Computational bounds for elevator control policies by large scale linear programming

Abstract

We computationally assess policies for the elevator control problem by a new column-generation approach for the linear programming method for discounted infinite-horizon Markov decision problems. By analyzing the optimality of given actions in given states, we were able to provably improve the well-known nearestneighbor policy. Moreover, with the method we could identify an optimal parking policy. This approach can be used to detect and resolve weaknesses in particular policies for Markov decision problems.

Optimization strategy of group control algorithm for small and medium-sized elevators with traffic pattern

“…The elevator group control system is an uncertain system [1] . When making an evaluation, it is necessary to consider multiple objectives such as passenger waiting time, congestion degree, operation energy consumption [2] , so as to achieve the optimal configuration of elevator group operation on the whole [3] .Many elevator group control technologies [4][5][6][7][8] have been proposed at home and abroad, but each has its advantages and disadvantages . Deterministic analytical algorithms have been widely used in practical projects of small and medium-sized elevator group control problems because of its simple program, fast operation speed, no need for verification, and can guarantee the optimal solution, which is convenient for design.…”

Hybrid Simulation Modeling of Hoist Down-Peak Operations in Construction Sites

“…During the period of design and construction of a tall building, some key parameters for the hoist operation have to be considered such as average waiting time for workers, or total time needed to send all workers to their destinations [6][7][8]. Some authors use mathematical methods to determine these parameters for hoist planning [9][10].…”