Clustering heuristic for time-dependent periodic routing problems with complex constraints

Periodic routing and scheduling is of utmost importance in many industries with mobile personnel working in the field: sales representatives, service technicians, suppliers, etc. The resulting optimization problems are of large scale and complexity, mostly due to discrete, combinatorial nature of the systems and due to complicated, nonuniform constraints. In many cases the long-term stability of the customer to personnel allocation is required, leading to the decomposition of the major problem into single employee subproblems.The paper deals with building clusters of customers visited by a single salesperson. The procedure takes into account diverse system requirements and constraints, possible traveling schedules and expected operational costs. The difficulty of the problem lies in its large scale and constraints complexity as well as in troublesome objective evaluation for the given solution. The general solution concept is presented. Its usefulness is supported by the results of the computational experiments.

Incorporating Maintenance Infeasibilities in an Aircraft Rotation Planning Model

In any airline’s schedule development process, aircraft rotations must be planned for individual fleet types after fleet assignment. The aircraft rotation plans must conform to stringent maintenance requirements and this problem can be formulated as a periodic routing problem on an Eulerian graph. We analyze the computational complexity of developing maintenance rotations when some overnighting aircraft may not have sufficient time on the ground to complete extended maintenance (referred to as a maintenance infeasibility). The paper also provides a theoretical analysis of heuristics for the aircraft maintenance rotation problem with maintenance infeasibilities.

Solving the assignment of customers to trucks and visiting days in a periodic routing real-world case

Introduction: this work proposes a model, and two heuristic algorithms to assign customers to trucks and visiting days, as a first phase in the solution of a real-world routing problem, which is closely related to the Periodic vehicle routing problem, but a strategic decision of the company imposes the additional constraint that every customer must always be visited by the same truck. Methods: The proposed model aims to group the customers that are visited the same day by the same truck as close as possible. The first proposed heuristic has a constructive stage, and five underlying improvement heuristic, the second one uses an exact linear programming algorithm. Results: The algorithms are evaluated by instances taken from the literature and generated, taking into account the characteristics presented in the real-world case addressed.