A Local Search Algorithm for the Single Machine Scheduling Problem with Setups and a Storage

2019 ◽  
Vol 13 (2) ◽  
pp. 239-249
Author(s):  
P. A. Kononova ◽  
Yu. A. Kochetov
2021 ◽  
Vol 22 (2) ◽  
pp. 211-223
Author(s):  
Bobby Kurniawan ◽  
Ade Irman ◽  
Evi Febianti ◽  
K Kulsum ◽  
Lely Herlina ◽  
...  

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.


2019 ◽  
Vol 53 (1) ◽  
pp. 289-302 ◽  
Author(s):  
Hanane Krim ◽  
Rachid Benmansour ◽  
David Duvivier ◽  
Abdelhakim Artiba

In this paper we propose to solve a single machine scheduling problem which has to undergo a periodic preventive maintenance. The objective is to minimize the weighted sum of the completion times. This criterion is defined as one of the most important objectives in practice but has not been studied so far for the considered problem. As the problem is proven to be NP-hard, and a mathematical model is proposed in the literature, we propose to use General Variable Neighborhood Search algorithm to solve this problem in order to obtain near optimal solutions for the large-sized instances in a small amount of computational time.


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