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2 Introduction Zeleny (1982) dalam bukunya "Multiple Criteria Decision Making", mengatakan: "Semakin sulit melihat dunia di sekitar kita secara unidimensional dan hanya menggunakan satu kriteria saat menilai apa yang kita lihat"

3 Examples of Multi-Criteria Problems Wife selection problem. This problem is a good example of multi-criteria decision problem. Criteria include: Religion Beauty Wealth Family status Family relationship Education 3

Agama Harta Keturunan Kecantikan/Kegantengan http://trial.

4 Agama Harta Keturunan Kecantikan/Kegantengan

6 Banyak masalah sektor publik dan bahkan keputusan pribadi melibatkan banyak pertimbangan, tujuan dan sasaran. Sebagai contoh: Menemukan pembangkit listrik tenaga nuklir melibatkan tujuan seperti: 1. Keamanan 2. Kesehatan 3. Lingkungan Hidup 4. Biaya

7 Luna wants to go on a vacation. She has 3 options Hogwarts Hogsmeade Azkaban How to decide..??? Pranav Mishra

8 Let us consider Each option can be evaluated against certain criteria. Criteria for vacation destinations can be: Entertainment Facilities Accommodation cost Travel cost

9 Important terms Weights These estimates relative importance of criteria. Each attribute is given certain points on 0-10 or rating scale by a team of experts or decision makers. Example: criteria weight rating scale Entertainment very good -1 none Facilities very good -1 none Travel cost low-1 very high Accomodation low-1 very high

10 Similarly Selecting a source of information (library, internet, etc ) involves various criteria such as: Reliability of information Time to gather information Cost of acquiring information These are examples of MULTI-CRITERIA problems and requires MCDM approach.

11 Land is a scarce resource: Identifying suitability for Where to build a dam Water flow Mountainous? Where to place a hospital Costs Access Greatest need Criteria can be based on human or physical geography factors

12 Beberapa istilah: Multi Criteria Analysis Multi Criteria Evaluation (MCE) Multi Criteria Preference Analysis Multi Criteria Decision Making Multi Objective Evaluation These methods are essentially one and the same! MCE = Multi-criteria evaluation is primarily concerned with how to combine the information from several criteria to form a single index of evaluation Professor Kenneth E., 2010

13 Mengapa DCDM: Bagus untuk mengambil keputusan dari masalah yang kompleks. Memungkinkan pengambil keputusan untuk menunjukkan pemikiran mereka. Berguna dalam analisis GIS dimana beberapa kriteria digabungkan. Termasuk kemampuan untuk menimbang kriteria. Sering digunakan untuk penentuan alokasi lahan.

14 MCDM Multiple Criteria Decision Making Selection of the best, from a set of alternatives, each of which is evaluated against multiple criteria. Some problem solving techniques are : SAW (Simple Additive Weighting) TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) ELECTRE (Elimination et Choice Translating Reality) AHP (The Analytical Hierarchy Process) SMART (The Simple Multi Attribute Rating Technique ) ANP (Analytic network process)

15 Definitions Decisions: a choice between alternatives Criterion: some basis for a decision. Two main classes: Factor: enhances or detracts from the suitability of a land use alternative (e.g. distance from a road) Constraint: limits the alternatives Goal or target: some characteristic that the solution must possess (a positive constraint)

16 The Basics

17 Principles of MCE Methodology: 1. Determine criteria (factors/constraints) to be included 2. Determining the weights for each factor 3. Sensitivity analysis of results

18 1 st Step: Determine the criteria to be included Criteria determine the alternatives Oversimplification of the decision problem could lead to too few criteria being used Using a large number of criteria reduces the influence of any one criteria Often proxies must be used since the criteria of interest may not be determinable

19 Example: Case study of a suitable dam and reservoir site Criteria used: River Urban Forest Accumulated water flow Existing reservoir Watershed boundary City Hydraulic head Undulation

22 Determine the weights A decision is the result of a comparison of one or more alternatives with respect to one or more criteria that we consider relevant for the task at hand. Among the relevant criteria we consider some as more important and some as less important; this is equivalent to assigning weights to the criterion according to their relative importance.

23 2 nd stage, assigning weights

24 Weights assigned using AHP

25 Weights assigned using the Rank Order method

27 Sensitivity analysis sensitivity analysis: vary the scores/weights of the factors to determine the sensitivity of the solution to minor changes Choice of criteria (e.g. why included?) Assesses the reliability of data: how stable is the final result? Choice for weighting factors is subjective Will the overall solution change if you use other weighing factors?

MCE pros and cons Cons: Dynamic problems strongly simplified into a linear model Static, lacks the time dimension Controversial method too subjective?

