A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths

DOI10.1016/j.apm.2013.08.023zbMath1427.90043OpenAlexW2088964647MaRDI QIDQ1994471

Nizaroyani Saibani, Rizauddin Ramli, Salah Alden Ghasimi

Publication date: 1 November 2018

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2013.08.023

zbMATH Keywords

genetic algorithmsupply chainCplexlength of each-cycleLingo

Mathematics Subject Classification ID

Transportation, logistics and supply chain management (90B06) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (5)

Genetic algorithms in supply chain management: a critical analysis of the literature ⋮ Supply chain network design with financial considerations: a comprehensive review ⋮ Optimal pricing and ordering in global supply chain management with constraints under random demand ⋮ Hybrid Genetic Algorithm and Invasive Weed Optimization via Priority Based Encoding for Location-Allocation Decisions in a Three-Stage Supply Chain ⋮ Multi-criteria risk classification to enhance complex supply networks performance

Uses Software

• CPLEX
• LINGO

Cites Work

• A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level
• A new approach to tactical and strategic planning in production-distribution networks
• Optimal models for a multi-stage supply chain system controlled by kanban under just-in-time philosophy
• An integrated model for logistics network design
• Inventory models for defective items incorporating marketing decisions with variable production cost
• A genetic algorithm approach for solving a closed loop supply chain model: a case of battery recycling
• Distribution network design: New problems and related models
• Supply chain models for an assembly system with preprocessing of raw materials: a simple and better algorithm
• Optimal batch sizing in a multi-stage production system with rework consideration
• A hybrid MCDM model for strategic vendor selection

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