Planning & Scheduling

Japanese government begins discussion on contingency plans in event of Mount Fuji eruption

The Japan Times

A government panel started discussions Tuesday on how to address repercussions in the Tokyo Metropolitan Area in the event Mount Fuji erupts, with falling volcanic ash likely to push the capital into chaos. Mount Fuji, Japan's highest peak at 3,776 meters and lying about 100 kilometers from central Tokyo, is an active volcano with a history of a number of major eruptions. The Central Disaster Management Council will assess the speed and scope in which volcanic ash will fall, and its influence on transportation infrastructure and electricity and water supplies, in the case of an eruption. The 14-member working group at the council, led by Toshitsugu Fujii, a professor emeritus at the University of Tokyo, aims to draw up a proposal within a year that will likely be reflected in local municipalities' disaster management plans. The group will assume numerous scenarios, reflecting volume of volcanic ash, wind direction and length of eruption, using references from the previous eruption of Mount Fuji in 1707.

iDriveSense: Dynamic Route Planning Involving Roads Quality Information Machine Learning

Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majority of the present trip planning applications and service providers are enabling their trip planning recommendations relying on shortest paths and/or fastest routes. However, such suggestions may discount drivers' preferences with respect to safe and less disturbing trips. Road anomalies such as cracks, potholes, and manholes induce risky driving scenarios and can lead to vehicles damages and costly repairs. Accordingly, in this paper, we propose a crowdsensing based dynamic route planning system. Leveraging both the vehicle motion sensors and the inertial sensors within the smart devices, road surface types and anomalies have been detected and categorized. In addition, the monitored events are geo-referenced utilizing GPS receivers on both vehicles and smart devices. Consequently, road segments assessments are conducted using fuzzy system models based on aspects such as the number of anomalies and their severity levels in each road segment. Afterward, another fuzzy model is adopted to recommend the best trip routes based on the road segments quality in each potential route. Extensive road experiments are held to build and show the potential of the proposed system.

Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks Artificial Intelligence

Monte Carlo Tree Search (MCTS) is particularly adapted to domains where the potential actions can be represented as a tree of sequential decisions. For an effective action selection, MCTS performs many simulations to build a reliable tree representation of the decision space. As such, a bottleneck to MCTS appears when enough simulations cannot be performed between action selections. This is particularly highlighted in continuously running tasks, for which the time available to perform simulations between actions tends to be limited due to the environment's state constantly changing. In this paper, we present an approach that takes advantage of the anytime characteristic of MCTS to increase the simulation time when allowed. Our approach is to effectively balance the prospect of selecting an action with the time that can be spared to perform MCTS simulations before the next action selection. For that, we considered the simulation time as a decision variable to be selected alongside an action. We extended the Hierarchical Optimistic Optimization applied to Tree (HOOT) method to adapt our approach to environments with a continuous decision space. We evaluated our approach for environments with a continuous decision space through OpenAI gym's Pendulum and Continuous Mountain Car environments and for environments with discrete action space through the arcade learning environment (ALE) platform. The evaluation results show that, with variable simulation times, the proposed approach outperforms the conventional MCTS in the evaluated continuous decision space tasks and improves the performance of MCTS in most of the ALE tasks.

Vulcan: A Monte Carlo Algorithm for Large Chance Constrained MDPs with Risk Bounding Functions Artificial Intelligence

Chance Constrained Markov Decision Processes maximize reward subject to a bounded probability of failure, and have been frequently applied for planning with potentially dangerous outcomes or unknown environments. Solution algorithms have required strong heuristics or have been limited to relatively small problems with up to millions of states, because the optimal action to take from a given state depends on the probability of failure in the rest of the policy, leading to a coupled problem that is difficult to solve. In this paper we examine a generalization of a CCMDP that trades off probability of failure against reward through a functional relationship. We derive a constraint that can be applied to each state history in a policy individually, and which guarantees that the chance constraint will be satisfied. The approach decouples states in the CCMDP, so that large problems can be solved efficiently. We then introduce Vulcan, which uses our constraint in order to apply Monte Carlo Tree Search to CCMDPs. Vulcan can be applied to problems where it is unfeasible to generate the entire state space, and policies must be returned in an anytime manner. We show that Vulcan and its variants run tens to hundreds of times faster than linear programming methods, and over ten times faster than heuristic based methods, all without the need for a heuristic, and returning solutions with a mean suboptimality on the order of a few percent. Finally, we use Vulcan to solve for a chance constrained policy in a CCMDP with over $10^{13}$ states in 3 minutes.

