PhD Researchers & Doctoral Candidates
A rigorous MCDM, DEA, or optimization study for a thesis—with consistency checks and sensitivity analysis built in.
Research Methods
Operations Research & Decision Sciences
Complex decisions involve competing criteria, interdependent factors, and imperfect judgment. We bring structure to them—optimization, multi-criteria decision analysis, and efficiency measurement—so a ranking, a solution, or a factor map rests on a transparent, defensible method rather than intuition.
Optimization & programming
AHP · TOPSIS · DEMATEL · DEA
Fuzzy MCDM & sensitivity
Reproducible, journal-ready
Sample output
top-ranked
alternatives
Overview
Structure for decisions with many moving parts
Real decisions rarely have a single objective or a clean answer. Which supplier, which strategy, which policy—each involves multiple criteria that trade off against one another, factors that influence each other, and expert judgment that is confident but imprecise. Operations research and multi-criteria decision analysis exist to bring transparent structure to exactly that, replacing "it felt right" with a method a reviewer or a board can follow.
We match the tool to the question. To rank alternatives against weighted criteria, AHP or TOPSIS; to understand which factors drive the others, DEMATEL; to build a hierarchy of how factors interrelate, ISM with MICMAC; to benchmark efficiency across units, DEA; to find an optimal allocation, linear or multi-objective programming. Where the inputs are genuinely vague expert assessments, the fuzzy variants—fuzzy AHP, fuzzy DEMATEL—carry that uncertainty through the analysis rather than pretending it away.
And because these methods rest entirely on the quality of their inputs, we design the elicitation, check consistency, and run sensitivity analysis to show whether the ranking or structure holds up—delivering a decision analysis that is reproducible and defensible.
Who We Work With
For structured, multi-criteria decisions
If you need to rank, optimize, or structure a complex decision defensibly, this is the right desk to write to.
Management & Operations Researchers
AHP, TOPSIS, DEMATEL, and ISM studies for supply chain, quality, and strategy research.
Engineering-Management Researchers
Optimization and multi-objective programming for design, allocation, and planning problems.
Sustainability & Policy Researchers
Multi-criteria evaluation of options and barriers, including fuzzy methods for expert-based assessment.
Efficiency & Benchmarking Analysts
Data envelopment analysis to benchmark firms, branches, or institutions on input-output efficiency.
Institutes & Decision-Support Teams
Applied decision analysis where the recommendation must be transparent, structured, and reproducible.
Capabilities
The full decision-science toolkit
Organized across optimization, multi-criteria ranking, and structural and efficiency methods. If your problem needs a method not listed here, ask—this is the core, not the boundary.
Finding the best allocation
Formal optimization and strategic decision modelling for problems with clear objectives and constraints.
- Operations research
- Optimization
- Linear programming
- Multi-objective optimization
- Game theory
- Decision modelling
Ranking against many criteria
The MCDM family for prioritizing alternatives when several, often conflicting, criteria matter.
- Multi-criteria decision making
- AHP
- TOPSIS
- PROMETHEE
- Fuzzy AHP
Relationships & performance
Methods for mapping how factors interrelate and for benchmarking efficiency across units.
- DEMATEL
- Fuzzy DEMATEL
- ISM
- MICMAC
- Data envelopment analysis
How the Analysis Works
Six steps from problem to defensible decision
A transparent sequence where the criteria, the judgments, and the sensitivity of the result are all made explicit. Nothing is a black box.
Steps are adapted to your problem: ranking vs. structuring vs. optimizing vs. benchmarking, and crisp vs. fuzzy inputs. We confirm the framing with you before analysis begins.
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1
Frame
Define the decision, the alternatives, the criteria, and whether the goal is ranking, structuring, or optimization.
Inputs: decision · alternatives · criteria · goal
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2
Select method
Choose the OR or MCDM method the problem structure justifies, and whether crisp or fuzzy inputs apply.
Methods: AHP · TOPSIS · DEMATEL · ISM · DEA · LP
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3
Elicit
Design and gather the expert judgments or data, and structure them into decision matrices.
