Hybrid Genetic — Ant Colony Algorithms for Solving Aggregate Production Plan

Kumar, G. Mohan; Haq, A. Noorul

doi:10.1142/s021968670500059x

Cited by 22 publications

(8 citation statements)

References 11 publications

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“…One of the most active application areas of ACO is fusing the ACO with other bionic methods and integrating the advantages from both of them. Most studies are combining ant colony algorithm with genetic algorithm [20,21] and Pilat et al [22] used the GA (Genetic Algorithm) method for the search of optimized ACO parameter values. Ding et al [23] used GA preliminary optimization which could achieve acceptable results.…”

Section: Background Information About the Acomentioning

confidence: 99%

The Application of ABP Method in High‐Density Resistivity Method Inversion

Zhang¹,

Liu²

2011

Chinese J of Geophysics

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As an important branch of geophysical inversion, non‐linear inversion method has played a unique role in geophysical inversion. In recent years, more researchers are laying emphasis on non‐linear joint inversion, but no substantial progresses have been made at present. This paper tries to realize a two‐dimensional non‐linear inversion based on the high density resistivity method by using a joint calculation of both Back Propagation neural network (BP neural network) method and ant colony algorithm which is called Ant colony optimized Back Propagation (ABP) method. After a comparison of results concluded from two models, calculation with BP and ABP methods could achieve better results than with conventional method. Furthermore, the ABP method is superior to the BP neural network method for it could reduce number of iterations, save computation time and obtain a higher calculation accuracy.

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Section: Background Information About the Acomentioning

confidence: 99%

The Application of ABP Method in High‐Density Resistivity Method Inversion

Zhang¹,

Liu²

2011

Chinese J of Geophysics

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“…Subsequently, a final solution was selected by examining a convex combination of these solutions. Kumar and Haq [15] resolved an APP problem by utilising genetic algorithm (GA), ant colony algorithm (AGA), and hybrid genetic-ant colony algorithm (HGA). Based on the results, HGA and GA exhibited relatively good performance.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Simulated Annealing for Solving Aggregate Production Planning

Bakar

Bakheet

Kamil

et al. 2016

Mathematical Problems in Engineering

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Simulated annealing (SA) has been an effective means that can address difficulties related to optimisation problems. SA is now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning (APP) is one of the most considerable problems in production planning, in this paper, we present multiobjective linear programming model for APP and optimised by SA. During the course of optimising for the APP problem, it uncovered that the capability of SA was inadequate and its performance was substandard, particularly for a sizable controlled APP problem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state will generate only one in next state that will make the search slower and the drawback is that the search may fall in local minimum which represents the best solution in only part of the solution space. In order to enhance its performance and alleviate the deficiencies in the problem solving, a modified SA (MSA) is proposed. We attempt to augment the search space by starting with + 1 solutions, instead of one solution. To analyse and investigate the operations of the MSA with the standard SA and harmony search (HS), the real performance of an industrial company and simulation are made for evaluation. The results show that, compared to SA and HS, MSA offers better quality solutions with regard to convergence and accuracy.

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“…Krishnamoorthy et al proposed a Lagrangean approach for solving the Personnel Task Scheduling Problem (PTSP) [14]. Besides, competent genetic algorithms [15], ant colony algorithms [16], particle swarm optimization (PSO) [17], simulation [18], simulated annealing (SA) [19] and many other algorithms and methods [20][21] were used in PCD scheduling.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

Yu¹,

Yang²,

Zhao³

et al. 2014

Int. j. simul. model.

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Disturbance is inevitable in product collaborative design (PCDSP), which has always posed a great challenge for enterprises making quick response. Thus, in the paper, a multi-objective rescheduling model and its solution algorithm are presented. Disturbance cases are analysed and dynamic scheduling procedure based on event-driven and lifecycle-driven is developed firstly. Then a multiobjective rescheduling model aiming to minimum the makespan and tardiness penalty is developed. As a solution, multi-objective dynamic adaptive scheduling algorithm (MODASA), based on bi-layer coding strategy, self-adaptive double point crossover and self-adaptive mutation, is proposed following closely. Finally, analytic results from a case of a wind turbine are used to illustrate the model and method proposed in this paper. Simulation results shows that the model and algorithm have full advantages in computing speed and precision. With the analysis, it can provide insight into ways of improving the strategic and operational decision making for enterprises.

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Hybrid Genetic — Ant Colony Algorithms for Solving Aggregate Production Plan

Cited by 22 publications

References 11 publications

The Application of ABP Method in High‐Density Resistivity Method Inversion

The Application of ABP Method in High‐Density Resistivity Method Inversion

Enhanced Simulated Annealing for Solving Aggregate Production Planning

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

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