The Berth Allocation and Scheduling Problem (BASP) is a critical optimization challenge in maritime logistics, aiming to assign arriving vessels to berths efficiently, while adhering to practical constraints. Exploiting the connection of BASP with the Heterogeneous Vehicle Routing Problem with Time Windows (HVRPTW), we propose a mixed integer linear programming (MILP) formulation for a variant of BASP which is of utmost importance in real-world scenarios: the Dynamic Discrete Berth Allocation and Scheduling Problem with Time Windows (DDBASPTW). Consequently, inspired by the wealth of constructive and improvement heuristics for VRP, we design, implement and experimentally evaluate three constructive heuristics, Nearest Neighbour (NN), Insertion (INS), a quick-and-dirty variant of Insertion (qd-INS), as well as two improvement heuristics, Swap and Reinsert, taking into consideration both the online and the offline scenario with respect to vessel arrivals. Finally, we propose, implement and experimentally evaluate, custom-tailored variants for DDBASPTW of a single-solution metaheuristic, the Adaptive Large Neighborhood Search (ALNS), and of two population-based metaheuristics, the Genetic Algorithm (GA) and the Cuckoo Search Algorithm (CSA), which are aimed to solve the offline version of the problem. An extensive experimental evaluation compares these techniques against a generic state-of-the-art MILP solver. Results demonstrate that certain variants of INS not only are extremely fast and deliver competitive solutions, achieving a practical trade-off between execution times and quality of solutions. The improvement heuristics further refine the initial solutions, especially for weaker constructive approaches, offering a lightweight yet effective enhancement mechanism. The metaheuristics consistently yield high-quality solutions with significantly lower computational times compared to the exact MILP solver, making them well-suited for use in real-time or large-scale operational environments.

A key task in multi-objective optimization is to compute the Pareto frontier (a.k.a. Pareto subset) P of a given d-dimensional objective space F; that is, a maximal subset P ⊆ F such that every element in P is non-dominated (i.e., it is better in at least one criterion, against any other point) within F. This process, called dominance-filtering, often involves handling objective spaces derived from either the union or the Minkowski sum of two given partial objective spaces which are Pareto sets themselves, and constitutes a major bottleneck in several multi-objective optimization techniques. In this work, we introduce three new data structures, ND^{+}-trees, QND^{+}-trees and TND^{+}-trees, which are designed for efficiently indexing non-dominated objective vectors and performing dominance-checks. We also devise three new algorithms that efficiently filter out dominated objective vectors from the union or the Minkowski sum of two Pareto sets. An extensive experimental evaluation on both synthetically generated and real-world data sets reveals that our new algorithms outperform state-of-art techniques for dominance-filtering of unions and Minkowski sums of Pareto sets, and scale well w.r.t. the number of d ≥ 3 criteria and the sets' sizes.

We present a new method for computing a set of alternative origin-to-destination routes in road networks with an underlying time-dependent metric. The resulting set is aggregated in the form of a time-dependent alternative graph and is characterized by minimum route overlap, small stretch factor, small size and low complexity. To our knowledge, this is the first work that deals with the time-dependent setting in the framework of alternative routes. Based on preprocessed minimum travel-time information between a small set of nodes and all other nodes in the graph, our algorithm carries out a collection phase for candidate alternative routes, followed by a pruning phase that cautiously discards uninteresting or low-quality routes from the candidate set. Our experimental evaluation on real time-dependent road networks demonstrates that the new algorithm performs much better (by one or two orders of magnitude) than existing baseline approaches. In particular, the entire alternative graph can be computed in less than 0.384sec for the road network of Germany, and in less than 1.24sec for that of Europe. Our approach provides also "quick-and-dirty" results of decent quality, in about 1/300 of the above mentioned query times for continental-size instances.

