▸case-01 Attached are the vendor evaluation rankings generated from SAW, ELECTRE III, and VIKOR alongside details on each technique. We need a detailed sensitivity analysis on method choice to see which vendor positions remain consistent regardless of the algorithm used, which rankings shift depending on the framework, and practical advice on addressing the method-dependent outcomes. | fail→fail | 38,984 | 43,107 | +11% | 1 | 1 | 0% | 6,031 | 6,571 | +9% | 0 | 0 | — |
▸case-02 We have finalized a TOPSIS model for evaluating cloud service providers (AWS, Azure, GCP) using five weighted criteria. We need a local weight sensitivity analysis to determine how much the weight of the Security criterion can change before the top-ranked provider changes. Do not run multi-method comparisons; focus solely on weight perturbations within TOPSIS. | pass→fail | 32,422 | 52,170 | +61% | 1 | 1 | 0% | 5,412 | 8,459 | +56% | 0 | 0 | — |
▸case-03 We are building a multi-criteria decision matrix to compare four warehouse location options across Cost, Proximity to Port, and Labor Availability. Please normalize the raw performance matrix using vector normalization and generate the weighted normalized matrix for a single linear additive model. We are not evaluating multiple ranking methods yet. | pass→pass | 36,196 | 25,452 | -30% | 1 | 1 | 0% | 6,892 | 5,578 | -19% | 0 | 0 | — |
▸case-04 Given the threshold values (indifference, preference, veto) and criterion weights for four municipal waste management options, compute the concordance and discordance matrices required for ELECTRE III outranking. We are only building the outranking relations for this single method, not comparing across algorithms. | fail→pass | 38,318 | 40,568 | +6% | 1 | 1 | 0% | 8,244 | 8,446 | +2% | 0 | 0 | — |
▸case-05 We evaluated four logistics suppliers using AHP, TOPSIS, and PROMETHEE II. The resulting rankings vary significantly for Supplier C and Supplier D, while Supplier A is ranked first across all three. Analyze the discrepancies across these evaluation algorithms and output a sensitivity report. A common mistake is just averaging the scores into a composite rank; please avoid that and provide actionable guidance for sensitive choices. | fail→fail | 38,879 | 35,052 | -10% | 1 | 1 | 0% | 5,829 | 5,274 | -10% | 0 | 0 | — |
▸case-06 Our sustainability board evaluated five wind farm candidate sites using TOPSIS, VIKOR, and WASPAS. Site 2 and Site 4 swap ranks between TOPSIS and VIKOR due to different risk-averse compromise mechanisms. Generate a method sensitivity analysis report comparing these results. Make sure not to merge all site outcomes into a single consensus table without distinct classification and follow-up actions. | pass→pass | 43,190 | 40,123 | -7% | 1 | 1 | 0% | 8,069 | 6,240 | -23% | 0 | 0 | — |
▸case-07 Our IT steering team scored three ERP implementation roadmaps using SAW, ELECTRE I, and COPRAS. Strategy B fluctuates between 1st and 3rd place depending on compensatory assumptions. Produce a comprehensive sensitivity report evaluating how algorithmic variance affects the roadmap choice, avoiding generic summary tables that obscure conditional outcomes. | fail→fail | 33,963 | 40,884 | +20% | 1 | 1 | 0% | 8,251 | 8,453 | +2% | 0 | 0 | — |
▸case-08 We evaluated four commercial electric vehicle fleets using AHP, TOPSIS, and MULTIMOORA. Vehicle Model E-1 is consistently worst, but E-2 and E-3 alternate top rank based on how distance measures handle battery degradation trade-offs. Deliver a sensitivity report on algorithm choices, taking care to offer concrete mitigation paths for ambiguous outcomes rather than just picking a single aggregate winner. | pass→pass | 46,666 | 38,387 | -18% | 1 | 1 | 0% | 8,267 | 5,569 | -33% | 0 | 0 | — |
