Constraints related to scenario variables

[XIZHO5@ulaval.ca]

Hi all,

I am currently designing a travel mode choice survey and would like to include scenario variables in the utility function. However, I have encountered several problems and would really appreciate any suggestions or advice.

1. When I add constraints to the scenario variables as suggested in the manual, Ngene always returns an error saying that it cannot find feasible candidates. The error message is:

"Error: The modified Federov candidate set size of 10000 could not be achieved. The percentages of candidates that failed are: 0% due dominance, 100% due constraints, and 0% due repeated alternatives. The candidate set size has been adjusted from 10000 to 0.

[Modified Fedorov] ERROR: The candidate set of the Modified Fedorov algorithm is smaller than the number of rows specified. That is, there are not enough unique choice sets to generate the number required as specified in the ;rows property. This problem sometimes appears when there are too many reject and/or reject constraints."

My code is:

```ngene
design
? 4km-Montreal city
; alts=bus,car,ebike,ecar,metro,walk
? efficient design
; eff = (mnl,d)+ 0.5*(imbalance)
; alg = mfederov(candidates = 10000)
; rows = 12
; block = 6

;require:
walk.weather = ebike.weather,
ebike.weather = bus.weather,
bus.weather = metro.weather,
metro.weather = ecar.weather,
walk.purpose = ebike.purpose,
ebike.purpose = bus.purpose,
bus.purpose = metro.purpose,
metro.purpose = ecar.purpose,
walk.GHG = ebike.GHG,
ebike.GHG = bus.GHG,
bus.GHG = metro.GHG,
metro.GHG = ecar.GHG,
walk.MET = ebike.MET,
ebike.MET = bus.MET,
bus.MET = metro.MET,
metro.MET = ecar.MET

;reject:
walk.walk_DN + ebike.ebike_DN + bus.bus_DN + metro.metro_DN + ecar.ecar_DN + car.car_DN >=6

;model:
U(walk) = b_weather_w.dummy[-0.2|-0.3] * weather[2,3,1] + b_purpose_w.dummy[0.1|0.2] * purpose[2,3,1] + b_GHG_w.dummy[0.2] * GHG[1,0] + b_MET_w.dummy[0.1] * MET[1,0]+ b_time[-0.01] * walk_time[48,61,74] + b_cost[-0.01] * walk_cost[0] + b_gov[0.1] * walk_gov[1,0] + b_use[0.1] * walk_use[1,0] + b_DN[0.15] * walk_DN[1,0] + asc_w[0.01] /

U(ebike) = b_weather_eb.dummy[-0.1|-0.2] * weather + b_purpose_eb.dummy[0.2|0.3] * purpose + b_GHG_eb.dummy[0.08] * GHG + b_MET_eb.dummy[0.2] * MET + b_time * ebike_time[12,21,27] + b_cost * ebike_cost[4.3,5.7,7.2] + b_gov * ebike_gov[1,0] + b_use * ebike_use[1,0] + b_DN * ebike_DN[1,0] + asc_ebike[0.01] /

U(bus) = b_weather_b.dummy[-0.05|-0.1] * weather + b_purpose_b.dummy[0.1|0.1] * purpose + b_GHG_b.dummy[0.05] * GHG + b_MET_b.dummy[0.05] * MET + b_time * bus_time[14,26,43] + b_cost * bus_cost[4.0] + b_gov * bus_gov[1,0] + b_use * bus_use[1,0] + b_DN * bus_DN[1,0] + asc_bus[0.01] /

U(metro) = b_weather_m.dummy[-0.05|-0.1] * weather + b_purpose_m.dummy[0.1|0.1] * purpose + b_GHG_m.dummy[0.1] * GHG + b_MET_m.dummy[0.05] * MET + b_time * metro_time[11,22,39] + b_cost * metro_cost[4.0] + b_gov * metro_gov[1,0] + b_use * metro_use[1,0] + b_DN * metro_DN[1,0] + asc_metro[0.01] /

U(ecar) = b_weather_ec.dummy[0.1|0.3] * weather + b_purpose_ec.dummy[0.05|0.05] * purpose + b_GHG_ec.dummy[0.02] * GHG + b_MET_ec.dummy[0.05] * MET + b_time * ecar_time[9,12,15] + b_cost * ecar_cost[3.2,4.3,5.4] + b_gov * ecar_gov[1,0] + b_use * ecar_use[1,0] + b_DN * ecar_DN[1,0] + asc_ecar[0.01]/

U(car) = b_time * car_time[7,8,11] + b_cost * car_cost[1.8,2.4,3.0] + b_park[-0.01] * car_parking_cost[3,4,5](2-4,2-4,2-4)+ b_use * car_use[1,0] + b_DN * car_DN[1,0]
$
```

The variables weather, purpose, GHG, and MET are scenario variables that should be the same across alternatives. In addition, the number of DN attributes should be less than 6.

I have tried many different methods to solve this problem, but none of them worked.

2. When I run other versions of the code with fewer constraints, the design can be generated successfully. However, the process takes a very long time, and the levels of some attributes are highly imbalanced.

These are the main problems I am facing at the moment. If anyone has experience with this kind of issue or any suggestions, I would really appreciate your help.

Thank you very much!