Ant Colony Optimisation approaches for the transportation assignment problem

D’Acierno, Luca; Gallo, Mariano; Montella, Bruno

doi:10.2495/ut100041

Cited by 14 publications

(4 citation statements)

References 17 publications

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“…Authors have declared that no competing interests exist. (12) 4 × = [9,12,9,6,9,10; 7,3,7,7,5,5; 6,5,9,11,3,11; 6,8,11,2,2,10], = [5,6,2,9], = [4,4,6,2,4,2] 3.BTP-3 (4) 3 × = [3,1,7,4; 2,6,5,9; 8,3,3,2] We want to schedule the shifting of tyers from factories to showroom with a minimum cost.…”

Section: Competing Interestsmentioning

confidence: 99%

“…This is based on the probabilistic technique for solving various types of optimization problems. Such as vehicle routing problem [2,3,4], traveling salesman problem, traffic assignment problem [5], shortest path problem, minimum spanning tree problem, etc. The algorithm is examining for an optimal path in the graph based on the behavior of ants.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Ant Colony Optimization Algorithm for Solving a Transportation Problem

Ekanayake

Perera

Daundasekara

et al. 2020

JAMCS

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Transportation of products from sources to destinations with minimal total cost plays a key role in logistics and supply chain management. The transportation problem (TP) is an extraordinary sort of Linear Programming problem where the objective is to minimize the total cost of disseminating resources from several various sources to several destinations. Initial feasible solution (IFS) acts as a foundation of an optimal cost solution technique to any TP. Better is the IFS lesser is the number of iterations to reach the final optimal solution. This paper presents a meta-heuristic algorithm, modified ant colony optimization algorithm (MACOA) to attain an IFS to a Transportation Problem. The proposed algorithm is straightforward, simple to execute, and gives us closeness optimal solutions in a finite number of iterations. The efficiency of this algorithm is likewise been advocated by solving validity and applicability examples An extensive numerical study is carried out to see the potential significance of our modified ant colony optimization algorithm (MACOA). The comparative assessment shows that both the MACOA and the existing JHM are efficient as compared to the studied approaches of this paper in terms of the quality of the solution. However, in practice, when researchers and practitioners deal with large-sized transportation problems, we urge them to use our proposed MACOA due to the time-consuming computation of JHM. Therefore this finding is important in saving time and resources for minimization of transportation costs and optimizing transportation processes which could help significantly to improve the organization’s position in the market.

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Section: Competing Interestsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modified Ant Colony Optimization Algorithm for Solving a Transportation Problem

Ekanayake

Perera

Daundasekara

et al. 2020

JAMCS

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“…The ant colony algorithm simulates the way that the ant releases pheromone on the route and that its behavior is influenced by other ants. The ant colony algorithm has been successfully used to improve traffic assignment by many scholars [19][20][21][22][23]. Concrete steps to determine the optimal path with the primary ant colony algorithm are as follows.…”

Section: Traffic Assignment Based On Improved Ant Colony Algorithmmentioning

confidence: 99%

Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

Wang

Feng

et al. 2014

Mathematical Problems in Engineering

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We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.

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“…The problem may be expressed in terms of a fixedpoint problem in the case of stationary hypotheses or in terms of similar analytical formulations. however, this problem has been extensively analysed elsewhere (see, for instance, cantarella [27], D'Acierno et al [28], Nguyen et al [29], Nuzzolo et al [30]). relation (3) represents the budget constraint which may be expressed in terms of both economic resources (i.e.…”

Section: Introductionmentioning

confidence: 99%

Assumptions and simulation of passenger behaviour on rail platforms

D’Acierno¹,

Botte²,

Montella³

2017

Int. J. TDI

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Current techniques of Travel Demand Management (TDM) are based on the simulation of users’ reactions to implemented strategies. Indeed, the correct modelling of user behaviour may be considered important for managing public transport systems. Especially in high density contexts, performance of the mass-transit system may represent one of the main tools of decision-makers for affecting users’ choices. In this paper, we focus on the behaviour of users waiting on rail/metro platforms, analysing boarding priorities when a train arrives based on the traditional First In – First Out (FIFO) approach and comparing it with Random In – First Out (RIFO) behaviour. The approaches are then applied in the case of a real metro line operating under different congestion levels

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Ant Colony Optimisation approaches for the transportation assignment problem

Cited by 14 publications

References 17 publications

A Modified Ant Colony Optimization Algorithm for Solving a Transportation Problem

A Modified Ant Colony Optimization Algorithm for Solving a Transportation Problem

Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

Assumptions and simulation of passenger behaviour on rail platforms

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