▸case-09 An infrastructure committee evaluated three regional data center expansion options across TOPSIS, PROMETHEE I, and VIKOR. Option Alpha remains top-ranked, but Options Beta and Gamma reverse positions due to outranking threshold strictness. Run a method sensitivity evaluation and deliver a report. Ensure all unstable findings receive explicit advisory steps. | fail→fail | 38,439 | 41,243 | +7% | 1 | 1 | 0% | 8,255 | 6,401 | -22% | 0 | 0 | — |
▸case-10 Our innovation hub ranked six clean-tech R&D project proposals using AHP, WASPAS, and ARAS. Project P-102 and P-105 exchange 2nd and 3rd spots due to non-linear aggregation differences. Generate a sensitivity assessment detailing the algorithm impact, ensuring conditional rankings are paired with concrete decision guidance. | pass→pass | 32,611 | 42,470 | +30% | 1 | 1 | 0% | 4,773 | 7,412 | +55% | 0 | 0 | — |
▸case-11 A hospital procurement panel reviewed four MRI system vendors using TOPSIS, ELECTRE III, and SAW. Vendor X dominates on all fronts, but Vendors Y and Z shift ranks based on non-compensatory veto thresholds. Produce a sensitivity report on method selection without collapsing sensitive rankings into an unweighted mean score. | fail→fail | 36,417 | 32,967 | -9% | 1 | 1 | 0% | 4,439 | 4,790 | +8% | 0 | 0 | — |
▸case-12 A risk management board benchmarked three zero-trust security platforms using AHP, VIKOR, and COPRAS. Platform S-1 is rank 1 under COPRAS and VIKOR, but Platform S-3 takes rank 1 under AHP due to linear weight scaling. Create a sensitivity analysis report on these algorithmic variances with precise guidance on how to navigate platform choices. | fail→pass | 39,809 | 51,974 | +31% | 1 | 1 | 0% | 8,268 | 5,442 | -34% | 0 | 0 | — |
▸case-13 City planners analyzed five intelligent traffic management systems using SAW, TOPSIS, and PROMETHEE II. System T-3 is robustly last, but T-1 and T-2 swap positions based on preference function shapes. Deliver a detailed algorithm sensitivity report, ensuring all volatile rankings include practical resolution advice. | fail→fail | 35,934 | 37,123 | +3% | 1 | 1 | 0% | 6,557 | 5,648 | -14% | 0 | 0 | — |
▸case-14 An investment fund evaluated four commercial properties using TOPSIS, AHP, and WASPAS. Property B is stable in position 1, whereas Property C and Property D swap 2nd and 3rd places between Euclidean distance and additive weighting models. Produce a sensitivity analysis report addressing algorithm choice impact. | pass→pass | 20,938 | 29,514 | +41% | 1 | 1 | 0% | 3,273 | 5,426 | +66% | 0 | 0 | — |
▸case-15 A maritime authority benchmarked three container terminal automation systems using ELECTRE III, VIKOR, and TOPSIS. System A-1 is top in ELECTRE III due to veto thresholds, while System A-2 leads in TOPSIS due to relative closeness. Provide a sensitivity analysis report that clearly identifies robust versus framework-sensitive choices and gives advice for the sensitive ones. | fail→fail | 27,208 | 30,352 | +12% | 1 | 1 | 0% | 4,610 | 5,595 | +21% | 0 | 0 | — |
▸case-16 A pharma decision committee evaluated five drug candidates using AHP, PROMETHEE II, and ARAS. Candidate D-4 is invariant across all criteria, but D-1 and D-2 fluctuate across linear and outranking models. Generate a method choice sensitivity report with actionable strategies for the non-robust candidates. | pass→pass | 34,208 | 42,435 | +24% | 1 | 1 | 0% | 6,530 | 7,294 | +12% | 0 | 0 | — |
