Kimberly Battle

Two-Stage inter-cell layout design for cellular manufacturing by using ant colony optimization algorithms

Facility layout planning plays an important role in the manufacturing process and seriously impacts a companys profitability A well-planned layout can significantly reduce the total material handling cost The purpose of this paper is to develop a two-stage inter-cell layout optimization approach by using one of the popular meta-heuristics — the Ant Colony Optimization algorithm At the first stage, the cells are formed based on the part-machine clustering results obtained through the ant system algorithm In other words, we get the initial inter-cell layout after this stage The work at the second stage uses a hybrid ant system algorithm to improve the solution obtained at previous stage Different performance measures are also employed in this paper to evaluate the results.

Part-Machine Clustering: The Comparison between Adaptive Resonance Theory Neural Network and Ant Colony System

The aim of part-machine clustering (PMC) in cellular manufacturing systems is to cluster parts that have similar processing requirements into part-families; and machines that meet these requirements into machine-groups. Although PMC problems are known as NP-complete in the literature, extensive research is still conducted in this field because of the considerable practical value of PMC for industries. In this paper, conventional adaptive resonance theory (ART1) neural network method and a novel meta-heuristic approach called ant colony system (ACS) are proposed for solving PMC problems. The experimental results show that ACS performs better than ART1 neural network on the same selected benchmark test problems. A PMC performance measure called grouping efficiency (GE) is also employed to evaluate the clustering result.

Ant colony optimization for automated storage and retrieval system

In this work, we first propose an NP-hard combinatorial problem, that is, the storage and retrieve (S/R) machine travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas such inventory items in logistics and work-in-process storage in manufacturing systems. And then, we investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the S/R machine operating performance measure index such as total travel distance and total travel time are employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem.

Cellular manufacturing system scheduling under fuzzy constraints: A group technology perspective

In this article, we attempt to solve cellular manufacturing system (CMS) scheduling problem from group technology point of view. Since the operations of a job in a CMS can be performed on more than one machine within a cell, the scheduling problem of the CMS is always considered as a computationally hard problem. Two approaches called fuzzy logic and fuzzy MAX-MIN ant system are employed respectively. Under the consideration of multiple criteria, the performance of different manufacturing cells in a CMS is improved by means of proposed methodologies.

TAC-RMTO: trading agent competition in remanufacture-to-order

TAC-RMTO is a multi-agent-based game that extends and enhances the trading agent competition (TAC) scenario to work in remanufacturing area. The game is a configurable Internet-mediated remanufacture-to-order (RMTO) platform that supports both human and software agents to interact. The main feature of the TAC-RMTO game is that it supports the design, development and execution of RMTO market scenarios involving auctions analogous to those of the TAC by third parties. Thus this platform can be used for conducting research on RMTO related markets analysis and strategies design as well as for educational purposes.

Intelligent travel route planning for bridge crane type of material handling equipment in cellular manufacturing

In this work, we propose the bridge crane travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas in manufacturing systems. We then investigate the feasibility of using ant colony optimization (ACO) meta-heuristics to address the proposed problem. Simulation tests are executed separately based on two ACO algorithms. Finally, the bridge crane operating performance measure index is employed to evaluate the experimental results achieved by different ACO algorithms. Experimental case study demonstrates the effectiveness and applicability of the selected ACO approaches to our proposed BOP problem for bridge crane.

Swarm intelligence supported e-remanufacturing

e-Remanufacturing has nowadays become a superior option for product recovery management system. So far, many different approaches have been followed in order to increase the efficiency of remanufacturing process. Swarm intelligence (SI), a relatively new bio-inspired family of methods, seeks inspiration in the behavior of swarms of insects or other animals. After applied in other fields with success, SI started to gather the interest of researchers working in the field of remanufacturing. In this paper we provide a survey of SI methods that have been used in e-remanufacturing.

The effects of customer perceived disposal hardship on post-consumer product remanufacturing: a multi-agent perspective

This research examines the impact of customer perceived disposal hardship on the post-consumer product remanufacturing activities. The post-consumer products are classified into three categories according to their corresponding level of intelligence. An agent-based simulation model is developed and used to explore different intelligent scenarios. The results suggest that the higher embedded product intelligence, the more effective post-consumer products acquisition management for remanufacturing.

Ant stigmergy shop floor control architecture for intelligent product oriented manufacturing system

In this paper we consider an alternative approach to the problem of infusing local shop floor control decisions with more global performance information. The approach proposed is that of using artificial ant colonies to decentralize and autonomize the shop floor routing. The proposed ant stigmergy shop floor control (ASSFC) has two functional levels: a virtual shop floor level in which ant agents explore optimal processing route stochastically, and a physical shop floor in which intelligent work pieces (IWPs) deterministically exploit the best paths that have been detected by their corresponding ant agents on virtual shop floor. The simple indirect communication and the robust adaptability to disturbances make the ASSFC more suitable for intelligent product oriented manufacturing system than tradition shop floor control.