scholarly journals A Column Generation Based Hyper-Heuristic to the Bus Driver Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Li ◽  
Ying Wang ◽  
Shi Li ◽  
Sujian Li
Keyword(s):  
Column Generation ◽  
Heuristic Method ◽  
Real Life ◽  
Complex Problem ◽  
Operating Costs ◽  
Driver Scheduling ◽  
Selection Mode ◽  
Bus Driver ◽  
Problem Instances ◽  
Improve Service Quality

Public transit providers are facing continuous pressure to improve service quality and reduce operating costs. Bus driver scheduling is among the most studied problems in this area. Based on this, flexible and powerful optimization algorithms have thus been developed and used for many years to help them with this challenge. Particularly, real-life large and complex problem instances often need new approaches to overcome the computational difficulties in solving them. Thus, we propose a column generation based hyper-heuristic for finding near-optimal solutions. Our approach takes advantages of the benefits offered by heuristic method since the column selection mode is driven by a hyper-heuristic using various strategies for the column generation subproblem. The performance of the proposed algorithm is compared with the approaches in the literature. Computational results on real-life instances are presented and discussed.

Evolutionary Driver Scheduling with Relief Chains

2001 ◽  
Vol 9 (4) ◽  
pp. 445-460 ◽  
Author(s):  
Raymond S. K. Kwan ◽  
Ann S. K. Kwan ◽  
Anthony Wren
Keyword(s):  
Hybrid Approach ◽  
Real Life ◽  
Data Sets ◽  
Scheduling Problems ◽  
New Approach ◽  
Driver Scheduling ◽  
Real Life Data ◽  
Construction Heuristic ◽  
Problem Instances

Public transport driver scheduling problems are well known to be NP-hard. Although some mathematically based methods are being used in the transport industry, there is room for improvement. A hybrid approach incorporating a genetic algorithm (GA) is presented. The role of the GA is to derive a small selection of good shifts to seed a greedy schedule construction heuristic. A group of shifts called a relief chain is identi-fied and recorded. The relief chain is then inherited by the offspring and used by the GA for schedule construction. The new approach has been tested using real-life data sets, some of which represent very large problem instances. The results are generally better than those compiled by experienced schedulers and are comparable to solutions found by integer linear programming (ILP). In some cases, solutions were obtained when the ILP failed within practical computational limits.

Optimal fuzzy scheduling and sequencing of multiple-period operating room

2017 ◽  
Vol 32 (1) ◽  
pp. 108-121 ◽  
Author(s):  
Abbas Al-Refaie ◽  
Mays Judeh ◽  
Ming-Hsien Li
Keyword(s):  
Optimization Model ◽  
Real Life ◽  
Operating Rooms ◽  
Decision Makers ◽  
Operating Costs ◽  
Optimization Models ◽  
Patients Satisfaction ◽  
Fuzzy Scheduling ◽  
Sequencing Problems

AbstractLittle research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

Using Texture Feature in Fruit Classification

2021 ◽  
Vol 39 (1B) ◽  
pp. 67-79
Author(s):  
Mauj H. Abd al kreem ◽  
Abd allameer A. Karim
Keyword(s):  
Fruits And Vegetables ◽  
Real Life ◽  
Texture Feature ◽  
Extraction Methods ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Tree Classifier ◽  
Fresh Products ◽  
Occurrence Matrix

Recent advances in computer vision have allowed wide-ranging applications in every area of ​​life. One such area of ​​application is the classification of fresh products, but the classification of fruits and vegetables has proven to be a complex problem and needs further development. In recent years, various machine learning techniques have been exploited with many methods of describing the different features of fruit and vegetable classification in many real-life applications. Classification of fruits and vegetables presents significant challenges due to similarities between layers and irregular characteristics within the class.Hence , in this work, three feature extractor/ descriptor which are local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and, histogram of oriented gradient(HoG) has been proposed to extract fruite features , the  extracted  features have been saved in three feature vectors , then desicion tree classifier has been proposed to classify the fruit types. fruits 360 datasets  is  used  in this work,   where 70% of the dataset were used  in the training phase while 30% of it used in the testing phase. The three proposed feature extruction methods plus the tree  classifier have been used to  classifying  fruits 360 images, results show that the the three feature extraction methods  give a promising results , while the HoG method yielded a poerfull results in which  the accuracy obtained is 96%.

scholarly journals Optimized Procedure to Schedule Physicians in an Intensive Care Unit: A Case Study

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1976
Author(s):  
Lotfi Hidri ◽  
Achraf Gazdar ◽  
Mohammed M. Mabkhot
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Cost Minimization ◽  
Optimal Schedule ◽  
Real Life ◽  
Real Data ◽  
Experimental Tests ◽  
Operating Costs ◽  
Healthcare Personnel ◽  
Total Cost

