A Two-Phase Method for Production Planning and Machine Speed Optimization Problem

TL;DR

This paper presents a two-phase heuristic method for optimizing machine speeds and production lot sizing in textile manufacturing to reduce costs and inventories, addressing a complex NP-hard nonlinear problem.

Contribution

It introduces a novel two-phase heuristic for solving a nonlinear production planning model, enabling efficient local-optimal solutions for complex textile manufacturing problems.

Findings

The heuristic effectively finds local-optimal solutions within acceptable computational times.

The model dynamically adjusts machine speeds based on demand to minimize inventories.

Computational tests demonstrate the heuristic's robustness across various scenarios.

Abstract

Textile industry is becoming a highly competitive area with the increase in demand for textile products. Since expanding the production capacity is not always feasible, optimizing the existing system is more practical. In particular, we consider a felt production system of a textile factory operating in Turkey in this study. We aim to minimize the production costs by optimizing machine operating speeds as well as building an efficient production lot sizing plan within the planning horizon. In this direction, we propose the Lot Sizing and Machine Speed (LSMS) nonlinear model to determine the optimal unit processing times and production quantities while minimizing the work-in-process and end item inventories by changing the machine operating speeds dynamically according to the demands. Since LSMS nonlinear optimization problem is NP-hard, we design a Two-Phase heuristic which iteratively processes a linear programming model by utilizing a commercial solver at each phase. We intensively test our Two-Phase heuristic via randomly generated demand, planning horizon and machine-hour capacity scenarios. Our computational experiments show that the introduced Two-Phase heuristic can find the local-optimal results in acceptable amount of time.