Solution strategies for a supply chain deterministic model

To most firms, intelligent supply chain decisions are essential to achieve competitiveness in an environment characterized with increasing globalization and relentless changes. As the supply chain concept largely evolved from traditional logistics management, practitioners and researchers have historically focused on the individual processes of a supply chain. A wide array of mathematical models have been developed to tackle such functional issues as inventory level, lead-time performance, delivery performance, customer satisfaction and so on. This research presents a model that aims to evaluate and optimize the overall performance of the supply chain. The key nodes and variables in the model are composed of plant location and production volume, storage location and volume, transportation mode and volume. Optimization of the model is to minimize the total supply chain operation cost, expressed as the sum of production cost, storage cost, transportation cost and lost-sale cost. Our methodology is a three-phased approach. First, we build a mixed integer-programming model of 3-tier supply chain with multi-plant, multi-warehouse, and multi-retailer, multi-period and restricted capacity. This mathematical model is solved by CPLEX/OPL. Due to excessive computation time to reach the Optimal Solution, we introduce the second phase to develop heuristic solutions to reduce the computation time. In the final phase, we evaluate the proposed heuristic solutions. Result analysis shows that the computation time is generally exponentially correlated to the data size in seeking Optimal Solutions, whereas it generally follows the polynomial distribution when the Heuristic Solutions are applied. Consequently, Heuristic Solution is preferred for models with large size data

Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm

The subdivision of large simplicial cones in Normaliz

Normaliz is an open-source software for the computation of lattice points in rational polyhedra, or, in a different language, the solutions of linear diophantine systems. The two main computational goals are (i) finding a system of generators of the set of lattice points and (ii) counting elements degree-wise in a generating function, the Hilbert Series. In the homogeneous case, in which the polyhedron is a cone, the set of generators is the Hilbert basis of the intersection of the cone and the lattice, an affine monoid. We will present some improvements to the Normaliz algorithm by subdividing simplicial cones with huge volumes. In the first approach the subdivision points are found by integer programming techniques. For this purpose we interface to the integer programming solver SCIP to our software. In the second approach we try to find good subdivision points in an approximating overcone that is faster to compute.Comment: To appear in the proceedings of the ICMS 2016, published by Springer as Volume 9725 of Lecture Notes in Computer Science (LNCS