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This research investigates analytical and quantitative methods for simulatingelevator optimizations. To maximize overall elevator usage, we concentrate oncreating a multiple-user positive-sum system that is inspired by agent-basedgame theory. We define and create basic "Dumbwaiter" models by attempting boththe Spatial Process Approach and the Gibbs Random Field Approach. These twomathematical techniques approach the problem from different points of view: thespatial process can give an analytical solution in continuous space and theGibbs Random Field provides a discrete framework to flexibly model the problemon a computer. Starting from the simplest case, we target the assumptions toprovide concrete solutions to the models and develop a "Multi-DumbwaiterSystem". This paper examines, evaluates, and proves the ultimate success ofsuch implemented strategies to design the basic elevator's optimal policy;consequently, not only do we believe in the results' practicality for industry,but also their potential for application.

Elevator Optimization: Application of Spatial Process and Gibbs Random Field Approaches for Dumbwaiter Modeling and Multi-Dumbwaiter Systems