Genetic Algorithm for Biobjective Urban Transit Routing Problem

Table 8

The best route sets for passenger and operator for all four cases.

Case	The best route sets for passenger	The best route sets for operator
I	13–14–10–8–6–3–2–1	5–4–2–1
	9–15–8–10–11–12–4–2	11–10–7–15–8–6–3–2
	11–10–7–15–6–3–2–5	9–15
	12–11–13–10–8–6–4–5	12–11–13–14
II	13–10–7–15–6–3–2–1	10–7–15–8–6–3–2–1
	11–10–8–6–4–5–2–1	11–12
	5–4–12–11–10–7–15–9	5–4–2
	10–14–13–11–12–4–2–1	14–13
	7–15–8–6–4–5–2–3	13–11–10
	12–11–10–8–6–3–2–1	9–15
III	13–10–7–15–8–6–4–5	1–2–3–6–8–15–7–10
	9–15–8–6–3–2–4–5	4–2
	9–15–7–10–11–12–4–2	12–11
	14–13–11–12–4–5–2–1	13–11–10
	5–4–6–8–10–11–13	5–4
	1–2–3–6–15–7–10–14	9–15
	1–2–3–6–8–10–11–13	14–13
IV	14–10–7–15–6–3–2–1	10–7–15–8–6–3–2–4
	11–10–8–6–4–5–2	12–11
	13–11–10–8–6–3–2–1	13–11
	11–12–4–2–1	2–1
	14–13–11–12–4–5–2–1	9–15
	9–15–8–6–3–2–4–5	4–5
	14–10–7–15–8–6–4–5	11–10
	12–11–10–7–15–9	14–13