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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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A multi-objective multi-period mathematical programming model for integrated project portfolio optimization and

Mostafa Zahedirad¹, Kaveh Khalili-Damghani¹, Vahidreza Ghezavati¹

¹Department of Industrial Engineering, ST.C., Islamic Azad University, Tehran, Iran.

|August 8, 2025

Summary

This summary is machine-generated.

This study presents two methods for project portfolio optimization and contractor selection. An integrated model simultaneously optimizing both aspects significantly outperforms a sequential approach, offering better results and faster computation.

Keywords:

Contractor ability planning Contractor selection Integrated planning Project portfolio optimization Project risk planning

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Area of Science:

Operations Research
Management Science
Mathematical Optimization

Background:

Project portfolio optimization and contractor selection are critical yet complex decisions.
Existing methods often treat these as separate problems, potentially leading to suboptimal outcomes.
Balancing multiple objectives like profit, risk, and technical capability under various constraints is challenging.

Purpose of the Study:

To develop and compare two distinct modeling approaches for integrated project portfolio optimization and contractor selection.
To evaluate the effectiveness of a simultaneous optimization model versus a sequential approach.
To analyze the computational performance and results of the proposed methods.

Main Methods:

Formulation of two mixed-integer mathematical programming models: one sequential and one integrated.
Application of goal programming (GP) to solve the multi-objective optimization problems.
Consideration of multiple objectives (profit, risk, capability, cost) and constraints (relationships, inflation, resources).
Validation through a practical case study and analysis of time complexity.

Main Results:

The integrated model (Scenario 2) demonstrated superior performance compared to the sequential model (Scenario 1).
Scenario 2 yielded better overall results in achieving project and contractor selection objectives.
The integrated approach exhibited significantly improved CPU time efficiency.

Conclusions:

Simultaneous optimization of project portfolio and contractor selection through an integrated model is more effective than sequential approaches.
Goal programming provides a robust framework for solving complex multi-objective optimization problems in this domain.
The integrated model offers a computationally efficient and practically superior solution for strategic project and contractor management.