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.

The Review of the Single Machine Scheduling Problem and its Solving Methods

2013 ◽  
Vol 411-414 ◽  
pp. 2081-2084 ◽  
Author(s):  
Ou Yang Quan ◽  
Hong Yun Xu
Keyword(s):  
Single Machine ◽  
Production Efficiency ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Efficiency And Effectiveness ◽  
History Of ◽  
Future Work ◽  
Difficult Subject

The research on the theory and method of single machine scheduling is a difficult subject, but it is very important for the companies to improve the production efficiency and effectiveness. The study on single machine scheduling problem has the history of 50 years, but there is still a gap between the classical scheduling theories and practical scheduling problems. According to this characteristic, the problems in practical scheduling area and the various factors need to be concerned are mentioned, and the main methods to solving the single machine scheduling problem and their applications are presented in details. Finally, the directions and suggestions of future work in single machine scheduling problem are summarized.

scholarly journals An Enhanced MOPSO Algorithm for Energy-Efficient Single-Machine Production Scheduling

2019 ◽  
Vol 11 (19) ◽  
pp. 5381 ◽  
Author(s):  
Yueyue Liu ◽  
Xiaoya Liao ◽  
Rui Zhang
Keyword(s):  
Energy Consumption ◽  
Local Search ◽  
Single Machine ◽  
Optimization Algorithm ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Scheduling Problem ◽  
Nsga Ii ◽  
Multi Objective

In recent years, the concerns on energy efficiency in manufacturing systems have been growing rapidly due to the pursuit of sustainable development. Production scheduling plays a vital role in saving energy and promoting profitability for the manufacturing industry. In this paper, we are concerned with a just-in-time (JIT) single machine scheduling problem which considers the deterioration effect and the energy consumption of job processing operations. The aim is to determine an optimal sequence for processing jobs under the objective of minimizing the total earliness/tardiness cost and the total energy consumption. Since the problem is NP -hard, an improved multi-objective particle swarm optimization algorithm enhanced by a local search strategy (MOPSO-LS) is proposed. We draw on the idea of k-opt neighborhoods and modify the neighborhood operations adaptively for the production scheduling problem. We consider two types of k-opt operations and implement the one without overlap in our local search. Three different values of k have been tested. We compare the performance of MOPSO-LS and MOPSO (excluding the local search function completely). Besides, we also compare MOPSO-LS with the well-known multi-objective optimization algorithm NSGA-II. The experimental results have verified the effectiveness of the proposed algorithm. The work of this paper will shed some light on the fast-growing research related to sustainable production scheduling.

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