Showing 3 results for Babazadeh
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Volume 23, Issue 2 (IJIEPR 2012)
Abstract
Design of a logistics network in proper way provides a proper platform for efficient and effective supply chain management. This paper studies a multi-period, multi echelon and multi-product integrated forward-reverse logistics network under uncertainty. First, an efficient complex mixed-integer linear programming (MILP) model by considering some real-world assumptions is developed for the integrated logistics network design to avoid the sub-optimality caused by the separate design of the forward and reverse networks. Then, the stochastic counterpart of the proposed MILP model is used to measure the conditional value at risk (CVaR) criterion, as a risk measure, that can control the risk level of the proposed model. The computational results show the power of the proposed stochastic model with CVaR criteria in handling data uncertainty and controlling risk levels.
Reza Babazadeh, Reza Tavakkoli-Moghaddam,
Volume 28, Issue 2 (IJIEPR 2017)
Abstract
A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND problem as a strategic level decision-making problem in supply chain management is an NP-hard class of computational complexity. To escape infeasible solutions emerged in the problem of interest due to realistic constraints, combination of a random key and priority-base encoding scheme is also used. To assess the quality of the proposed hybrid GA-TLBO algorithm, some numerical examples are conducted. Then, the results are compared with the GA, TLBO, differential evolution (DE) and branch-and -bound algorithms. Finally, the conclusion is provided.
Leila Rezaei, Reza Babazadeh,
Volume 33, Issue 4 (IJIEPR 2022)
Abstract
The introduction of blockchain technology into the food supply chain represents a digital revolution that has led to widespread advances in tracking food security. This article presents a comprehensive review of the literature on the use of blockchain in the food supply chain. This article is a review of the synthesis evidence Best group. We have focused on the supply chains of meat, fruits and vegetables. The Literature review has been conducted from seven different databases. For more insight, we categorized meat, fruit, and vegetable articles into four groups: descriptive, prescriptive, conceptual, and predictive. Due to the small number of case studies in research, the theoretical and conceptual frameworks proposed in most food supply chain articles, including the supply chain of meat, fruits and vegetables, have been less tested in reality. These surveys and small-scale case studies do not clearly and completely identify the impact of blockchain on the meat, fruit and vegetable supply chain and the challenges that blockchain implementation may pose to these supply chains. Findings indicate that little valid and quality research has been done in this field and more research is needed on the use of blockchain in the supply chain of fresh products.
