A heuristic algorithm for the just-in-time single machine scheduling problem with setups: a comparison with simulated annealing

2006 ◽  
Vol 32 (3-4) ◽  
pp. 326-335 ◽  
Author(s):  
Ghaith Rabadi ◽  
Georgios C. Anagnostopoulos ◽  
Mansooreh Mollaghasemi
Keyword(s):  
Simulated Annealing ◽  
Heuristic Algorithm ◽  
Single Machine ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Just In Time ◽  
Scheduling Problem

scholarly journals Solving Just-in-Time Single Machine Scheduling with Variable Discrete Speed Machine using Hybrid NSGA-II

2021 ◽  
Vol 22 (2) ◽  
pp. 211-223
Author(s):  
Bobby Kurniawan ◽  
Ade Irman ◽  
Evi Febianti ◽  
K Kulsum ◽  
Lely Herlina ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Local Search ◽  
Single Machine ◽  
Energy Demand ◽  
Production Efficiency ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Just In Time ◽  
Scheduling Problem ◽  
Nsga Ii

Due to industrialization and population growth, increasing energy demand can lead to energy scarcity because non-renewable resources are primarily used as energy sources. In addition, carbon dioxide gas, the waste of industrialization, can harm the environment. Therefore, environmentally friendly methods are encouraged in the industrial environment as energy preservation and climate change mitigation. This research discusses just-in-time single machine scheduling that takes into account energy consumption. In this research, energy consumption depends on the machine’s speed. The objectives are minimizing the just-in-time (JIT) penalty (the sum of weighted earliness/tardiness) and energy consumption. This research proposed a hybrid NSGA-II with a local search to solve the multi-objective scheduling problem. Thus, solving the JIT single-machine scheduling problem considers energy consumption to conserve energy and increase production efficiency. Numerical experiments demonstrated that the hybrid NSGA-II with local search is more effective than the standard NSGA-II in solving the problem. Therefore, decision-makers can use the scheduling model to select alternative solutions that consider energy and the environment without sacrificing efficiency.

scholarly journals Hybridization Simulated Annealing Algorithm in a Single Machine Scheduling Problem

2021 ◽  
Vol 20 ◽  
pp. 597-605
Author(s):  
Hafed M. Motair
Keyword(s):  
Simulated Annealing ◽  
Single Machine ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Scheduling Problem ◽  
Metaheuristic Algorithm ◽  
Annealing Algorithm ◽  
Completion Times

In this paper, we investigate a single machine scheduling problem (SMSP). We try to reach the optimal or near optimal solution which minimize the sum of three objective functions: total completion times, total tardiness and total earliness. Firstly, we solve this problem by Branch and bound algorithm (BAB alg) to find optimal solutions, dominance rules (DR)s are used to improve the performance of BAB alg, the resulting is BABDR, secondly, we solve this problem by simulated annealing algorithm (SA alg) as metaheuristic algorithm (MET alg). It is known that combining MET alg with other algorithms can improve the resulting solutions. In this paper we developed the concept of insertion preselected jobs one by one through all positions of remaining jobs of considered sequence, the proposed MET alg called Insertion Metaheuristic Algorithm (IMA). This procedure improves the performance of SA alg in two directions: in the first one, we use the IMA to generate initial solution for SA alg, in the second one, we use the IMA to improve the solution obtained through the iterations of SA alg. The experiments showed that IMA can improve the performance of SA alg in these two directions.

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