State Space Reduced Dynamic Programming for the Aircraft Sequencing Problem with Constrained Position Shifting

2014 ◽  
pp. 267-279 ◽  
Author(s):  
Fabio Furini ◽  
Martin Philip Kidd ◽  
Carlo Alfredo Persiani ◽  
Paolo Toth
Keyword(s):  
Dynamic Programming ◽  
State Space ◽  
Sequencing Problem ◽  
Aircraft Sequencing Problem ◽  
Reduced Dynamic

scholarly journals Research on Optimization Algorithm Design Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1991-1992
Keyword(s):  
Dynamic Programming ◽  
State Space ◽  
Branch And Bound ◽  
Optimization Algorithm ◽  
Feasible Solution ◽  
Algorithm Design ◽  
Optimization Techniques ◽  
Greedy Method ◽  
Design Techniques

This paper discusses various optimization algorithm design techniques. So, optimization techniques which are discussed in this paper are greedy method, dynamic programming and branch and bound. Problem comes under optimization are used to find either maximum or minimum. All these techniques we have multiple inputs and some constraints and we have to find feasible solution using these inputs and constraints. In greedy method we follow some predefined method. Using that predefined method, we reach to the solution. On contrary to this in dynamic programming we take decision at every step and in the end we reach to the solution. In branch and bound we create state space tree and explore all possibilities of live node. Based on some constraint we start killing some alive nodes. Here, firstly I will discuss all the design techniques. Then types of problems that can be solved under each design techniques and their time complexities.

A dynamic programming formulation of a production sequencing problem

1983 ◽  
Vol 7 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Uriel G. Rothblum ◽  
Reuven Karni ◽  
Erna Gelfand
Keyword(s):  
Dynamic Programming ◽  
Programming Formulation ◽  
Sequencing Problem

scholarly journals A Ripple-Spreading Genetic Algorithm for the Aircraft Sequencing Problem

2011 ◽  
Vol 19 (1) ◽  
pp. 77-106 ◽  
Author(s):  
Xiao-Bing Hu ◽  
Ezequiel A. Di Paolo
Keyword(s):  
Combinatorial Problems ◽  
Natural Phenomenon ◽  
Binary Representation ◽  
Optimal Sequence ◽  
Sequencing Problem ◽  
Spreading Model ◽  
Memory Efficiency ◽  
Aircraft Landing ◽  
Study Case ◽  
Aircraft Sequencing Problem

When genetic algorithms (GAs) are applied to combinatorial problems, permutation representations are usually adopted. As a result, such GAs are often confronted with feasibility and memory-efficiency problems. With the aircraft sequencing problem (ASP) as a study case, this paper reports on a novel binary-representation-based GA scheme for combinatorial problems. Unlike existing GAs for the ASP, which typically use permutation representations based on aircraft landing order, the new GA introduces a novel ripple-spreading model which transforms the original landing-order-based ASP solutions into value-based ones. In the new scheme, arriving aircraft are projected as points into an artificial space. A deterministic method inspired by the natural phenomenon of ripple-spreading on liquid surfaces is developed, which uses a few parameters as input to connect points on this space to form a landing sequence. A traditional GA, free of feasibility and memory-efficiency problems, can then be used to evolve the ripple-spreading related parameters in order to find an optimal sequence. Since the ripple-spreading model is the centerpiece of the new algorithm, it is called the ripple-spreading GA (RSGA). The advantages of the proposed RSGA are illustrated by extensive comparative studies for the case of the ASP.

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