Multi-manned assembly line balancing problem with stochastic processing times

Abstract

This paper investigates the multi-manned assembly line balancing problem (MALBP) with stochastic processing times, concentrating on a novel feature of MALBP that incorporates uncertainty and unpredictability. Our study introduces stochastic processing times and pioneers the linearization of the resulting chance constraints, which has not before been investigated in the literature. Our goal is to create a more realistic and practical model that accurately reflects real-world settings. This study proposed a mathematical approach to improve assembly line productivity and optimize resource allocation. The approach employs chance-constrained programming methodologies to increase operational stability and resilience, allowing industries to successfully manage uncertainty and minimize risks. A comparative study of stochastic and deterministic models is carried out. The study finds that stochastic models improve system resilience by providing realistic representation and risk estimation, while deterministic models struggle to capture the complexity and uncertainty of real-world systems due to constant inputs. This highlights the value of using stochastic models as they improve system dynamics and provide a more solid basis for decision-making. Numerical instances indicate that our approach creates a more realistic and practical model that effectively reflects real-world scenarios, leading to better decision-making along with understanding of MALBP's complexity.