Data-Driven Models and Methodologies to Optimize Production Schedules

Prabhakar Sastri

doi:10.11648/j.acis.20160401.11

Abstract

Data driven Models based on production parameters in combination with modern optimization algorithms are shown to be useful in industry to optimize production schedules and improve profitability. Based on real data obtained from an existing facility, we have developed models for time and costs of heat treatment. Using these data statistical models have been developed and used to find an optimal solution to the Job-Shop scheduling problem using three algorithms namely Particle Filter, Particle Swarm Optimization and Genetic Algorithm. The algorithm is useful when we would like to arrive at job schedules based on a mix of both time and cost optimization. The results are compared and future work discussed with respect to the data used.

Highlights

Introduction and BackgroundA heat treatment facility typically consists of many furnaces
Based on the type of individual jobs to be processed, it is necessary to optimize the overall profitability of the facility and work out the production schedule and resource allocation
We have considered Particle Filter, Particle Swarm Optimization and Genetic Algorithm for our work

Summary

Introduction

A heat treatment facility typically consists of many furnaces. The parts undergoing heat treatment can be processed in one or more furnaces. Each furnace can process a variety of parts. A decision on price and resource allocation has to be made every time a part gets processed in a facility consisting of many furnaces. Delivery of some parts is time critical and others are cost critical. There are tactical and operational level decisions to be addressed including the cost of a single job, which may be optimized either for cost or time. Based on the type of individual jobs to be processed, it is necessary to optimize the overall profitability of the facility and work out the production schedule and resource allocation

Objectives

Results

Conclusion