Integrated short and medium term underground mine production scheduling

The development of short- and medium-term mine production schedules in isolation from each other has meant that only a local optimum can be achieved when each scheduling phase is carried out. The globally optimal solution, however, can be achieved when integrating scheduling phases and accounting for the interaction between short-term and medium-term activities simultaneously. This paper addresses the task of integrating short- and medium term production plans by combining the short-term objective of minimizing deviation from targeted mill feed grade with the medium-term objective of maximizing net present value (NPV) into a single mathematical optimization model. A conceptual sublevel stoping operation comprising 30 stopes is used for trialling segregated and integrated scheduling approaches. Segregated medium- and short-term scheduling using separate models achieved an NPV of $42 654 456. The final scheduling approach involved integrating the two scheduling horizons using the newly-developed globally optimal integrated production scheduling model to achieve an NPV of$42 823 657 with smoother mill feed grade. The larger the stope data set, the larger the difference between the two scheduling approaches is likely to be. At the very least, an integrated approach ensures feasibility across the two scheduling horizons, which cannot always be assumed when using a segregated approach

Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process

The increasing challenges to the grid stability posed by the penetration of renewable energy resources urge a more active role for demand response programs as viable alternatives to a further expansion of peak power generators. This work presents a methodology to exploit the demand flexibility of energy-intensive industries under Demand-Side Management programs in the energy and reserve markets. To this end, we propose a novel scheduling model for a multi-stage multi-line process, which incorporates both the critical manufacturing constraints and the technical requirements imposed by the market. Using mixed integer programming approach, two optimization problems are formulated to sequentially minimize the cost in a day-ahead energy market and maximize the reserve provision when participating in the ancillary market. The effectiveness of day-ahead scheduling model has been verified for the case of a real metal casting plant in the Nordic market, where a significant reduction of energy cost is obtained. Furthermore, the reserve provision is shown to be a potential tool for capitalizing on the reserve market as a secondary revenue stream

PhD Thesis Proposal: Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Resource optimization in health care, manufacturing, and military operations requires the careful choreography of people and equipment to effectively fulfill the responsibilities of the profession. However, resource optimization is a computationally challenging problem, and poorly utilizing resources can have drastic consequences. Within these professions, there are human domain experts who are able to learn from experience to develop strategies, heuristics, and rules-of-thumb to effectively utilize the resources at their disposal. Manually codifying these heuristics within a computational tool is a laborious process and leaves much to be desired. Even with a codified set of heuristics, it is not clear how to best insert an autonomous decision-support system into the human decision-making process. The aim of this thesis is to develop an autonomous computational method for learning domain-expert heuristics from demonstration that can support the human decision-making process. We propose a new framework, called apprenticeship scheduling, which learns and embeds these heuristics within a scalable resource optimization algorithm for real-time decision-support. Our initial investigation, comprised of developing scalable methods for scheduling and studying shared control in human-machine collaborative resource optimization, inspires the development of our apprenticeship scheduling approach. We present a promising, initial prototype for learning heuristics from demonstration and outline a plan for our continuing work

On the Tradeoff between Speedup and Energy Consumption in High Performance Computing – A Bioinformatics Case Study

High Performance Computing has been very useful to researchers in the Bioinformatics, Medical and related fields. The bioinformatics domain is rich in applications that require extracting useful information from very large and continuously growing sequence of databases. Automated techniques such as DNA sequencers, DNA microarrays & others are continually growing the dataset that is stored in large public databases such as GenBank and Protein DataBank. Most methods used for analyzing genetic/protein data have been found to be extremely computationally intensive, providing motivation for the use of powerful computers or systems with high throughput characteristics. In this paper, we provide a case study for one such bioinformatics application called BLAT running in a high performance computing environment. We use sequences gathered from researchers and parallelize the runs to study the performance characteristics under three different query and data partitioning models. This research highlights the need to carefully develop a parallel model with energy awareness in mind, based on our understanding of the application and then appropriately designing a parallel model that works well for the specific application and domain. We found that the BLAT program is highly parallelizable and a high degree of speedup is achievable. The experiments suggest that the speed up depends on model used for query and database segmentation

