Application of particle swarm intelligence algorithms in supply chain network architecture optimization

Kadadevaramath, Rajeshwar S.; Chen, Jason C.H.; Shankar, Balakrishnan; Rameshkumar, K.

doi:10.1016/j.eswa.2012.02.116

Cited by 62 publications

(28 citation statements)

References 31 publications

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“…In this context, several approaches for dealing with production scheduling problem indicate a very high efficiency in the use of metaheuristics such as genetic algorithms (Yildiz 2013;Sadrzadeh 2012;Wong and Ngan 2013;Kadadevaramath et al 2012;Lin et al 2010). However, it is reported that the simple GA often suffers from the troubles of premature convergence, difficulty in constructing fitness functions and parameter dependence Yin et al (2004).…”

Section: Introductionmentioning

confidence: 98%

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

Sanches

Rocha

Castoldi

et al. 2015

J Control Autom Electr Syst

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The problem of production scheduling of manufacturing systems involves the system modeling task and the application of a technique to solve it. This kind of scheduling is characterized by the large number of possible solutions, where several researches have been using the genetic algorithms as a search method to solve this problem, since these algorithms have the capacity of globally exploring the search space and to find good solutions quickly. This paper proposes the use of adaptive genetic algorithm to solve this kind of scheduling problem. The aim of this paper was to obtain a good production schedule considering simultaneous use of machines and automated guided vehicles that minimize the makespan with low running time. The results of this paper were validated in large scenarios and compared with other approach. These results are presented and discussed in this paper.

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Section: Introductionmentioning

confidence: 98%

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

Sanches

Rocha

Castoldi

et al. 2015

J Control Autom Electr Syst

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“…It is to setup a service supply chain network which is composed of a supplier, a service provider, a customer, and a service partner in order to transfer resources which are connected with a yacht service to servitized products [10], deliver to a customer, and to gain a profit by all comers of the yacht service supply chain. The proposed optimization method of a yacht service supply chain is approached in aspect of assets and the suggested method is distinct from the previous method [5]. This paper explains the motivation and the objective of this research in Section 1 and the characteristics of the suggested model in Section 2.…”

Section: Introductionmentioning

confidence: 99%

“…Kadadevaramath et al, [5] explained that a supply chain which has cost efficiency should be managed to succeed in the industry such as a yacht service because various markets, logistics, and manufacturing circumstance are unclear. Also the paper elucidated an optimization method such as Particle Swarm Optimization (PSO) is needed to optimize a supply chain network.…”

Section: Introductionmentioning

confidence: 99%

An Supply Chain Network Optimization for Yacht Service Using C2C Cycle

Park

2014

IJCA

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“…Numerical results have shown that the PSO algorithm is more efficient than the genetic algorithm, especially for solving problems involving continuous solution space. For instance, Kadadevaramath et al [27] and Govindan et al [28] solved, respectively, a three-echelon SCN problem and an optimization problem involving both the economic and environmental benefits of a perishable food SCN by both the genetic algorithm and the PSO algorithm. Numerical results indicated that the PSO algorithm is more efficient than the genetic algorithm, in terms of the accuracy of the optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Optimization Algorithms: A Stochastic Closed-Loop Supply Chain Network Problem Involving Oligopolistic Competition for Multiproducts and Their Product Flow Routings

Zhou

Chan

Wong

et al. 2015

Mathematical Problems in Engineering

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Recently, the first oligopolistic competition model of the closed-loop supply chain network involving uncertain demand and return has been established. This model belongs to the context of oligopolistic firms that compete noncooperatively in a Cournot-Nash framework. In this paper, we modify the above model in two different directions. (i) For each returned product from demand market to firm in the reverse logistics, we calculate the percentage of its optimal product flows in each individual path connecting the demand market to the firm. This modification provides the optimal product flow routings for each product in the supply chain and increases the optimal profit of each firm at the Cournot-Nash equilibrium. (ii) Our model extends the method of finding the Cournot-Nash equilibrium involving smooth objective functions to problems involving nondifferentiable objective functions. This modification caters for more real-life applications as a lot of supply chain problems involve nonsmooth functions. Existence of the Cournot-Nash equilibrium is established without the assumption of differentiability of the given functions. Intelligent algorithms, such as the particle swarm optimization algorithm and the genetic algorithm, are applied to find the Cournot-Nash equilibrium for such nonsmooth problems. Numerical examples are solved to illustrate the efficiency of these algorithms.

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Application of particle swarm intelligence algorithms in supply chain network architecture optimization

Cited by 62 publications

References 31 publications

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

An Supply Chain Network Optimization for Yacht Service Using C2C Cycle

Intelligent Optimization Algorithms: A Stochastic Closed-Loop Supply Chain Network Problem Involving Oligopolistic Competition for Multiproducts and Their Product Flow Routings

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