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Formulating and Solving Sustainable Stochastic Dynamic Facility Layout Problem: A Key to Sustainable Operations

Tayal, Akash, Gunasekaran, Angappa, Singh, Surya Prakash, Dubey, Rameshwar, Papadopoulos, Thanos (2016) Formulating and Solving Sustainable Stochastic Dynamic Facility Layout Problem: A Key to Sustainable Operations. Annals of Operations Research, 253 (1). pp. 621-655. ISSN 0254-5330. (doi:10.1007/s10479-016-2351-9) (KAR id:57689)

Abstract

Facility layout design, a NP Hard problem, is associated with the arrangement of facilities in a manufacturing shop floor, which impacts the performance, and cost of system. Efficient design of facility layout is a key to the sustainable operations in a manufacturing shop floor. An efficient layout design not only optimizes the cost and energy due to proficient handling but also increase flexibility and easy accessibility. Traditionally, it is solved using meta-heuristic techniques. But these algorithmic or procedural methodologies do not generate effective and efficient layout design from sustainable point of view, where design should consider multiple criteria such as demand fluctuations, material handling cost, accessibility, maintenance, waste and more. In this paper, to capture the sustainability in the layout design these parameters are considered, and a new Sustainable Stochastic Dynamic Facility Layout Problem (SDFLP) is formulated and solved. SDFLP is optimized for material handling cost and rearrangement cost using various meta-heuristic techniques. The pool of layouts thus generated is then analyzed by Data Envelopment Analysis (DEA) to identify efficient layouts. A novel hierarchical methodology of consensus ranking of layouts is proposed which combines the multiple attributes/criteria. Multi Attribute decision-making (MADM) Techniques such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Interpretive Ranking Process (IRP) and Analytic hierarchy process (AHP), Borda-Kendall and Integer Linear Programming based rank aggregation techniques are applied. To validate the proposed methodology data sets for facility size N=12 for time period T=5 having Gaussian demand are considered.

Item Type: Article
DOI/Identification number: 10.1007/s10479-016-2351-9
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Thanos Papadopoulos
Date Deposited: 05 Oct 2016 16:37 UTC
Last Modified: 05 Nov 2024 10:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57689 (The current URI for this page, for reference purposes)

University of Kent Author Information

Papadopoulos, Thanos.

Creator's ORCID: https://orcid.org/0000-0001-6821-1136
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