A Preference Biobjective Evolutionary Algorithm for the Payment Scheduling Negotiation Problem.

Area of Science:

• Project Management
• Operations Research
• Computational Economics

Background:

• The resource-constrained project scheduling problem (RCPSP) is a fundamental challenge in project management.
• Existing research often focuses solely on the contractor's financial gains (Net Present Value - NPV).
• There is a need to consider both contractor and client financial interests for a balanced project outcome.

Purpose of the Study:

• To address the limitations of existing RCPSP studies by introducing the Payment Scheduling Negotiation Problem (PSNP).
• To develop a method that maximizes both the client's and contractor's NPV, achieving a win-win scenario.
• To overcome challenges including conflicting objectives, participant preferences, and the NP-Hard nature of RCPSP.

Main Methods:

• Reformulating the problem as a biobjective optimization problem incorporating preferences.
• Presenting a multilevel region of interest (ROI) strategy to accommodate diverse client and contractor preferences.
• Integrating this strategy with the nondominated sorting genetic algorithm II (NSGA-II) for efficient PSNP solving.

Main Results:

• The proposed algorithm effectively searches within the specified region of interest (ROI).
• Experimental comparisons show the new method outperforms traditional double-level and multiobjective optimization approaches.
• The algorithm provides more satisfactory solutions by balancing conflicting objectives.

Conclusions:

• The developed approach offers a novel solution for the PSNP, enhancing project financial outcomes for all stakeholders.
• The multilevel ROI strategy combined with NSGA-II provides an efficient and effective method for complex project scheduling and payment negotiations.
• This research contributes to more equitable and financially optimized project management practices.