In this research, we address the application of uncertaintyprogramming to design a multi-site, multi-product, multi-period,closed-loop supply chain (CLSC) network. In order to make theresults of this article more realistic, a CLSC for a case study inthe iron and steel industry has been explored. The presentedsupply chain covers three objective functions: maximization ofprofit, minimization of n...

In this paper, a bi-objective multi-product single-vendor single-buyer supply chain problem is studied under green vendor-managed inventory (VMI) policy based on the economic production quantity (EPQ) model. To bring the model closer to real-world supply chain, four constraints of model including backordering cost, number of orders, production budget and warehouse space are considered stochasti...

This paper focuses on the logistics aspect of supply chain management. It proposes a randomized flow management algorithm for a time-varying, random, supply chain network. A constrained stochastic optimization problem that maximizes the profit function in terms of the long-run, time-average of the flows in the supply chain is formulated. The algorithm is distributed and based on queueing theory...

The development of optimization and mathematical models for closed loop supply chain (CLSC) design has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. The aim of this paper, therefore, is to propose a n...

Conflicting objectives becomes a common issue in many supply chain network optimization problems. In this paper, a new model is formulated to design a green supply chain network through a new mixed integer linear programming problem. Uncertain demand and stochastic environmental respect levels are the main parameters of the formulation. The first objective function minimizes the cost of the sup...

Supply chains continually face the challenge of efficient decision-making in a complex environment coupled with uncertainty. While plenty of forecasting and analytical tools are available in the market to evaluate and enhance Supply Chains’ performance, the current functionalities are not sufficient to address issues related to efficient decision making under uncertainty. In this paper we discu...

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. This paper develops a robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply our approach to address the problem of analyzing competing biomass supply chains subject to stochastic de...

This paper explores the merits of hedging stochastic input costs (i.e., reducing the risk of adverse changes in costs) in a decentralized, risk neutral supply chain. Specifically, we consider a generalized version of the well-known ‘selling-to-the-newsvendor’ model in which both the upstream and the downstream firms face stochastic input costs. The firms’ operations are intertwined – i.e., the ...

This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy,...

Today’s business has inevitably been set in the global supply chain management environment. International transportation has never played such an important role in the global supply chain network, because movement of shipments from one country to another tends to be more frequent than ever before. This paper studies international transportation problems experienced by an international transport...