We aim at exploiting parallelism in shared-memory multiprocessing systems, in order to speed up the execution time with as small redundancy in work as possible, for an elementary task that comes up frequently as a subroutine in the daily maintenance of large-scale time-dependent graphs representing real-world relationships or technological networks: the many-to-all time-dependent shortest paths (MATDSP) problem. MATDSP requires the computation of one time-dependent shortest-path tree (TDSPT) per origin-vertex and departure-time, from an arbitrary collection of pairs of origins and departure-times, towards all reachable destinations in the graph. Our goal is to explore the potential and highlight the limitations of amorphous data parallelism, when dealing with MATDSP in multicore computing environments with a given amount of processing elements and a shared memory to exploit. Apart from speeding-up execution time, consumption of resources (and energy) is also critical. Therefore, we aim at limiting the work overhead for solving a MATDSP instance, as measured by the overall number of arc relaxations in shortest-path computations, while trying to minimize the overall execution time. Towards this direction, we provide several algorithmic engineering interventions for solving MATDSP concerning: (i) the compact representation of the instance; (ii) the choice and the improvement of the time-dependent single-source shortest path algorithm that is used as a subroutine; (iii) the way according to which the overall work is allocated to the processing elements; (iv) the adoption of the amorphous data parallelism rationale, in order to avoid costly synchronization among the processing elements while doing their own part of the work. Our experimental evaluations, both on real-world and on synthetic benchmark instances of time-dependent road networks, provide insight how one should organize heavy MATDSP computations, depending on the application scenario. This insight is in some cases rather unexpected. For instance, it is not always the case that pure data parallelism (among otherwise totally independent processors) is the best choice for minimizing execution times. In certain cases it may be worthwhile to limit the level of data parallelism in favor of algorithmic parallelism, in order to achieve more efficient MATDSP computations.

Providing efficient, sustainable and personalized mobility services in urban environments that combine a spectrum of transport modes (e.g., public transport, electric vehicles, vehicle sharing, low energy and/or emission routes) constitutes a great challenge. In this work, we present MOVESMART, a holistic approach (and integrated platform) for the provision of renewable personal mobility services, leveraging crowd-sourcing data, tools for collecting real-time information by multimodal travelers, and traffic prediction mechanisms. MOVESMART guarantees real-time responses to renewable (on-demand) mobility queries for efficient multi-modal route planning that are time-dependent as well as sensitive to aperiodic incidents and traffic prediction forecasts. This paper focuses on the cloudbased (backend) services of the MOVESMART platform.

A novel landmark-based oracle (CFLAT) is presented, which provides earliest-arrival-time route plans in time-dependent road networks. To our knowledge, this is the first oracle that preprocesses combinatorial structures (collections of time-stamped min-travel-time-path trees) rather than travel-time functions. The preprocessed data structure is exploited by a new query algorithm (CFCA) which also computes (and pays for it) the actual connecting path that preserves the theoretical approximation guarantees. To make it practical and tackle the main burden of landmark-based oracles (the large preprocessing requirements), CFLAT is extensively engineered. A thorough experimental evaluation on two real-world benchmark instances shows that CFLAT achieves a significant improvement on preprocessing, approximation guarantees and query-times, in comparison to previous landmark-based oracles. It also achieves competitive query-time performance compared to state-of-art speedup heuristics for time-dependent road networks, whose query-times in most cases do not account for path construction.

Mobility of people and goods in urban environments raises several quality and sustainability concerns. While ICTs have established the ground for developing intelligent transport services, their effective use for supporting cleaner urban mobility still represents a major research challenge. The eCOMPASS research project addressed this challenge through introducing new mobility concepts and establishing a methodological framework for route planning optimization, delivering a comprehensive set of innovative tools and services for end-users to enable eco-awareness in urban transport. eCOMPASS innovative tools are based on new algorithmic technology concerning tools and methods for vehicle routing (cars and vehicle fleets) and multimodal human mobility for city dwellers and tourists. eCOMPASS involved a generic architecture that considered all types and scenarios of human and goods mobility in urban environments minimizing their environmental impact. In this work, we report on the main scientific innovations and end-products of eCOMPASS for vehicle routing, including car route planning and vehicle fleets.