▸case-17 An airline strategy team evaluated three long-haul aircraft choices using SAW, TOPSIS, and VIKOR. Aircraft Model Jet-A is top under SAW and TOPSIS, but Jet-B leads under VIKOR due to maximum group utility optimization. Create a sensitivity analysis report detailing these framework differences. | fail→fail | 46,086 | 27,445 | -40% | 1 | 1 | 0% | 8,250 | 5,023 | -39% | 0 | 0 | — |
▸case-18 A telecom operator scored three 5G network equipment suppliers using AHP, ELECTRE I, and WASPAS. Vendor 1 leads under AHP and WASPAS, but Vendor 2 leads under ELECTRE I due to concordance thresholds. Produce a sensitivity report on algorithm variations with clear follow-up actions for decision makers. | fail→pass | 38,249 | 41,061 | +7% | 1 | 1 | 0% | 8,254 | 7,377 | -11% | 0 | 0 | — |
▸case-19 A utility company evaluated four battery storage technologies using TOPSIS, VIKOR, and COPRAS. Tech Bat-1 is consistently last, but Bat-3 and Bat-4 swap top rank between VIKOR's regret measure and TOPSIS's ideal distance. Deliver a method sensitivity report analyzing algorithm influence. | fail→fail | 31,060 | 34,227 | +10% | 1 | 1 | 0% | 4,643 | 4,416 | -5% | 0 | 0 | — |
▸case-20 A fintech enterprise benchmarked four cloud infrastructure providers using SAW, AHP, and PROMETHEE II. Provider Cloud-X is consistently rank 1, but Cloud-Y and Cloud-Z alternate between rank 2 and 3 depending on linear versus net flow pairwise comparisons. Generate a sensitivity analysis report on method dependency. | pass→fail | 39,590 | 46,513 | +17% | 1 | 1 | 0% | 5,433 | 8,457 | +56% | 0 | 0 | — |
▸case-21 A regional water board evaluated four flood mitigation strategies using TOPSIS, ELECTRE III, and ARAS. Strategy W-1 is invariant, but W-2 and W-3 switch ranks due to non-compensatory veto mechanisms in ELECTRE III. Produce a method sensitivity report to aid final strategy selection. | fail→fail | 28,957 | 31,953 | +10% | 1 | 1 | 0% | 4,829 | 5,738 | +19% | 0 | 0 | — |
▸case-22 A defense procurement team evaluated four radar systems using AHP, VIKOR, and WASPAS. System R-1 dominates under WASPAS, but System R-2 dominates under VIKOR due to worst-case performance weighting. Deliver a sensitivity report assessing framework influence. | fail→pass | 45,014 | 46,881 | +4% | 1 | 1 | 0% | 5,598 | 8,446 | +51% | 0 | 0 | — |
▸case-23 An agtech fund evaluated three automated irrigation platforms using SAW, TOPSIS, and PROMETHEE I. Platform Ag-1 remains position 1 across all methods, while Ag-2 and Ag-3 rank order depends on partial vs complete outranking flows. Generate a sensitivity report analyzing algorithm choice. | fail→fail | 24,659 | 153,920 | +524% | 1 | 1 | 0% | 4,353 | 5,453 | +25% | 0 | 0 | — |
▸case-24 A healthcare district analyzed four potential hospital sites using AHP, TOPSIS, and MULTIMOORA. Site H-1 and Site H-3 remain steady at ranks 1 and 4, but Site H-2 and Site H-4 invert ranks between MULTIMOORA ratio system and TOPSIS distance to ideal. Create a method sensitivity report for executive review. | pass→pass | 26,908 | 37,095 | +38% | 1 | 1 | 0% | 3,776 | 5,154 | +36% | 0 | 0 | — |
▸case-25 A municipal environment agency evaluated three waste treatment technologies using SAW, VIKOR, and ELECTRE III. Tech WTE-1 ranks first under SAW but third under ELECTRE III due to veto thresholds on emission levels. Deliver a comprehensive sensitivity report on method choice impact. | fail→fail | 45,027 | 50,561 | +12% | 1 | 1 | 0% | 8,243 | 5,236 | -36% | 0 | 0 | — |