Modeling and analysis of a mixed‐model assembly line with stochastic operation times

Abstract

Abstract:We consider a mixed-model assembly line (MMAL) comprised a set of workstations and a conveyor. The workstations are arranged in a serial configuration. The conveyor moves at a constant speed along the workstations. Initial units belonging to different models are successively fed onto the conveyor, and they are moved by the conveyor to pass through the workstations to gradually generate final products. All assembling tasks are manually performed with operation times to be stochastic. An important performance measure of MMALs is overload times that refer to uncompleted operations for operators within their work zones. This paper establishes a method to analyze the expected overload times for MMALs with stochastic operation times. The operation processes of operators form discrete time nonhomogeneous Markov processes with continuous state spaces. For a given daily production schedule, the expected overload times involve in analyzing the Markov processes for finite horizon. Based on some important properties of the performance measure, we propose an efficient approach for calculating the expected overload times. Numerical computations show that the results are very satisfactory.

Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles

“…Table 1 provides an overview of existing studies on the stochastic MMS problem. The seminal study by Zhao et al (2007) proposed an approach based on Markov chains to calculate the expected work overload time. In a nutshell, this approach approximates the current positions of the workers within their stations by dividing the interval of possible positions into several subintervals.…”

A constraint programming model for mixed model type 2 assembly line balancing problem

“…Ancak bu tip hatlarda sürelerin değişkenliğinden kaynaklı olarak deterministik görev süreli hatlara göre kullanılan amaç değerleri de farklılaşmaktadır. Bunlar; üretim, işçilik, envanter ve kurulum maliyetleri gibi maliyet fonksiyonları [21]- [23], hat etkinliğinin maksimizasyonu [22], [23], belirlenen süre içinde tamamlanan işlerin maksimizasyonu veya tamamlanamayan işlerin minimizasyonu [22]- [25], işlem gören ürün sayısı, çıktı hızı, sistem akış zamanı ve kullanım oranı gibi sistemin performans değerleri [26]- [28]. Bunlara ek olarak, Manavizadeh ve diğerleri [29] Sezgisel çalışmalar içinde meta sezgisel kullanan çalışmalara bakıldığında ise genetik algoritma kullanan çalışmaların daha fazla olduğu görülmektedir [6], [10], [12], [15], [17], [18], [29], [30].…”

Second order conic approximation for disassembly line design with joint probabilistic constraints

“…To deal with this uncertainty, the following models were proposed in the literature. Task times were assumed to be random variables with either known continuous probability distributions (Zhao, Liu, Ohno, & Kotani, 2007), or known or unknown symmetric probability distributions (Betts & Mahmoud, 1989;Raouf & Tsui, 1982), or known independent normal probability distributions. This third case has received quite some attention: earlier papers have focused on optimizing straight assembly lines where heuristic (Carter & Silverman, 1984;Chakravarty & Shtub, 1986;Fazlollahtabar, Hajmohammadi, & Es'haghzadeh, 2011;Kao, 1979;Lyu, 1997;Shin, 1990;Silverman & Carter, 1986), metaheuristic (Cakir, Altiparmak, & Dengiz, 2011;Erel, Sabuncuoglu, & Sekerci, 2005) and exact solution methods (Henig, 1986;Kao, 1976;Sarin, Erel, & Dar-el, 1999) were proposed.…”