Inputs: expert panel · pairwise comparisons · data
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4
Compute
Run the analysis—derive weights, rank alternatives, map relationships, or solve the model.
Output: weights · rankings · cause-effect maps · solutions
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5
Validate
Check consistency, aggregate experts appropriately, and run sensitivity analysis on the ranking.
Checks: consistency ratio · aggregation · sensitivity
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6
Report
Deliver ranked results or solutions, figures, methodology, and reproducible computation files you keep.
Output: rankings · figures · methods · computation files
Rigor by default
The checks that make a decision analysis credible
MCDM results depend entirely on their inputs and calibration. The safeguards that make a ranking defensible are standard on every engagement.
Included on every project
- Justified criteria and method selection
- Consistency checks on expert judgments
- Appropriate aggregation of multiple experts
- Sensitivity analysis on weights and rankings
- Reproducible decision matrices and files you keep
What You Receive
Every engagement, delivered in full
Not a black-box result and a number, but a complete, documented package you can submit, defend, and reproduce.
- Criteria hierarchy and method rationale
- Structured decision matrices
- Criteria weights and consistency results
- Alternative rankings, factor maps, or solutions
- Sensitivity analysis of the results
- Interpretation and decision recommendation
- Publication-ready tables and figures
- Reproducible R or Python computation files
- Journal-ready methodology and results sections
Where this fits
Part of a larger arc
Decision analysis is strongest when the problem framing ahead of it is deliberate and the reporting after it is precise—each handled with the same care.
Research Design & Planning
Identification strategy, power, and specification decided before estimation begins.
Explore methodsStatistical & Methodological Audit
An independent check of assumptions, specification, and reproducibility before submission.
Explore auditPublication & Research Support
Methods and results reporting, journal selection, and reviewer-response support.
Explore support
FAQ
Common questions
Answers to what most researchers and project leads ask before we begin an operations-research or decision-analysis engagement.
It depends on the decision. AHP suits structured hierarchies with pairwise judgments; TOPSIS ranks alternatives by distance from an ideal solution; PROMETHEE handles outranking with preference functions; DEMATEL maps cause-effect relationships among criteria. We match the method to the structure of your problem, and often combine them—for example, using DEMATEL or AHP to derive weights that feed into TOPSIS.
They answer different questions. AHP prioritizes criteria or alternatives through pairwise comparison; DEMATEL identifies which factors drive others (cause versus effect groups); ISM (with MICMAC) builds a hierarchical structure of how factors interrelate and classifies them by driving and dependence power. For understanding a system of interrelated factors, DEMATEL and ISM are the right tools; for ranking, AHP.
When the judgments are inherently vague. Human assessments like important or very high are imprecise, and fuzzy AHP or fuzzy DEMATEL capture that uncertainty in the input rather than forcing crisp numbers. They are appropriate when expert opinion is the data and the ambiguity is real, not noise to be ignored.
Carefully—these methods live or die on input quality. We design the elicitation, check consistency (for AHP, the consistency ratio), aggregate multiple experts appropriately, and run sensitivity analysis to show whether the ranking is robust to reasonable changes in the judgments.
Yes—that is data envelopment analysis (DEA), which benchmarks decision-making units (firms, branches, business units) by how efficiently they convert inputs into outputs, without assuming a functional form. We run the appropriate DEA model (CCR, BCC, and extensions) and interpret the efficiency scores and targets.
Yes. We formulate and solve linear, integer, and multi-objective optimization problems, and apply game-theoretic and decision-modelling approaches where strategic interaction or uncertainty is central to the decision.
R and Python for MCDM, DEA, and optimization, plus specialized solvers and MATLAB where a model requires them. You receive the decision matrices, computation files, and a methods section written to journal standards.
Yes, and it is the best time. Defining the criteria, the alternatives, and the expert panel up front—and choosing whether the question is about ranking, structuring, or efficiency—determines which method is valid and prevents an analysis that answers the wrong question.
Related Expertise
Often paired with econometrics
Structuring a complex decision?
Tell us the alternatives and the criteria—we'll recommend the right MCDM or optimization method and make the ranking defensible.