Hospitals are facing an important financial pressure due to the increasing of the operating costs. Indeed, the growth for the hospitals’ services demand causes a rising in the number of required qualified personnel. Enlarging the personnel number increases dramatically the fixed total cost. Based on some studies, 50% of operating costs in US hospitals are allocated to healthcare personnel. Therefore, reducing these types of costs without damaging the service quality becomes a priority and an obligation. In this context, several studies focused on minimizing the total cost by producing optimal or near optimal schedules for nurses and physicians. In this paper, a real-life physicians scheduling problem with cost minimization is addressed. This problem is encountered in an Intensive Care Unit (ICU) where the current schedule is manually produced. The manual schedule is generating a highly unbalanced load within physicians in addition to a high cost overtime. The manual schedule preparation is a time consuming procedure. The main objective of this work is to propose a procedure that systematically produces an optimal schedule. This optimal schedule minimizes the total overtime within a short time and should satisfies the faced constraints. The studied problem is mathematically formulated as an integer linear program. The constraints are real, hard, and some of them are non-classical ones (compared to the existing literature). The obtained mathematical model is solved using a state-of-the-art software. Experimental tests on real data have shown the performance of the proposed procedure. Indeed, the new optimal schedules reduce the total overtime by up to 69%. In addition, a more balanced workload for physicians is obtained and several physician preferences are now satisfied.

scholarly journals Simple Constructive, Insertion, and Improvement Heuristics Based on the Girding Polygon for the Euclidean Traveling Salesman Problem

Algorithms ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 5 ◽  
Author(s):  
Víctor Pacheco-Valencia ◽  
José Alberto Hernández ◽  
José María Sigarreta ◽  
Nodari Vakhania
Keyword(s):  
Traveling Salesman Problem ◽  
Real Life ◽  
Traveling Salesman ◽  
Computational Time ◽  
Problem Instance ◽  
Dimensional Euclidean Space ◽  
Benchmark Problem ◽  
Small Constant ◽  
Euclidean Traveling Salesman Problem ◽  
Problem Instances

The Traveling Salesman Problem (TSP) aims at finding the shortest trip for a salesman, who has to visit each of the locations from a given set exactly once, starting and ending at the same location. Here, we consider the Euclidean version of the problem, in which the locations are points in the two-dimensional Euclidean space and the distances are correspondingly Euclidean distances. We propose simple, fast, and easily implementable heuristics that work well, in practice, for large real-life problem instances. The algorithm works on three phases, the constructive, the insertion, and the improvement phases. The first two phases run in time O ( n 2 ) and the number of repetitions in the improvement phase, in practice, is bounded by a small constant. We have tested the practical behavior of our heuristics on the available benchmark problem instances. The approximation provided by our algorithm for the tested benchmark problem instances did not beat best known results. At the same time, comparing the CPU time used by our algorithm with that of the earlier known ones, in about 92% of the cases our algorithm has required less computational time. Our algorithm is also memory efficient: for the largest tested problem instance with 744,710 cities, it has used about 50 MiB, whereas the average memory usage for the remained 217 instances was 1.6 MiB.

scholarly journals An Instance Data Repository for the Round-robin Sports Timetabling Problem

2020 ◽  
Vol 45 (2) ◽  
pp. 184-200
Author(s):  
David Van Bulck ◽  
Dries Goossens ◽  
Jo¨rn Scho¨nberger ◽  
Mario Guajardo
Keyword(s):  
Optimization Problem ◽  
Real Life ◽  
Short Description ◽  
Problem Instance ◽  
Data Repository ◽  
Timetabling Problem ◽  
Classification Framework ◽  
Different Types ◽  
Problem Instances ◽  
Complex Matter

The sports timetabling problem is a combinatorial optimization problem that consists of creating a timetable that defines against whom, when and where teams play games. This is a complex matter, since real-life sports timetabling applications are typically highly constrained. The vast amount and variety of constraints and the lack of generally accepted benchmark problem instances make that timetable algorithms proposed in the literature are often tested on just one or two specific seasons of the competition under consideration. This is problematic since only a few algorithmic insights are gained. To mitigate this issue, this article provides a problem instance repository containing over 40 different types of instances covering artificial and real-life problem instances. The construction of such a repository is not trivial, since there are dozens of constraints that need to be expressed in a standardized format. For this, our repository relies on RobinX, an XML-supported classification framework. The resulting repository provides a (non-exhaustive) overview of most real-life sports timetabling applications published over the last five decades. For every problem, a short description highlights the most distinguishing characteristics of the problem. The repository is publicly available and will be continuously updated as new instances or better solutions become available.

scholarly journals Solving a real-life roll-on–roll-off waste collection problem with column generation

2019 ◽  
Vol 2 (1-4) ◽  
pp. 41-54 ◽  
Author(s):  
Joël Raucq ◽  
Kenneth Sörensen ◽  
Dirk Cattrysse
Keyword(s):  
Column Generation ◽  
Real Life ◽  
Waste Collection
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