28 MCE pros and cons Cons: Dynamic problems strongly simplified into a linear model Static, lacks the time dimension Controversial method too subjective? Pros: Gives a structured and traceable analysis. Possibility to use different evaluation factors makes it a good tool for discussion. Copes with large amounts of information. It works!

29 Approaches For MCDM Several approaches for MCDM exist. We will cover the following: Weighted score method ( Section 5.1 in text book). TOPSIS method Analytic Hierarchy Process (AHP) Goal programming? 29

30 Approaches For MCDM Some problem solving techniques are : SAW (Simple Additive Weighting) TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) ELECTRE (Elimination et Choice Translating Reality) BAYESIAN NETWORK BASED FRAMEWORK AHP (The Analytical Hierarchy Process) SMART (The Simple Multi Attribute Rating Technique ) ANP (Analytic network process) 30

31 MCDM problem has four elements: Goal Objectives Criteria Alternatives 31

32 An example of hierarchical value tree: 32

33 Criteria characteristics Completeness: It is important to ensure that all of the important criteria are included. Redundancy: In principle, criteria that have been judged relatively unimportant or to be duplicates should be removed at a very early stage. Operationality: It is important that each alternative can be judged against each criterion. 33

34 Problem solving steps: 1. Establish the decision context, the decision objectives (goals), and identify the decision maker(s). 2. Identify the alternatives. 3. Identify the criteria (attributes) that are relevant to the decision problem. 34

35 Azas dalam Analisis Overlay Azas Dominan (Dominance Rule): satu nilai dominan Azas Kontribusi (Contributory Rule) masing-masing nilai attribut berkontribusi terhadap hasil Azas Interaksi (Interaction Rule) pasangan-pasangan dari nilai-nilai berkontribusi terhadap hasil.

36 2 nd stage, assigning weights

37 AHP merupakan salah satu alat bantu (proses) dalam pengambilan keputusan yang dikembangkan oleh Thomas L Saaty pada tahun 70an.

41 STRUKTUR ANALYTIC HIERARCHY PROCESS (AHP) Konsep dasar AHP adalah penggunaan matriks pairwise comparison (matriks perbandingan berpasangan) untuk menghasiikan bobot relative antar kriteria maupun alternative. Suatu kriteria akan dibandingkan dengan kriteria lainnya dalam hal seberapa penting terhadap pencapaian tujuan di atasnya (Saaty, 1986).

45 Combining performance indicators to one Key Performance Indicator (KPI) You can give each one with different WEIGHTS Klaus Goepel

46 How to derive the weights? Mathematical method: Klaus Goepel

47 AHP has many applications: 1. Select key performance indicators (KPIs) 2. Evaluate product features 3. Select from srategic alternatives 4. Make consilifated decisions with multiple inputs from different stakeholders Klaus Goepel

48 Analytic Hierarchy Process (AHP) Deriving ratio scales from paired comparisons Allows some small inconsistency in judgment INPUT: Actual measurement Subjective opinion Price, weight etc satisfaction feelings, preferences OUTPUT: Ratio scales Consistency index from Eigen vectors from Eigen value Klaus Goepel

49 Analytic Hierarchy Process (AHP) Step 1 : Define objective Step 2: Structure elements in criteria, sub-criteria, alternatives etc. Step 3: Make a pair wise compatison of elements in each group Step 4: Calculate weighting and consistency ratio Step 5: Evaluate alternatives according weighting Get Ranking Klaus Goepel

65 Price not taken as criterion separating benefits form cost allows for a costbenefit analysis

71 Paired comparison matrix

72 HASIL REKLASIFIKASI Mengetahui rekomendasi jalur pipa PDAM yang optimum dalam melayani konsumen Kemiringan Lereng Arah Hadap Lereng Penggunaan Lahan Jalan Sungai

73 HASIL PERHITUNGAN AHP Parameter Jalan Penggunaan Lahan Kemiringan Lereng Arah Hadap Lereng Sungai Priority Vector Bobot Jalan % Penggunaan Lahan % Kemiringan Lereng % Arah Hadap Lereng % Sungai % Jumlah % Principle Eigen Value : Consistency Index (CI) : Hasil CR dapat diterima karena lebih kecil dari 0,1 Consistency Ratio (CR) : 0.077

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Introduction. Zeleny (1982) dalam bukunya "Multiple Criteria Decision Making", mengatakan:

Introduction. Zeleny (1982) dalam bukunya Multiple Criteria Decision Making, mengatakan: Introduction Zeleny (1982) dalam bukunya "Multiple Criteria Decision Making", mengatakan: "Semakin sulit melihat dunia di sekitar kita secara unidimensional dan hanya menggunakan satu kriteria saat menilai