Celebrating Faculty Achievement 2015

https://digitalcommons.lasalle.edu/celebratingfaculty/1003/thumbnail.jp

A Three-Echelon Multi-Objective Multi-Period Multi-Product Supply Chain Network Design Problem: A Goal Programming Approach

In this paper, a multi-objective multi-period multi-product supply chain network design problem is introduced. This problem is modeled using a multi-objective mixed integer mathematical programming. The objectives are maximizing the total profit of logistics, maximizing service level, and minimizing inconsistency of operations. Several sets of constraints are considered to handle the real situations of three-echelon supply chains. As the optimum value of conflictive objective functions of the proposed model cannot be met concurrently, so a goal programming approach is used. An illustrative numerical example is provided to show the mechanism of proposed model and the solution procedure. In a numerical example, 1 manufacture, 2 warehouses, 2 distribution centers (DCs), and 2 types of final products are considered in a planning horizon consists of 3 time periods. Products are shipped from the manufacturer to warehouses and then are shipped from the two warehouses to two distribution centers. The distribution centers are the point from which the product are shipped to final consumers. The Model is coded using GAMS software on a Core i7 CPU, using 8GB of RAM with MS-Windows 8.0. The optimum design of supply chain, inventory level for warehouses and distributors, and amount of shipments between echelons are determined

A Decision Support System for Solving Multi-Objective Redundancy Allocation Problems

The Redundancy Allocation Problem (RAP) is a reliability optimization problem in designing series-parallel systems. The reliability optimization process is intended to select multiple components with appropriate levels of redundancy by maximizing the system reliability under some predefined constraints. Several methods have been proposed to solve the RAPs. However, most of these methods often treat RAP as a single objective problem of maximizing the system reliability (or minimizing the system design cost). We propose a Decision Support System for solving Multi-Objective RAPs. Initially, we use the Technique for Order Performance by Similarity to Ideal Solution method to reduce the multiple objective dimensions of the problem. We then propose an efficient ε-constraint method to generate non-dominated solutions on the Pareto front. Finally, we use a Data Envelopment Analysis model to prune the non-dominated solutions. A benchmark case is presented to assess the performance of the proposed system, demonstrate the applicability of the proposed framework, and exhibit the efficacy of the procedures and algorithms. Copyright © 2013 John Wiley & Sons, Ltd

A Decision Support System for Solving Multi-Objective Redundancy Allocation Problems

The Redundancy Allocation Problem (RAP) is a reliability optimization problem in designing series-parallel systems. The reliability optimization process is intended to select multiple components with appropriate levels of redundancy by maximizing the system reliability under some predefined constraints. Several methods have been proposed to solve the RAPs. However, most of these methods often treat RAP as a single objective problem of maximizing the system reliability (or minimizing the system design cost). We propose a Decision Support System for solving Multi-Objective RAPs. Initially, we use the Technique for Order Performance by Similarity to Ideal Solution method to reduce the multiple objective dimensions of the problem. We then propose an efficient ε-constraint method to generate non-dominated solutions on the Pareto front. Finally, we use a Data Envelopment Analysis model to prune the non-dominated solutions. A benchmark case is presented to assess the performance of the proposed system, demonstrate the applicability of the proposed framework, and exhibit the efficacy of the procedures and algorithms. Copyright © 2013 John Wiley & Sons, Ltd

Model for Prioritization of High Variation Elements in Discrete Production Systems

The complexity of the modern manufacturing enterprise has led companies to look for techniques and methodologies for improving production performance. Lean manufacturing techniques have been applied in the US with varying degrees of success, and Theory of Constraints (TOC) has been used to emphasize the flow of production and identify performance improvement projects. One aspect of manufacturing for which there has been limited academic or industrial research till date is the impact of variation on production performance and the identification of improvement projects based on variation. This thesis develops a methodology to incorporate random and simultaneous occurrence of variability in a manufacturing facility, e.g., equipment failure, variabilities in the arrival time of raw materials and in-station processing time, to model system performance. Two measures of performance are developed corresponding to time and material. A prioritization algorithm is developed to utilize the “Coefficient of Variation” to identify a Bundle of High Variation Elements (BHVs) affecting the performance of a production system. The Bundled Variation-based Project Prioritization Model (BVPM) is a closed-loop model designed to provide decision makers with a list of projects to improve system performance while monitoring the implementation of projects