Sequencing in mixed model assembly lines: a genetic algorithm approach
Computers and Operations Research
Design and Analysis of Experiments
Design and Analysis of Experiments
Cloud theory-based simulated annealing algorithm and application
Engineering Applications of Artificial Intelligence
A hybrid immune simulated annealing algorithm for the job shop scheduling problem
Applied Soft Computing
A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II
Computers and Industrial Engineering
New methods for system planning
Applied Soft Computing
Two-machine robotic cell scheduling problem with sequence-dependent setup times
Computers and Operations Research
Nested simulated annealing approach to periodic routing problem of a retail distribution system
Computers and Operations Research
A simulated annealing algorithm based approach for balancing and sequencing of mixed-model U-lines
Computers and Industrial Engineering
Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects
Information Sciences: an International Journal
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In spite of many studies, investigating balancing and sequencing problems in Mixed-Model Assembly Line (MMAL) individually, this paper solves them simultaneously aiming to minimize total utility work. A new Mixed-Integer Linear Programming (MILP) model is developed to provide the exact solution of the problem with station-dependent assembly times. Because of NP-hardness, a Simulated Annealing (SA) is applied and compared to the Co-evolutionary Genetic Algorithm (Co-GA) from the literature. To strengthen the search process, two main hypotheses, namely simultaneous search and feasible search, are developed contrasting Co-GA. Various parameters of SA are reviewed to calibrate the algorithm by means of Taguchi design of experiments. Numerical results statistically show the efficiency and effectiveness of the proposed SA in terms of both the quality of solution and the time of achieving the best solution. Finally, the contribution of each hypothesis in this superiority is analyzed.