Jörg Homberger

Bio: Jörg Homberger is an academic researcher from University of Applied Sciences Stuttgart. The author has contributed to research in topics: Metaheuristic & Parallel metaheuristic. The author has an hindex of 13, co-authored 23 publications receiving 525 citations. Previous affiliations of Jörg Homberger include University of Stuttgart.

A (μ, λ)-coordination mechanism for agent-based multi-project scheduling

Abstract: A new generic negotiation-based mechanism to coordinate project planning software agents to share resources among projects is described. The mechanism, which takes into account asymmetric information and opportunistic behavior, is concretized for the decentralized resource constrained multi-project scheduling problem, and evaluated on 80 benchmark instances taken from the literature and 60 newly generated instances. Computational tests show that the proposed mechanism comes close to results obtained by central solution methods. For twelve benchmark instances new best solutions could be computed.

A parallel two-phase metaheuristic for routing problems with time windows

Abstract: This paper describes the parallelization of a two-phase metaheuristic for the vehicle routing problem with time windows and a central depot (VRPTW). The underlying objective function combines the minimization of the number of vehicles in the first search phase of the metaheuristic and the minimization of the total travel distance in the second search phase. The parallelization of the metaheuristic is based on the concept of cooperative autonomy, i.e., several autonomous two-phase metaheuristics cooperate through the exchange of solutions. The parallelized two-phase metaheuristic was subjected a comparative test on the basis of 356 problems from the literature with sizes varying from 100 to 1000 customers. The derived results seem to justify the proposed parallelization concept.

Decentralized multi-level uncapacitated lot-sizing by automated negotiation

Abstract: An automated negotiation mechanism for decentralized production coordination is presented and evaluated. The coordination problem contains a set of self-interested software agents, representing the production facilities of a supply chain, searching for a mutually agreeable production plan, while taking private information into account. The negotiation mechanism is applied and evaluated using a multi-facility production coordination problem, which is a reformulation of the well-known multi-level uncapacitated lot-sizing problem (MLULSP). The basic element of the mechanism is a decentralized simulated annealing method, consisting of a transition rule carried out by a neutral mediator agent and a cooperative acceptance rule carried out by negotiating agents. We use 176 benchmark problems from relevant literature for the evaluation. Experimental results show that the proposed negotiation mechanism comes close to those results which are obtained by centralized planning. Furthermore, the developed simulated annealing method applied in a single, centralized planning task is competitive with the best known solution methods for the MLULSP. It was possible to compute new best solutions for 24 of the benchmark problems.

Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows

Abstract: The Vehicle Routing Problem with Time windows (VRPTW) is an extension of the capacity constrained Vehicle Routing Problem (VRP). The VRPTW is NP-Complete and instances with 100 customers or more are very hard to solve optimally. We represent the VRPTW as a multi-objective problem and present a genetic algorithm solution using the Pareto ranking technique. We use a direct interpretation of the VRPTW as a multi-objective problem, in which the two objective dimensions are number of vehicles and total cost (distance). An advantage of this approach is that it is unnecessary to derive weights for a weighted sum scoring formula. This prevents the introduction of solution bias towards either of the problem dimensions. We argue that the VRPTW is most naturally viewed as a multi-objective problem, in which both vehicles and cost are of equal value, depending on the needs of the user. A result of our research is that the multi-objective optimization genetic algorithm returns a set of solutions that fairly consider both of these dimensions. Our approach is quite effective, as it provides solutions competitive with the best known in the literature, as well as new solutions that are not biased toward the number of vehicles. A set of well-known benchmark data are used to compare the effectiveness of the proposed method for solving the VRPTW.