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In this paper we investigate a particular two-stage supply chain network design problem with fixed costs. In order to solve this complex optimization problem, we propose an efficient hybrid algorithm, which was obtained by incorporating a linear programming optimization problem within the framework of a genetic algorithm. In addition, we integrated within our proposed algorithm a powerful local search procedure able to perform a fine tuning of the global search. We evaluate our proposed solution approach on a set of large size instances. The achieved computational results prove the efficiency of our hybrid genetic algorithm in providing high-quality solutions within reasonable running-times and its superiority against other existing methods from the literature. [ABSTRACT FROM AUTHOR]

The two-stage supply chain problem with fixed costs consists of designing a mimimum distribution cost configuration of the manufacturers, distribution centers and retailers in a distribution network, satisfying the capacity constraints of the manufacturers and distribution centers so as to meet the retailers specific demands. The aim of this work is to pinpoint some inaccuracies regarding the paper entitled "A two-stage supply chain problem with fixed costs: An ant colony optimization approach" by Hong et al. published in International Journal of Production Economics, Vol. 204, pp. 214-226 (2018) and to propose a valid mixed integer programming based mathematical model of the problem. The comments are related to the mathematical formulation proposed by Hong et al. and the considered test instances. [ABSTRACT FROM AUTHOR]

Modern supply chains security solutions need to also solve transport security. Greenhouse gas (GHG) emissions have a direct influence on the structure and behaviour of supply chains. The more complex the supply chains, the more critical these two dimensions become. This paper introduces a mathematical model for computing the risk associated with Greenhouse emissions, within the (SCN) context. The supply model chosen is a two-stage SCN, called the Secure & Green Supply Chain Network (SGSCN). In the SGSCN a Manufacturer is directly connected to several Distribution Centres, each of them connected to one or more Customers. The objective of SGSCN is to minimize transportation costs whilst also maintaining a specified overall security level, including a specified GHG level. The applicability of the proposed model for several supply chain configurations and scenarios is illustrated and discussed. [ABSTRACT FROM AUTHOR]

In this article, we consider the capacitated fixed-charge transportation problem in a two-stage supply chain network, in which the manufacturer can ship to any of the existing m distribution centres and each of the distribution centres can ship to any of the n customers aiming at minimizing the cost of the physical distribution flow. Due to the complexity of the problem, we propose an improved hybrid algorithm combining the Nearest Neighbour search heuristic with a powerful local search procedure. The extensive computational experiments on benchmark instances from the literature show that our hybrid algorithm is competitive in comparison with the known heuristics published to date. [ABSTRACT FROM AUTHOR]

This paper deals with a complex optimization problem, more specifically the two-stage transportation problem with fixed costs. In our investigated transportation problem, we are modeling a distribution network in a two-stage supply chain. The considered two-stage supply chain includes manufacturers, distribution centers, and customers, and its principal feature is that in addition to the variable transportation costs, we have fixed costs for the opening of the distribution centers, as well as associated with the routes. In this paper, we describe a different approach for solving the problem, which is an effective hybrid genetic algorithm. Our proposed hybrid genetic algorithm is constructed to fit the challenges of the investigated supply chain network design problem, and it is achieved by incorporating a linear programming optimization problem within the framework of a genetic algorithm. Our achieved computational results are compared with the existing solution approaches on a set of 150 benchmark instances from the literature and on a set of 50 new randomly generated instances of larger sizes. The outputs proved that we have developed a very competitive approach as compared to the methods that one can find in the literature. [ABSTRACT FROM AUTHOR]

This article considers the two-stage fixed-charge transportation problem which models an important transportation application in a supply chain, from manufacturers to customers through distribution centers. For solving this optimization problem we describe a hybrid algorithm that combines a steady-state genetic algorithm with a local search procedure. The computational results for an often used collection of benchmark instances show that our proposed hybrid method delivers results that are competitive to those of other state-of-the-art algorithms for solving the two-stage fixed-charge transportation problem. [ABSTRACT FROM AUTHOR]