scholarly journals Scheduling deferrable electric appliances in smart homes: a bi-objective stochastic optimization approach

2021 ◽  
Vol 19 (1) ◽  
pp. 34-65
Author(s):  
Diego G. Rossit ◽  
◽  
Segio Nesmachnow ◽  
Jamal Toutouh ◽  
Francisco Luna ◽  
...  
Keyword(s):  
Simulation Optimization ◽  
Electricity Consumption ◽  
Greedy Heuristic ◽  
Optimization Approach ◽  
Real World Data ◽  
Conflicting Objectives ◽  
Decision Making Processes ◽  
Problem Instances ◽  
Sustainable Usage ◽  
The Cost

<abstract><p>In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios.</p></abstract>

scholarly journals LBA: Online Learning-Based Assignment of Patients to Medical Professionals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3021
Author(s):  
Hanan Rosemarin ◽  
Ariel Rosenfeld ◽  
Steven Lapp ◽  
Sarit Kraus
Keyword(s):  
Empirical Evaluation ◽  
Wait Time ◽  
Medical Professional ◽  
Decision Makers ◽  
Optimization Approach ◽  
Real World Data ◽  
Medical Settings ◽  
Medical Institutions ◽  
Conflicting Objectives ◽  
The Right

Central to any medical domain is the challenging patient to medical professional assignment task, aimed at getting the right patient to the right medical professional at the right time. This task is highly complex and involves partially conflicting objectives such as minimizing patient wait-time while providing maximal level of care. To tackle this challenge, medical institutions apply common scheduling heuristics to guide their decisions. These generic heuristics often do not align with the expectations of each specific medical institution. In this article, we propose a novel learning-based online optimization approach we term Learning-Based Assignment (LBA), which provides decision makers with a tailored, data-centered decision support algorithm that facilitates dynamic, institution-specific multi-variate decisions, without altering existing medical workflows. We adapt our generic approach to two medical settings: (1) the assignment of patients to caregivers in an emergency department; and (2) the assignment of medical scans to radiologists. In an extensive empirical evaluation, using real-world data and medical experts’ input from two distinctive medical domains, we show that our proposed approach provides a dynamic, robust and configurable data-driven solution which can significantly improve upon existing medical practices.

scholarly journals Simulation Optimization for the Multihoist Scheduling Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Ricardo Pérez-Rodríguez
Keyword(s):  
Job Shop ◽  
Simulation Optimization ◽  
Optimization Method ◽  
Shop Floor ◽  
Optimization Approach ◽  
Scheduling Problem ◽  
Chemical Production ◽  
Operation Conditions ◽  
Conflicting Objectives ◽  
Biobjective Optimization

Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison.

scholarly journals Characteristics of the cost of electricity consumption in agricultural production

2021 ◽  
Vol 677 (3) ◽  
pp. 032087
Author(s):  
G S Kudryashev ◽  
A N Tretyakov ◽  
S V Batishchev ◽  
V A Bochkarev ◽  
V D Ochirov
Keyword(s):  
Agricultural Production ◽  
Electricity Consumption ◽  
Cost Of Electricity ◽  
The Cost

Aircraft Assembly Ramp-Up Planning Using a Hybrid Simulation-Optimization Approach

2020 ◽  
Author(s):  
Amos H.C. Ng ◽  
Jacob Bernedixen ◽  
Martin Andersson ◽  
Sunith Bandaru ◽  
Thomas Lezama
Keyword(s):  
Simulation Optimization ◽  
Hybrid Simulation ◽  
Optimization Approach ◽  
Aircraft Assembly

scholarly journals Robust Maintenance Scheduling of Aircraft Fleet: A Hybrid Simulation-Optimization Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 17854-17865
Author(s):  
Hani Shahmoradi-Moghadam ◽  
Nima Safaei ◽  
Seyed Jafar Sadjadi
Keyword(s):  
Simulation Optimization ◽  
Hybrid Simulation ◽  
Maintenance Scheduling ◽  
Optimization Approach

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

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