![]() |
Design, Operations, & Production Systems Lab (DeOPSys) |
![]() ![]() ![]() |
DeOPSys lab presentation in the Supply Chain & Logistics magazine (text in Greek)
Supply Chain & Logistics Magazine - Τεύχος 57, 15 August - 01October 2013
Supply Chain & Logistics Magazine - Issue 57, 15 August - 01 October 2013
Scheduled Paratransit Transport Systems
G. Dikas, I. Minis
In this report we focus on ways to provide individualized services to people with mobility challenges using existing modes of public transport. We study the design of an interesting case, in which a bus operating in a public transport route may diverge from its nominal path to pick-up passengers with limited mobility and drop them off at their destination. We have modeled the design problem by a mixed integer-linear program, and we developed an exact Branch and Price approach to solve it to optimality. The proposed approach includes a labeling algorithm in which we introduced appropriate dominance rules to guarantee optimality. We have compared the efficiency of our approach with that of related algorithms from the literature. Furthermore, we have used the proposed approach to study key aspects of the system design problem, such as the effect of various constraints on the service level, and the tuning of the system’s parameters to address different transport environments.
Generation of test data for Scheduled Paratransit Transport Systems
G. Dikas, I. Minis
In this report we describe the creation of random test data for the case of Scheduled Paratransit Transport Systems (SPTS). SPTS focus on flexible public transport services that address the needs of the elderly, as well as of people with a disability. In this flexible transport services a bus operating in a public transport route may diverge from its nominal path to pick-up passengers with limited mobility and drop them off at their destination. The generator described in this report aims to create appropriate test instances that simulate practical problems within three distinct environments: a) Urban, b) Suburban, and c) Rural. The data generator uses special techniques to produce test instances that incorporate the characteristics of each of the three aforementioned environments.