The environmental impact of the steadily increasing demand for mobility of people and goods, especially in urban environments, raises public concern and presents challenges that need to be addressed in the interest of long-term sustainability. Along this line, the inherently eco-friendly human mobility which involves the use of urban public transit networks must be encouraged and eased. This necessitates the development of context-aware services that hide the complexity of public transit networks while considering all available transportation modalities (e.g. bus, metro, tram, walking and cycling) in order to provide sophisticated route planning tailored to both residents and visitors of urban areas. The EU-funded eCOMPASS research project has addressed this challenge through establishing a methodological framework for route planning optimization. A core objective of the project has been to employ novel algorithm engineering approaches for delivering a comprehensive set of tools and services (accessible from web/mobile application interfaces) for mobile end users to enable eco-awareness in urban multi-modal transfers. eCOMPASS delivered web and mobile services providing multimodal public transportation route planning, considering contextual information as well as various restrictions and/or user constraints. Herein, we report the motivation, main scientific innovations and present the end-products of eCOMPASS with respect to multimodal human mobility.

We implement and experimentally evaluate landmark-based oracles for min-cost paths in two different types of road networks with time-dependent arc-cost functions, based on distinct real-world historic traffic data: the road network for the metropolitan area of Berlin, and the national road network of Germany. Our first contribution is a significant improvement on the implementation of the FLAT oracle, which was proposed and experimentally tested in previous works. Regarding the implementation, we exploit parallelism to reduce preprocessing time and real-time responsiveness to live-traffic reports. We also adopt a lossless compression scheme that severely reduces preprocessing space and time requirements. As for the experimentation, apart from employing the new data set of Germany, we also construct several refinements and hybrids of the most prominent landmark sets for the city of Berlin. A significant improvement to the speedup of FLAT is observed: For Berlin, the average query time can now be as small as 83μsec, achieving a speedup (against the time-dependent variant of Dijkstra's algorithm) of more than 1, 119 in absolute running times and more than 1, 570 in Dijkstra-ranks, with worst-case observed stretch less than 0.781%. For Germany, our experimental findings are analogous: The average query-response time can be 1.269msec, achieving a speedup of more than 902 in absolute running times, and 1, 531 in Dijkstra-ranks, with worst-case stretch less than 1.534%. Our second contribution is the implementation and experimental evaluation of a novel hierarchical oracle (HORN). It is based on a hierarchy of landmarks, with a few “global” landmarks at the top level possessing travel-time information for all possible destinations, and many more “local” landmarks at lower levels possessing travel-time information only for a small neighborhood of destinations around them. As it was previously proved, the advantage of HORN over FLAT is that it achieves query times sublinear, not just in the size of the network, but in the Dijkstra-rank of the query at hand, while requiring asymptotically similar preprocessing space and time. Our experimentation of HORN in Berlin indeed demonstrates improvements in query times (more than 30.37%), Dijkstra-ranks (more than 39.66%), and also worst-case error (more than 35.89%), at the expense of a small blow-up in space. Finally, we implement and experimentally test a dynamic scheme to provide responsiveness to live-traffic reports of incidents with a small timelife (e.g., a temporary blockage of a road segment due to an accident). Our experiments also indicate that the traffic-related information can be updated in seconds.

We study networks obeying time-dependent min-cost path metrics, and present novel oracles for them which provably achieve two unique features: (i) subquadratic preprocessing time and space, independent of the metric’s amount of disconcavity; (ii) sublinear query time, in either the network size or the actual Dijkstra-Rank of the query at hand.

Urban road networks are represented as directed graphs, accompanied by a metric which assigns cost functions (rather than scalars) to the arcs, e.g. representing time-dependent arc-traversal-times. In this work, we present oracles for providing time-dependent min-cost route plans, and conduct their experimental evaluation on a real-world data set (city of Berlin). Our oracles are based on precomputing all landmark-to-vertex shortest travel-time functions, for properly selected landmark sets. The core of this preprocessing phase is based on a novel, quite efficient and simple one-to-all approximation method for creating approximations of shortest travel-time functions. We then propose three query algorithms, including a PTAS, to efficiently provide min-cost route plan responses to arbitrary queries. Apart from the purely algorithmic challenges, we deal also with several implementation details concerning the digestion of raw traffic data, and we provide heuristic improvements of both the preprocessing phase and the query algorithms. We conduct an extensive, comparative experimental study with all query algorithms and six landmark sets. Our results are quite encouraging, achieving remarkable speedups (at least by two orders of magnitude) and quite small approximation guarantees, over the time-dependent variant of Dijkstra's algorithm.

