A simulated annealing heuristic for the dynamic layout problem with budget constraint (original) (raw)
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Simulated annealing heuristics for the dynamic facility layout problem
Computers & Operations Research, 2006
Today's 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. In addition, studies show that material-handling cost make up between 20 and 50 percent of the total operating cost. Therefore, this paper considers the problem of arranging and rearranging, when there are changes in product mix and demand, manufacturing facilities such that the sum of material handling and rearrangement costs is minimized. This problem is called the dynamic facility layout problem (DFLP). In this paper, hybrid ant systems (HASs) are developed to solve the DFLP. To test the performance of the meta-heuristics, two data sets taken from the literature are used in the analysis. The results show that the HASs are efficient techniques for solving the DFLP. More importantly, HASs found new best solutions for more than one-half of all the test problems. ᭧
A solution to the facility layout problem using simulated annealing
Computers in Industry, 1998
. In this paper a solution in the continual plane to the Facility Layout Problem FLP is presented. It is based on Simulated Ž . Annealing SA , a relatively recent algorithm for solving hard combinatorial optimization problems, like FLP. This approach Ž . may be applied either in General Facility Layout Problems GFLP considering facilities areas, shapes and orientations or in Ž . Machine Layout problems MLP considering machine's pick-up and drop-off points. It has been applied to real-life situations with useful results, indicating the effectiveness of this approach. q
Operations Research and Computing: Algorithms and Software for Analytics, 2015
In this paper, an unequal area Cyclic Facility Layout Problem (CFLP) is studied. Dynamic and seasonal nature of the product demands results in the necessity for considering the CFLP where product demands as well as the departmental area requirements are changing from one period to the next one. Since the CFLP is NP-hard, we propose a Simulated Annealing (SA) metaheuristic with a dynamic temperature schedule to solve the CFLP. In the SA algorithm, both relative department locations and dimensions of departments are simultaneously determined. We benchmark the performance of the proposed SA algorithm with earlier approaches on different test problems from the literature and find out that the SA algorithm is promising.
International Journal of Industrial Engineering Computations, 2018
In this article, we propose Simulated Annealing (SA) heuristic to solve Unequal Area Dynamic Facility Layout Problem (FBS) with Flexible Bay Structure (UA-DFLPs with FBS). The UA-DFLP with FBS is the problem of determining the facilities dimension and their location coordinates with flexible bays formation in the layout for various periods of the planning horizon. The UA-DFLP with FBS is more constrained than general UA-DFLP and it is an NP-complete problem. The proposed SA is tested with the available UA-DFLPs instances in the literature. The proposed SA heuristic has given new best solution or the same solution for FBS based problems as compared with the best-known reported in the UA-DFLPs with FBS literature. The proposed SA heuristic is also tested on standard UA-DFLPs used in non-FBS approaches. The SA heuristic solution is not significantly different from the best solution reported in the literature for non-FBS approaches. Equal area DFLP instances are also solved with the proposed SA and the results obtained are promising with the solutions reported in the literature. Hence the results obtained indicate that the proposed SA for UA-DFLP with FBS is effective and versatile for both equal and unequal area dynamic facility layout problems. The computational efficiency of the proposed SA heuristic is very much competitive as compared to other meta-heuristics computational timings reported in the literature.
A combined zone-LP and simulated annealing algorithm for unequal-area facility layout problem
Advances in Production Engineering & Management
Facility layout problem (FLP) is one of well-known NP-hard problems and has been demonstrated to be useful in enhancing the productivity of manufacturing systems in practice. This paper focuses on the unequal-area FLP (UA-FLP) whose goal is to locate departments with different areas within a given facility so as to minimize the total material handling cost. A novel approach, which we call a combined zone-linear programming (zone-LP) and simulated annealing algorithm, is developed for solving the UA-FLP. The zone-LP approach is a layout construction technique for the unequal-area departments and consists of two phases. In the first phase, a zoning algorithm is implemented to determine the relative positions between the departments. In this algorithm, for the sake of problem simplification and computational efficiency, each department is treated as a rectangle with an allowable aspect ratio and the area of the facility is assumed to be unbounded. In the second phase, by using the relative positions obtained in the first phase as input, a linear programming (LP) model is developed to identify the exact locations and dimensions of departments within the facility with specified sizes while satisfying their maximum aspect ratio requirement and the shape constraints. We also design a simulated annealing algorithm to improve the placing sequence. Finally, our computational results suggest that our proposed algorithm is efficient compared with the best existing approach in the literature.
