Optimizing Nozzle Travel Time in Proton Therapy

Proton therapy is a cancer therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Since there is also a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time. The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy. In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesman Problem (TSP) solver, Concorde, and an efficient Vehicle Routing Problem (VRP) heuristic, VROOM

Towards a permanent deep sea observatory,: the GEOSTAR European Experiment.

GEOSTAR is the prototype of the first European long-term, multidisciplinary deep sea observatory for continuous monitoring of geophysical, geochemical and oceanographic parameters. Geostar is the example of a strong synergy between science and tecnology addressed to the development of new technological solutions for the observatory realisation and management. The GEOSTAR system is described outlining the enhancements introduced during five years of project activity. An example of data retrieved from the observatory being the deep sea mission running is also given.Published111-1202.5. Laboratorio per lo sviluppo di sistemi di rilevamento sottomarinireserve

Some results on shop scheduling with S-precedence constraints among job tasks

We address some special cases of job shop and flow shop scheduling problems with s-precedence constraints. Unlike the classical setting, in which precedence constraints among the tasks of a job are finish-start, here the task of a job cannot start before the task preceding it has started. We give polynomial exact algorithms for the following problems: a two-machine job shop with two jobs when recirculation is allowed (i.e., jobs can visit the same machine many times), a two-machine flow shop, and an m-machine flow shop with two jobs. We also point out some special cases whose complexity status is open

Assignment and sequencing of parts to autonomous workstations

We present an optimization-based coordination protocol among autonomous workstations in a multiprocessor stage devoted to painting of the shutters in a furniture production process. The coordination aims to maximize the number of parallel operations executable at each machine cycle, while fulfilling constraints on the unique-copy tools. The mechanism is derived by a distributed implementation of a bipartite matching algorithm. The resulting procedure is shown to be compatible with the several autonomous decisions characterizing the process

A Lagrangian Heuristic for Satellite Range Scheduling with Resource Constraints

The data exchange between ground stations and satellite constellations is becoming a challenging task, as more and more communication requests must be daily scheduled on a few, expensive stations located all around the Earth. Most of the scheduling procedures adopted in practice cannot cope with such complexity, and the development of optimization-based tools is strongly spurred. We show that the problem can be formulated as a multiprocessor task scheduling problem in which each job (communication) requires a time dependent pair of resources (ground station and satellite) to be processed, and the objective consists of maximizing the total revenue of on-time jobs. A time-indexed 0,1-linear programming formulation is then introduced able to include all the complex technological constraints of current constellations. Unfortunately, relevant real-world scenarios yield integer programs with hundreds of thousands variables and a few million constraints, which cannot be tackled by standard integer programming (either exact or heuristic) techniques. To overcome this difficulty, we developed a Lagrangian version of the Fix-and-Relax MIP heuristic. It is based on a Lagrangian relaxation of the problem which is shown to be equivalent to a sequence of maximum weighted independent set problems on interval graphs. The heuristic has been implemented in a tool used by the Italian reference operator for the GALILEO constellation, providing near optimal solutions to relevant large scale test problems

A Three-dimensional Matching Model for Perishable Production Scheduling

AbstractIn the production of perishable goods, particular stress is often given to performance indicators generally less critical in such manufacturing settings as metal-cutting, or mechanical/electronic assembly. For instance, in food or biochemical productions, a prominent interest of the producer is to reduce the time from distribution to the so-called best-before end. A scheduling problem with a goal of this sort is here addressed. The decision variables considered are launching and completion times of parts in a production line with critical aspects in the initial and/or final stages. The basic problem is to find an assignment of a maximum number of products to launching and completion times, so that no two products are assigned the same launching or completion time: feasible solutions have therefore the form of three-dimensional matchings. The problem is studied under two independent respects, assuming either (i) the relative perishability of products or (ii) the feasibility of launching/completion time pairs not affected by the intermediate transformation stage. We show that the problem is NP-Complete, even under such a ranking assumption as (i), whereas is in P under assumption (ii). Polynomial-time algorithms are also proposed to solve other optimization versions of the problem (in two cases, based on the identification of a matroid structure)