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

Intelligent Systems; Investigators at Multimedia University Detail Research in Intelligent Systems

According to news reporting originating from Melaka, Malaysia, by VerticalNews correspondents, research stated, "Since Facility Layout Problem (FLP) affects the total manufacturing cost significantly, it can be considered as a critical issue in the early stages of designing Flexible Manufacturing Systems (FMSs), particularly in volatile environments where uncertainty in product demands is inevitable

Intelligent Systems; Investigators at Multimedia University Detail Research in Intelligent Systems

According to news reporting originating from Melaka, Malaysia, by VerticalNews correspondents, research stated, "Since Facility Layout Problem (FLP) affects the total manufacturing cost significantly, it can be considered as a critical issue in the early stages of designing Flexible Manufacturing Systems (FMSs), particularly in volatile environments where uncertainty in product demands is inevitable

Manufacturing; Findings from Multimedia University Broaden Understanding of Manufacturing

Dynamic and robust layouts are flexible enough to cope with fluctuations and uncertainties in product demands in volatile environment of flexible manufacturing systems. Since the facility layout is a hard combinatorial optimization problem, intelligent approaches are the most appropriate methods for solving the large size of this problem in reasonable computational time

Gravitational search algorithm optimization for bi-objective flow shop scheduling using weighted dispatching rules

A framework based on weighted dispatching rules is proposed in this study in order to obtain optimum job-machine allocation for a bi-objective flow shop system. In the proposed method, bi-objective based dispatching rules are integrated into a single weightage function. Unlike traditional dispatching rules (DRs) which only provide a fixed schedule for a particular job, this method iteratively improves the schedules by finding the optimum weightages for every stage. However, such weightage function is rather complex and attaining an optimum solution is a rather arduous task. Hence, Gravitational Search Algorithm (GSA) optimization is applied to overcome this issue. The proposed method is indeed an improvement over the conventional dispatching rule method and it can be easily implemented in the semiconductor industry, in which flexible flow shop system is the standard practice. A number of random generated cases are used in this study to demonstrate the performance of the framework by introducing a series of weightages in the selected dispatching rules

Reduction of Computational Load in Robust Facility Layout Planning Considering Temporal Production Efficiency

Part 3: Production Management Theory and MethodologyInternational audienceMost researches of facility layout planning (FLP) have aimed at finding a layout with which evaluation indices based on distance are minimized. Because temporal efficiency has not been considered in this stage but in post stages, the resultant temporal efficiency may not be optimal enough. The authors have developed an FLP method considering temporal efficiency, in which facility layout is optimized using genetic algorithm (GA), and have enhanced it so that robustness against changes in production environment can be taken into consideration. However, the enhanced method involves a large computational load, since numerous production scenarios need to be considered. This paper provides a method for reducing computational load in the robust FLP based on the sampling approach where each layout plan is evaluated with only a limited number of production scenarios in the optimization process by GA. Numerical experiments showed the potential of the proposed method to efficient robust FLP considering temporal efficiency

A hybrid CS-SA intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demands

Abstract This paper aims at proposing a quadratic assignment-based mathematical model to deal with the stochastic dynamic facility layout problem. In this problem, product demands are assumed to be dependent normally distributed random variables with known probability density function and covariance that change from period to period at random. To solve the proposed model, a novel hybrid intelligent algorithm is proposed by combining the simulated annealing and clonal selection algorithms. The proposed model and the hybrid algorithm are verified and validated using design of experiment and benchmark methods. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time points of view. Besides, the proposed model can be used in both of the stochastic and deterministic situations

Intelligent design of a dynamic machine layout in uncertain environment of flexible manufacturing systems

Since Facility Layout Problem (FLP) affects the total manufacturing cost significantly, it can be considered as a critical issue in the early stages of designing Flexible Manufacturing Systems (FMSs), particularly in volatile environments where uncertainty in product demands is inevitable. This paper proposes a new mathematical model by using the Quadratic Assignment Problem formulation for designing an optimal machine layout for each period of a dynamic machine layout problem in FMSs. The product demands are considered as independent normally distributed random variables with known Probability Density Function (PDF), which changes from period to period at random. In this model, the decision maker's defined confidence level is also considered. The confidence level represents the decision maker's attitude about uncertainty in product demands in such a way that it affects the results of the problem significantly. To validate the proposed model, two different size test problems are generated at random. Since the FLP, especially in multi-period case is a hard Combinatorial Optimization Problem (COP), Simulated Annealing (SA) meta-heuristic resolution approach programmed in Matlab is used to solve the mathematical model in a reasonable computational time. Finally, the computational results are evaluated statistically