Constraint programming solvers are known to perform remarkably well for most scheduling problems. However, when comparing the performance of different available solvers, there is usually no clear winner over all relevant problem instances. This gives rise to the question of how to select a promising solver when knowing the concrete instance to be solved. In this article, we aim to provide first insights into this question for the flexible job shop scheduling problem. We investigate relative performance differences among five constraint programming solvers on problem instances taken from the literature as well as randomly generated problem instances. These solvers include commercial and non-commercial software and represent the state-of-the-art as identified in the relevant literature. We find that two solvers, the IBM ILOG CPLEX CP Optimizer and Google’s OR-Tools, outperform alternative solvers. These two solvers show complementary strengths regarding their ability to determine provably optimal solutions within practically reasonable time limits and their ability to quickly determine high quality feasible solutions across different test instances. Hence, we leverage the resulting performance complementarity by proposing algorithm selection approaches that predict the best solver for a given problem instance based on instance features or parameters. The approaches are based on two machine learning techniques, decision trees and deep neural networks, in various variants. In a computational study, we analyze the performance of the resulting algorithm selection models and show that our approaches outperform the use of a single solver and should thus be considered as a relevant tool by decision makers in practice.

There is a finite number of non-cooperating clients, who are averse to uncertain loss and compete for execution of their jobs not later than by their respective due dates in a parallel service eironment. For each client, a due date violation implies a cost. In order to address the minimization of the total scheduling cost of all clients as a social criterion, a game mechanism is suggested. It is designed such that no client has an incentive to claim a false due date or cost. The game mechanism allows the clients to move their jobs to complete earlier in a given schedule. However, they must compensate costs of those clients whose jobs miss their due dates because of these moves. Algorithmic aspects are analyzed. Furthermore, that determines an equilibrium of the considered game is suggested and embedded into the game mechanism. Computational tests analyze the performance and practical suitability of the resulting game mechanism.

We consider the problem of designing truthful mechanisms for machine scheduling problems with parallel identical machines where some of the jobs’ characteristics are private information of their respective owners and a central decision maker is in charge of computing the schedule. We study a two-parameter setting, where weights and due dates are private information while processing times are publicly known. The global objective is to minimize the sum of the weights of those jobs that are completed after their due dates. We derive a set of properties that is equivalent to the well known condition of cycle monotonicity, which is a general condition for truthful mechanisms in non-coex valuation function domains. Our results utilize knowledge about the underlying scheduling problem, so that the resulting properties are easier to implement and verify than the general condition of cycle monotonicity. We illustrate the use of our results by analyzing an example algorithm that has recently been proposed in the literature for the case of one machine.

Globalization and digitalization have lead to new challenges and perspectives in intermodal transport logistics of large city sea ports and megahubs. In particular, due to an enormous increase of the container throughput over the last decades and the automatization of megahubs, new planning problems in this field must consistently be addressed by smart software solutions. In this research article, we sketch some challenges that arise at megahubs and outline how mechanism design, as a popular tool that combines ideas from game theory and computer science, can be an approach to tackle logistics problems that iolve multiple selfish players.

In this paper we analyze the effect of including price competition into a classical (market entrant’s) competitive location problem. The multinomial logit approach is applied to model the decision process of utility maximizing customers. We provide complexity results and show that, given the locations of all facilities, a fixed-point iteration approach that has previously been introduced in the literature can be adapted to reliably and quickly determine local price equilibria. We present examples of problem instances that demonstrate the potential non-existence of price equilibria and the case of multiple local equilibria in prices. Furthermore, we show that different price sensitivity levels of customers may actually affect optimal locations of facilities, and we provide first insights into the performance of heuristic algorithms for the location problem.