A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load

Global supply chains of high value low volume products

Imperial Users onl

Applying Scenario Reduction Heuristics in Stochastic Programming for Phlebotomist Scheduling

Laboratory services in healthcare play a vital role in inpatient care. Studies have indicated laboratory data affect approximately 65% of the most critical decisions on admission, discharge, and medication. This research focuses on improving phlebotomist performance in laboratory facilities of large hospital systems. A two-stage stochastic integer linear programming (SILP) model is formulated to determine better weekly phlebotomist schedules and blood collection assignments. The objective of the two-stage SILP model is to balance the workload of the phlebotomists within and between shifts, as reducing workload imbalance will result in improved patient care. Due to the size of the two-stage SILP model, a scenario reduction model has been proposed as a solution approach. The scenario reduction heuristic is formulated as a linear programming model and the results indicate the scenarios with the largest likelihood of occurrence. These selected scenarios will be tested in the two-stage SILP model to determine weekly scheduling policies and blood draw assignments that will balance phlebotomist workload and improve overall performance