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Sustainable supply chain management with pricing, greening and governmental tariffs determining strategies: A game-theoretic approach

Journal Paper
Seyed RezaMadani, MortezaRasti-Barzoki
Madani, Seyed Reza, and Morteza Rasti-Barzoki. "Sustainable supply chain management with pricing, greening and governmental tariffs determining strategies: A game-theoretic approach." Computers & Industrial Engineering 105 (2017): 287-298.

Despite the considerable influence of the governmental regulations on the green supply chain, in the most of the studies in the literature of green supply chain, almost the role of the government and interactions between the government and supply chains members’ decisions are disregarded. In this study, a competitive mathematical model of government as the leader and two competitive green and non-green supply chains as the followers is developed and for the first time in this paper, pricing policies, greening strategies and governance tariffs determining in supply chains competition under government financial intervals are discussed. In the presented framework, the government seeks social benefits and determines subsidy and tax rates for green and non-green products respectively. The sale prices of products and the green degree of the green product are supply chains’ decision variables. In centralized and decentralized models, the optimal values of decision variables are gained and some important sensitivity analyses of governance decisions are done. In the governmental decisions area, it is observed that the impact of raising subsidy rate is significantly more than tax rate and it leads to increase in profits of government and supply chains and sustainability of products. Also among the competition of supply chains, cooperating between members makes more profit for them and leads to produce more eco-friendly products.

A bi-objective, reliable single allocation p-hub maximal covering location problem: Mathematical formulation and solution approach

Journal Paper
Seyed Reza Madani, Ali Shahandeh Nookabadi, Seyed Reza Hejazi
Madani, S.R., et al., A bi-objective, reliable single allocation p-hub maximal covering location problem: Mathematical formulation and solution approach, Journal of Air Transport Management (2017), http://dx.doi.org/10.1016/ j.jairtraman.2017.09.001

In the last few years, the p-hub maximal covering problem (pHMCP) has been applied in a variety of applications, including the design of air transportation networks, distribution systems for perishable products, postal delivery networks, and tourism routing. In hub-based systems, disruptions at hubs or unavailability of routes significantly affect service level and result in excessive costs. To tackle these problems, selecting backup hubs for unavailable hubs and rerouting the related flows are often proposed. This paper develops a bi-objective reliable single allocation p-hub maximal covering problem (BRSApHMCP) considering two objectives: maximizing expected covered flows and minimizing congestion. After formulating an initial non-linear model, a linear model is presented; the NP-Completeness of the developed model is proved and a non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve it. In order to show the superior performance of the proposed NSGA-II, a well-known evolutionary algorithm, the multi-objective particle swarm optimization (MOPSO), is utilized and the results are analyzed and compared. The parameters of the proposed algorithms are calibrated using the Taguchi approach. Also, a case study and some parametric analyses are done. The results show that NSGA-II is able to find the better solutions in comparison with MOPSO and by opting this proactive strategy in the investigated case study, NSGA-II could recover up to 73% of lost flow in a well-balanced system.

Formulating and solving the two-stage robust facility location problem considering the queue systems

Conference Paper
Seyed Reza Madani, Milad Riahi, Mehdi Alinaghian
Seyed Reza Madani *, Milad Riahi, Mehdi Alinaghian. “Formulating and solving the two-stage robust facility location problem considering the queue systems” the 9th international Iranian Operations Research Society conference, Shiraz University of Technology. Shiraz, Iran, 2016.

A novel mathematical model for the classic facility location problem is presented in this study. This paper tries to allocate the customers to the facilities in a way that the total costs are reduced and the welfare level of the customers is increased. As in the real world, the demands of the customers are uncertain, we have considered the customers’ demand probabilistic. Then, in order to prepare a robust solution for the problem, we introduced some scenarios and formulated the two-stage robust model of the problem. Finally, the two well-known genetic and simulated annealing algorithms are employed to solve the proposed model.

Reliable single allocation p-hub maximal covering location problem with single and multiple backups and considering congestion avoidance: Mathematical formulations and solution approaches

Thesis
Seyed Reza Madani
Supervisor: Prof. Shahandeh Nookabadi Advisor: Prof. Hejazi --- master degree(2016)

In the last few years, the p-hub maximal covering problem has been applied in a variety of applications, including the design of air transportation networks, distribution systems for perishable products, postal delivery networks, and tourism routing. In hub-based systems, disruptions at hubs or unavailability of routes significantly affect service levels and result in excessive costs; to tackle these problems; selecting (single or multiple) backup hubs for unavailable hubs and rerouting the related flows are often proposed. This study develops two bi-objective reliable single allocation p-hub maximal covering problems considering two objectives: maximizing expected covered flows and minimizing congestion. After formulating the initial non-linear models, their linearized models are presented; after proving the NP-Completeness of the developed models, the non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve them. In order to show the superior performance of the proposed NSGA-II, a well-known evolutionary algorithm, the multi-objective particle swarm optimization (MOPSO) and the epsilon constraint methods are utilized and the results are analyzed and compared. The parameters of the proposed algorithms are calibrated using the Taguchi approach. Also, a case study and some parametric analyses are done.

Risk analysis in the production of gasoline process in the Tehran refinery, using Monte Carlo simulation approach

Thesis
Seyed Reza Madani
Supervisor: Dr. Nasrollah Mohammadi --- bachelor degree (2014)

A New Robust Mathematical Model for the Multi-product Capacitated Single Allocation Hub Location Problem with Maximum Covering Radius

Journal Paper
Mahdi Alinaghain, Seyed Reza Madani, Hossain Moradi
Alinaghian, M., S.R. Madani, and H. Moradi, A New Robust Mathematical Model for the Multi-product Capacitated Single Allocation Hub Location Problem with Maximum Covering Radius. International Journal of Supply and Operations Management, 2017. 4(3): p. 248-262.

This paper presents a new robust mathematical model for the multi-product capacitated single allocation hub location problem with maximum covering radius. The objective function of the proposed model minimizes the cost of establishing hubs, the expected cost of preparing hubs for handling products, shipping and transportation in all scenarios, and the cost variations over different scenarios. In the proposed model, a single product of a single node cannot be allocated to more than one hub, but different products of one node can be allocated to different hubs. Also, a product can be allocated to a hub only if equipment related to that product is installed on that hub. Considering the NP-Hard complexity of this problem, a GA-based meta-heuristic algorithm is developed to solve the large scale variants of the problem. To evaluate the performance of the proposed algorithm, its results are compared with the results of exact method and simulated annealing algorithm. These results show the good performance of the proposed algorithm.