We address a location-allocation problem that requires deciding the location of a set of facilities, the allocation of customers to those facilities under facility capacity constraints, and the allocation of the customers to trucks at those facilities under per truck traveldistance constraints. We present a hybrid approach that combines integer programming and constraint programming using logic...

In this paper we propose improved Benders decomposition schemes for solving a remanufacturing supply chain design problem (RSCP). We introduce a set of valid inequalities in order to improve the quality of the lower bound and also to accelerate the convergence of the classical Benders algorithm. We also derive quasi Pareto-optimal cuts for improving convergence and propose a Benders decompositi...

We consider decomposition approaches for the solution of multistage stochastic programs that appear in financial applications. In particular, we discuss the performance of two algorithms that we test on the mean-variance portfolio optimization problem. The first algorithm is based on a regularized version of Benders decomposition, and we discuss its extension to the quadratic case. The second a...

This study presents a multimodal hub location problem which has the capability to split commodities by limited-capacity hubs and transportation systems, based on the assumption that demands are stochastic for multi-commodity network flows. In the real world cases, demands are random over the planning horizon and those which are partially fulfilled, are lost. Thus, the present study handles dema...

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,...

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...

The design of resilient supply chains under the risk of disruptions at candidate locations for distribution centers (DCs) is formulated as a two-stage stochastic program. The problem involves selecting DC locations, determining storage capacities for multiple commodities, and establishing the distribution strategy in scenarios that describe disruptions at potential DCs. The objective is to mini...

When solving combinatorial optimization problems it can happen that using a single technique is not efficient enough. In this case, simplifying assumptions can transform a huge and hard to solve problem in a manageable one, but they can widen the gap between the real world and the model. Heuristic approaches can quickly lead to solutions that can be far from optimality. For some problems, that ...

We describe a hybrid bi-level decomposition scheme that addresses the challenge of solving a large-scale two-stage stochastic programming problem with mixed-integer recourse, which results from a multi-scale capacity planning problem as described in part I of this paper series. The decomposition scheme combines bi-level decomposition with Benders decomposition, and relies on additional strength...

This paper describes a complete and efficient solution to the stochastic allocation and scheduling for Multi-Processor System-on-Chip (MPSoC). Given a conditional task graph characterizing a target application and a target architecture with alternative memory and computation resources, we compute an allocation and schedule minimizing the expected value of communication cost, being the communica...