Spatiotemporal Critical Opportunity and Link Identification for Joint Participation Scheduling - Space-Time Integration in Geography and GIScience - page 113

7.4 Modified Multi-objective Optimization Approach

for Scheduling Joint Participation

Several researchers have focused on activities for joint activity scheduling. For
example, Ronald et al. ( 2012 ) modeled social interactions between individuals for
joint activity scheduling. Nijland et al. ( 2012 ) extended the need-based model by
incorporating planned activities and events into a dynamic multi-day activity agenda
generator. Zhou and Golledge ( 2007 ) proposed a unified data collection framework
for real-time tracking of activity scheduling and schedule execution. Auld and
Mohammadian ( 2012 ) represented the activity planning process in an activity-
based microsimulation model called ADAPTS (Agent-based Dynamic Activity
Planning and Travel Scheduling). This model can dynamically simulate activity and
travel planning and scheduling. Most work on activity scheduling procedures has
been done from a transportation planning perspective, for example, SCHEDULER
(Gärling et al. 1989 ), ALBATROSS (Arentze and Timmermans 2003 ), FRBS (fuzzy
rule based system) (Olaru and Smith 2002 ), TASHA (travel activity scheduler for
household agents) (Miller and Roorda 2003 ; Roorda et al. 2009 ), and AURORA (an
agent for utility-driven rescheduling of routinized activities) (Joh et al. 2001 ;Joh
2004 ). Very few research studies have attempted to solve the activity scheduling
problem under constraints from time geography theory.
This chapter modifies the multi-objective approach of Fang et al. ( 2011 )to
schedule joint participation using the extended TVNBP and with the help of the
critical link and activity opportunity concepts.
The modified multi-objective optimization problem for scheduling joint activities
for multiple persons can then be defined as follows:

0 1

X path_ dist opp k j ;p i

X

@ D D A

min (7.13)

p i 2 P;k j 2 K

0 1

X t ravel _ time opp k j ;p i

X

@ T D A

min (7.14)

p i 2 P;k j 2 K

0 1

X desire_DT opp k j ;p i X

@ R D A

max (7.15)

p i 2 P;k j 2 K

0 1

X added_T opp k j ;p i

X

@ AT A

min D (7.16)

p i 2 P;k j 2 K

0 1

X U opp k j X

@ U D A

max (7.17)

p i 2 P;k j 2 K