Application of ant colony optimization metaheuristic on job shop scheduling problem FT06 for minimization of makespan

2014 ◽  
Author(s):  
Muhammad Umer ◽  
Nasir Mehmood ◽  
Riaz Ahmad
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

scholarly journals Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Wang ◽  
Jingcao Cai ◽  
Ming Li ◽  
Zhihu Liu
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Computational Time ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP) plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO) has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO) is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

Simulation Model of Ant Colony Optimization for the FJSSP

2011 ◽  
pp. 3468-3474 ◽  
Author(s):  
Li-Ning Xing ◽  
Ying-Wu Chen ◽  
Ke-Wei Yang
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

The job shop scheduling problem (JSSP) is generally defined as decision-making problems with the aim of optimizing one or more scheduling criteria. Many different approaches, such as simulated annealing (Wu et al., 2005), tabu search (Pezzella & Merelli, 2000), genetic algorithm (Watanabe, Ida, & Gen, 2005), ant colony optimization (Huang & Liao, 2007), neural networks (Wang, Qiao, &Wang, 2001), evolutionary algorithm (Tanev, Uozumi, & Morotome, 2004) and other heuristic approach (Chen & Luh, 2003; Huang & Yin, 2004; Jansen, Mastrolilli, & Solis-Oba, 2005; Tarantilis & Kiranoudis, 2002), have been successfully applied to JSSP. Flexible job shop scheduling problem (FJSSP) is an extension of the classical JSSP which allows an operation to be processed by any machine from a given set. It is more complex than JSSP because of the addition need to determine the assignment of operations to machines. Bruker and Schlie (1990) were among the first to address this problem.

scholarly journals PENERAPAN ALGORITMA ANT COLONY OPTIMIZATION (ACO) PADA JOB SHOP SCHEDULING PROBLEM (JSSP) DI PT. SIEMENS INDONESIA (CILEGON FACTORY)

2018 ◽  
Vol 1 (01) ◽  
pp. 35-51
Author(s):  
Widyawati Widyawati
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Raw Material ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Ant System ◽  
Shop Scheduling ◽  
Manufacturing Time

Fabrication process is often disrupted by non–deterministic job, this create a problem in the Pre-Fabrication department schedule because often the manufacture of raw material for non-deterministic job should given priority. This problem also affected by the existing system which is not yet fully developed to solve the problem of optimize rescheduling master line (seen from total makespan time). Ant Colony Optimization (ACO) variant Ant System (AS) was proposed to solve Job Shop Scheduling Problem (JSSP) with the objective to propose the best schedule that give shortest makespan. The algorithm tested to perform scheduling of 5 projects (consist of 10 parts) as the initial job, and another 2 projects (consist of 4 parts) as the non-deterministic job. For the initial job, makespan was 287 days and after the arrival of non-deterministic job, makespan was 362 days compare with the actual manufacturing time (7 project consist of 14 parts) which is ± 511 days

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