scholarly journals Stratisfimal Layout: A modular optimization model for laying out layered node-link network visualizations

2021 ◽  
Author(s):  
Sara Di Bartolomeo ◽  
Mirek Riedewald ◽  
Wolfgang Gatterbauer ◽  
Cody Dunne
Keyword(s):  
Optimization Model ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Layout Optimization ◽  
Web Based ◽  
Trade Offs ◽  
Network Generator ◽  
Linear Programming Formulation ◽  
Layer Groups ◽  
Integer Linear Programming Formulation

Node-link visualizations are a familiar and powerful tool for displaying the relationships in a network. The readability of these visualizations highly depends on the spatial layout used for the nodes. In this paper, we focus on computing layered layouts, in which nodes are aligned on a set of parallel axes to better expose hierarchical or sequential relationships. Heuristic-based layouts are widely used as they scale well to larger networks and usually create readable, albeit sub-optimal, visualizations. We instead use a layout optimization model that prioritizes optimality— as compared to scalability— because an optimal solution not only represents the best attainable result, but can also serve as a baseline to evaluate the effectiveness of layout heuristics. We take an important step towards powerful and flexible network visualization by proposing STRATISFIMAL LAYOUT, a modular integer-linear-programming formulation that can consider several important readability criteria simultaneously— crossing reduction, edge bendiness, and nested and multi-layer groups. The layout can be adapted to diverse use cases through its modularity. Individual features can be enabled and customized depending on the application. We provide open-source and documented implementations of the layout, both for web-based and desktop visualizations. As a proof-of-concept, we apply it to the problem of visualizing complicated SQL queries, which have features that we believe cannot be addressed by existing layout optimization models. We also include a benchmark network generator and the results of an empirical evaluation to assess the performance trade-offs of our design choices. A full version of this paper with all appendices, data, and source code is available at osf.io/3vqmswith live examples at https://visdunneright.github.io/stratisfimal/.

A Mixed Integer Linear Programming Formulation for Unrestricted Wind Farm Layout Optimization

2015 ◽  
Author(s):  
Ning Quan ◽  
Harrison Kim
Keyword(s):  
Mixed Integer Linear Programming ◽  
Wind Farm ◽  
Optimization Problem ◽  
Layout Optimization ◽  
Mixed Integer ◽  
Programming Formulation ◽  
Downstream Distance ◽  
Wind Farm Layout Optimization ◽  
Linear Programming Formulation ◽  
Integer Linear Programming Formulation

This paper proposes a mixed integer linear programming formulation of the unrestricted wind farm layout optimization problem. The formulation is achieved in part by changing the objective from power generation maximization to the maximization of the smallest penalized downstream distance between any pair of turbines, where downstream distance is defined as the distance between any pair of turbines with overlapping wake cones. The proposed formulation also linearizes other non-linear characteristics of the unrestricted layout optimization problem such as proximity constraints and wake-cone membership. The main advantage of the proposed approach is that an optimal solution to the proposed formulation is guaranteed to be globally optimal. This is in contrast to previous approaches with non-linear formulations that do not come with such guarantees.

scholarly journals A Fuzzy Optimization Model for the Berth Allocation Problem and Quay Crane Allocation Problem (BAP + QCAP) with n Quays

2021 ◽  
Vol 9 (2) ◽  
pp. 152
Author(s):  
Edwar Lujan ◽  
Edmundo Vergara ◽  
Jose Rodriguez-Melquiades ◽  
Miguel Jiménez-Carrión ◽  
Carlos Sabino-Escobar ◽  
...  
Keyword(s):  
Optimization Model ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Triangular Fuzzy Numbers ◽  
Berth Allocation Problem ◽  
Arrival Times ◽  
Decision Variables ◽  
Short Time

