Simulated annealing heuristics for the dynamic facility layout problem

Today\u27s consumer market demands that manufacturers must be competitive. This requires the efficient operation of manufacturing plants and their ability to quickly respond to changes in product mix and demand. Studies show that material handling cost makes up between 20 and 50 percent of the total operating cost. Therefore, this thesis considers the problem of arranging and rearranging (when there are changes in product mix and demand) manufacturing facilities such that material handling and rearrangement costs are minimized. This problem is called the dynamic facility layout problem. In this thesis, three simulated annealing heuristics are presented for the dynamic facility layout problem. The first is the direct implementation of the simulated annealing algorithm. The second heuristic uses a reheating strategy within simulated annealing. The third heuristic combines the simulated annealing algorithm, time windows concept, and the backward pairwise exchange method. The performance of the heuristics was evaluated using two measures: solution quality and computational time. Results obtained show that the proposed heuristics are effective for the dynamic facility layout problem

Overview of Dynamic Facility Layout Planning as a Sustainability Strategy

[EN] The facility layout design problem is significantly relevant within the business operations strategies framework and has emerged as an alternate strategy towards supply chain sustainability. However, its wide coverage in the scientific literature has focused mainly on the static planning approach and disregarded the dynamic approach, which is very useful in real-world applications. In this context, the present article offers a literature review of the dynamic facility layout problem (DFLP). First, a taxonomy of the reviewed papers is proposed based on the problem formulation current trends (related to the problem type, planning phase, planning approach, number of facilities, number of floors, number of departments, space consideration, department shape, department dimensions, department area, and materials handling configuration); the mathematical modeling approach (regarding the type of model, type of objective function, type of constraints, nature of market demand, type of data, and distance metric), and the considered solution approach. Then, the extent to which recent research into DFLP has contributed to supply chain sustainability by addressing its three performance dimensions (economic, environmental, social) is described. Finally, some future research guidelines are provided.This research was funded by the Spanish Ministry of Science, Innovation and Universities Project CADS4.0, grant number RTI2018-101344-B-I00; and the Valencian Community ERDF Programme 2014-2020, grant number IDIFEDER/2018/025.Pérez-Gosende, P.; Mula, J.; Díaz-Madroñero Boluda, FM. (2020). Overview of Dynamic Facility Layout Planning as a Sustainability Strategy. Sustainability. 12(19):1-16. https://doi.org/10.3390/su12198277S1161219Ghassemi Tari, F., & Neghabi, H. (2015). A new linear adjacency approach for facility layout problem with unequal area departments. Journal of Manufacturing Systems, 37, 93-103. doi:10.1016/j.jmsy.2015.09.003Kheirkhah, A., Navidi, H., & Messi Bidgoli, M. (2015). Dynamic Facility Layout Problem: A New Bilevel Formulation and Some Metaheuristic Solution Methods. IEEE Transactions on Engineering Management, 62(3), 396-410. doi:10.1109/tem.2015.2437195Altuntas, S., & Selim, H. (2012). 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The Single Row Facility Layout Problem: State of the Art

The single row facility layout problem (SRFLP) is a NP-hard problem concerned with the arrangement of facilities of given lenghs on a line so as to minimize the weighted sum of the distances between all the pairs of facilities. The SRFLP and its special cases often arise while modeling a large variety of applications. It was actively researched until the mid-nineties. It has again been actively studied since 2005. Interestingly, research on many aspects of this problem is still in the initial stages, and hence the SRFLP is an interesting problem to work on. In this paper, we review the literature on the SRFLP and comment on its relationship with other location problems. We then provide an overview of different formulations of the problem that appear in the literature. We provide exact and heuristic approaches that have been used to solve SRFLPs, and finally point out research gaps and promising directions for future research on this problem.

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

Metaheuristics for the Generalized Quadratic Assignment Problem

The generalized quadratic assignment problem (GQAP) is the task of assigning a set of facilities to a set of locations such that the sum of the assignment and transportation costs is minimized. The facilities may have different space requirements, and the locations may have varying space capacities. Also, multiple facilities may be assigned to each location such that space capacity is not exceeded. In this research, an application of the GQAP is presented for assigning a set of machines to a set of locations on the plant floor. Two meta-heuristics are proposed for solving the GQAP: tabu search (TS) and simulated annealing (SA). In addition, two types of neighborhood structures are considered for each meta-heuristic. A set of 21 test problems, available in the literature, is used to evaluate the performances of the meta-heuristics using one or two neighborhood structures. Computational experiments show that the proposed SA heuristics performed better than the proposed TS heuristics. The SA heuristics obtained results better than those presented in the literature for three of the test problems. On the other hand, the TS heuristics did not perform well for the problems with high space capacity utilization

Tabu search heuristics for the dynamic facility layout problem

The facility layout dramatically influences the efficiency of material handling within a manufacturing system. In order to ensure optimal performance within a manufacturing system, the facility layout should reflect changes throughout time. However, the static facility layout problem with constant material flows between departments may not be a realistic scenario because a manufacturing facility is a dynamic system that constantly evolves. In other words, product demand constantly changes over time. As a result, the dynamic facility layout problem (DFLP) considers these changes and is defined as the problem of assigning departments to locations during a multi-period planning horizon such that the sum of the material handling and rearrangement costs is minimized. In this research, tabu search heuristics and a probabilistic tabu search heuristic are developed to solve the DFLP. The proposed tabu search heuristics are a simple tabu search heuristic, a tabu search heuristic with diversification and intensification strategies, and a probabilistic tabu search heuristic. Two data sets taken from the literature are used to test the performances of the proposed heuristics. Computational experiments show that the proposed heuristics out-performed the heuristics presented in the literature with respect to solution quality and computational time

Facility Layout Problem for Cellular Manufacturing Systems

Good layout plan leads to in improve machine utilization, part demand quality, efficient setup time, less work-in-process inventory and material handling cost. Cellular Manufacturing (CM) is an application of GTCM is the combination of job shop and/or flow shop. Facility Layout Problem (FLP) for CMS includes both inter-cell layout and intra-cell layout. A bi-level mixed-integer non-linear programming continuous model has been formulated to fully define the problem and the relationship between intra-cell and inter-cell layout design. Facilities are assumed unequal size; operation sequences, part demands, overlap elimination, aisle are considered. The problem is NP-hard; hence, a simulated annealing meta-heuristic employing a novel constructive radial-based heuristic for initialization have been designed and implemented. For the first time, a novel heuristic algorithm has been designed to allocate and displace facilities in radial direction. In order to improve the search efficiency of the developed SA algorithm, the cell size used in the initialization heuristic algorithm is assumed twice as that of the original size of the cells. A real case study from the metal cutting inserts industry has been used. Results demonstrate the superiority of the developed SA algorithm against rival comparable meta-heuristics and algorithms from the literature

Ant colony heuristics for the dynamic facility layout problem

As global economic competition and cooperation become more and more drastic, the enterprise\u27s facility layout needs to be more flexible to adapt to the rapidly changing environment. Therefore, attention should be given to the dynamical nature of the facility layout. In other words, the flow of materials between departments changes during the planning horizon (multiple periods) and should be considered. This problem is known as the dynamic facility layout problem (DFLP).;This research proposes three heuristics based on the ant colony optimization (ACO) heuristic to solve the DFLP. The performance of the heuristics was evaluated using two data sets taken from the literature. Results obtained show that the proposed heuristics are effective for the dynamic facility layout problem