The objective of the Bar Crawl Optimization Problem is to select a set of bars and determine an ordering of them in such a way as to maximize the total benefit minus the total distance subject to a budget constraint.
The Multiple Traveling Salesman Problem (mTSP) is a generalization of the Traveling Salesman Problem (TSP) in which more than one salesman is allowed. Given a set of cities, one depot where \(m\) salesmen are located, and a cost metric, the objective of the \(m\)TSP is to determine a tour for each salesman such that the total tour cost is minimized and that each city is visited exactly once by only one salesman.
This application determines a flight path to avoid thunderclouds. The vertical extent of thunderclouds can be input as data, and a minimum cost flight path (under certain restrictions is determined (via a network flow formulation). This is implemented using GAMS/MIRO. Users can run the model on default data, view the results, then change data to generate a new scenario, comparing two saved scenarios in the comparison tool.
This case study demonstrates how supermarkets can make use of ridesharing vehicles to augment their delivery fleet and how to develop a model that routes both the supermarkets vehicles and the ridesharing vehicles to satisfy customer demands. The problem can be modeled as a Heterogeneous Fleet Vehicle Routing Problem with Time Windows (HVRPTW).