The milk collection problem is well known in rural areas of the world. This paper considers this real-life problem for an Italian dairy company that collects raw milk from farmers. In our milk collection problem, we address the constraint that some farms are small and inaccessible by large vehicles; moreover, these farmers produce different milk types, and the tank trucks used for transporting the milk have multiple compartments. This generates the additional constraint that at most one milk type can be assigned to a tank compartment. The goal of this paper is to show how operations research techniques helped the company to improve its daily performance. In particular, we present a solution approach based on two mathematical formulations and local search, all embedded within a multiple-restart mechanism. The first mathematical formulation minimizes the number of vehicles to be routed in the network; the second minimizes the tour length. We also discuss experiments we conducted as part of our case study and compare our solution with the process that the company used previously.
Examination timetabling assigns examinations to a given number of time slots so that there are no conflicts. A conflict occurs if a student has to take more than one examination at the same time, or when the number of students that must take an exam exceeds the capacity of the classroom assigned. The objective is to minimize penalties from proximity constraints. We present new algorithms based on local search and report on an extensive experimental study. We consider also a variant where the concern is to produce conflict-free timetables minimizing the number of time slots, regardless of how close exams appear in the schedule. The algorithms proposed also manage the trade-off between the two objective functions and produce the best results on several standard benchmark instances, compared to the best existing algorithms.
I n this paper, we study a novel toll setting policy to regulate hazardous material (hazmat) transportation, where the regulator (e.g., a government authority) aims at minimizing not only the network total risk but also at spreading the risk in an equitable way over a given road network. The idea is to use a toll setting policy to discourage carriers transporting hazmat from overloading portions of the network with the consequent increase of the risk exposure of the population involved. Specifically, we assume that the toll paid by a carrier on a network link depends on the usage of that link by all carriers. Therefore the route choices of each carrier depend on the other carrier's choices, and the tolls deter the carriers from using links with a high total risk. The resulting model is a mathematical programming with equilibrium constraints (MPEC) problem, where the inner problem is a Nash equilibrium problem (game) having as players the carriers, each one wishing to minimize his or her travel cost (including tolls); the outer problem is addressed by the government authority, whose aim is finding the link tolls that induce the carriers to choose route plans that minimize both the network total risk and the maximum link total risk among the network links (to address risk equity). To guarantee the stability of the solution, we study conditions for the existence and uniqueness of the Nash equilibrium, and propose a local search heuristic for the MPEC problem. Computational results are carried out on a real road network, comparing the performance of our toll setting policy with the toll setting approach proposed in the literature.
We study the problem of levelling resources in a project with generalized precedence relationships, given a deadline for the completion of all the activities and variable execution intensities and flexible durations of the activities. Variable execution intensities have been taken into account firstly by Kis in 2005 applied to a real world scenario in which, due to the physical characteristics of some manufacturing processes, the effort associated with a certain activity for its execution may vary over time. Generalized precedence relationships and variable intensity execution and duration have not been dealt with together to the best of our knowledge. For this novel problem we propose a mixed integer linear programming formulation, a lower bound based on Lagrangian relaxation, and a branch and bound algorithm. Computational results on known benchmarks are provided
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