Maarten Lipmann

On-Line Dial-a-Ride Problems Under a Restricted Information Model

Abstract In on-line dial-a-ride problems servers are traveling in some metric space to serve requests for rides which are presented over time. Each ride is characterized by two points in the metric space, a source, the starting point of the ride, and a destination, the endpoint of the ride. Usually it is assumed that at the release of a request, complete information about the ride is known. We diverge from this by assuming that at the release of a ride, only information about the source is given. At visiting the source, the information about the destination will be made available to the servers. For many practical problems, our model is closer to reality. However, we feel that the lack of information is often a choice, rather than inherent to the problem: additional information can be obtained, but this requires investments in information systems. In this paper we give mathematical evidence that for the problem under study it pays to invest.

On-Line Dial-a-Ride Problems under a Restricted Information Model

In on-line dial-a-ride problems, servers are traveling in some metric space to serve requests for rides which are presented over time. Each ride is characterized by two points in the metric space, a source, the starting point of the ride, and a destination, the end point of the ride. Usually it is assumed that at the release of such a request complete information about the ride is known. We diverge from this by assuming that at the release of such a ride only information about the source is given. At visiting the source, the information about the destination will be made available to the servers. For many practical problems, our model is closer to reality. However, we feel that the lack of information is often a choice, rather than inherent to the problem: additional information can be obtained, but this requires investments in information systems. In this paper we give mathematical evidence that for the problem under study it pays to invest.

Non-abusiveness Helps: An % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfKttLearuqr1ngBPrgarmWu51MyVXgatC% vAUfeBSjuyZL2yd9gzLbvyNv2CaeHbuLwBLnhiov2DGi1BTfMBaeHb% d9wDYLwzYbItLDharqqtubsr4rNCHbGeaGqiVu0Je9sqqrpepC0xbb% L8F4rqqrFfpeea0xe9Lq-Jc9vqaqpepm0xbba9pwe9Q8fs0-yqaqpe% pae9pg0FirpepeKkFr0xfr-xfr-xb9adbaqaaeGaciGaaiaadeWaaq% aadaqbaaGcbaGaaGOmamaaCaaaleqabaGagiiBaWMaei4Ba8Maei4z% aCgaaOWaaWbaaSqabeaadaahaaadbeqaamaaBaaabaWaaWbaaeqaba% GaaGymaiabgkHiTiabgIGiodaaaeqaaaaaaaGcdaahaaWcbeqaaiab% d6gaUbaaaaa!4546!\[2^{\log } ^{^{_{^{1 - \in } } } } ^n \](1)-Competitive Algorithm for Minimizing the Maximum Flow Time in the Online Traveling Salesman Problem

In the online traveling salesman problem OLTSP requests for visits to cities arrive online while the salesman is traveling. We study the Fmax-OLTSP where the objective is to minimize the maximum flow time. This objective is particularly interesting for applications. Unfortunately, there can be no competitive algorithm, neither deterministic nor randomized. Hence, competitive analysis fails to distinguish online algorithms. Not even resource augmentation which is helpful in scheduling works as a remedy. This unsatisfactory situation motivates the search for alternative analysis methods.We introduce a natural restriction on the adversary for the Fmax-OLTSP on the real line. A non-abusive adversary may only move in a direction if there are yet unserved requests on this side. Our main result is an algorithm which achieves a constant competitive ratio against the nonabusive adversary.In the online traveling salesman problem OlTsp requests for visits to cities arrive online while the salesman is traveling. We study the F max-OlTsp where the objective is to minimize the maximum flow time. This objective is particularly interesting for applications. Unfortunately, there can be no competitive algorithm, neither deterministic nor randomized. Hence, competitive analysis fails to distinguish online algorithms. Not even resource augmentation which is helpful in scheduling works as a remedy. This unsatisfactory situation motivates the search for alternative analysis methods.

On minimizing the maximum flow time in the online dial-a-ride problem

In the online dial-a-ride problem (OlDarp), objects must be transported by a server between points in a metric space. Transportation requests (“rides”) arrive online, specifying the objects to be transported and the corresponding source and destination. We investigate the OlDarp for the objective of minimizing the maximum flow time. It has been well known that there can be no strictly competitive online algorithm for this objective and no competitive algorithm at all on unbounded metric spaces. However, the question whether on metric spaces with bounded diameter there are competitive algorithms if one allows an additive constant in the definition competitive ratio, had been open for quite a while. We provide a negative answer to this question already on the uniform metric space with three points. Our negative result is complemented by a strictly 2-competitive algorithm for the Online Traveling Salesman Problem on the uniform metric space, a special case of the problem.

Tight bounds for online TSP on the line

We consider the online traveling salesperson problem (TSP), where requests appear online over time on the real line and need to be visited by a server initially located at the origin. We distinguish between closed and open online TSP, depending on whether the server eventually needs to return to the origin or not. While online TSP on the line is a very natural online problem that was introduced more than two decades ago, no tight competitive analysis was known to date. We settle this problem by providing tight bounds on the competitive ratios for both the closed and the open variant of the problem. In particular, for closed online TSP, we provide a 1.64-competitive algorithm, thus matching a known lower bound. For open online TSP, we give a new upper bound as well as a matching lower bound that establish the remarkable competitive ratio of 2.04. Additionally, we consider the online D ial -A-R ide problem on the line, where each request needs to be transported to a specified destination. We provide an improved non-preemptive lower bound of 1.75 for this setting, as well as an improved preemptive algorithm with competitive ratio 2.41. Finally, we generalize known and give new complexity results for the underlying offline problems. In particular, we give an algorithm with running time O(n2) for closed offline TSP on the line with release dates and show that both variants of offline D ial -A-R ide on the line are NP-hard for any capacity c ≥ 2 of the server.