The optimal scheduling of crude-oil operation in refineries has been researched by various groups during the past decade. Different mixed integer linear programming or mixed nonlinear programming formulations are derived. This paper proposes a novel MINLP formulation with multiple charging tanks charging a distiller overlapingly and oil residency time constraint that is based on multi-operation...

In this paper we study an extension of the Resource-Constrained Project Scheduling Problem (RCPSP) with minimum makespan objective by introducing as precedence constraints the so called “Feeding Precedences” (FP). For the RCPSP with FP we propose a new mathematical formulation and a branch and bound algorithm exploiting the latter formulation. The exact algorithm takes advantage also of a lower...

This document describes an implementation of Lagrangian Relaxation using GAMS.

We propose two new Lagrangian dual problems for chance-con-strained stochastic programs based on relaxing nonanticipativity constraints. We compare the strength of the proposed dual bounds and demonstrate that they are superior to the bound obtained from the continuous relaxation of a standard mixed-integer programming (MIP) formulation. For a given dual solution, the associated Lagrangian rela...

A novel relaxation labeling (RL) algorithm is proposed. RL is posed as a constrained optimization problem and the solution is found by using the Lagrangian multiplier method and a technique used in the graded Hoppeld neural network. In terms of the optimized objective value, the algorithm performs almost as well as simulated annealing, as shown by the experimental results. Also, the resulting a...

In this paper, we propose combining augmented Lagrangian optimization with the dual decomposition method to obtain a fast algorithm for approximate MAP (maximum a posteriori) inference on factor graphs. We also show how the proposed algorithm can efficiently handle problems with (possibly global) structural constraints. The experimental results reported testify for the state-of-the-art performa...

Much effort has been directed at algorithms for obtaining the highest probability configuration in a probabilistic random field model – known as the maximum a posteriori (MAP) inference problem. In many situations, one could benefit from having not just a single solution, but the top M most probable solutions – known as the M-Best MAP problem. In this paper, we propose an efficient message-pass...

We present the moment cone (MC) relaxation and a hierarchy of sparse LagrangianSDP relaxations of polynomial optimization problems (POPs) using the unified framework established in Part I. The MC relaxation is derived for a POP of minimizing a polynomial subject to a nonconvex cone constraint and polynomial equality constraints. It is an extension of the completely positive programming relaxati...

W describe a new algorithm for computing the efficient frontier of the “bi-objective maximum-flow network-interdiction problem.” In this problem, an “interdictor” seeks to interdict (destroy) a set of arcs in a capacitated network that are Pareto-optimal with respect to two objectives, minimizing total interdiction cost and minimizing maximum flow. The algorithm identifies these solutions throu...

In this paper, we address a new integration of column generation and Lagrangian relaxation for solving flowshop scheduling problems to minimize the total weighted tardiness. In the proposed method, the initial columns are generated by using near-optimal dual variables for linear programming relaxation of Dantzig-Wolfe decomposition derived by the Lagrangian relaxation method. The column generat...