprises. Furthermore, it is based on an analysis of 28 commercial project management software systems. RefMod PM has already been applied in several projects and is the basis of the forthcoming German DIN norm for a standardized project management data model.

Teaching project management using Excel

In this paper, we present an application of Excel spreadsheets in teaching the traditional Program Evaluation and Review Technique/Critical Path Method. This spreadsheet approach can be used in class with little cost in time or effort because it eliminates the need for tedious calculations and extensive algebraic manipulations, yet the students can understand and apply the algorithm without the black-box features that characterise most of the commercial project management software. To maintain a direct relationship with the traditional method, the spreadsheet uses the same familiar forward and backward operations required in the traditional manual calculations to find the critical path. Similar spreadsheet models are combined with the Solver in Excel to perform cost analysis by linear programming.

Modified But-For Method for Delay Analysis

“But-for” is a widely used method for analyzing and apportioning project delays among the responsible parties. Despite its acceptability, the traditional but-for method suffers from serious drawbacks; namely its narrow focus on the point of view of a single party and its inability to accurately consider concurrent delays. In this technical note, several improvements have been made to the but-for method to produce repeatable results and to account for concurrent delays. Details of the modified but-for (MBF) method are provided along with an example to demonstrate its advantages. To automate the MBF method, it has been coded in a macro program on commercial project management software. The method is simple and can help practitioners in apportioning project delays in an accurate and equitable manner.

Analysis of Scheduling Schemes and Heuristic Rules Performance in Resource-Constrained Multiproject Scheduling

Frequently, the availability of resources assigned to a project is limited and not sufficient to execute all the concurrent activities. In this situation, decision making about their schedule is necessary. Many times this schedule supposes an increase in the project completion time. Additionally, companies commonly manage various projects simultaneously, sharing a pool of renewable resources. Given these resource constraints, we often can only apply heuristic methods to solve the scheduling problem. In this work the effect of the schedule generation schemes – serial or parallel – and priority rules – MINLFT, MINSLK, MAXTWK, SASP or FCFS – with two approaches – multi-project and single-project – are analysed. The time criteria considered are the mean project delay and the multiproject duration increase. Through an extensive computational study, results show that with the parallel scheduling generation scheme and the multi-project approach the project manager can obtain a good multiproject schedule with the time criterion selected: minimising mean project delay or minimising multiproject duration increase. New heuristics – based on priority rules with a two-phase approach – that outperform classical ones are proposed to minimise mean project delay with a multi-project approach. Finally, the best heuristics analysed are evaluated together with a representative sample of commercial project management software.

Resource Allocation Capabilities of Commercial Project Management Software Packages

This study concerns the quality of seven commercial project management software packages in allocating resources. Since the methods employed in these packages are proprietary, the quality is measured in terms of the deviation from an upper bound of the minimum makespan. As benchmark instances, a set of problems generated under a factorial design is used. Statistical tests are performed to investigate the impact of the problem parameters and to detect significant differences in quality among the packages.

Optimization of Resource Allocation and Leveling Using Genetic Algorithms

Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA proced...