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SUSTAINABILITY MCDM MODEL COMPARISONS

SUSTAINABILITY MCDM MODEL COMPARISONS. Yuan-Sheng Lee, Tamkang University Hsu-Shih Shih, Tamkang University David L. Olson, University of Nebraska. SUSTAINABILITY Tzeng et al. [2005] Energy Policy. DECISION: select bus type from 12 choices Eleven criteria Our use:

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SUSTAINABILITY MCDM MODEL COMPARISONS

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  1. SUSTAINABILITY MCDM MODEL COMPARISONS Yuan-Sheng Lee, Tamkang University Hsu-Shih Shih, Tamkang University David L. Olson, University of Nebraska European DSI 2014, Kolding, Denmark

  2. SUSTAINABILITYTzeng et al. [2005] Energy Policy • DECISION: select bus type from 12 choices • Eleven criteria • Our use: • Demonstration of features of various multi-criteria methods European DSI 2014, Kolding, Denmark

  3. Multi-Criteria Models of Sustainability • Non-dominated Identification • Lotov et al. [2004]; Bouchery et al. [2012] • Cardinal weighting • Equal weights; Tchebychev; Ordinal; SMART; AHP • Outranking • ELECTRE; PROMETHEE • TOPSIS (Technique for Preference by Similarity to the Ideal Solution) • Min distance to ideal while Max distance from nadir • Hwang & Yoon [1981] • TODIM • From cumulative prospect theory, S-shaped value function • Gomes & Lima [1992] European DSI 2014, Kolding, Denmark

  4. Urban Transportation Selection DecisionSelect a bus type – CRITERIA (Tzeng et al., 2005) • Energy supply • Energy efficiency • Air pollution • Noise pollution • Industrial relations • Employment cost • Maintenance cost • Capability of vehicle • Road facility • Speed of traffic • Sense of comfort European DSI 2014, Kolding, Denmark

  5. TODIM • Classify multiple criteria into benefits, costs • STEP 1: DM constructs normalized decision matrix (see next slide) • STEP 2: Value alternatives on each criterion with 0 the worst and 1 the best • STEP 3: Compute matrix of relative dominance • STEP 4: Calculate global measure for each alternative • STEP 5: Rank alternatives by global measures European DSI 2014, Kolding, Denmark

  6. Part 1: European DSI 2014, Kolding, Denmark

  7. Part II European DSI 2014, Kolding, Denmark

  8. NON-DOMINANCE • A1 (Diesel Bus) • A3 (LPG Bus) {> A2 on energy supply, = on all others} • A8 (Electric bus with exchangeable batteries) {>A7 on capability, roads} • A6 (Electric bus with opportunity charging) • A9 (Hybrid electric bus with gasoline engine) • A10 (Hybrid electric bus with diesel engine) • A11 (Hybrid electric bus with CNG engine) • A12 (Hybrid electric bus with LPG engine) identical ratings to A11 • A4, A5 dominated by combinations European DSI 2014, Kolding, Denmark

  9. WEIGHTING • EQUAL WEIGHTING (LaPlace) • A8 Electric bus with exchange batteries wins • A7 a very close second • PROVIDES FULL RANKING • Uses cardinal (continuous?) numbers • TCHEBYCHEV WEIGHTS • Maximize worst rating – A2 (CNG – dominated by A3), A3(LPG), A9 (Hybrid) • ORDINAL WEIGHTS (centroid) • A8 Electric bus with exchange batteries wins • A7 a very close second • CARDINAL WEIGHTS (from Tzeng et al. - AHP) • A8 Electric bus with exchange batteries wins • A7 a very close second European DSI 2014, Kolding, Denmark

  10. Simulation European DSI 2014, Kolding, Denmark

  11. PROMETHEE European DSI 2014, Kolding, Denmark

  12. Distance methods • TOPSIS • A8 Electric exchange batteries • A6 Electric optional charge close behind • A7 Electric direct exchange (dominated solution) close behind • TODIM • A8 Electric exchange batteries • A7 Electric direct exchange (dominated solution) second • A11/A12 Hybrid CNG or LPG third European DSI 2014, Kolding, Denmark

  13. Rankings European DSI 2014, Kolding, Denmark

  14. SELECTION European DSI 2014, Kolding, Denmark

  15. DISCUSSION • Fair consistency in rankings • No two identical • Continuous allows close second to be ranked even if dominated (A7) • Tchebychef the most extreme • Only looks at worst • Thus is sensitive to scale • A2 considered, though dominated European DSI 2014, Kolding, Denmark

  16. CONCLUSIONS • Many multiple criteria methods • All valuable to some degree • more • SIMULATION preferred by author • Nondominance might be useful in selection, not in ranking • You can always come up with another criterion • Accuracy of data critical • A11/A12 identical, but might vary on some additional factor • Outranking methods help explore • PREFERENCE important • Machine-methods {omit preference as much as possible} (TOPSIS) • Individual preference well-studied • Group preference problematic European DSI 2014, Kolding, Denmark

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