New Mathematical Modeling of Three-Level Supply Chain with Multiple Transportation Vehicles and Different Manufacturers

Supply Chain Management (SCM) is the practice of coordinating the flow of goods, services, information and finances as they move from raw materials to parts supplier to manufacturer to wholesaler to retailer to consumer. Different supply chains have been designed for a variety of firms and this chapter discusses some issues in this regard. This chapter attempts to find why we require different supply chain for different companies. In this chapter we discuss the role of stochastic models in supply chain management system, and also discuss other mathematical models for SCM.

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Mathematical Modeling of Automotive Supply Chain Networks

Periodica Polytechnica Transportation Engineering ◽

10.3311/pptr.9544 ◽

2016 ◽

Vol 44 (3) ◽

pp. 181-186 ◽

Cited By ~ 4

Author(s):

Ákos Dömötörfi ◽

◽

Tamás Péter ◽

Krisztián Szabó ◽

◽

...

Keyword(s):

Mathematical Modeling ◽

Supply Chain ◽

Supply Chain Networks ◽

Automotive Supply Chain ◽

Automotive Supply

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Mathematical Modeling of the Unit Load Formation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.309.358 ◽

2013 ◽

Vol 309 ◽

pp. 358-365 ◽

Cited By ~ 3

Author(s):

Béla Illés ◽

Gabriella Bognár

Keyword(s):

Mathematical Modeling ◽

Supply Chain ◽

Significant Role ◽

Mathematical Description ◽

Raw Materials ◽

Transport Processes ◽

Optimization Process ◽

Objective Functions ◽

Unit Load ◽

Major Factors

A fast and efficient way of handling and storing components, raw materials, semi-finished and finished products play a significant role in the development of transport, storage and distribution design within the supply chain. One of the most important issues in storage and transport processes is the formation of unit loads. Our main goal is to give a mathematical description of a model involving the major factors of the unit load formation. The optimization process is exhibited applying the determined objective functions and constraints related to the basic tasks.

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A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study)

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-008-1648-5 ◽

2008 ◽

Vol 44 (1-2) ◽

pp. 194-200 ◽

Cited By ~ 36

Author(s):

S. J. Sadjadi ◽

M. Jafari ◽

T. Amini

Keyword(s):

Mathematical Modeling ◽

Genetic Algorithm ◽

Supply Chain ◽

Auto Industry ◽

Milk Run ◽

Algorithm Search

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Techno-economic evaluation of microalgae-based supply chain: Review on recent approaches

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1195/1/012026 ◽

2021 ◽

Vol 1195 (1) ◽

pp. 012026

Author(s):

S L Y Lo ◽

K G H Kong ◽

B S How ◽

J Y Lim ◽

P L Show ◽

...

Keyword(s):

Mathematical Modeling ◽

Supply Chain ◽

Economic Evaluation ◽

Alternative Energy ◽

First Generation ◽

Experimental Studies ◽

Third Generation ◽

Feasibility Evaluation ◽

Technical Evaluation ◽

Microalgae Productivity

Abstract Third generation biomass-derived products such as biofuel has been garnering attention as a viable alternative energy source recently as it does not necessarily require fresh water and vast land for cultivation as compared to first-generation and second-generation biomass. However, extensive studies have to go into the feasibility evaluation for third generation biomass utilization prior to upscaling the process to commercial level. Other than comprehensive technical evaluation such as experimental studies to understand the microalgae productivity, economic evaluation of the utilization of third-generation biomass is also critical specifically in the perspective of supply chain. Therefore, the objective of this review is to lay out an overall picture to the readers the various option of approaches or methods utilized in feasibility evaluation of the microalgae-based supply chain. The outcome of the review paper indicated that approximately 58% of the papers reviewed opted for mathematical modeling with optimization whereas the remaining 42% opted for mathematical modeling without optimization.

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