This work introduces a fuzzy optimization model, which solves in an integrated way the berth allocation problem (BAP) and the quay crane allocation problem (QCAP). The problem is solved for multiple quays, considering vessels’ imprecise arrival times. The model optimizes the use of the quays. The BAP + QCAP, is a NP-hard (Non-deterministic polynomial-time hardness) combinatorial optimization problem, where the decision to assign available quays for each vessel adds more complexity. The imprecise vessel arrival times and the decision variables—berth and departure times—are represented by triangular fuzzy numbers. The model obtains a robust berthing plan that supports early and late arrivals and also assigns cranes to each berth vessel. The model was implemented in the CPLEX solver (IBM ILOG CPLEX Optimization Studio); obtaining in a short time an optimal solution for very small instances. For medium instances, an undefined behavior was found, where a solution (optimal or not) may be found. For large instances, no solutions were found during the assigned processing time (60 min). Although the model was applied for n = 2 quays, it can be adapted to “n” quays. For medium and large instances, the model must be solved with metaheuristics.

scholarly journals Competitive Caching with Machine Learned Advice

2021 ◽  
Vol 68 (4) ◽  
pp. 1-25
Author(s):  
Thodoris Lykouris ◽  
Sergei Vassilvitskii
Keyword(s):  
Online Algorithms ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Poor Performance ◽  
Machine Learning Algorithms ◽  
Average Error ◽  
Generalization Error ◽  
Worst Case ◽  
Future Events ◽  
Real World Datasets

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.

scholarly journals Multi-Objective Optimisation of Tyre and Suspension Parameters during Cornering for Different Road Roughness Profiles

2021 ◽  
Vol 11 (13) ◽  
pp. 5934
Author(s):  
Georgios Papaioannou ◽  
Jenny Jerrelind ◽  
Lars Drugge
Keyword(s):  
Optimal Solution ◽  
Design Parameters ◽  
Road Vehicles ◽  
Propulsion Systems ◽  
Vehicle Handling ◽  
Optimum Parameters ◽  
Trade Offs ◽  
Road Roughness ◽  
Control Technologies ◽  
Tyre Wear

Effective emission control technologies and novel propulsion systems have been developed for road vehicles, decreasing exhaust particle emissions. However, work has to be done on non-exhaust traffic related sources such as tyre–road interaction and tyre wear. Given that both are inevitable in road vehicles, efforts for assessing and minimising tyre wear should be considered. The amount of tyre wear is because of internal (tyre structure, manufacturing, etc.) and external (suspension configuration, speed, road surface, etc.) factors. In this work, the emphasis is on the optimisation of such parameters for minimising tyre wear, but also enhancing occupant’s comfort and improving vehicle handling. In addition to the search for the optimum parameters, the optimisation is also used as a tool to identify and highlight potential trade-offs between the objectives and the various design parameters. Hence, initially, the tyre design (based on some chosen tyre parameters) is optimised with regards to the above-mentioned objectives, for a vehicle while cornering over both Class A and B road roughness profiles. Afterwards, an optimal solution is sought between the Pareto alternatives provided by the two road cases, in order for the tyre wear levels to be less affected under different road profiles. Therefore, it is required that the tyre parameters are as close possible and that they provide similar tyre wear in both road cases. Then, the identified tyre design is adopted and the optimum suspension design is sought for the two road cases for both passive and semi-active suspension types. From the results, significant conclusions regarding how tyre wear behaves with regards to passenger comfort and vehicle handling are extracted, while the results illustrate where the optimum suspension and tyre parameters have converged trying to compromise among the above objectives under different road types and how suspension types, passive and semi-active, could compromise among all of them more optimally.

A fuzzy multi-objective programming optimization model for emergency resource dispatching under equitable distribution principle

2021 ◽  
pp. 1-10
Author(s):  
Zhaoping Tang ◽  
Wenda Li ◽  
Shijun Yu ◽  
Jianping Sun
Keyword(s):  
Optimization Model ◽  
Programming Model ◽  
Ideal Point ◽  
Optimal Solution ◽  
Multi Objective ◽  
Emergency Rescue ◽  
Satisfaction Degree ◽  
Multi Objective Programming ◽  
Parameter Interval ◽  
Objective Programming

In the initial stage of emergency rescue for major railway emergencies, there may be insufficient emergency resources. In order to ensure that all the emergency demand points can be effectively and fairly rescued, considering the fuzzy properties of the parameters, such as the resource demand quantity, the dispatching time and the satisfaction degree, the railway emergency resources dispatching optimization model is studied, with multi- demand point, multi-depot, and multi-resource. Based on railway rescue features, it was proposed that the couple number of relief point - emergency point is the key to affect railway rescue cost and efficiency. Under the premise of the maximum satisfaction degree of quantity demanded at all emergency points, a multi-objective programming model is established by maximizing the satisfaction degree of dispatching time and the satisfaction degree of the couple number of relief point - emergency point. Combined with the ideal point method, a restrictive parameter interval method for optimal solution was designed, which can realize the quick seek of Pareto optimal solution. Furthermore, an example is given to verify the feasibility and effectiveness of the method.

