Scheduling of parts and robot activities in a two machine robotic cell
Computers and Operations Research - Special issue on the traveling salesman problem
Complexity of one-cycle robotic flow-shops
Journal of Scheduling
Robotic cell scheduling with operational flexibility
Discrete Applied Mathematics
Sequencing and Scheduling in Robotic Cells: Recent Developments
Journal of Scheduling
Scheduling in a three-machine robotic flexible manufacturing cell
Computers and Operations Research
Identical part production in cyclic robotic cells: Concepts, overview and open questions
Discrete Applied Mathematics
A general model for cyclic machine scheduling problems
Discrete Applied Mathematics
Multiple Part-Type Production in Robotic Cells: Equivalence of Two Real-World Models
Manufacturing & Service Operations Management
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
Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups
Computers and Operations Research
An automatic machine vision-guided grasping system for Phalaenopsis tissue culture plantlets
Computers and Electronics in Agriculture
Survey: Complexity of cyclic scheduling problems: A state-of-the-art survey
Computers and Industrial Engineering
A polynomial algorithm for multi-robot 2-cyclic scheduling in a no-wait robotic cell
Computers and Operations Research
An analysis of cyclic scheduling problems in robot centered cells
Computers and Operations Research
Cycles and permutations in robotic cells
Mathematical and Computer Modelling: An International Journal
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In this paper, we introduce a new and practical two-machine robotic cell scheduling problem with sequence-dependent setup times (2RCSDST) along with different loading/unloading times for each part. Our objective is to simultaneously determine the sequence of robot moves and the sequence of parts that minimize the total cycle time. The proposed problem is proven to be strongly NP-hard. Using the Gilmore and Gomory (GnG) algorithm, a polynomial-time computable lower bound is provided. Based on the input parameters, a dominance condition is developed to determine the optimal sequence of robot moves for a given sequence of parts. A mixed-integer linear programming (MILP) model is provided and enhanced using a valid inequality based on the given dominance condition. In addition, a branch and bound (BnB) algorithm is exploited to solve the problem, and due to the NP-hardness, an improved simulated annealing (SA) algorithm is proposed to address large-sized test problems. All the solution methods are evaluated using small-, medium- and large-sized test problems. The numerical results indicate that the optimal solution of the MILP model is attained for the medium- and some large-sized test problems, and the proposed SA, tuned using the Taguchi technique, provides an acceptable, near-optimal solution with markedly reduced CPU time. Moreover, the lower bound is observed to be significantly near the optimal solution. Thus, this lower bound is exploited to validate the results of the SA algorithm for large-sized test problems.