Introduction to modeling and simulation
Proceedings of the 29th conference on Winter simulation
Binary integer formulation for mixed-model assembly line balancing problem
Computers and Industrial Engineering
Verification, validation, and accreditation
Proceedings of the 30th conference on Winter simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Proceedings of the 33nd conference on Winter simulation
Arena: the Arena product family: enterprise modeling solutions
Proceedings of the 33nd conference on Winter simulation
A new heuristic method for mixed model assembly line balancing problem
Computers and Industrial Engineering
Simulation with Arena
Dynamic programming solution to the batching problem in just-in-time flow-shops
Computers and Industrial Engineering
Computers and Industrial Engineering
Process-oriented simulation for mixed-model assembly lines
Proceedings of the 2007 Summer Computer Simulation Conference
Computers and Industrial Engineering
Mixed model assembly line balancing problem with fuzzy operation times and drifting operations
Proceedings of the 40th Conference on Winter Simulation
A job assignment model for conveyor-aided picking system
Computers and Industrial Engineering
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In this paper, a mixed-model PC camera assembly line balancing case study is presented. The aim of the study is to establish different line configurations, for varying levels of demand. In the first stage of the study, a heuristic procedure previously developed by some of the authors, based on the simulated annealing meta-heuristic, is used to derive line configurations with a minimum number of workstations and a smooth workload balance between and within the workstations. In the second stage, the solutions provided by the heuristic are used as an input to discrete event simulation models in which certain manufacturing parameters that analytical procedures have difficulty to accommodate, namely, stochastic times, machine breakdowns, rework, etc. are introduced. These simulation models derive different performance measures (e.g. flow times and resources utilization) that provide operational support and help fine-tune the line configurations.This paper reports on the collaborative study between the Department of Economics, Management and Industrial Engineering of University of Aveiro and a major manufacturer of electronic consumer goods.