Cooperative Transport Planning An Experimental Environment Jonne Zutt ( ) TU Delft Parallel Distributed Systems CABS Project

Contents Transport Planning Problem Architecture Algorithm MARS (TNO-TPD) The demo Results (concept)

Transport Planning Problem Infrastructure Trucks (capacity, speed) Orders (source, destination, profits, volume, timewindow) Plans (sequence of locations) Costs (distance, nr of trucks, timewindows) TPP Architecture Algorithm MARS Demo Results

TPP (2) Objective: Find a plan for every truck that minimizes the total costs, such that all orders are pickup up and delivered. TPP Architecture Algorithm MARS Demo Results

Broker Company 1Company 3 Company 2 Architecture Customer TPP Architecture Algorithm MARS Demo Results

Planning TPP Architecture Algorithm MARS Demo Results OrdersPlans First Phase Second Phase between all trucks of the company; Insertion, savings, incremental local optimization. cooperation between trucks in a coalition; clustering is used to decide which agents will cooperate; exchanging of orders.

Multi-Agent Real-time Simulator TNO TPD Written in the Java language, platform independent Scalable Two parts: (i) Basic (generic) simulator (ii) Experiment TPP Architecture Algorithm MARS Demo Results

MARS Experiment 1.Entities 2.Infrastructure 3.Scenario 4.Visual Model TPP Architecture Algorithm MARS Demo Results

TPP Architecture Algorithm MARS Demo Results

TPP Architecture Algorithm MARS Demo Results 17 runs, 10 random orders, 9 trucks. Average solution quality = 103.5%. Average cooperation gain = 76.1%.

Future Work Implement other heuristics for creating the initial plans; Try other forms of exchange, e.g. exchange combinations of orders; Incident handling: trucks are not executing exactly as planned.

The End

TPP (1) Directed graph G = ( Locations, Arcs ). Orders O = { id, source, target, profits, volume, timewindow }. Trucks T = { id, capacity, speed }. Plan = Cost function = (distance, nr of trucks, timewindows) TPP Architecture Algorithm MARS Demo Results

TPP (2) Total costs = The problem is to find a plan for every truck, such that all orders are picked up and delivered, thereby minimizing the total costs (i.e. the sum of the costs of the plans of all individual trucks). TPP Architecture Algorithm MARS Demo Results

Multi-Agent Real-time Simulator Basic Simulator: (Un)register objects Communication Loading experiment Initializing – Simulation steps – Termination TPP Architecture Algorithm MARS Demo Results