Accessibility vs. Feasibility: Optimizing Function Allocation for Accommodation of Heterogeneous Populations

2021 ◽  
pp. 1-36
Author(s):  
Benjamin Knisely ◽  
Monifa Vaughn-Cooke
Keyword(s):  
Optimization Model ◽  
Engineering Students ◽  
Product Variety ◽  
Human Beings ◽  
Heterogeneous Populations ◽  
Trade Offs ◽  
Potential System ◽  
Wide Range ◽  
Need To Evaluate ◽  
Design Case Study

Abstract Human beings are physically and cognitively variable, leading to a wide array of potential system use cases. To design safe and effective systems for highly heterogeneous populations, engineers must cater to this variability to minimize the chance of error and system failure. This can be a challenge because of the increasing costs associated with providing additional product variety. Most guidance for navigating these trade-offs is intended for late-stage design, when significant resources have been expended, thus risking expensive redesign or exclusion of users when new human concerns become apparent. Despite the critical need to evaluate accommodation-cost trade-offs in early stages of design, there is currently a lack of structured guidance. In this work, an approach to function modeling is proposed that allows the simultaneous consideration of human and machine functionality. This modeling approach facilitates the allocation of system functions to humans and machines to be used as an accessible baseline for concept development. Further, a multi-objective optimization model was developed to allocate functions with metrics for accommodation and cost. The model was demonstrated on a design case study. 16 senior mechanical engineering students were recruited and tasked with performing the allocation task manually. The results were compared to the output of the optimization model. Results indicated that participants were unable to produce concepts with the same accommodation-cost efficiency as the optimization model. Further, the optimization model successfully produced a wide range of potential product concepts, demonstrating its utility as a decision-aid.

scholarly journals Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2190 ◽  
Author(s):  
Rafael Dawid ◽  
David McMillan ◽  
Matthew Revie
Keyword(s):  
Wind Farm ◽  
Real Life ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Decision Support Tool ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Support Tool ◽  
Trade Offs ◽  
The Impact

This paper for the first time captures the impact of uncertain maintenance action times on vessel routing for realistic offshore wind farm problems. A novel methodology is presented to incorporate uncertainties, e.g., on the expected maintenance duration, into the decision-making process. Users specify the extent to which these unknown elements impact the suggested vessel routing strategy. If uncertainties are present, the tool outputs multiple vessel routing policies with varying likelihoods of success. To demonstrate the tool’s capabilities, two case studies were presented. Firstly, simulations based on synthetic data illustrate that in a scenario with uncertainties, the cost-optimal solution is not necessarily the best choice for operators. Including uncertainties when calculating the vessel routing policy led to a 14% increase in the number of wind turbines maintained at the end of the day. Secondly, the tool was applied to a real-life scenario based on an offshore wind farm in collaboration with a United Kingdom (UK) operator. The results showed that the assignment of vessels to turbines generated by the tool matched the policy chosen by wind farm operators. By producing a range of policies for consideration, this tool provided operators with a structured and transparent method to assess trade-offs and justify decisions.

scholarly journals Multi-Agent Path Finding with Deadlines

2018 ◽  
Author(s):  
Hang Ma ◽  
Glenn Wagner ◽  
Ariel Felner ◽  
Jiaoyang Li ◽  
T. K. Satish Kumar ◽  
...  
Keyword(s):  
Flow Problem ◽  
Search Algorithms ◽  
The Other ◽  
Path Finding ◽  
Combinatorial Search ◽  
Programming Formulation ◽  
Commodity Flow ◽  
Linear Programming Formulation ◽  
Multi Agent ◽  
Integer Linear Programming Formulation

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.

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