Morteza Ghomi-Avili

Bio: Morteza Ghomi-Avili is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Supply chain network & Supply chain. The author has an hindex of 5, co-authored 6 publications receiving 110 citations.

Competitive green supply chain network design model considering inventory decisions under uncertainty: a real case of a filter company

Abstract: A robust bi-level model of the single-product multi-period network design problem is proposed for a competitive green supply chain considering pricing and inventory decisions under uncertainty and ...

A Network Design Model for a Resilient Closed-Loop Supply Chain with Lateral Transshipment

Abstract: This paper develops a model for the closed-loop supply chain network design with disruption risk. By considering supply disruption, two factors including extra inventory and lateral transshipment are used as resilience strategies. The main purpose is to reduce the supply chain costs due to the location decisions, quantity of products between different levels and lost sale. Disruption in a supply is assumed completely by different scenarios, and then the problem is formulated by a mixed-integer programming model. Furthermore, a two-stage stochastic approach is implemented to tackle uncertainty. Finally, a sensitivity analysis is carried out to examine the effects of the resilience strategies on the structure of the supply chain and to propose some managerial insight for using the model in real world situations.

A Flexible Integrated Forward/ Reverse Logistics Model with Random Path-based Memetic Algorithm

Abstract: Due to business and environmental issues, the efficient design of an integrated forward/reverse logistics network has recently attracted more attention from researchers. The significance of transportation cost and customer satisfaction spurs an interest in developing a flexible network design model with different delivery paths. This paper proposes a flexible mixed-integer programming model to deal with such issues. The model integrates the network design decisions in both forward and backward logistics networks, and also applies three kinds of delivering modes (normal delivery, direct shipment, and direct delivery) which enrich the model to be able to deliver the products to customers by distribution-skipping the mid-process strategy in order to deliver products in more flexible paths to customer zones. To tackle with such an NP-hard problem, a memetic algorithm (MA) with random path-based direct representation and combinatorial local search methods is developed. Numerical experiments are conducted to demonstrate the significance and applicability of the model as well as the efficiency and accuracy of the proposed solution approach.

Joint Inventory-Location Problem Under Risk of Probabilistic Facility Disruptions

Abstract: This paper studies a reliable joint inventory-location problem that optimizes facility locations, customer allocations, and inventory management decisions when facilities are subject to disruption risks (e.g., due to natural or man-made hazards). When a facility fails, its customers may be reassigned to other operational facilities in order to avoid the high penalty costs associated with losing service. The authors propose an integer programming model that minimizes the sum of facility construction costs, expected inventory holding costs and expected customer costs under normal and failure scenarios. The authors develop a Lagrangian relaxation solution framework for this problem, including a polynomial-time exact algorithm for the relaxed nonlinear subproblems. Numerical experiment results show that this proposed model is capable of providing a near-optimum solution within a short computation time. Managerial insights on the optimal facility deployment, inventory control strategies, and the corresponding cost constitutions are drawn.

A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk

Abstract: One of the challenges facing supply chain designers is designing a sustainable and resilient supply chain network. The present study considers a closed-loop supply chain by taking into account sustainability, resilience, robustness, and risk aversion for the first time. The study suggests a two-stage mixed-integer linear programming model for the problem. Further, the robust counterpart model is used to handle uncertainties. Furthermore, conditional value at risk criterion in the model is considered in order to create real-life conditions. The sustainability goals addressed in the present study include minimizing the costs, \begin{document}$ \text{CO}_2 $\end{document} emission, and energy, along with maximizing employment. In addition, effective environmental and social life-cycle evaluations are provided to assess the associated effects of the model on society, environment, and energy consumption. The model aims to answer the questions regarding the establishment of facilities and amount of transported goods between facilities. The model is implemented in a car assembler company in Iran. Based on the results, several managerial insights are offered to the decision-makers. Due to the complexity of the problem, a constraint relaxation is applied to produce quality upper and lower bounds in medium and large-scale models. Moreover, the LP-Metric method is used to merge the objectives to attain an optimal solution. The results revealed that the robust counterpart provides a better estimation of the total cost, pollution, energy consumption, and employment level compared to the basic model.