Nested-Solution Facility Location Models.

Area of Science:

• Operations Research
• Optimization Theory
• Logistics Management

Background:

• Classical facility location models often lack consistency in facility selection as the number of facilities changes.
• Variations in the number of future utilized facilities (r) can lead to suboptimal or inconsistent solutions.

Purpose of the Study:

• To introduce and define the concept of nested facility locations.
• To address the inconsistency issue in classical facility location models.
• To improve decision-making under uncertainty regarding the number of future facilities.

Main Methods:

• Developed the nested facility location concept, where solutions for p facilities are subsets of solutions for q facilities (i <= p < q <= j).
• Applied the nested approach to the p-median model.
• Conducted computational testing to evaluate the performance of the new models.

Main Results:

• Demonstrated that nested facility location models maintain subset consistency.
• Showcased reductions in average regret when the actual number of utilized facilities (r) differs from the planned number (p).
• Achieved reductions in worst-case regret under similar conditions (r != p).

Conclusions:

• Nested facility location models offer improved consistency and reduced regret compared to classical models.
• The proposed framework provides a more robust approach to facility planning, especially when future needs are uncertain.
• This research advances optimization techniques for strategic facility location decisions.