SSOS 2013 Abstracts

Full Papers
Paper Nr: 1

A Scheduling Strategy for Global Scientific Grids - Minimizing Simultaneously Time and Energy Consumption


Fábio Coutinho, Leizer L. Pinto and Cláudio T. Bornstein

Abstract: Grid computing has consolidated itself as a solution able of integrating, on a global scale, heterogeneous resources distributed geographically. This fact has contributed significantly to increase the IT infrastructure. However, all this computer power results in a lot of energy consumption, raising concerns not only with respect to economic aspects, but also regarding environmental impacts. Current data shows that the information technology and communication industry has been responsible for 2% of the carbon dioxide global emission, equivalent to the entire aviation industry. This paper proposes a biobjective strategy for resource allocation on global scientific grids, considering both energy consumption and execution times. An algorithm is presented which generates the minimal complete set of Pareto-optimal solutions in polynomial time. Computation experience is reported for three distinct scenarios.

Paper Nr: 4

A Framework for Optimizing the Supply Chain Performance of a Steel Producer


Ali Diabat, Raid Al-Aomar, Mahmoud Alrefaei, Ameen Alawneh and Mohd Nishat Faisal

Abstract: Supply Chain Management (SCM) is focused on developing, optimizing, and operating efficient supply chains. Efficient supply chains are characterized by cost effective decisions, lean flow and structure, high degree of integration, and well-chosen Key Performance Indicators (KPIs). Although there exists a large body of literature on optimizing individual supply chain elements (transportation, distribution, inventory, location, etc.), the literature does not provide an effective methodology that can address the complexity of the supply chain of a large scale industry such as steel producers. This paper, therefore, builds on existing research methods of supply chain modeling and optimization to propose a framework for optimizing supply chain performance of a steel producer. The framework combines deterministic modeling using Linear Programming (LP) with stochastic simulation modeling and optimization. A holistic LP deterministic optimization model is first used to characterize and optimize the supply chain variables. The model minimizes the annual operating cost of the steel company’s supply chain. Simulation-based optimization with Simulated Annealing is then used to determine the operational levels of the supply chain drivers that meet a desired level of customer satisfaction. The proposed approach is applied to the supply chain of a major steel producer in the Arabian Gulf.

Short Papers
Paper Nr: 3

A Lagrangian Relaxation based Heuristic for the Static Berth Allocation Problem using the Cutting Plane Method


A. S. Simrin, N. N. Alkawaleet and A. H. Diabat

Abstract: One of the important seaside operations problems that received a lot of attention in the literature is the assignment of quay space and service time to vessels that have to be unloaded and loaded at a terminal. This problem is commonly referred to as the Berth Allocation Problem (BAP). Different approaches exist in the literature for the berth allocation problem (BAP). Some of those approaches consider static arrival of vessels, so called the static berth allocation problem (SBAP), while other approaches consider dynamic arrival of vessels, called the dynamic berth allocation problem (DBAP). Approaches also differ in the layout used for the quay. In this paper we study one of the SBAP models presented in literature. Since the SBAP is a non-deterministic polynomial-time (NP) problem, we applied a Lagrangian Relaxation heuristic technique with the application of cutting plane method on our problem. We coded the cutting plane method in Matlab, and ran it on different instances of the problem. In most of the cases that we studied, our solution technique converged to an optimal solution.

Paper Nr: 6

A Novel Mathematical Formulation for the Strategic Planning of a Reverse Supply Chain Network - Theoretical and Computational Results


Ernesto D. R. Santibanez-Gonzalez and Nelson Maculan

Abstract: In the last decade, literature on strategic planning of a supply chain network grew rapidly. In this paper we address a classical three-layer remanufacturing supply chain network design problem that covers sourcing, reprocessing and remanufacturing activities, in which strategic decisions regarding the number, location of reprocessing units and the flow of returns through the logistics network are made. First, we propose an alternative mixed-integer mathematical programming (MILP) formulation for this problem and provide theoretical proof of equivalence between the classical and the proposed mathematical formulation. Second, the goodness of both formulations is compared by means of a computational study, and the results for large instances of the problem are discussed. We empirically prove that the proposed formulation provides tighter linear relaxation lower bounds and obtains the integer solutions several times faster than the classical formulation.