Simulated annealing for the multi-objective aircrew rostering problem

Lučić, Panta; Teodorović, Dušan

doi:10.1016/s0965-8564(98)00021-4

Cited by 45 publications

(39 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…3 Some are corrected expressions for similar constraints proposed by Lucic and Teodorovic [1]. Using the expressions from [1] may lead to errors in counting the working time of the cockpit crews (see fourth paragraph of Section 3 for details) that result in incompact rosters.…”

Section: Crew Scheduling;mentioning

confidence: 99%

“…Using the expressions from [1] may lead to errors in counting the working time of the cockpit crews (see fourth paragraph of Section 3 for details) that result in incompact rosters. Moreover, in the case of a rostering problem where the number of cockpit crews is very restricted, the use of the expressions from [1] leads to an infeasible problem. The expressions become the constraints of the optimization model.…”

Section: Crew Scheduling;mentioning

confidence: 99%

“…A larger flight network leads to a more complex crew rostering Rostering means assigning each cockpit crew pairing 1 A pairing is a sequence of flights starting an Math. Fund.…”

Section: Introductionmentioning

confidence: 99%

“…Rostering means assigning each cockpit crew pairing, 1 which is an element of A pairing is a sequence of flights starting and ending at the cockpit crew base.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization Model for an Airline Crew Rostering Problem: Case of Garuda Indonesia

et al. 2014

View full text Add to dashboard Cite

Abstract. This paper discusses Indonesia, taking into account regulations. These internal labor regulations are in general more restrictive at Garuda Indonesia than at other airlines, so that modeling the cockpit crew rostering problem for Garuda Indonesia is challenging. We ha mathematical expressions for the cockpit crew labor regulations and some technical matters. We model problem, using the average relative deviation of total flight time to the ideal flight time as the objective function. for all classes of cockpit crews of Garuda Indonesia, using a simulated annealing method for solving the problem. We obtained satisfactory ro members in a short amount of computing time. This shows that the optimization problem is well-defined.

show abstract

Section: Crew Scheduling;mentioning

confidence: 99%

Section: Crew Scheduling;mentioning

confidence: 99%

“…A larger flight network leads to a more complex crew rostering Rostering means assigning each cockpit crew pairing 1 A pairing is a sequence of flights starting an Math. Fund.…”

Section: Introductionmentioning

confidence: 99%

“…Rostering means assigning each cockpit crew pairing, 1 which is an element of A pairing is a sequence of flights starting and ending at the cockpit crew base.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization Model for an Airline Crew Rostering Problem: Case of Garuda Indonesia

et al. 2014

View full text Add to dashboard Cite

show abstract

“…This means that the run cutting (crew scheduling) problem must be studied very carefully taking into account operator's as well as crew's objectives [47,48]. Once the vehicle schedule has been designed, scheduling crews and their assignment to planned duties are among the most important problems transit operators face.…”

Section: Some Potential Applications Of Swarm Intelligence In Trafficmentioning

confidence: 99%

Transport modeling by multi-agent systems: a swarm intelligence approach

Teodorovic

2003

Transportation Planning and Technology

127

View full text Add to dashboard Cite

There are a number of emergent traffic and transportation phenomena that cannot be analyzed successfully and explained using analytical models. The only way to analyze such phenomena is through the development of models that can simulate behavior of every agent. Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual 'agents' and that each agent interacts with other agents according to localized knowledge. The agent-based approach is a 'bottom-up' approach to modeling where special kinds of artificial agents are created by analogy with social insects. Social insects (including bees, wasps, ants and termites) have lived on Earth for millions of years. Their behavior in nature is primarily characterized by autonomy, distributed functioning and self-organizing capacities. Social insect colonies teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. On the other hand, a large number of traditional engineering models and algorithms are based on control and centralization. In this article, we try to obtain the answer to the following question: Can we use some principles of natural swarm intelligence in the development of artificial systems aimed at solving complex problems in traffic and transportation?

show abstract

9.1.2 Two‐stage Multi‐objective Analysis in the Assignment of Federal Air Marshals

Philie

Kwinn

Henderson

2014

INCOSE International Symp

View full text Add to dashboard Cite

The United States Federal Air Marshal Service (FAMS) plays a critical role in ensuring safe travel aboard United States (US) air carriers. Federal Air Marshall agents, who operate out of field offices throughout the US, are assigned to accompany flights in order to deter and respond to hostile acts. In an era of limited resources, the FAMS is interested in optimally locating its field offices and agents to minimize the overall risk to the US air transportation system. This represents a highly constrained and complex optimization problem that resists standard techniques. In this paper, we demonstrate an approach that leverages the value‐focused thinking to model risk and optimize risk mitigation. Our approach uses a novel two‐stage approach to risk analysis that reduces the optimization burden and provides a solution to the FAMS.

show abstract

Simulated annealing for the multi-objective aircrew rostering problem

Cited by 45 publications

References 14 publications

Optimization Model for an Airline Crew Rostering Problem: Case of Garuda Indonesia

Optimization Model for an Airline Crew Rostering Problem: Case of Garuda Indonesia

Transport modeling by multi-agent systems: a swarm intelligence approach

9.1.2 Two‐stage Multi‐objective Analysis in the Assignment of Federal Air Marshals

Contact Info

Product

Resources

About