Kenneth Sörensen

In this paper, we study a public bus line operating between a terminal and a city center for which there exists a clear demand imbalance between the two directions during peak hours. Despite the demand imbalance, traditional public bus systems typically operate with a fixed‐route and fixed‐timetable scheme in both directions. To increase the efficiency of service during peak hours, in this paper, we propose an on‐demand operation that allows vehicles to take some shortcuts between the city center and the terminal, based on the passenger requests that are collected before the peak hours. We develop a variable neighborhood search (VNS) algorithm to optimize the operational decisions of this system. Based on the received requests, the VNS algorithm decides the following: (1) shortcut decisions: which shortcuts to take by each bus in each direction, (2) departure time decisions: when to depart from the city center and the terminal, and (3) passenger assignment decisions: which service serves each passenger. Experiments show that the total passenger travel time improves up to 45% on a real‐size line compared to its traditional fixed‐route operation. The performance of the system is also analyzed under different circumstances.

A demand‐responsive feeder system (DRFS) presents an alternative to traditional feeder bus systems (TFS) in areas with low demand. This paper introduces an optimization problem to support the planning and operation of a DRFS. A variable neighborhood search (VNS) is employed to optimize the DRFS, focusing on minimizing passengers' travel times. The performance of the VNS is compared with that obtained by solving the mathematical model using a commercial solver (CPLEX) across two networks: a small network for validation and a larger suburban network that simulates a TFS. The results indicate that the VNS is a viable and efficient alternative for optimizing DRFS operations, providing flexibility in route and departure time adjustments, and achieving significant reductions in passenger travel times. The results also demonstrate that the DRFS outperforms the TFS in low‐demand areas. On average, the VNS improves upon CPLEX results, obtained within one hour of calculation time for the small network, by 2.3%. Using the same resources, the DRFS reduces the average passenger travel time by 9.6% compared to the TFS.

Conflicts among tow trains pose a significant challenge in just‐in‐time manufacturing systems, impacting both safety and efficiency. This paper proposes an innovative solution to achieve conflict‐free tow train routing. In our approach, the production layout is partitioned into “pixels.” The A‐star (A*) algorithm is then employed on this pixel‐based layout to create a distance matrix between workstations. Subsequently, a simulated annealing heuristic optimizes tow train routes to deliver parts demanded at the workstations. Additionally, a conflict detection algorithm identifies collisions among tow trains on the pixel layout, which are then resolved through two distinct conflict avoidance strategies. The algorithms are implemented and tested on a set of benchmark instances, demonstrating their effectiveness.