scholarly journals An Ant Optimization Model for Unrelated Parallel Machine Scheduling with Energy Consumption and Total Tardiness

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Peng Liang ◽  
Hai-dong Yang ◽  
Guo-sheng Liu ◽  
Jian-hua Guo
Keyword(s):  
Energy Consumption ◽  
Parallel Machines ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Taguchi Methods ◽  
Total Tardiness ◽  
Unrelated Parallel Machine ◽  
Heuristic Rule ◽  
Unrelated Parallel Machine Scheduling

This research considers an unrelated parallel machine scheduling problem with energy consumption and total tardiness. This problem is compounded by two challenges: differences of unrelated parallel machines energy consumption and interaction between job assignments and machine state operations. To begin with, we establish a mathematical model for this problem. Then an ant optimization algorithm based on ATC heuristic rule (ATC-ACO) is presented. Furthermore, optimal parameters of proposed algorithm are defined via Taguchi methods for generating test data. Finally, comparative experiments indicate the proposed ATC-ACO algorithm has better performance on minimizing energy consumption as well as total tardiness and the modified ATC heuristic rule is more effectively on reducing energy consumption.

scholarly journals Mathematical Models of Energy-Conscious Bi-Objective Unrelated Parallel Machine Scheduling

2020 ◽  
Vol 21 (2) ◽  
pp. 115-125
Author(s):  
Bobby Kurniawan
Keyword(s):  
Energy Consumption ◽  
Production Efficiency ◽  
Human Life ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Mixed Integer ◽  
Unrelated Parallel Machine ◽  
Weighted Sum Method ◽  
Unrelated Parallel Machine Scheduling

The industrialization has led to the prosperity of human life. However, it causes the side effect that harms the environment. Moreover, the source of energy used to drive the industrialization comes from non-renewable resources that can be extinct. As the extensive energy user, the manufacturing sector can use energy efficiently by scheduling and planning. A scheduling system that incorporates environmental and the energy consumption is one of the initiatives to reduce energy consumption and reduce environmental effects. Therefore, this study addresses bi-objective unrelated parallel machine scheduling to minimize the total tardiness and energy consumption. The energy consumption follows the Time-Of-Use (TOU) tariffs price scheme. The problem is formulated as two mixed-integer programming (MIP) models, using the time-indexed and disjunctive formulation, and solved using the weighted sum method. We perform complexity and computational analysis to evaluate the performance of models. Numerical experiments show that the time-indexed formulation is more efficient than the disjunctive formulation. The results provide useful insights for decision-makers in the manufacturing sectors to be energy-conscious without neglecting the production efficiency.

scholarly journals The Unrelated Parallel Machine Scheduling with a Dependent Time Setup using Ant Colony Optimization Algorithm

2021 ◽  
Vol 23 (1) ◽  
pp. 65-74
Author(s):  
Farida Pulansari ◽  
Triyono Dwi Retno M.
Keyword(s):  
Ant Colony Optimization ◽  
Manufacturing Industry ◽  
Parallel Machines ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Unrelated Parallel Machine ◽  
Unrelated Parallel Machine Scheduling

The unrelated parallel machine scheduling (PMS) problem is essential for the manufacturing industry. Scheduling will save company resources, especially time management. By solving scheduling problems quickly and precisely, the company can get more profit. On that note, this paper focused on unrelated PMS problems, which did not consider the inherent uncertainty in processing time and set up time by minimizing the makespan and tardiness. This paper aimed to minimize the makespan and tardiness using timing considerations. This paper described how to schedule unrelated parallel machines using the Ant Colony Optimization (ACO) Algorithm approach. The ACO is beneficial for inherent parallelism problems and can provide fast and reasonable solutions. This study revealed that the results of ACO Algorithm scheduling were obtained under a steady condition in iteration 30467. This condition can be interpreted that the makespan and tardiness value is close to 2.75%. By minimizing the makespan and tardiness, the delay of product delivery to consumers can be anticipated. Moreover, a company can maintain customer satisfaction and increase its profit.

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