[雙語翻譯]--外文翻譯--基于隨機(jī)多準(zhǔn)則接受程度分析的電梯規(guī)劃（英文）

1、Omega 36 (2008) 352–362www.elsevier.com/locate/omegaElevator planning with stochastic multicriteria acceptability analysis?Tommi Tervonena,b,?, Henri Hakonenb,c, Risto LahdelmabaCEG-IST, Centre for Management Studies, In

2、stituto Superior Técnico, Departamento de Engenharia e Gestão, 1049-001 Lisbon, Portugal bDepartment of Information Technology, University of Turku, FIN 20520 Turku, Finland cKone Oyj, Keilasatama 3, FIN 02150

3、Espoo, FinlandReceived 29 September 2004; accepted 26 April 2006 Available online 27 November 2006AbstractModern elevator systems in high-rise buildings consist of groups of elevators with centralized control. The goal i

4、n elevator planning is to configure a suitable elevator group to be built. The elevator group must satisfy specific minimum requirements for a number of standard performance criteria. In addition, it is desirable to opti

5、mize the configuration in terms of other criteria related to the performance, economy and service level of the elevator group. Different stakeholders involved in the planning phase emphasize different criteria. Most of t

6、he criteria measurements are by nature uncertain. Some criteria can be estimated by using analytical models, while others, especially those related to the service level in different traffic patterns, require simulations.

7、 In this paper we formulate the elevator planning problem as a stochastic discrete multicriteria decision-making problem. We compare 10 feasible elevator group configurations for a 20-floor building. We evaluate the crit

8、eria related to the service level in different traffic situations using the KONE Building Traffic Simulator, and use analytical models and expert judgments for other criteria. The resulting decision problem contains mixe

9、d type criteria. Some criteria are represented by the multivariate Gaussian distribution, others by deterministic values and ordinal (ranking) information. To identify configurations that can best satisfy the goals of th

10、e stakeholders, we analyze the problem using the stochastic multicriteria acceptability analysis (SMAA) method. ? 2006 Elsevier Ltd. All rights reserved.Keywords: Stochastic multicriteria acceptability analysis (SMAA); E

11、levator planning; Multicriteria; Simulation1. IntroductionIn modern high-rise buildings workers and inhabi- tants are transported between floors mainly by means of multiple elevators. Elevators are usually operated by? T

12、his paper was processed by Guest Editors Margaret M. Wiecek, Matthias Ehrgott, Georges Fadel and José Rui Figueira. ? Corresponding author. CEG-IST, Centre for Management Studies, Instituto Superior Técnico, De

13、partamento de Engenharia e Gestão, 1049-001 Lisbon, Portugal. E-mail addresses: tommi.tervonen@it.utu.fi (T. Tervonen),henri.hakonen@kone.com (H. Hakonen), risto.lahdelma@cs.utu.fi (R. Lahdelma).0305-0483/$ - see fr

14、ont matter ? 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.omega.2006.04.017elevator group control systems in order to provide efficient transportation. When a high-rise building is designed, a suitable configura

15、tion for the elevator group has to be designed. The decision makers (DMs) should consider performance as well as price and other non-performance criteria of alternative elevator group configurations. Because analytical m

16、ethods are limited to the up-peak traffic situation and cannot evaluate the effect of a group control algorithm, the performance has to be measured using computer simulation, which produces stochastic measurements for th

17、e performance criteria of alternative configurations. The performance of an elevator group can be measured using several354 T. Tervonen et al. / Omega 36 (2008) 352–362and because there are analytical formulas for calcul

18、at- ing the up-peak handing capacity and interval [14]. The usual recommendations state that the up-peak handling capacity for an office building should be 11–17% and interval 20–30s [15]. Non-performance criteria, such

19、as cost and occupied floor area should also be considered. The cost of an elevator system consists of build and maintenance costs. The floor area occupied by the elevator group consists of the shaft space and the waiting

20、 area for passengers. In high-rise buildings the population is large and distances are long, thus the portion of shafts is large compared to the total floor area. This means more costs, since the rentable area is reduced

21、. In some cases the building design constrains the occupied area, sometimes there is more freedom to use space. The elevator planning is not independent of building design; the architect should take advice from the eleva

22、tor planner. Instead of considering only up-peak traffic, we take into account the entire daily traffic and consider all cri- teria simultaneously. In the study presented in this paper we consider the following six crite

23、ria. The cost and area criteria take into account the building owners point of view. Passengers point of view is taken into account by WT, journey time (JT), the percentage of WTs exceed- ing 60s (WT60), and the percenta

24、ge of JTs exceeding 120s (JT120). The WT is measured from the moment a passenger enters the waiting area to the moment he/she enters the elevator. The JT is the total time from en- tering the waiting area to exiting the

25、elevator. The last two criteria measure unsatisfactory service, which may happen especially in intense traffic peaks.3. The SMAA methodsThe SMAA-2 method [1] has been developed for dis- crete stochastic multicriteria dec

26、ision-making problems with multiple DMs. SMAA-2 applies inverse weight space analysis to describe for each alternative what kind of preferences make it the most preferred one, or place it on any particular rank. The deci

27、sion problem is rep- resented as a set of m alternatives {x1, x2, ..., xm} that are evaluated in terms of n criteria. The DMs’preference structure is represented by a real-valued utility or value function u(xi, w). The v

28、alue function maps the differ- ent alternatives to real values by using a weight vector w to quantify DMs’ subjective preferences. SMAA-2 has been developed for situations where neither criteria measurements nor weights

29、are precisely known. Uncer- tain or imprecise criteria are represented by stochastic variables ?ij with joint density function fX(?) in the space X ? Rm×n. We denote the stochastic criteria mea-surements of alternat

30、ive xi with ?i. The DMs’ unknown or partially known preferences are represented by a weight distribution with joint density function fW(w) in the feasible weight space W. Total lack of prefer- ence information is represe

31、nted in ‘Bayesian’ spirit by a uniform weight distribution in W, that is, fW(w) = 1/vol(W). The weight space can be defined according to needs, but typically, the weights are non-negative and normalized, that is; the wei

32、ght space is an n?1 dimen- sional simplex in n dimensional space:W =? ??w ∈ Rn : w?0 andn ?j=1 wj = 1? ?? . (1)The value function is used to map the stochastic cri- teria and weight distributions into value distributions

33、 u(?i, w). Based on the value distributions, the rank of each alternative is defined as an integer from the best rank (=1) to the worst rank (=m) by means of a rank- ing functionrank(i, ?, w) = 1 +m ?k=1 ?(u(?k, w) >

34、u(?i, w)), (2)where ?(true) = 1 and ?(false) = 0. SMAA-2 is then based on analyzing the stochastic sets of favorable rank weightsW r i (?) = {w ∈ W : rank(i, ?, w) = r}. (3)Any weight w ∈ W r i (?) results in such values

35、 for dif- ferent alternatives, that alternative xi obtains rank r. The first descriptive measure of SMAA-2 is the rank acceptability index br i , which measures the variety of different preferences that grant alternative

36、 xi rank r. It is the share of all feasible weights that make the alternative acceptable for a particular rank, and it is most conveniently expressed percentage wise. The rank acceptability index br i is computed numeric

37、ally as a multidimensional integral over the criteria distributions and the favorable rank weights asbr i =??∈X fX(?)?w∈W r i (?) fW(w) dw d?. (4)The most acceptable (best) alternatives are those with high acceptabilitie