Finite LTL Synthesis with Environment Assumptions and Quality Measures Artificial Intelligence

In this paper, we investigate the problem of synthesizing strategies for linear temporal logic (LTL) specifications that are interpreted over finite traces -- a problem that is central to the automated construction of controllers, robot programs, and business processes. We study a natural variant of the finite LTL synthesis problem in which strategy guarantees are predicated on specified environment behavior. We further explore a quantitative extension of LTL that supports specification of quality measures, utilizing it to synthesize high-quality strategies. We propose new notions of optimality and associated algorithms that yield strategies that best satisfy specified quality measures. Our algorithms utilize an automata-game approach, positioning them well for future implementation via existing state-of-the-art techniques.

The reactive multiple operating room surgical case sequencing problem Artificial Intelligence

In this paper we consider the surgical case sequencing problem (SCSP) under stochastic conditions. In addition to implementing a robust surgical schedule, we investigate the use of a number of reactive strategies that can be used to maintain schedule feasibility. We present a mixed integer nonlinear programming (MINLP) model for the reactive multiple operating room (OR) SCSP that may be suitable for direct implementation on small problem instances. A machine scheduling perspective is considered and the model is equivalent to a resource-constrained parallel-machine scheduling problem with identical machines, machine eligibility restrictions, and machine and job release dates. The explicit objective of the model is to reduce OR idle time, although other common objectives (including time to surgery and overtime) are discussed. The work here is based on a case study of a large Australian public hospital with long surgical waiting lists and high non-elective demand. Results of computational experiments show that the reactive strategies presented in this paper can be used to reduce idle time without putting excessive pressure on surgeons.

The real-time reactive surgical case sequencing problem Artificial Intelligence

In this paper, the multiple operating room (OR) surgical case sequencing problem (SCSP) is addressed. The objective is to maximise total OR utilisation during standard opening hours. The work here is based on a case study of a large Australian public hospital with long surgical waiting lists and high levels of non-elective demand. Due to the complexity of the SCSP and the size of the instances considered herein, heuristic techniques are required to solve the problem. Constructive heuristics are presented based on both a modified block scheduling policy and an open scheduling policy. A number of real-time reactive strategies are presented that can be used to maintain schedule feasibility in the case of disruptions. Results of computational experiments show that the approach presented in this paper can be used to maintain schedule feasibility in real-time, whilst increasing OT utilisation and throughput, and reducing the waiting time of non-elective patients. The framework presented here is applicable to the real-life scheduling of OT departments, and recommendations have been provided regarding implementation of the approach.

UK outlines contingency plan in event of 'no-deal' Brexit

Al Jazeera

Consumers and businesses in the United Kingdom would have to pay more for goods and services in the event of a "No-deal" Brexit between the UK and the European Union, the British government has warned. Brexit Secretary Dominic Raab on Thursday released 25 so-called "technical notices" that covered everything from financial services to nuclear materials, advising companies and the public on how to prepare for such a scenario. He said he remained confident the two sides would reach a deal before March, and said the failure of the talks was "unlikely". "If the EU responds with the same level of ambition and pragmatism, we will strike a strong deal that benefits both sides. But we must be ready to consider the alternative," he said.

LSTM-Based Goal Recognition in Latent Space Artificial Intelligence

Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.

Oracle updates transportation, trade management clouds


Oracle on Tuesday released a series of updates to its Transportation Management and Global Trade Management clouds. The updates aim to help companies streamline and simplify compliance with shifting global trade regulations, as well as speed up customer fulfillment, Oracle said. Key to the new features is the injection of data into shipment routes and automated event handling. For instance, routing decisions will now take into account factors such as historic traffic patterns, hazardous material restrictions and tolls when planning shipments. Changes to transportation planning software are designed to improve outbound order fulfillment.