Frontmatter, Table of contents, Preface, Workshop Organization

We consider algorithmic questions concerning the existence, tractability, and quality of Nash equilibria, in atomic congestion games among users participating in selfish coalitions. We introduce a coalitional congestion model among atomic players and demonstrate many interesting similarities with the noncooperative case. For example, there exists a potential function proving the existence of pure Nash equilibria (PNE) in the unrelated parallel links setting; in the network setting, the finite improvement property collapses as soon as we depart from linear delays, but there is an exact potential (and thus PNE) for linear delays. The price of anarchy on identical parallel links demonstrates a quite surprising threshold behavior: It persists on being asymptotically equal to that in the case of the noncooperative KP-model, unless the number of coalitions is sublogarithmic . We also show crucial differences, mainly concerning the hardness of algorithmic problems that are solved efficiently in the noncooperative case. Although we demonstrate convergence to robust PNE, we also prove the hardness of computing them. On the other hand, we propose a generalized fully mixed Nash equilibrium that can be efficiently constructed in most cases. Finally, we propose a natural improvement policy and prove its convergence in pseudopolynomial time to PNE which are robust against (even dynamically forming) coalitions of small size.

In this paper we study the problem of assigning unit-size tasks to related machines when only limited online information is provided to each task. This is a general framework whose special cases are the classical multiple-choice games for the assignment of unit-size tasks to identical machines. The latter case was the subject of intensive research for the last decade. The problem is intriguing in the sense that the natural extensions of the greedy oblivious schedulers, which are known to achieve near-optimal performance in the case of identical machines, are proved to perform quite poorly in the case of the related machines.(MATH) In this work we present a rather surprising lower bound stating that any oblivious scheduler that assigns an arbitrary number of tasks to $n$ related machines would need $\Omega\left(\frac{\log n}{\l2 n}\right)$ polls of machine loads per task, in order to achieve a constant competitive ratio versus the optimum offline assignment of the same input sequence to these machines. On the other hand, we prove that the missing information for an oblivious scheduler to perform almost optimally, is the amount of tasks to be inserted into the system. In particular, we provide an oblivious scheduler that only uses $\O(\l2 n)$ polls, along with the additional information of the size of the input sequence, in order to achieve a constant competitive ratio vs. the optimum offline assignment. The philosophy of this scheduler is based on an interesting exploitation of the slowfit concept ([1, 5, 3]; for a survey see [6, 9, 16]) for the assignment of the tasks to the related machines despite the restrictions on the provided online information, in combination with a layered induction argument for bounding the tails of the number of tasks passing from slower to faster machines. We finally use this oblivious scheduler as the core of an adaptive scheduler that does not demand the knowledge of the input sequence and yet achieves almost the same performance.

In this paper we present an efficient general simulation strategy for computations designed for fully operational BSP machines of n ideal processors, on n-processor dynamic-fauhprone BSP machines.The fault occurrences are fail-stop and fully dynamic, i.e., they are ahowed to happen on-line 'This work was partially

In this paper general simulations of algorithms designed for fully operational BSP machines on BSP machines with faulty processors or unavailable processors are developed. The fail-stop model is considered, that is, if a processor fails or becomes unavailable it remains so until the end of the computation. The faults are random, that is, a processor may fail independently with probablility a, a is a constant. Two possible settings for fault occurence are considered: the faults are either static (the faulty or unavailable processors are already known at the start of the computation) or dynamic (the processors become faulty or unavailable during the computation). In the case of static faults, a simulation of an n-processor fault-free BSP machine on a faulty n-processor BSP machine is presented with constant slowdown per local computation step and O(log n \\Delta maxfL; gg) slowdown per communication step, given that a preprocessing has been done that needs O(log 2 n \\Delta maxfL; gg)...