Analysis of a New Vehicle Scheduling and Location Problem

We consider a container terminal discharging containers from a ship and locating them in the terminal yard. Each container has a number of potential locations in the yard where it can be stored. Containers are moved from the ship to the yard using a fleet of vehicles, each of which can carry one container at a time. The problem is to assign each container to a yard location and dispatch vehicles to the containers so as to minimize the time it takes to download all the containers from the ship. We show that the problem is NP-hard and develop a heuristic algorithm based on formulating the problem as an assignment problem. The effectiveness of the heuristic is analyzed from both worst-case and computational points of view. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 363–385, 200

Estimation and monitoring of traffic intensities with application to control of stochastic systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106982/1/asmb1961.pd

Autonomous mobile robots in manufacturing : Highway Code development, simulation and testing

A dynamic and flexible manufacturing environment presents many challenges in the movement of Autonomous Mobile Robots (AMRs), leading to delays due to the complexity of operations while negotiating even a simple route. Therefore, an understanding of rules related to AMR movement is important both from a utility perspective as well as a safety perspective. Our survey from literature and industry has revealed a gap in methodology to test rules related to AMR movement in a factory environment. Testing purely through simulations would not able to capture the nuances of shop floor interactions whereas physical testing alone would be incredibly time-consuming and potentially hazardous. This work presents a new methodology that can make use of observations of AMR behaviour on selected cases on the shop floor and build up the fidelity of those simulations based on observations. This paper presents the development of a Highway Code for AMRs, development of simulation models for an ideal-AMR (based on the rules from the Highway Code) and physical testing of real-AMR in an industrial environment. Finally, a behavioural comparison of an ideal-AMR and a real-AMR in five scenarios (taken from the shop floor of an industrial partner) is presented. This work could enable informed decisions regarding the implementation of AMRs through identification of any adverse behaviours which could then be mitigated either through improvements on the AMR or through establishing shop floor protocols that reduce the potential impact of these behaviours