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Showing 2 results for Esfandiari

Sahebe Esfandiari, Hamid Mashreghi, Saeed Emami,
Volume 30, Issue 2 (IJIEPR 2019)

We study the problem of order acceptance, scheduling and pricing (OASP) in parallel machine environment. Each order is characterized by due date, release date, deadline, controllable processing time, sequence dependent set up time and price in MTO system. We present a MILP formulation to maximize the net profit. Then under joint optimization approach, the pricing decisions set for unrelated parallel machine environment. The results show that the basic developed problem can solve the scheduling decisions based on different levels of products’ priced. Thus the problem solves these two categories of decisions simultaneously. Moreover, the changes of accepted orders in pricing levels can be analyzed regarding its dependency to price elasticity of items for future research.
Dr. Zahra Esfandiari, Prof. Mahdi Bashiri, Prof. Reza Tavakkoli-Moghaddam,
Volume 31, Issue 1 (IJIEPR 2020)

One of the major risks that can affect supply chain design and management is the risk of facility disruption due to natural hazards, economic crises, terrorist attacks, etc. Static resiliency of the network is one of the features that is considered when designing networks to manage disruptions, which increases the network reliability. This feature refers to the ability of the network to maintain its operation and connection in the lack of some members of the chain. Facility hardening is one of the strategies used for this purpose. In this paper, different reliable capacitated fixed-charge location allocation models are developed for hedging network from failure. In these proposed models, hardening, resilience, and hardening and resilience abilities are considered respectively. These problems are formulated as a nonlinear programming models and their equivalent linear form are presented. The sensitivity analysis confirms that the proposed models construct more effective and reliable network comparing to the previous networks. A Lagrangian decomposition algorithm (LDA) is developed to solve the linear models. Computational results show that the LDA is efficient in computational time and quality of generated solutions for instances with different sizes. Moreover, the superiority of the proposed model is confirmed comparing to the classical model.

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