Stochastic Dynamic Facility Layout Problem Research Articles

Routing flexibility for unequal–area stochastic dynamic facility layout problem in flexible manufacturing systems

It is indispensable that any manufacturing system is consistent with potential changes such as fluctuations in demand. The uncertainty also makes it more essential. Routing Flexibility (RF) is one of the necessities to any modern manufacturing system such as Flexible Manufacturing System (FMS). This paper suggests three mixed integer nonlinear programming models for the Unequal–Area Stochastic Dynamic Facility Layout Problems (UA–SDFLPs) by considering the Routing Flexibility. The models are proposed when the independent demands follow the random variable with the Poisson, Exponential, and Normal distributions. To validation of the proposed models, many small-sized test problems has solved that derived from a real case in literature. The large-sized test problems are solved by the Genetic Algorithm (GA) at a reasonable computational time. The obtained results indicate that the discussed models for the UA–SDFLPs are valid and the managers can take these models to the manufacturing floor to adapt to the potential changes in today's competitive market.

Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning

AbstractThe facility layout problem (FLP) is a combinatorial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedural design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three‐stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learning (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simulated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta‐heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (efficiency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA and improves the prediction accuracy of layouts. A Gaussian distribution product demand dataset for time period T = 5 and facility size N = 12 is used to prove the effectiveness of the methodology.

Analysis of simulated annealing cooling schemas for design of optimal flexible layout under uncertain dynamic product demand

Manufacturing facilities are subjected to many uncertainties such as variability in demand, queuing delays, variable task times, rejects and machine breakdown. These volatilities have a large impact on leap time, inventory cost and delivery performance of a manufacturing unit. To operate efficiently the manufacturing facilities should adapt to these variations. The paper explores the way uncertainties are addressed in designing of flexible optimal layout. Such facility layout problem is known as stochastic dynamic facility layout problem (SDFLP). SDFLP is an NP-hard combinatorial optimization problem, which means the time taken to solve increases exponentially with problem size. To solve SDFLP, the paper presents an adaptation of simulated annealing (SA) meta-heuristic. Various SA cooling schemas are discussed, computed and evaluated for generating the optimal flexible layout. An optimal layout is one that minimises the distance travelled by materials taking into account uncertain product demand (material handling cost). A computer-based tool was developed and analysis was conducted on small to large size problem set. The results showed that SA with exponential cooling schedule provides better solution in terms of layout efficiency and gave better solution as compared to literature.

An Improved Genetic Algorithm Based Robust Approach for Stochastic Dynamic Facility Layout Problem

This paper deals with stochastic dynamic facility layout problem under demand uncertainty in terms of material flow between facilities. A robust approach suggests a robust layout in each period as the most frequent one falling within a prespecified percentage of the optimal solution for multiple scenarios. Mont Carlo simulation method is used to randomly generate different scenarios. A mathematical model is established to describe the dynamic facility layout problem with the consideration of transport device assignment. As a solution procedure for the proposed model, an improved adaptive genetic algorithm with population initialization strategy is developed to reduce the search space and improve the solving efficiency. Different sized instances are compared with Particle Swarm Optimization (PSO) algorithm to verify the effectiveness of the proposed genetic algorithm. The experiments calculating the cost deviation ratio under different fluctuation level show the good performance of the robust layout compared to the expected layout.

Formulating multi-objective stochastic dynamic facility layout problem for disaster relief

Facility layout problem is an NP-hard problem where two basic approaches have commonly been used to optimize layouts i.e. qualitative and quantitative. Qualitative aspect provide layout based on maximizing of closeness rating while quantitative involve minimization of material handling cost. Previous work undertaken by scholars in which they have studied or focussed on optimizing product flow to optimize both objectives either considering constant or varying demand across all period. Here, the facility layout problem is addressed from the point of demand based disaster for disaster relief operations. Therefore, the paper first presents mathematical formulation for multi objective stochastic dynamic facility layout problem (MO-SDFLP) where the product demand is varying over multiple periods for multiple products. Huge demand variations in multiple products across multiple periods may cause disaster as it can impact heavily the supply chain. Hence, the layout problem considered here mainly focus on considering product demand variations to capture disaster for disaster relief operations. MO-SDFLP being NH-hard is solved using simulated annealing (SA) and chaotic simulated annealing (CSA) meta-heuristics. The paper reports results of SA and CSA algorithms using data sets available in literature for facility size N = 12, period T = 5 and Gaussian distribution product demand. In addition, to show the applicability of MO-SDFLP for bigger demand based disaster, SA and CSA is also tested on bigger problem i.e. N = 30 for T = 5. It is observed that CSA performs better than SA.

