Advances in Industrial Engineering

Advances in Industrial Engineering

Design a Supply Chain Network based on Lean, agile and pivotal values and solving by multi objective metaheuristic algorithms

Document Type : Research Paper

Authors
Hossein Ali Hassan pour 1
morteza jabale 2
1 Department of Industrial Engineering and Logistics, Faculty of Engineering, Imam Hossein Comprehensive University, Tehran, Iran
2 Industrial Engineering Group, Imam Hossein University, Tehran, Iran
10.22059/jieng.2019.255368.1563
Abstract
In this research, a three objective (lean, agile, pivotal values) nonlinear integer programming mathematical model is proposed to optimize general commodities supply chain in this model, all parameters for objective functions and restrictions are assumed to have fixed and known values. Proposed model have three objective functions including: minimizing transportation costs, delivery tardiness and making contracts. Maximizing agility and pivotal values. Intended model designs a lean, agile and value supply chain network. This network consists of Producers, Distributers and Customers that which indexes and criteria values and agility is assumed for producers and distributers. To solve the model, first suitable weights for agility and observance the value for producers and distributers are examined using multi-criteria decision making. These weights and other problem parameters are input data for proposed mathematical model. Proposed mathematical model is solved in a case-study form using GAMS program. Then two multi objective meta heuristic algorithms including genetic algorithm and Frog leaping algorithm using non dominated sorting is proposed and Meta heuristic algorithms and GAMS`s results are Compared to Validate proposed algorithms. Results of two algorithms NSGA-II and NSMOSFLA for comparing criteria`s of multi objective algorithms are Analyzed.
Keywords
Lean Supply Chain- Agile- Value
Nonlinear integer mathematical model
NSGA-II
NSMOSFLA
Subjects
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Advances in Industrial Engineering
Volume 52, Issue 3
Autumn 2018
Pages 349-366