The concept of an idealised optimal benchmark (IOB) is used in many engineering disciplines. An example of an IOB from the area of thermodynamics is the formula for evaluating the maximum possible efficiency of a heat engine. This paper explores the concept of an IOB in the area of elevator traffic analysis. It shows that the classical method of elevator traffic design by calculating the value of the round trip time is an example of an IOB; it also lists the assumptions that lie behind the formulae to illustrate this. It then extends the concept of an IOB to calculating the maximum performance of an elevator system when destination group control is applied under incoming traffic conditions. Formulae are derived for finding the minimum values of the expected number of stops (S) and the highest reversal floor (H) under destination group control during incoming traffic conditions. The assumption is that the L elevators in the group are sequenced (or rotated) to the L virtual sectors in the building, in order to equalise the handling capacities of the L sectors in the group. A numerical example is presented to illustrate the calculation of the maximum possible handling capacity and comparing it to the handling capacity that is achieved under conventional incoming traffic group control. Three numerical algorithms are also used to find the practical minimum values of H and S, the results of which are compared to the IOB using the equations derived above. Practical application: The concept and the accompanying formulae presented in this paper allow the elevator traffic designer to assess the improvement in the handling capacity of the elevator traffic system when he/she changes the group controller from a conventional group controller to a destination group controller. This improvement could be as much as 200%.

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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.

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