Multi-agent system specification for distributed scheduling in home health care

Nowadays, scheduling and allocation of resources and tasks becomes a huge and complex challenge to the most diverse industrial areas, markets, services and health. The problem with current scheduling systems is that their management is still done manually or using classical optimization methods (usually static, time-consuming) and centralized approaches. However, opportunities arise to decentralize solutions with smart systems, which enable the distribution of the computational effort, the flexibility of behaviours and the minimization of operating times and operational planning costs. The paper proposes the specification of a Multi-agent System (MAS) for the Home Health Care (HHC) scheduling and allocation. The MAS technology enables the scheduling of intelligent behaviours and functionalities based on the interaction of agents, and allows the evolution of current strategies and algorithms, as it can guarantee the fast response to condition changes, flexibility and responsiveness in existing planning systems. An experimental HHC case study was considered to test the feasibility and effectiveness of the proposed MAS approach, the results demonstrating promising qualitative and quantitative indicators regarding the efficiency and responsiveness of the HHC scheduling.This work has been supported by FCT—Fundação para a Ciência e a Tecnologia within the R&D Units Projects Scope: UIDB/00319/2020 and UIDB/05757/2020. Filipe Alves is supported by FCT Doctorate Grant Reference SFRH/BD/143745/2019

Scheduling flights at hub airports

In a typical hub airport, incoming flights from many origins feed outgoing flights to many destinations. If an incoming flight is late, outgoing flights which are fed by it may also be delayed eventually. Alternately, planes may leave before some feeding flights arrive, thereby incurring high misconnection penalties. Clearly, if we plan for very long scheduled ground time between the last incoming flight and the first outgoing one, we can reduce the risk of unscheduled delays or misconnections. However, such a schedule may cost the airline too much in terms of idle personnel and equipment and will not be attractive to the passenger either. On the other hand, if we plan for very short scheduled ground time, we run the risk of excessive unscheduled delays, and/or misconnection penalties. In this paper we develop models designed to optimize the scheduled ground time under two pure policies (i) to wait as long as necessary to ensure all connections, and (ii) not to wait at all (i.e., pay misconnection penalties rather than delay penalties). The models can also be applied to similar problems such as express parcel deliveries and ground transportation hubs. Keywords: Scheduling; Air transportation; Airports; Airline schedulingsupported by the NPS Foundation Research Programhttp://archive.org/details/schedulingflight00trieApproved for public release; distribution is unlimited

Utility-Aware Scheduling of Stochastic Real-Time Systems

Time utility functions offer a reasonably general way to describe the complex timing constraints of real-time and cyber-physical systems. However, utility-aware scheduling policy design is an open research problem. In particular, scheduling policies that optimize expected utility accrual are needed for real-time and cyber-physical domains. This dissertation addresses the problem of utility-aware scheduling for systems with periodic real-time task sets and stochastic non-preemptive execution intervals. We model these systems as Markov Decision Processes. This model provides an evaluation framework by which different scheduling policies can be compared. By solving the Markov Decision Process we can derive value-optimal scheduling policies for moderate sized problems. However, the time and memory complexity of computing and storing value-optimal scheduling policies also necessitates the exploration of other more scalable solutions. We consider heuristic schedulers, including a generalization we have developed for the existing Utility Accrual Packet Scheduling Algorithm. We compare several heuristics under soft and hard real-time conditions, different load conditions, and different classes of time utility functions. Based on these evaluations we present guidelines for which heuristics are best suited to particular scheduling criteria. Finally, we address the memory complexity of value-optimal scheduling, and examine trade-offs between optimality and memory complexity. We show that it is possible to derive good low complexity scheduling decision functions based on a synthesis of heuristics and reduced-memory approximations of the value-optimal scheduling policy