Maximizing Revenues for Online-Dial-a-Ride

In the classic Dial-a-Ride Problem, a server travels in some metric space to serve requests for rides. Each request has a source, destination, and release time. We study a variation of this problem where each request also has a revenue that is earned if the request is satisfied. The goal is to serve requests within a time limit such that the total revenue is maximized. We first prove that the version of this problem where edges in the input graph have varying weights is NP-complete. We also prove that no algorithm can be competitive for this problem. We therefore consider the version where edges in the graph have unit weight and develop a 2-competitive algorithm for this problem

Online Optimization of Complex Transportation Systems

This paper discusses online optimization of real-world transportation systems. We concentrate on transportation problems arising in production and manufacturing processes, in particular in company internal logistics. We describe basic techniques to design online optimization algorithms for such systems, but our main focus is decision support for the planner: which online algorithm is the most appropriate one in a particular setting? We show by means of several examples that traditional methods for the evaluation of online algorithms often do not suffice to judge the strengths and weaknesses of online algorithms. We present modifications of well-known evaluation techniques and some new methods, and we argue that the selection of an online algorithm to be employed in practice should be based on a sound combination of several theoretical and practical evaluation criteria, including simulation

Comparison of Tabu/2‐opt heuristic and optimal tree search method for assignment problems

A nonlinear cooperative control problem involving several vehicles is detailed and solved. The vehicles must be assigned to perform many tasks such that they obey constraints on the order of task completion and minimize a nonlinear objective function, the total time to finish all tasks. This is an example of a combinatorial task assignment problem. A novel heuristic is introduced that represents a new combination of two combinatorial optimization tools. The quality of the solutions produced by this heuristic is demonstrated through comparison with a branch and bound search method. The branch and bound method is a well‐known procedure and finds optimal solutions to the constrained, nonlinear task assignment problem. Copyright © 2011 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86839/1/1717_ftp.pd

Online k-server routing problems

In an online k-server routing problem, a crew of k servers has to visit points in a metric space as they arrive in real time. Possible objective functions include minimizing the makespan (k-Traveling Salesman Problem) and minimizing the sum of completion times (k-Traveling Repairman Problem). We give competitive algorithms, resource augmentation results and lower bounds for k-server routing problems in a wide class of metric spaces. In some cases the competitive ratio is dramatically better than that of the corresponding single server problem. Namely, we give a 1+O((log¿k)/k)-competitive algorithm for the k-Traveling Salesman Problem and the k-Traveling Repairman Problem when the underlying metric space is the real line. We also prove that a similar result cannot hold for the Euclidean plane

Solving the Sequential Ordering Problem with Automatically Generated Lower Bounds

The Sequential Ordering Problem (SOP) is a version of the Asymmetric Traveling Salesman Problem (ATSP) where precedence constraints on the vertices must also be observed. The SOP has many real life applications and it has proved to be a great challenge (there are SOPs with 40-50 vertices which have not been solved optimally yet with significant computational effort). We use novel branch&bound search algorithms with lower bounds obtained from homomorphic abstractions of the original state space. Our method is asymptotically optimal. In one instance, it has proved a solution value to be optimal for an open problem while it also has matched best known solutions quickly for many unsolved problems from the TSPLIB. Our method of deriving lower bounds is general and applies to other variants of constrained ATSPs as well

On identifying in polynomial time violated subtour elimination and precedence forcing constraints for the sequential ordering problem

On identifying in polynomial time violated subtour elimination and precedence forcing constraints for the sequential ordering problem / N. Ascheuer ... - In: Integer programming and combinatorial optimization / Ravi Kannan ... - Waterloo, Ont. : Univ. of Waterloo Pr., 1990. - S. 19 ff