A new multi objective optimization model for designing a green supply chain network under uncertainty

Abstract

Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with -constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE) is applied.

Multi-objective optimization for supply chain management problem: A literature review

“…The other publications discuss about multi-objective problem for CLSC i.e. conducted by Gupta andEvans, (2009), Zarandi et al (2011), Saffar andRazmi (2015) andvahdani andMohamadi (2015).…”

“…Liang (2008) developed fuzzy mixed integer linear programming model to solve integrated multi-product and multi-time period production/distribution planning decisions problems with fuzzy objectives. Saffar et al (2015) developed mixed integer linear programming to formulate green supply chain network under uncertainty. The authors considered model parameter such as facility locating costs, transportation costs, production and maintenance costs, rate of CO2 emission, rate of returned products, rate of recoverability as form of fuzzy parameters.…”

A fuzzy dynamic multi-objective, multi-item model by considering customer satisfaction in supply chain

“…Pasandideh et al [17] used a Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve the proposed complicated bi-objective multi-product multi-period three-layer SC network problem. Some authors have employed the "-constraint method to nd Pareto so- lutions to the multi-objective problems [28,29]. Yu et al [30] presented a bi-objective model under uncertainty in which minimizing costs and risks are considered as the two objectives.…”

“…This limitation is represented by Constraint (28). Constraint (29) shows delivery quantity consisting of perfect, imperfect, and defective items. This model considers minimum rates for defective and imperfect items shown in Eqs.…”

A dynamic bi-objective closed-loop supply chain network design considering supplier selection and remanufacturer subcontractors

“…Note that the main reason they implemented the reverse settings is the rework process. Saffar et al (2015) investigated a bi-objective green supply chain problem under a fuzzy programming method to be able to face with uncertain conditions. They applied Jimenez approach (Jiménez, 1996) to change the problem to the crisp model and solved it using the ε-constraint method.…”