AbstractEnvironmental, social and economic concerns motivate the operation of closedloop supply chain networks (CLSCN) in many industries. We propose a novel profit maximization model for CLSCN design as a mixed-integer linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the firm's policies. Our major contribution is to dev...

Abstract: In order to improve the efficiency for solving MINLP problems, we present in this paper three computational strategies. These include multiple-generation cuts, hybrid methods and partial surrogate cuts for the Outer Approximation and Generalized Benders Decomposition. The properties and convergence of the strategies are analyzed. Five new MINLP algorithms are described based on the pr...

• A location-allocation problem under uncertainty is investigated. two-stage stochastic programming model proposed. The mode solved by sample average approximation. Emergencies, especially those considered routine (i.e., occurring on a daily basis), pose great threats to health, life, and property. Immediate response treatment can greatly mitigate these threats. This research conducted optimize...

Benders decomposition [1] is a solving strategy based on the separation of the variables of the problem. It is often introduced as a basis for models and techniques using the complementary strengths of constraint programming and optimization techniques. Hybridization schemes have appeared recently and provided interesting computational results [4, 5, 7, 8]. They have been extended [2, 3, 6] to ...

We consider the 3-staged Strip Packing Problem, in which rectangular items have to be arranged onto a rectangular strip of fixed width, such that the items can be obtained by three stages of guillotine cuts while the required strip height is to be minimized. We propose a new logic-based Benders decomposition with two kinds of Benders cuts and compare it with a compact integer linear programming...

In this paper, we propose an extended relocation model for warehouses configuration in a supply chain network, in which uncertainty is associated to operational costs, production capacity and demands whereas, existing researches in this area are often restricted to deterministic environments. In real cases, we usually deal with stochastic parameters and this point justifies why the relocation m...

In this paper we describe a two-stage optimization model for determining robust rolling stock circulations for passenger trains. Here robustness means that the rolling stock circulations can better deal with large disruptions of the railway system. The two-stage optimization model is formulated as a large Mixed-Integer Linear Programming model. We first use Benders decomposition to determine op...

Dynamic multistage stochastic linear programming has many practical applications for problems whose current decisions have to be made under future uncertainty. There are a variety of methods for solving these problems, including nested Benders decomposition. In this method, recently shown to be superior to the alternatives for large problems, the problem is decomposed into a set of smaller line...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy REFORMULATION AND CUTTING-PLANE APPROACHES FOR SOLVING TWO-STAGE OPTIMIZATION AND NETWORK INTERDICTION PROBLEMS By Siqian Shen August 2011 Chair: J. Cole Smith Major: Industrial and Systems Engineering This dissertation investigates mode...

This paper addresses the solution of a two-stage stochastic programming model for a supply chain investment planning problem applied to the petroleum products supply chain. In this context, we present the development of acceleration techniques for the stochastic Benders decomposition that aim to strengthen the cuts generated, as well as to improve the quality of the solutions obtained during th...