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

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

APPLIED ARTIFICIAL INTELLIGENCE

This paper deals with the modeling of conceptual knowledge to capture the major customer requirements effectively and to transform these requirements systematically into the relevant design requirements. Quality Function Deployment (QFD) is a well-known planning and problem-solving tool for translating customer needs (CNs) into the engineering characteristics (ECs) and can be employed for this modeling. In this study, an integrated methodology is presented to rank ECs for implementing QFD in a fuzzy environment. The proposed methodology uses fuzzy weighted average method as a fuzzy group decision making approach to fuse multiple preference rankings for determining the weights of the customer needs. It adopts a fuzzy Analytic Network Process (ANP) approach which enables the consideration of inner dependencies in a cluster as well as the interdependencies between the clusters to determine the importance of ECs. The proposed approach is illustrated through a case study in ready-mixed concrete industry

Simulation approach in comparison of a pull system in a cell production system with a push system in a conventional production system according to flexible cost: A case study

Due to globalization of the world markets, industrial firms in an increasing competitive environment are required to reduce their production costs and to produce with superior quality at short delivery times in a flexible way. The firms in the automotive industry, which have a big share in the world industry, should produce cheaper, high-quality products in short-delivery times with flexible production systems in order to be able to meet customers changing needs. In this study, a case study to apply JIT philosophy to a company that is a sub-contractor of the automotive industry will be presented. This new system and the existing system are modeled and simulated by SIMAN after which an economic analysis is performed using the simulation results. (C) 1998 Published by Elsevier Science B.V. All rights reserved