Esmaeil Najafi

Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk

We formulate a robust GrOSCN by the bi-level programming.We are the first to introduce the CVaR for GrOSCD.We achieve an exact solution by using KKT conditions.We integrate risk parameters and cost to design a GrSCN.The resulted robust model is better-managed in the case of operational risks. The main objective of this research is to introduce the concept of Green Opportunistic Supply Chain (GrOSC) and to design it in a lean and agile manufacturing setting under uncertain and risky environment. The model considers the uncertainties of the market-related information, i.e. the demand and transportation and shortage costs, under Vendor Managed Inventory (VMI) strategy. Addressing the retailers risk aversion level through Conditional Value at Risk (CVaR) to deal with these uncertainties leads to a bi-level programming problem. The Karush-Kuhn-Tucker (KKT) conditions are adopted to transform the model into a single-level mixed integer linear programming problem. Since, the realization of the uncertain parameters is the only information available, a data-driven approach is employed to avoid distributional assumptions. The effectiveness of the model is finally demonstrated through a numerical example.

An electromagnetism-like metaheuristic for open-shop problems with no buffer

This paper considers open-shop scheduling with no intermediate buffer to minimize total tardiness. This problem occurs in many production settings, in the plastic molding, chemical, and food processing industries. The paper mathematically formulates the problem by a mixed integer linear program. The problem can be optimally solved by the model. The paper also develops a novel metaheuristic based on an electromagnetism algorithm to solve the large-sized problems. The paper conducts two computational experiments. The first includes small-sized instances by which the mathematical model and general performance of the proposed metaheuristic are evaluated. The second evaluates the metaheuristic for its performance to solve some large-sized instances. The results show that the model and algorithm are effective to deal with the problem.

A new data envelopment analysis in fully fuzzy environment on the base of the degree of certainty of information

Data Envelopment Analysis (DEA) is a linear programming (LP) based technique for evaluating the comparative efficiency of decision-making units (DMUs) based on multiple inputs and outputs. Conventional DEA forms require precise data. However in real problems, it is not easy to measure inputs and outputs in an exact way to find precise data. Although, fuzzy set theory has been introduced as a powerful approach to quantify vague data and several authors have suggested various fuzzy DEA models, there is a key flaw in previous approaches. When coping with real information, fuzziness is not sufficient to consider and a reliability of the information is very vital too. A Z-number has extra ability to depict the uncertain information. This concept relates to the topic of reliability of information. Z-number can portray fuzziness and reliability of information concurrently. In this paper, we consider a different fuzzy DEA form, where all the inputs and outputs and also their weights are Z-numbers. This Z-numbers DEA model turned into fully fuzzy LP on the basis of fuzzy expectation. Finally, we transform the fully fuzzified DEA model to the classical LP model. This method has very straightforward calculations and the key benefit of the proposed method is its low computational intricacy.

A Network-Based Data Envelope Analysis Model in a Dynamic Balanced Score Card

Performance assessment during the time and along with strategies is the most important requirements of top managers. To assess the performance, a balanced score card (BSC) along with strategic goals and a data envelopment analysis (DEA) are used as powerful qualitative and quantitative tools, respectively. By integrating these two models, their strengths are used and their weaknesses are removed. In this paper, an integrated framework of the BSC and DEA models is proposed for measuring the efficiency during the time and along with strategies based on the time delay of the lag key performance indicators (KPIs) of the BSC model. The causal relationships during the time among perspectives of the BSC model are drawn as dynamic BSC at first. Then, after identifying the network-DEA structure, a new objective function for measuring the efficiency of nine subsidiary refineries of the National Iranian Oil Refining and Distribution Company (NIORDC) during the time and along with strategies is developed.