Facility layout problem: Bibliometric and benchmarking analysis

Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems

Compressive sensing adaptation for polynomial chaos expansions

Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adaptations with different convergence properties. In this paper we present a new adaptation mechanism that builds on compressive sensing algorithms, resulting in a reduced polynomial chaos approximation with optimal sparsity. The developed adaptation algorithm consists of a two-step optimization procedure that computes the optimal coefficients and the input projection matrix of a low dimensional chaos expansion with respect to an optimally rotated basis. We demonstrate the attractive features of our algorithm through several numerical examples including the application on Large-Eddy Simulation (LES) calculations of turbulent combustion in a HIFiRE scramjet engine.Comment: Submitted to Journal of Computational Physic

Addressing the facilities layout design problem through constraint logic programming

One of the most difficult problems that face researchers experimenting with complex systems in real world applications is the Facility Layout Design Problem. It relies with the design and location of production lines, machinery and equipment, inventory storage and shipping facilities. In this work it is intended to address this problem through the use of Constraint Logic Programming (CLP) technology. The use of Genetic Algorithms (GA) as optimisation technique in CLP environment is also an issue addressed. The approach aims the implementation of genetic algorithm operators following the CLP paradigm

Simulation-based Flexible Layout Planning Considering Stochastic Effects

Layout planning is an important practical problem for manufacturing companies. In today's market conditions - characterized with continuously changing product portfolio and shortening product lifecycles - frequent reconfiguration is requested if the primary goal for the company is to remain competitive. The key to win customers is to widen the product portfolio and customize the products, however, this leads to the problem that the manufacturing system has to be re-organized several times during its life cycle that requires solving design problems frequently. In the paper, a novel layout planning method is introduced that can be applied efficiently to solve real industrial problems. The method applies automated simulation model building to create the different layouts. It focuses on minimizing the objective function that is specified according to the predefined key performance indicators (KPI). The solution is a hybrid optimization method, in which evaluation of the lay out alternatives is done by simulation and the improvement of the solution is performed by a near-to-optimal search algorithm. The optimization is separated from the simulation model in order to boost the computations. Important advantage of the solution is the efficiency consideration of stochastic parameters that improve the applicability of the results

Optimizing shipping routes to minimize cost using particle swarm optimization

Product shipping is important in the economic process in the company. Efficient product shipping routes should provide low transportation costs. This study based on a case company of CV. Kayana, a distributor of “Sari Rotiâ€, has 4 motorbikes and 2 cars. Each vehicle has their own shipping routes. Nowadays, high distance for each route results on high transportation cost. Therefore, the objective of this study to minimize the distance and cost of product shipping by developing shipping algorithm using Particle Swarm Optimization (PSO) for Traveling Salesman Problem (TSP). The MATLAB software was employed to solve this problem. The solution is obtained by varying the amount of particles and number of iterations. Experimental results proved that the developed PSO is enough effective and efficient to solve shipping routes problem. The results show the proposed model have lower distance and transportation cost. It helps the company in determining the routes for product shipping with minimum transportation cost

Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem

Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost

A Multiple Objective Formulation and Algorithm for the Layout Design of Food Processing Facilities.

A multiple objective formulation, which incorporates robustness and constraint enforcement as design criteria, is utilized to model the layout of food processing facilities. These facilities are subject to the compliance with guidelines dictated by public health agencies, changes in product mix, and variation in production levels due to seasonality, which render existing layout design algorithms unsuitable for their design. The solution of the robust multiple objective formulation is implemented using a construction heuristic algorithm, MORCH, and an improvement heuristics, MOLAD. The MORCH/MOLAD hybrid algorithm performs comparably to well known heuristic algorithms where materials handling cost is used as the only design criterion. Also, the MORCH/MOLAD solutions are more robust than those of robust heuristic algorithms. Moreover, through the use of a qualitative constraint matrix, the hybrid algorithm generates layouts that conform to guidelines imposed by U.S. regulatory agencies without significantly penalizing materials handling cost. As a qualitative constraint matrix in conjunction with materials handling cost are present in the model, a multicriteria decision making aid that deals with qualitative and quantitative factors, the Analytic Hierarchy Process, is used to select the most suitable layout and to guide the generation of and search for good alternative layout solutions by the hybrid algorithm