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A survey on the Software Project Scheduling Problem

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  • Vega-Velázquez, Miguel Ángel
  • García-Nájera, Abel
  • Cervantes, Humberto

Abstract

Creating a plan for a software project is a recurring activity in software development organizations that plays a critical role in the project success. When creating a plan for a project, these organizations must deal with the problem of allocating resources to tasks in the project. Because of its importance, there has been considerable research focused on finding ways to solve this problem, which is known as the Software Project Scheduling Problem (SPSP). Solving this problem usually focuses on creating a schedule for a project with minimal duration and cost. As part of our work, we have found only one survey about the SPSP, however it focuses primarily on the methods used to solve it, while the rest of the surveys focus primarily on other scheduling problems such as the Resource-Constrained Project Scheduling Problem. In this paper, we present a survey of the current research focused on solving the SPSP. For this survey, we have analyzed and classified a number of research studies considering a set of criteria that include: the model used to represent the problem, the optimization goals, the optimization techniques used to solve the problem, the methodology used to evaluate the different approaches, and the main results. From our analysis, we produce a set of general observations and provide suggestions that we believe can be useful for future research in this field.

Suggested Citation

  • Vega-Velázquez, Miguel Ángel & García-Nájera, Abel & Cervantes, Humberto, 2018. "A survey on the Software Project Scheduling Problem," International Journal of Production Economics, Elsevier, vol. 202(C), pages 145-161.
    • Handle: RePEc:eee:proeco:v:202:y:2018:i:c:p:145-161
    DOI: 10.1016/j.ijpe.2018.04.020
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    References listed on IDEAS

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    1. Sönke Hartmann, 1998. "A competitive genetic algorithm for resource‐constrained project scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 45(7), pages 733-750, October.
    2. Hanne, Thomas & Nickel, Stefan, 2005. "A multiobjective evolutionary algorithm for scheduling and inspection planning in software development projects," European Journal of Operational Research, Elsevier, vol. 167(3), pages 663-678, December.
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    4. Hartmann, Sonke & Kolisch, Rainer, 2000. "Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 127(2), pages 394-407, December.
    5. Hartmann, Sönke & Kolisch, R., 2000. "Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 11180, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Hartmann, Sönke & Briskorn, Dirk, 2010. "A survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 207(1), pages 1-14, November.
    7. Drexl, Andreas & Nissen, Rudiger & Patterson, James H. & Salewski, Frank, 2000. "ProGen/[pi]x - An instance generator for resource-constrained project scheduling problems with partially renewable resources and further extensions," European Journal of Operational Research, Elsevier, vol. 125(1), pages 59-72, August.
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    2. Ben Issa, Samer & Patterson, Raymond A. & Tu, Yiliu, 2021. "Solving resource-constrained multi-project environment under different activity assumptions," International Journal of Production Economics, Elsevier, vol. 232(C).
    3. Zsolt T. Kosztyán & Eszter Bogdány & István Szalkai & Marcell T. Kurbucz, 2022. "Impacts of synergies on software project scheduling," Annals of Operations Research, Springer, vol. 312(2), pages 883-908, May.
    4. Bruno Mota & Luis Gomes & Pedro Faria & Carlos Ramos & Zita Vale & Regina Correia, 2021. "Production Line Optimization to Minimize Energy Cost and Participate in Demand Response Events," Energies, MDPI, vol. 14(2), pages 1-14, January.
    5. Xuejun Hu & Jianjiang Wang & Kaijun Leng, 2019. "The Interaction Between Critical Chain Sequencing, Buffer Sizing, and Reactive Actions in a CC/BM Framework," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(03), pages 1-22, June.

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