Simulated annealing heuristics for the average flow-time and the number of tardy jobs bi-criteria identical parallel machine problem

1997 ◽  
Vol 33 (1-2) ◽  
pp. 257-260 ◽  
Author(s):  
Alex J. Ruiz-Torres ◽  
E. Emory Enscore ◽  
Russell R. Barton
Keyword(s):  
Simulated Annealing ◽  
Parallel Machine ◽  
Flow Time ◽  
Average Flow ◽  
Identical Parallel Machine ◽  
Number Of Tardy Jobs ◽  
Tardy Jobs ◽  
Parallel Machine Problem ◽  
Machine Problem

scholarly journals Parallel-Batch Scheduling with Two Models of Deterioration to Minimize the Makespan

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Cuixia Miao
Keyword(s):  
Polynomial Time ◽  
Single Machine ◽  
Processing Time ◽  
Parallel Machine ◽  
Batch Scheduling ◽  
Approximation Schemes ◽  
Time Approximation ◽  
Identical Parallel Machine ◽  
Parallel Machine Problem ◽  
Machine Problem

We consider the bounded parallel-batch scheduling with two models of deterioration, in which the processing time of the first model ispj=aj+αtand of the second model ispj=a+αjt. The objective is to minimize the makespan. We presentO(n log n)time algorithms for the single-machine problems, respectively. And we propose fully polynomial time approximation schemes to solve the identical-parallel-machine problem and uniform-parallel-machine problem, respectively.

scholarly journals MINIMIZING THE MAKESPAN FOR UNRELATED PARALLEL MACHINES

2007 ◽  
Vol 16 (03) ◽  
pp. 399-415 ◽  
Author(s):  
YUNSONG GUO ◽  
ANDREW LIM ◽  
BRIAN RODRIGUES ◽  
LIANG YANG
Keyword(s):  
Simulated Annealing ◽  
Lower Bound ◽  
Tabu Search ◽  
Parallel Machines ◽  
Parallel Machine ◽  
Experimental Results ◽  
Neighborhood Search ◽  
Unrelated Parallel Machines ◽  
Parallel Machine Problem ◽  
Machine Problem

In this paper, we study the unrelated parallel machine problem for minimizing the makespan, which is NP-hard. We used Simulated Annealing (SA) and Tabu Search (TS) with Neighborhood Search (NS) based on the structure of the problem. We also used a modified SA algorithm, which gives better results than the traditional SA and developed an effective heuristic for the problem: Squeaky Wheel Optimization (SWO) hybrid with TS. Experimental results average 2.52% from the lower bound and are within acceptable timescales improving current best results for the problem.

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