An Improved Multi-objective Algorithm for the Urban Transit Routing Problem

The determination of efficient routes and schedules in public transport systems is complex due to the vast search space and multi- ple constraints involved. In this paper we focus on the Urban Transit Routing Problem concerned with the physical network design of pub- lic transport systems. Historically, route planners have used their local knowledge coupled with simple guidelines to produce network designs. Several major studies have identified the need for automated tools to aid in the design and evaluation of public transport networks. We propose a new construction heuristic used to seed a multi-objective evolutionary al- gorithm. Several problem specific mutation operators are then combined with an NSGAII framework leading to improvements upon previously published results

A Systematic Evaluation of Cost-Saving Dosing Regimens for Therapeutic Antibodies and Antibody-Drug Conjugates for the Treatment of Lung Cancer

Background: Expensive novel anticancer drugs put a serious strain on healthcare budgets, and the associated drug expenses limit access to life-saving treatments worldwide. Objective: We aimed to develop alternative dosing regimens to reduce drug expenses. Methods: We developed alternative dosing regimens for the following monoclonal antibodies used for the treatment of lung cancer: amivantamab, atezolizumab, bevacizumab, durvalumab, ipilimumab, nivolumab, pembrolizumab, and ramucirumab; and for the antibody-drug conjugate trastuzumab deruxtecan. The alternative dosing regimens were developed by means of modeling and simulation based on the population pharmacokinetic models developed by the license holders. They were based on weight bands and the administration of complete vials to limit drug wastage. The resulting dosing regimens were developed to comply with criteria used by regulatory authorities for in silico dose development. Results: We found that alternative dosing regimens could result in cost savings that range from 11 to 28%, and lead to equivalent pharmacokinetic exposure with no relevant increases in variability in exposure. Conclusions: Dosing regimens based on weight bands and the use of complete vials to reduce drug wastage result in less expenses while maintaining equivalent exposure. The level of evidence of our proposal is the same as accepted by regulatory authorities for the approval of alternative dosing regimens of other monoclonal antibodies in oncology. The proposed alternative dosing regimens can, therefore, be directly implemented in clinical practice.</p

A Parallel Hybrid Heuristic for the TSP

In this paper we investigate the design of a coarse-grained parallel implementation of Cga-LK, a hybrid heuristic for the Traveling Salesman Problem (TSP). Cga-LK exploits a compact genetic algorithm in order to generate high-quality tours which are then refined by means of an e#cient implementation of the Lin-Kernighan local search heuristic. The results of several experiments conducted on a cluster of workstations with di#erent TSP instances show the e#cacy of the parallelism exploitation. Keywords: Parallel algorithms, TSP, compact genetic algorithm, LinKernighan algorithm, hybrid GA.

Collaboratively Solving the Traveling Salesman Problem with Limited Disclosure

International audienceWith increasing resource constraints, optimization is necessary to make the best use of scarce resources. Given the ubiquitous connectivity and availability of information, collaborative optimization problems can be formulated by different parties to jointly optimize their operations. However, this cannot usually be done without restraint since privacy/security concerns often inhibit the complete sharing of proprietary information. The field of privacy-preserving optimization studies how collaborative optimization can be performed with limited disclosure. In this paper, we develop privacy-preserving solutions for collaboratively solving the traveling salesman problem (TSP), a fundamental combinatorial optimization problem with applications in diverse fields such as planning, logistics and production. We propose a secure and efficient protocol for multiple participants to formulate and solve such a problem without sharing any private information. We formally prove the protocol security under the rigorous definition of secure multiparty computation (SMC), and demonstrate its effectiveness with experimental results using real data