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

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.

Integrated SA-DEA-TOPSIS-based solution approach for multi objective stochastic dynamic facility layout problem

Layout design often has a significant impact on the performance of a manufacturing industry. In the real world, a facility layout is subjected to various quantitative and qualitative conditions or criteria such as, total material handling cost, flow distance, closeness factor, maintenance etc. Researchers have devised various algorithmic and procedural methodologies for finding a layout that is applicable for practical scenarios. The paper proposes an integrated methodology of combining algorithmic (meta-heuristic) and procedural (decision-making) techniques to identify a layout that has the highest degree of satisfaction for all the criteria. A computer program was developed for simulated annealing. It was used to generate a set of layouts based on qualitative and quantitative aspect of facility layout problem for multiple time periods and uncertain product demand. DEA was applied to identify a subset of efficient layouts. TOPSIS was used to rank these layouts based on criteria and their weights as per expert's opinion.

Integrated SA-DEA-TOPSIS-based solution approach for multi objective stochastic dynamic facility layout problem

Integrated SA-DEA-TOPSIS-based solution approach for multi objective stochastic dynamic facility layout problem

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 are then analyzed by data envelopment analysis 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 techniques such as technique for order preference by similarity to ideal solution, interpretive ranking process and analytic hierarchy process, 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.

Designing Flexible Stochastic Dynamic Layout: An Integrated Firefly and Chaotic Simulated Annealing-Based Approach

Facility layout problem (FLP) is non-deterministic and a polynomial time problem (NP-hard). In practice, the FLP is affected due to the presence of fluctuating products demand and thus results in increased material handling cost. Such layout problems are known as stochastic dynamic facility layout problem (SDFLP). It is well known that the computational time to solve SDFLP is exponential; therefore, it is hard to solve using exact approaches. Various meta-heuristic algorithms are used for solving such problems. The aim of the paper is to formulate a novel meta-heuristic for solving SDFLP, which minimizes the material handling cost better than the prevailing meta-heuristic. A hybridized methodology of combining firefly algorithm (FA) and chaotic simulated annealing (CSA), i.e. hybrid FA/CSA is proposed and evaluated to find an optimal solution for SDFLP. The performance of the algorithm, in terms of total material handling cost, is compared with simulated annealing, chaotic simulated annealing and hybrid ant colony optimization/simulated annealing using data from the literature.

Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem

Manufacturing industries have become larger, diverse and the factors affecting a facility layout design have grown rapidly. Handling and evaluating these large set of criteria (factors) is difficult in designing and solving facility layout problems. These factors and uncertainties have a large impact on manufacturing time, manufacturing cost, product quality and delivery performance. In order to operate efficiently, these facilities should adapt to these variations over multiple time periods and this must be addressed while designing an optimal layout. This paper proposes a novel integrated framework by combining Big Data Analtics and Hybrid meta-heuristic approach to design an optimal facility layout under stochastic demand over multiple periods. Firstly, factors affecting a facility layout design are identified. The survey is conducted to generate data reflecting 3V’s of Big Data. Secondly, a reduced set of factors are obtained using Big Data Analytics. These reduced set of factors are considered to mathematically model a weighted aggregate objective for Multi-objective Stochastic Dynamic Facility Layout Problem (MO-SDFLP). Hybrid Meta-heuristic based on Firefly (FA) and Chaotic simulated annealing is used to solve the MO-SDFLP. To show the working methodology of proposed integrated framework an exemplary case is presented.