Load dependent lead times and sustainability

Pahl, Julia; Voß, Stefan

doi:10.1109/ssci.2016.7850057

Cited by 1 publication

(1 citation statement)

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“…Ozceylan and Paksoy [41] propose a multiperiod multiproduct mix integer nonlinear program (MINLP) model to optimize strategic decisions for the CLSC specifically accounting for uncertainty in capacities, demand, reverse rates, and objectives, the latter aiming at minimizing total costs for transportation, purchasing, refurbishing, and fixed costs. In fact, reverse rates significantly complicate mathematical models [42].…”

Section: Single-objective Modelsmentioning

confidence: 99%

Closed-Loop Supply Chain Design with Sustainability Aspects and Network Resilience under Uncertainty: Modelling and Application

Baghizadeh

Pahl

2021

Mathematical Problems in Engineering

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In this study, we present a multiobjective mixed-integer nonlinear programming (MINLP) model to design a closed-loop supply chain (CLSC) from production stage to distribution as well as recycling for reproduction. The given network includes production centers, potential points for establishing of distribution centers, retrieval centers, collecting and recycling centers, and the demand points. The presented model seeks to find optimal locations for distribution centers, second-hand product collection centers, and recycling centers under the uncertainty situation alongside the factory’s fixed points. The purpose of the presented model is to minimize overall network costs including processing, establishing, and transportation of products and return flows as well as environmental impacts while maximizing social scales and network flexibility according to the presence of uncertainty parameters in the problem. To solve the proposed model with fuzzy uncertainty, first, the improved epsilon (ε)-constraints approach is used to transform a multiobjective to a single-objective problem. Afterward, the Lagrangian relaxation approach is applied to effectively solve the problem. A real-world case study is used to evaluate the performance of the proposed model. Finally, sensitivity analysis is performed to study the effects of important parameters on the optimal solution.

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Section: Single-objective Modelsmentioning

confidence: 99%