Planning and Scheduling Drainage Infrastructure Maintenance Operations Under Hard and Soft Constraints: A Simulation Study

2020 ◽  
Author(s):  
Monjurul Hasan ◽  
Ming Lu ◽  
Simaan AbouRizk ◽  
Jason Neufeld
Keyword(s):  
Simulation Study ◽  
Soft Constraints ◽  
Planning And Scheduling ◽  
Hard And Soft Constraints ◽  
Infrastructure Maintenance

A multi-objective approach to nurse scheduling with both hard and soft constraints

1996 ◽  
Vol 30 (3) ◽  
pp. 183-193 ◽  
Author(s):  
Ilham Berrada ◽  
Jacques A. Ferland ◽  
Philippe Michelon
Keyword(s):  
Soft Constraints ◽  
Nurse Scheduling ◽  
Multi Objective ◽  
Hard And Soft Constraints

scholarly journals Laplacian Mesh Deformation

2013 ◽  
Author(s):  
Arnaud Gelas
Keyword(s):  
Mesh Deformation ◽  
Soft Constraints ◽  
Surface Mesh ◽  
Flexible Design ◽  
3D Surface ◽  
Hard And Soft Constraints ◽  
Local Detail

Deforming a 3D surface mesh while preserving its local detail is useful for editing anatomical atlases or for mesh based segmentation. This contribution introduces new classes for performing hard and soft constraints deformation in a flexible design which allows user to switch easily in between Laplacian discretization operators, area weighing and solvers. The usage of these new classes is demonstrated on a sphere.

Chapter 23. MaxSAT, Hard and Soft Constraints

2021 ◽  
Author(s):  
Chu Min Li ◽  
Felip Manyà
Keyword(s):  
Combinatorial Optimization ◽  
Lower Bounds ◽  
Branch And Bound ◽  
Data Structures ◽  
Optimization Problems ◽  
Soft Constraints ◽  
Inference Rules ◽  
Combinatorial Optimization Problems ◽  
Definition Of ◽  
Hard And Soft Constraints

MaxSAT solving is becoming a competitive generic approach for solving combinatorial optimization problems, partly due to the development of new solving techniques that have been recently incorporated into modern MaxSAT solvers, and to the challenge problems posed at the MaxSAT Evaluations. In this chapter we present the most relevant results on both approximate and exact MaxSAT solving, and survey in more detail the techniques that have proven to be useful in branch and bound MaxSAT and Weighted MaxSAT solvers. Among such techniques, we pay special attention to the definition of good quality lower bounds, powerful inference rules, clever variable selection heuristics and suitable data structures. Moreover, we discuss the advantages of dealing with hard and soft constraints in the Partial MaxSAT formalims, and present a summary of the MaxSAT Evaluations that have been organized so far as affiliated events of the International Conference on Theory and Applications of Satisfiability Testing.

Graph coloring approach with new upper bounds for the chromatic number: team building application

2018 ◽  
Vol 52 (3) ◽  
pp. 807-818
Author(s):  
Assia Gueham ◽  
Anass Nagih ◽  
Hacene Ait Haddadene ◽  
Malek Masmoudi
Keyword(s):  
Chromatic Number ◽  
Graph Coloring ◽  
Upper Bound ◽  
Team Building ◽  
Upper Bounds ◽  
Real Case ◽  
Soft Constraints ◽  
Empirical Comparison ◽  
Hard And Soft Constraints

In this paper, we focus on the coloration approach and estimation of chromatic number. First, we propose an upper bound of the chromatic number based on the orientation algorithm described in previous studies. This upper bound is further improved by developing a novel coloration algorithm. Second, we make a theoretical and empirical comparison of our bounds with Brooks’s bound and Reed’s conjecture for class of triangle-free graphs. Third, we propose an adaptation of our algorithm to deal with the team building problem respecting several hard and soft constraints. Finally, a real case study from healthcare domain is considered for illustration.

scholarly journals University Course Timetabling using Bayesian based Optimization Algorithm

2018 ◽  
Vol 6 (2) ◽  
pp. 14
Author(s):  
Alinaswe Siame ◽  
Douglas Kunda
Keyword(s):  
Mathematical Optimization ◽  
Soft Constraints ◽  
Simulation Research ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Hard Constraints ◽  
Time Simulation ◽  
Feasible Solutions ◽  
Hard And Soft Constraints ◽  
University Course

<p>The timetabling problem has traditionally been treated as a mathematical optimization, heuristic, or human-machine interactive problem. The timetabling problem comprises hard and soft constraints. Hard constraints must be satisfied in order to generate feasible solutions. Soft constraints are sometimes referred to as preferences that can be contravened if necessary. In this research, we present is as both a mathematical and a human-machine problem that requires acceptable and controlled human input, then the algorithm gives options available without conflicting the hard constraints. In short, this research allows the human agents to address the soft-constraints as the algorithm works on the hard constraints, as well as the algorithm being able to learn the soft constraints over time. Simulation research was used to investigate the timetabling problem. Our proposed model employs the use a naïve Bayesian Algorithm, to learn preferred days and timings by lecturers and use them to resolve the soft constraints.  </p>

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