scholarly journals Tabu Search-Based Algorithm for Large Scale Crew Scheduling Problems

2005 ◽  
Author(s):  
Marco Caserta
Keyword(s):  
Tabu Search ◽  
Large Scale ◽  
Optimal Solution ◽  
Crew Scheduling ◽  
Set Covering ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Dual Algorithm ◽  
Airline Crew ◽  
Primal Dual

<p>In this paper, the problem of finding a work schedule for airline crew members in a given time horizon is tackled. This problem is known in the literature as airline crew scheduling. The objective is to define the minimum cost schedules where each crew, associated to a combination of commercial flights or "legs" called "pairing", is assigned to one or more flights ensuring that the whole set of flights is covered by crew members. The crew scheduling problem can be modeled by using the set covering formulation. This paper presents a new algorithm whose centerpiece is a primal-to-dual scheme aimed at linking any primal solution to the dual feasible vector that best reflects the quality of the primal solution. This new mechanism is used to intertwine a tabu search based, primal intensive, scheme with a lagrangian based, dual intensive, scheme to design a primal-dual algorithm that progressively reduces the gap between upper and lower bound. The algorithm has been tested on benchmark problems from the literature. In this paper, results on real-world airline instances are presented: out of six well-known problems, the algorithm is able to match the optimal solution for four of them while for the last two, whose optimal solution is not known, a new best known solution is found.</p>

scholarly journals Convergence of a short-step primal-dual algorithm based on the Gauss-Newton direction

2003 ◽  
Vol 2003 (10) ◽  
pp. 517-534 ◽  
Author(s):  
Serge Kruk ◽  
Henry Wolkowicz
Keyword(s):  
Optimal Solution ◽  
Path Following ◽  
Singular Value ◽  
Dual Algorithm ◽  
Strict Complementarity ◽  
Semidefinite Programs ◽  
Primal Dual ◽  
Newton Direction ◽  
Proof Of Convergence ◽  
Smallest Singular Value

We prove the theoretical convergence of a short-step, approximate path-following, interior-point primal-dual algorithm for semidefinite programs based on the Gauss-Newton direction obtained from minimizing the norm of the perturbed optimality conditions. This is the first proof of convergence for the Gauss-Newton direction in this context. It assumes strict complementarity and uniqueness of the optimal solution as well as an estimate of the smallest singular value of the Jacobian.

Solving large scale crew scheduling problems

1997 ◽  
Vol 97 (2) ◽  
pp. 260-268 ◽  
Author(s):  
Hai D. Chu ◽  
Eric Gelman ◽  
Ellis L. Johnson
Keyword(s):  
Large Scale ◽  
Crew Scheduling ◽  
Scheduling Problems

Application and Evaluation of Bee-Based Algorithms in Scheduling

2018 ◽  
pp. 20-42
Author(s):  
Ayse Aycim Selam ◽  
Ercan Oztemel
Keyword(s):  
Project Scheduling ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Manufacturing Processes ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Project Scheduling Problem ◽  
Multi Mode ◽  
Vital Element

Scheduling is a vital element of manufacturing processes and requires optimal solutions under undetermined conditions. Highly dynamic and, complex scheduling problems can be classified as np-hard problems. Finding the optimal solution for multi-variable scheduling problems with polynomial computation times is extremely hard. Scheduling problems of this nature can be solved up to some degree using traditional methodologies. However, intelligent optimization tools, like BBAs, are inspired by the food foraging behavior of honey bees and capable of locating good solutions efficiently. The experiments on some benchmark problems show that BBA outperforms other methods which are used to solve scheduling problems in terms of the speed of optimization and accuracy of the results. This chapter first highlights the use of BBA and its variants for scheduling and provides a classification of scheduling problems with BBA applications. Following this, a step by step example is provided for multi-mode project scheduling problem in order to show how a BBA algorithm can be implemented.

scholarly journals Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shaopei Chen ◽  
Ji Yang ◽  
Yong Li ◽  
Jingfeng Yang
Keyword(s):  
Neural Network ◽  
Vehicle Routing ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Neural Network Models ◽  
Network Systems

Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems.

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