Profit-oriented scheduling of resource-constrained projects with flexible capacity constraints

Schnabel, André; Kellenbrink, Carolin; Helber, Stefan

doi:10.1007/s40685-018-0063-5

Cited by 9 publications

(1 citation statement)

References 38 publications

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“…Therefore, owing to the time value of money (TVoM), a small variation in cash flow timing may have a substantial impact on the overall project's profitability [7]. As a result, from financial point of view, project scheduling focusing solely on the minimization of makespan is not always desirable [7]- [9]. Consequently, in RCPSP, financial perspectives have become an important point in decision-making by the project managers.…”

Section: Introductionmentioning

confidence: 99%

An Immune Genetic Algorithm for Solving NPV-Based Resource Constrained Project Scheduling Problem

et al. 2021

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The net present value (NPV)-based resource constrained project scheduling problem (RCPSP) is a well-known scheduling problem in many industries, such as construction, software development, and manufacturing. Over the last five decades, although different approaches have been proposed to solve the problem, no single approach has been shown to achieve satisfactory performances with quality solutions for a wide range of problems. This study presents a hybrid immune genetic algorithm (IGA) to solve NPV-based RCPSPs. Hybridizing a genetic algorithm (GA) with an immune algorithm (IA) enhances the overall performance of their standalone components (i.e., only GA or IA). Performance of the proposed IGA is further improved by applying a variable insertion based local search (VINS) and forward-backward improvement (FBI). A restart mechanism is presented to the algorithm which induces diversity and helps to avoid becoming trapped in local optima. Moreover, an activity move rule (AMR) is implemented to shift the negative cash flow associated activities to further improve the NPV. Taguchi Design of Experiment (DOE) is conducted to investigate the impact of various parameters and to determine the appropriate set of parameters for the proposed IGA. The performances of the proposed algorithms are tested on 17,280 standard benchmark instances ranging from 25 to 100 activities. Comparison with the state-of-art algorithms through extensive numerical experiments reveal the effectiveness of the proposed algorithms. Overall, the proposed algorithm outperforms existing algorithms, particularly the projects with 0% and 100% negative cash flow associated activities, the 75-activity instances, and the projects with two resources usage in terms of a lower value of average percentage deviation.

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Section: Introductionmentioning

confidence: 99%

An Immune Genetic Algorithm for Solving NPV-Based Resource Constrained Project Scheduling Problem

et al. 2021

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Solving Logistics Scheduling Problems Using an Extended Many-Project Optimization Model

Mihály,

Kulcsárné Forrai,

Kulcsár

2024

Lecture Notes in Logistics

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Resource overload problems with tardiness penalty: structural properties and solution approaches

Wohlert,

Zimmermann

2024

Ann Oper Res

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In this paper, we consider a resource overload problem and add a tardiness penalty to the objective function when a prescribed project makespan is exceeded, which enables a trade-off between a balanced resource utilization and a project delay. For the tardiness penalty, we distinguish between a constant and variable delay cost variant. Based on the structural properties of the resource overload problem, we show that the search space of the resource overload problem with tardiness penalty can also be reduced utilizing quasistable schedules. In addition, we discuss the application of these findings to further problems, which include objectives composed of a locally concave and a concave function or a reward structure for an early project completion instead of a tardiness penalty. As solution approaches, we present mixed-integer linear model formulations as well as a novel genetic algorithm with a decoding procedure, which exploits the devised structural properties. The performance of the genetic algorithm is improved by implementing learning methods and utilizing lower bounds. Finally, we present results from experiments on small to medium sized problem instances.

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Profit-oriented scheduling of resource-constrained projects with flexible capacity constraints

Cited by 9 publications

References 38 publications

An Immune Genetic Algorithm for Solving NPV-Based Resource Constrained Project Scheduling Problem

An Immune Genetic Algorithm for Solving NPV-Based Resource Constrained Project Scheduling Problem

Solving Logistics Scheduling Problems Using an Extended Many-Project Optimization Model

Resource overload problems with tardiness penalty: structural properties and solution approaches

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