Stochastic Lead Time Models for Supply Chain Networks

Author(s):  
N. R. Srinivasa Raghavan ◽  
N. Viswanadham
2006 ◽  
Vol 173 (2) ◽  
pp. 617-636 ◽  
Author(s):  
Jeon G. Kim ◽  
Dean Chatfield ◽  
Terry P. Harrison ◽  
Jack C. Hayya

2021 ◽  
Author(s):  
Sepehr Habibollahi

This report examines the supply chain strategies for a specific perishable product, or fresh produce and uses green beans as an example. The quality of the products which are in direct correlation with the value of the product are put into the supply chain model, this type of model is also known as “cold chain”. This report in addition to recent researches in cold chain, looks into multi aspect quality degradation and a stochastic lead time from warehouse to retailer. This model developed creates greater insight into the supply chain strategies of such products.


2015 ◽  
Vol 26 (7) ◽  
pp. 1069-1084 ◽  
Author(s):  
Kanda Boonsothonsatit ◽  
Sami Kara ◽  
Suphunnika Ibbotson ◽  
Berman Kayis

Purpose – The purpose of this paper is to propose a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to support decision makers to design their supply chain networks using three key objectives: the lowest cost and environmental impact and the shortest lead time by incorporating the decision maker’s inputs. Design/methodology/approach – GOOG aims to suggest the best-fitted parameters for supply chain partners and manufacturing plant locations, their order allocations, and appropriate transportation modes and lot-sizes for cradle-to-gate. It integrates Fuzzy Goal Programming and weighted max-min operator for trade-off conflicting objectives and overcome fuzziness in specifying target values of individual objectives. It is solved using exact algorithm and validated using an industrial case study. Findings – The comparative analysis between actual, three single-objective, and multi-objective decisions showed that GOOG is capable to optimising three objectives namely cost, lead time, and environmental impact. Research limitations/implications – Further, GOOG requires validation for different supply chain scenarios and manufacturing strategic decisions. It can improve by including multi-echelon supply chain networks, entire life cycle and relevant environmental legislations. Practical implications – GOOG helps the decision makers to configuring those supply chain parameters whilst minimising those three objectives. Social implications – Companies can use GOOG as a tool to strategically select their supply chain that reduces their footprint and stop rebound effect which imposes significant impact to the society. Originality/value – GOOG includes overlooked in the previous study in order to achieve the objectives set. It is flexible for the decision makers to change the relative weightings of the inputs for those contradicting objectives.


2021 ◽  
Author(s):  
Sepehr Habibollahi

This report examines the supply chain strategies for a specific perishable product, or fresh produce and uses green beans as an example. The quality of the products which are in direct correlation with the value of the product are put into the supply chain model, this type of model is also known as “cold chain”. This report in addition to recent researches in cold chain, looks into multi aspect quality degradation and a stochastic lead time from warehouse to retailer. This model developed creates greater insight into the supply chain strategies of such products.


2021 ◽  
Author(s):  
Muhammad Kamran

Two echelon cold supply chain model is developed in which warehouse and retailer are the two main actors of supply chain. The model is based on energy consumption cost for chiller system and the stochastic lead time. To incorporate the quality degradation, global stability index (GSI) method is used. The objective is to analyze the effect of retailer’s storage temperature and nutritional index weightage on the total cost of supply chain. A breakdown structure of all the associated costs is developed to formulate the total cost of cold chain. A numerical example is used for better understanding. To find the optimal solution, the model is numerically solved by using matlab genetic algorithm. The sensitivity analysis is being performed to study the model behavior against different parameters. Keywords: Cold chain, echelon valuation, quality degradation, global stability index, stochastic lead time


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