The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints.

This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search.

The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems.

The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.

/pdf/an-adaptive-large-neighborhood-search-heuristic-for-the-54a14dq2lk.pdf

An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows

https://www.iro.umontreal.ca/~ferland/Seminars/Local_search/Pisinger,Ropke_ALNS_2007.pdf

A general heuristic for vehicle routing problems

This paper presents a survey of the research on the vehicle routing problem with time windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from one depot to a set of geographically scattered points. The routes must be designed in such a way that each point is visited only once by exactly one vehicle within a given time interval, all routes start and end at the depot, and the total demands of all points on one particular route must not exceed the capacity of the vehicle. Both traditional heuristic route construction methods and recent local search algorithms are examined. The basic features of each method are described, and experimental results for Solomon's benchmark test problems are presented and analyzed. Moreover, we discuss how heuristic methods should be evaluated and propose using the concept of Pareto optimality in the comparison of different heuristic approaches. The metaheuristic methods are described in the second part of this article.

/pdf/vehicle-routing-problem-with-time-windows-part-i-route-4bqv6nr8uf.pdf

Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms

Charged particle swarm optimization (CPSO) is well suited to the dynamic search problem since inter-particle repulsion maintains population diversity and good tracking can be achieved with a simple algorithm. This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity. Two types of charged swarms and an adapted neutral swarm are compared for a number of different dynamic environments which include extreme ‘needle-inthe-haystack’ cases. The results suggest that charged swarms perform best in the extreme cases, but neutral swarms are better optimizers in milder environments.

Genetic and Evolutionary Computation -- GECCO-2003

The key idea underlying iterated local search is to focus the search not on the full space of all candidate solutions but on the solutions that are returned by some underlying algorithm, typically a local search heuristic. The resulting search behavior can be characterized as iteratively building a chain of solutions of this embedded algorithm. The result is also a conceptually simple metaheuristic that nevertheless has led to state-of-the-art algorithms for many computationally hard problems. In fact, very good performance is often already obtained by rather straightforward implementations of the metaheuristic. In addition, the modular architecture of iterated local search makes it very suitable for an algorithm engineering approach where, progressively, the algorithms’ performance can be further optimized. Our purpose here is to give an accessible description of the underlying principles of iterated local search and a discussion of the main aspects that need to be taken into account for a successful application of it. In addition, we review the most important applications of this method and discuss its relationship to other metaheuristics.

/pdf/iterated-local-search-framework-and-applications-4e6bvb7vlv.pdf

Iterated Local Search: Framework and Applications

http://polymorphe.free.fr/cours/ia/tsp/schrimpf-record_breaking_opt_ruin_recreate.pdf

Record Breaking Optimization Results Using the Ruin and Recreate Principle

Means and a computerized method for recommending items such as books and audio compact disks. For each item, a user profile includes ratings provided by users of the system. Unlike present recommendation systems, the user profiles do not include pre-computed similarity factors measuring similarity between users. Rather, when an advisee requests a recommendation, similarity measures are computed comparing the advisee to other users, and the similarity measures are associated with the other users. A subset of the users is selected, where the subset includes the users most similar to the advisee. A recommendation is made based on the ratings by the members of the selected subset.

System and method for item recommendations

A computerized method and corresponding means for rating an item within a recommendation system, that exploits additional external knowledge or the relationships between the ratable items to implicitly derive, from a first item rated explicitly by a certain user, implicit ratings for items related to the explicitly rated item. In response to a first explicit rating for a first item, the following steps are performed: determining, for the first item, a first set related items based on a predefined item relationship; storing, within the recommendation system, the first explicit rating of the first item; and storing, within the recommendation system, first implicit ratings for the first set of related items.

Method for rating items within a recommendation system based on additional knowledge of item relationships

A method and system for providing and receiving location-based services for a planned trip of a user having access to a portable telecommunication device. The user creates, e.g., via the Internet, a trip profile. The profile contains base data about the user and the trip. After the profile is created at the Service provider's site, a scheduled delivery of information is activated. The position of the user is computed repeatedly during the trip. When new messages are available for an individual user at his individual current position, the user receives the information. If the information does not match the user's needs, he is able to react and submit his current position in order to receive the right information.

Personal travel agent using push services

The present invention relates to an automatic method of computerized optimization of a technical system like for instance routing systems such as a transportation routing system or a communication network. The invention teaches an optimization method of a technical system comprising new ruin- and recreate-steps operating on the state-variables of a current system state. The ruin-step destroys the current state in a “larger” extend and thus introduces increased freedom for the recreate-step to create a new state. This allows for recalculating a new state in accordance with additional constraints which also improves a quality measure of the state; i.e. every recalculated state always fulfills the additional constraints. Moreover, the recreate-step is not guided by the values of said destroyed state variables, which opens a larger space for determining a new state.

Gerhard Schrimpf

Papers

Record Breaking Optimization Results Using the Ruin and Recreate Principle

System and method for item recommendations

Method for rating items within a recommendation system based on additional knowledge of item relationships

Personal travel agent using push services

Optimization with ruin recreate