Throughput optimization for the Robotic Cell Problem with Controllable Processing Times

2017 ◽  
Vol 51 (3) ◽  
pp. 805-818 ◽  
Author(s):  
Mohammed Al-Salem ◽  
Mohamed Kharbeche
Keyword(s):  
Controllable Processing Times ◽  
Throughput Optimization ◽  
Cell Problem ◽  
Robotic Cell ◽  
Processing Times

scholarly journals Bicriteria robotic cell scheduling with controllable processing times

2011 ◽  
Vol 49 (2) ◽  
pp. 569-583 ◽  
Author(s):  
Serdar Yildiz ◽  
M. Selim Akturk ◽  
Oya Ekin Karasan
Keyword(s):  
Controllable Processing Times ◽  
Robotic Cell ◽  
Processing Times ◽  
Cell Scheduling

Bicriterion scheduling with truncated learning effects and convex controllable processing times

2020 ◽  
Vol 28 (3) ◽  
pp. 1573-1593
Author(s):  
Ji‐Bo Wang ◽  
Dan‐Yang Lv ◽  
Jian Xu ◽  
Ping Ji ◽  
Fuqiang Li
Keyword(s):  
Controllable Processing Times ◽  
Learning Effects ◽  
Processing Times

Unrelated Parallel-Machine Scheduling with Controllable Processing Times and Impact of Deteriorating Maintenance Activities under Consideration

2016 ◽  
Vol 33 (01) ◽  
pp. 1650001 ◽  
Author(s):  
Chun-Lai Liu ◽  
Jian-Jun Wang
Keyword(s):  
Processing Time ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Controllable Processing Times ◽  
Maintenance Activity ◽  
Processing Times ◽  
Unrelated Parallel Machine Scheduling ◽  
Deteriorating Maintenance ◽  
Maintenance Activities

In this paper, we study the problem of unrelated parallel machine scheduling with controllable processing times and deteriorating maintenance activity. The jobs are nonresumable. The processing time of each job is a linear function of the amount of a continuously divisible resource allocated to the job. During the planning horizon, there is at most one maintenance activity scheduled on each machine. Additionally, if the starting time of maintenance activity is delayed, the length of the maintenance activity required to perform will increase. Considering the total completion times of all jobs, the impact of maintenance activity in the form of the variation in machine load and the amounts of compression, we need to determine the job sequence on each machine, the location of maintenance activities and processing time compression of each job simultaneously. Accordingly, a polynomial time solution to the problem is provided.

scholarly journals Multi-Objective Optimal Model for Task Scheduling and Allocation in a Two Machines Robotic Cell Considering Breakdowns

2020 ◽  
Keyword(s):  
Optimal Allocation ◽  
Solution Procedure ◽  
Optimal Solutions ◽  
Operational Cost ◽  
Optimal Model ◽  
Robotic Cell ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Processing Times ◽  
Two Machines

This paper aimed to demonstrate a metaheuristic as a solution procedure to schedule a two-machine, identical parts robotic cell under breakdown. The proposed previous model enabled one to determine optimal allocation of operations to the machines and corresponding processing times of each machine. For the proposed mathematical model to minimize cycle time and operational cost, multi-objective particle swarm optimization (MOPSO) algorithm was provided. Through some numerical examples, the optimal solutions were compared with the previous results. MOPSO algorithm could find the solutions for problems embeds up to 50 operations in a rationale time.

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