A facility layout problem is concerned with determining the best position of departments, cells, or machines on the plant. An efficient layout contributes to the overall efficiency of operations. It’s been proved that, when system characteristics change, it can cause a significant increase in material handling cost. Consequently, the efficiency of the current layout decreases or is lost and it does necessitate rearrangement. On the other hand, the rearrangement of the workstations may burden a lot of expenses on the system. The problem that considers balance between material handling cost and the rearrangement cost is known as the Dynamic Facility Layout Problem (DFLP). The objective of a DFLP is to find the best layout for the company facilities in each period of planning horizon considering the rearrangement costs. Due to the complex structure of the problem, there are few researches in the literature which tried to find near optimum solutions for DFLP with budget constraint. In this paper, a new heuristic approach has been developed by combining Genetic Algorithm (GA) and Parallel Simulated Annealing Algorithm (PSAA) which is the main contribution of the current study. The results of applying the proposed algorithm were tested over a wide range of test problems taken from the literature. The results show efficiency of the hybrid algorithm GA- to solve the Dynamic Facility Layout Problem with Budget Constraint (DFLPBC). Keywords: Dynamic facility layout problem; Budget constraint; Genetic algorithm; Parallel Simulated annealing algorithm.
Neural Computing and Applications, 2014
Today's manufacturing plants tend to be more flexible due to rapid changes in product mix and market demand. Therefore, this paper investigates the problem of location and relocation (when there are changes incurred to the material flows between departments) manufacturing facilities such that the total cost of material flows and relocation costs are minimized. This problem is known as the dynamic facility layout problem (DFLP), which is a general case of static facility layout problem. This paper proposes a robust and simply structured hybrid technique based on integrating three meta-heuristics: imperialist competitive algorithms, variable neighborhood search, and simulated annealing, to efficiently solve the DFLP. The novel aspect of the proposed algorithm is taking advantage of features of all above three algorithms together. To test the efficiency of our algorithm, a data set from the literature is used for the experimental purpose. The results obtained are quite promising in terms of solution quality for most of the test problems.
Multi Objective Simulated Annealing Approach for Facility Layout Design
Facility layout design problem considers the departments' physcial layout design with area requirements in some restrictions such as material handling costs, remoteness and distance requests. Briefly, facility layout problem related to optimization of the layout costs and working conditions. This paper proposes a new multi objective simulated annealing algorithm for solving of the unequal area in layout design. Using of the different objective weights are generated with entropy approach and used in the alternative layout design. Multi objective function takes into the objective function and constraints. The suggested heuristic algorithm used the multi-objective parameters for initialization. Then prefered the entropy approach determines the weight of the objective functions. After the suggested improved simulated annealing approach applied to whole developed model. A multi-objective simulated annealing algorithm is implemented to increase the diversity and reduce the chance of g...
Modelling Sequence Unequal Size Facility Layout Problem Using Simulated Annealing
Science, Education and Innovations in the context of modern problems
It truly is suggested with this papers that will improved managed annealing (SA) methods be applied to deal with an ideal large continuous rough dimension service style problem. It really is offered with this papers how you can resolve the particular automated bumpy sizing support design design and style a significant in an attempt to produce the most effective structure whilst considering typically the quickest variety journeyed simply by models associated with circulation (people, materials, info, along with other assisting services), along with the least edge utilization (for the particular layout). The particular bumpy dimensions FLP (UFLP) has been understood to be a consistent Bumpy Service Style Problem (SUFLP) with this study, plus handled annealing (SA) had been utilized to fix the problem to get the perfect answer. To deal with the actual manufacturing atmosphere, this particular analysis offers up-to-date INTERPERSONAL WORRY methods with regard to heat environment techniques in addition heat decrease recommendations having a number of beginning temps in addition to preventing problems which are good outcomes of ruse. The specific assessments had been carried out to cope with the best concern regarding unequal sizing practical devices like devices, assistance gear, and even operate the process region, that was resolved within the books. Typically the method which has been produced increases the high quality involving SUFLP's options although dealing with large matter sizes. Based on the assessment outcomes along with recommended strategies, typically the iteration-based temp lowering guideline together with amalgamated plan functions the very best design design around the real software when it comes to answer top quality.
Metaheuristic in facility layout problems: current trend and future direction
A state-of-the-art review, spanning the last two decades, on application of metaheuristic methods in facility layout problems (FLPs) to gauge the current and emerging trends involving new design objectives, algorithms and methodologies to the combinatorial optimisation aspects is presented in this work. Fresh developments in emerging layout research, as analysed in this study, provide a perspective on what the future of the field will be like. A tendency of using metaheuristics, such as genetic algorithm (GA), simulated annealing (SA), ant colony optimisation (ACO) and particle swarm optimisation (PSO) with a trend towards multi-objective approaches to layout and material handling system design is observed.