Hybrid Meta-Heuristics for Robust Scheduling

The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.Meta-Heuristics;Multi-Objective Genetic Optimization;Robust Scheduling;Supply Networks

Hybrid Meta-Heuristics for Robust Scheduling

The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete

Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete

The coordination of just-in-time production and transportation in a network of partially independent facilities to guarantee timely delivery to distributed customers is one of the most challenging aspects of supply chain management. From the theoretical perspective, the timely production/distribution can be viewed as a hybrid combination of planning, scheduling and routing problem, each notoriously affected by nearly prohibitive combinatorial complexity. From a practical viewpoint, the problem calls for a trade-off between risks and profits. This paper focuses on the ready-made concrete delivery: in addition to the mentioned complexity, strict time-constraints forbid both earliness and lateness of the supply. After developing a detailed model of the considered problem, we propose a novel meta-heuristic approach based on a hybrid genetic algorithm combined with constructive heuristics. A detailed case study derived from industrial data is used to illustrate the potential of the proposed approach

Real-world efficacy and safety of nivolumab in previously-treated metastatic renal cell carcinoma, and association between immune-related adverse events and survival: the Italian expanded access program

Background: The Italian Renal Cell Cancer Early Access Program was an expanded access program that allowed access to nivolumab, for patients (pts) with metastatic renal cell carcinoma (mRCC) prior to regulatory approval. Methods: Pts with previously treated advanced or mRCC were eligible to receive nivolumab 3 mg/kg every 2 weeks. Pts included in the analysis had received ≥1 dose of nivolumab and were monitored for drug-related adverse events (drAEs) using CTCAE v.4.0. Immune-related (ir) AEs were defined as AEs displaying a certain, likely or possible correlation with immunotherapy (cutaneous, endocrine, hepatic, gastro-intestinal and pulmonary). The association between overall survival (OS) and irAEs was assessed, and associations between variables were evaluated with a logistic regression model. Results: A total of 389 pts were enrolled between July 2015 and April 2016. Overall, the objective response rate was 23.1%. At a median follow-up of 12 months, the median progression-free survival was 4.5 months (95% CI 3.7-6.2) and the 12-month overall survival rate was 63%. Any grade and grade 3-4 drAEs were reported in 124 (32%) and 27 (7%) of pts, respectively, and there were no treatment-related deaths. Any grade irAEs occurred in 76 (20%) of patients, 8% cutaneous, 4% endocrine, 2% hepatic, 5% gastro-intestinal and 1% pulmonary. Of the 22 drAEs inducing treatment discontinuation, 10 (45%) were irAEs. Pts with drAEs had a significantly longer survival than those without drAEs (median OS 22.5 versus 16.4 months, p = 0.01). Pts with irAEs versus without irAEs had a more significant survival benefit (median OS not reached versus 16.8 months, p = 0.002), confirmed at the landmark analysis at 6 weeks. The occurrence of irAEs displayed a strong association with OS in univariable (HR 0.48, p = 0.003) and multivariable (HR 0.57, p = 0.02) analysis. Conclusions: The appearance of irAEs strongly correlates with survival benefit in a real-life population of mRCC pts treated with nivolumab