scholarly journals Scheduling in Manufacturing Systems – Ant Colony Approach

10.5772/51487 ◽  
2013 ◽  
Author(s):  
Mieczyslaw Drabowski ◽  
Edward Wantuch
Keyword(s):  
Manufacturing Systems ◽  
Ant Colony

scholarly journals Ant Colony Intelligence in Flexible Manufacturing Scheduling Using Contract-Net Model

2011 ◽  
Vol 2-3 ◽  
pp. 608-613
Author(s):  
Ying Zi Wei ◽  
Yi Jun Feng ◽  
Kan Feng Gu
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Ant Colony ◽  
Contract Net Protocol ◽  
Agent Based ◽  
Contract Net ◽  
Multi Agent ◽  
Bidding Mechanism ◽  
Alternative Processes ◽  
Alternative Machines

This paper builds an efficient agent-based flexible scheduling for real-world manufacturing systems. Considering the alternative processes and alternative machines, the allocation of manufacturing resources is achieved through negotiation among the job and machine agents in a multi-agent system (MAS). Ant Colony Intelligence (ACI) is proposed to be combined with Contract Net Protocol (CNP) so as to make agents adaptive to changing circumstances. ACI is integrated into both machine agents and job agents to solve the task allocation and sequencing problem. CNP is introduced to allow the agents to cooperate and coordinate their local schedules in order to find globally near-optimal robust schedules. The negotiation protocol is an interactive bidding mechanism based on the hybrid contract net protocol. The implementation of the issues using CNP model is discussed. Experimental results verify the effectiveness and efficiency of the proposed algorithm integrated with ant-inspired coordination.

Solving Machine Loading Problem of FMS

2011 ◽  
pp. 19-43 ◽  
Author(s):  
Anoop Prakash ◽  
Nagesh Shukla ◽  
Ravi Shankar ◽  
Manoj Kumar Tiwari
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Random Search ◽  
Search Algorithms ◽  
Ant Colony ◽  
Complex Nature ◽  
Machine Loading ◽  
Loading Problem

Artificial intelligence (AI) refers to intelligence artificially realized through computation. AI has emerged as one of the promising computer science discipline originated in mid-1950. Over the past few decades, AI based random search algorithms, namely, genetic algorithm, ant colony optimization, and so forth have found their applicability in solving various real-world problems of complex nature. This chapter is mainly concerned with the application of some AI based random search algorithms, namely, genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA), artificial immune system (AIS), and tabu search (TS), to solve the machine loading problem in flexible manufacturing system. Performance evaluation of the aforementioned search algorithms have been tested over standard benchmark dataset. In addition, the results obtained from them are compared with the results of some of the best heuristic procedures in the literature. The objectives of the present chapter is to make the readers fully aware about the intricate solutions existing in the machine loading problem of flexible manufacturing systems (FMS) to exemplify the generic procedure of various AI based random search algorithms. Also, the present chapter describes the step-wise implementation of search algorithms over machine loading problem.

A Hybrid Ant Colony Optimization Algorithm for Permutation Flow-Shop Scheduling in Manufacturing Systems and Industrial Process

2013 ◽  
Vol 345 ◽  
pp. 438-441
Author(s):  
Jing Chen ◽  
Xiao Xia Zhang ◽  
Yun Yong Ma
Keyword(s):  
Ant Colony Optimization ◽  
Hybrid Algorithm ◽  
Manufacturing Systems ◽  
Flow Shop ◽  
Ant Colony ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

This paper presents a novel hybrid ant colony optimization approach (ACO&VNS) to solve the permutation flow-shop scheduling problem (PFS) in manufacturing systems and industrial process. The main feature of this hybrid algorithm is to hybridize the solution construction mechanism of the ant colony optimization (ACO) with variable neighborhood search (VNS) which can also be embedded into the ACO algorithm as neighborhood search to improve solutions. Moreover, the hybrid algorithm considers both solution diversification and solution quality. Finally, the experimental results for benchmark PFS instances have shown that the hybrid algorithm is very efficient to solve the permutation flow-shop scheduling in manufacturing engineering compared with the best existing methods in terms of solution quality.

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