Purpose -The purpose of this paper is to focus on the use of analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to select an optimum maintenance strategy for a textile industry. Design/methodology/approach -The relative importance of multiple evaluation criteria and the extension of the TOPSIS are prioritized using AHP. The TOPSIS method is applied to compensate for the imprecise ranking of the AHP in the selection of a maintenance policy mix. Findings -An efficient ranking of alternatives can be achieved for maintenance strategy selection through the combination of AHP and TOPSIS. Originality/value -The paper highlights a new insight into multi-criteria decision-making techniques to select an optimum maintenance policy for a process industry with the use of a case study.

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Purpose -The purpose of this paper is to focus on the use of analytic hierarchy process (AHP) under fuzzy environment and technique for order preference by similarity to ideal solution (TOPSIS) to select an optimum maintenance strategy for a textile industry. Design/methodology/approach -First by using improved AHP with fuzzy set theory, the weight of each criterion is calculated to overcome the criticism of unbalanced scale of judgments, uncertainty, and imprecision in the pair-wise comparison process. Then this paper introduces a model that integrates improved fuzzy AHP with TOPSIS algorithm to support maintenance strategy selection decisions. Findings -An efficient pair-wise comparison process and ranking of alternatives can be achieved for maintenance strategy selection through the integration of AHP with fuzzy set theory and TOPSIS. Originality/value -The paper points out a new insight of multi-criteria decision making techniques to select optimum maintenance policy for a process industry with the use of a case study.

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Flexible Manufacturing System (FMS), which is equipped with several CNC machines and Automated Guided Vehicle (AGV) based material handling system is designed and implemented to gain the flexibility and efficiency of production. After the implementation of FMS, in practice, the scheduling of the resources, such as frequent variation in the parts, tools, AGV routings, becomes a complex task. This is being done traditionally using various mathematical programming techniques. In recent years, random search algorithms have been attempted for scheduling. Most of the research has been emphasized only on single objective optimization. Multi objective problems in scheduling with conflicting objectives are more complex and combinatorial in nature and hardly have a unique solution. This paper addresses multi objective task scheduling of AGV in a flexible manufacturing environment using nontraditional optimization algorithms. In this paper the authors made an attempt to find the nearoptimum schedule for two AGVs based on the balanced workload and the minimum traveling time for maximum utilization. The proposed methods are exemplified with illustrations.