Computers and Operations Research
A heuristic algorithm for master planning that satisfies multiple objectives
Computers and Operations Research
The two-stage assembly scheduling problem to minimize total completion time with setup times
Computers and Operations Research
Expert Systems with Applications: An International Journal
Scheduling Flexible Servers with Convex Delay Costs in Many-Server Service Systems
Manufacturing & Service Operations Management
Robotics and Computer-Integrated Manufacturing
Computers and Operations Research
The production scheduling problem in a multi-page invoice printing system
Computers and Operations Research
Branch-and-bound and simulated annealing algorithms for a two-agent scheduling problem
Expert Systems with Applications: An International Journal
Solving the integrated product mix-outsourcing problem using the Imperialist Competitive Algorithm
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
An agent-based parallel approach for the job shop scheduling problem with genetic algorithms
Mathematical and Computer Modelling: An International Journal
Minimizing total expected costs in the two-machine, stochastic flow shop
Operations Research Letters
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This paper addresses the two-stage assembly flow-shop scheduling problem with non-identical assembly machines at the second stage to minimize a sum of holding and delay costs. Although there are more than one assembly machine in many manufacturing systems, to the best of our knowledge, the two-stage assembly flow-shop scheduling problem (TSAFSP) has never been addressed with more than one assembly machine at stage two. Moreover, setup time is an inevitable factor in many cases and so in this paper, for more reality, sequence dependent setup times are considered for both stages. After extending mathematical modeling, to solve the addressed problem, four hybrid meta-heuristics are developed. A simulated annealing algorithm (SA) and an imperialist competitive algorithm (ICA) in order to find a sequence of jobs at the first stage and a heuristic (HEU) and again SA for assigning addressed jobs to assembly machines in stage two; therefore, these hybrid meta-heuristics are SA+HEU, ICA+HEU, SA+SA and ICA+SA. Computational results reveal that ICA+HEU outperforms all other algorithms; however, the run time of SA+HEU is the smallest among the algorithms.