Solving a tri-objective supply chain problem with modified NSGA-II algorithm

Bandyopadhyay, Susmita; Bhattacharya, Ranjan

doi:10.1016/j.jmsy.2013.12.001

Cited by 71 publications

(21 citation statements)

References 40 publications

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“…The algorithm gives the population a rank starting from level 1 for the best level, level 2 second best level and so on. The next step is calculation of “crowding distance” Eq (23) between members of each level [53]. In order to operate binary tournament selection, it is required to compute both the crowding distance and the rank for the members of the population.…”

Section: Problem Definition and Modellingmentioning

confidence: 99%

Development of closed–loop supply chain network in terms of corporate social responsibility

Pedram

Yusoff

et al. 2017

PLoS ONE

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Due to the rise in awareness of environmental issues and the depletion of virgin resources, many firms have attempted to increase the sustainability of their activities. One efficient way to elevate sustainability is the consideration of corporate social responsibility (CSR) by designing a closed loop supply chain (CLSC). This paper has developed a mathematical model to increase corporate social responsibility in terms of job creation. Moreover the model, in addition to increasing total CLSC profit, provides a range of strategic decision solutions for decision makers to select a best action plan for a CLSC. A proposed multi-objective mixed-integer linear programming (MILP) model was solved with non-dominated sorting genetic algorithm II (NSGA-II). Fuzzy set theory was employed to select the best compromise solution from the Pareto-optimal solutions. A numerical example was used to validate the potential application of the proposed model. The results highlight the effect of CSR in the design of CLSC.

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Section: Problem Definition and Modellingmentioning

confidence: 99%

Development of closed–loop supply chain network in terms of corporate social responsibility

Pedram

Yusoff

et al. 2017

PLoS ONE

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“…They applied an intelligent multi-objective hybrid particle swarm optimization algorithm optimizer. As another study, Bandyopadhyay and Bhattacharya (2014) proposed a tri-objective problem for a twoechelon serial supply chain. The considered objectives were the minimization of the total cost the chain, minimization of the variance of order quantity and minimization of the total inventory.…”

Section: Multi-objective Optimization Problem In Scmentioning

confidence: 99%

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

et al. 2017

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There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multiproduct multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms.

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“…Finally, the paper by Bandyopadhyay and Bhattacharya (2014) reports the study of a two-echelon supply chain. Their objectives are the minimization of the total cost, minimization of the variance of order quantities, and minimization of the total quantity of inventory.…”

Section: Literature Reviewmentioning

confidence: 99%

Supply chain network design with efficiency, location, and inventory policy using a multiobjective evolutionary algorithm

Loaiza

Olivares-Benítez

Miranda-Gonzalez

et al. 2016

Int Trans Operational Res

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This study presents a metaheuristic based on a multiobjective evolutionary algorithm to solve a biobjective mixed-integer nonlinear programming model for supply chain design with location-inventory decisions and supplier selection. The supply chain has four echelons with suppliers, plants, distribution centers, and retailers. The decision variables are the opening of plants and distribution centers and the flow of materials between the different facilities, considering a continuous review inventory policy. The conflicting objectives are to minimize total costs on the entire chain, and to maximize a combined value of overall equipment effectiveness from suppliers. Small-and medium-sized scenarios are solved and compared with Pareto fronts obtained with commercial optimization software applying the epsilon-constraint method. The numerical results show the effectiveness of the proposed metaheuristic. The main contributions of this work are a new practical problem that has not been analyzed before, and the development of the evolutionary algorithm.

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Solving a tri-objective supply chain problem with modified NSGA-II algorithm

Cited by 71 publications

References 40 publications

Development of closed–loop supply chain network in terms of corporate social responsibility

Development of closed–loop supply chain network in terms of corporate social responsibility

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

Supply chain network design with efficiency, location, and inventory policy using a multiobjective evolutionary algorithm

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