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Evaluating the performance of elevator group control algorithms using a three-element new paradigm

This paper presents a new paradigm for assessing the effectiveness of up-peak elevator group control algorithms. The new paradigm can be very effective in providing a mechanism for objectively assessing and comparing elevator group control algorithms. It is built around three essential components: idealised optimal benchmarks; random scenario testing; and progressive introduction of reality. An idealised optimal benchmark is the starting point for calculating an analytical upper bound for the performance of any algorithm. It provides a reference for comparing the performance of all algorithms. Random scenario testing is used to subject the elevator group controller to a randomly generated scenario (usually of passenger origin-destination pairs). The response of the group controller to a randomly generated scenario is recorded, and more scenarios are generated and added. The overall response (e.g., average of all responses) of the group control algorithm to the large number of scenarios represents an objective measure of its efficacy. The random scenario testing is first carried out under idealised or partially idealised conditions. Under the third component of the new paradigm, the conditions are gradually made more realistic and better reflective of reality. This third element is called the progressive introduction of reality. Practical application : This paper presents to the designer of the elevator group controller a new paradigm for assessing the benchmark against which he/she is working. The designer can be confident that whatever up-peak group control algorithm he/she develops, it cannot exceed this upper benchmark. This has important practical applications in benchmarking the performance of up-peak group controllers. It can also be used by consultants and clients to mediate elevator group controller performance claims presented by elevator manufacturers.