Abstract

The acquisition of Advanced Manufacturing Technologies (AMT), such as high-power fiber or CO2 laser cutting equipment, generally involves high investment levels. Its payback period is usually more extended, and there is a moderate-to-high risk involved in adopting these technologies. In this work, we present a robust model that optimizes equipment investing decisions, considers the process’s technical constraint and finds an optimal production plan based on the available machinery. We propose a linear investment model based on historical demand information and take physical process parameters for a LASER cutting equipment, such as cutting speed and gas consumption. The model is then transformed into a robust optimization model which considers demand uncertainty. Second, we determine the optimal production plan based on the results of the robust optimization model and assuming that demand follows a normal distribution. As a case study, we decided on the investment and productive plan for a company that offers Laser-Beam Cutting (LBC) services. The case study validates the effectiveness of the proposed model and proves the robustness of the solution. For this specific application of the model, results showed that the optimal robust solution could increase the company’s expected profits by 6.4%.

Highlights

  • Investment in new technologies, such as Advanced Manufacturing Technologies (AMT), has become a requirement for companies to stay competitive

  • We present the results obtained from the deterministic linear optimization (LO) model and the robust optimization (RO) model

  • We have developed and implemented deterministic and robust optimization investment and production models for the laser cutting process service

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Summary

Introduction

Investment in new technologies, such as AMT, has become a requirement for companies to stay competitive. Robust optimization of laser-beam cutting equipment investment decision generates solutions that are progressively less sensitive to data uncertainty; it does not need detailed probabilistic knowledge, nor specific distributions of the uncertain parameters [18, 55]. Our contribution is to propose and solve a linear robust optimization model which accounts for the demand variability and uses physical process parameters for LASER cutting equipment such as cutting speed and gas consumption. The latter to determine the optimal equipment selection and production planning. Robust optimization of laser-beam cutting equipment investment decision approach that takes into account the parameters ambiguity and stochastic uncertainty [62]. We outline some conclusions and present potential further research that could be continued on this topic

Equipment investment and production optimization planning model
Laser cutting machine parameters
P1 À P2 r1 r2
Deterministic optimization model formulation
Robust reformulation
Robust optimization methodology
Uncertainty in demand
Numerical case study
Results and discussion
Conclusion and managerial insights
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