Multi Objective Simulated Annealing Approach for Facility Layout Design

Robust university course timetabling problem subject to single and multiple disruptions

This paper presents the capacitated Windy Rural Postman Problem with several vehicles. For this problem, two objectives are considered. One of them is the minimization of the total cost of all vehicle routes expressed by the sum of the total traversing cost and another one is reduction of the maximum cost of vehicle route in order to find a set of equitable tours for the vehicles. Mathematical formulation is provided. The multi-objective simulated annealing (MOSA) algorithm has been modified for solving this bi-objective NP-hard problem. To increase algorithm performance, Taguchi technique is applied to design experiments for tuning parameters of the algorithm. Numerical experiments are proposed to show efficiency of the model. Finally, the results of the MOSA have been compared with MOCS (multi-objective Cuckoo Search algorithm) to validate the performance of the proposed algorithm. The experimental results indicate that the proposed algorithm provides good solutions and performs significantly better than the MOCS.

This paper focuses on facility layout design problem in microfluidic device manufacturing. Microfluidics device manufacturing requires exquisite work of operators and relatively much higher material cost than other types of manufacturing. Facility layout problem can directly affect the overall cost of manufacturing, particularly in handling and transport cost. Therefore, the objective of this study is to minimize the total handling and transport cost for facility layout design problem. A genetic algorithm (GA) is employed to find a good alternative facility layout design which is based on the performance metrics. Experimental results show that this approach is more feasible in dealing with the facility layout in microfluidics device fabrication.

This work aims at comparing different many-objective techniques for the optimization of mission and parallel hybrid electric power system for aircraft. In particular, this work considers, as input of the optimization, the specification of the flight mission, the size of the main components and the energy management strategy for a Medium Altitude Long Endurance Unmanned Aerial Vehicle (MALE-UAV). The goals of the optimization are maximization of electric endurance, minimization of overall fuel consumption, improvement of take-off performance and minimization of the additional volume of the hybrid electric solution with respect to the initial conventional power system. The optimization methods considered in this study are those included in the ModeFRONTIER optimization environment: NSGA-II, MOGA-II, MOSA (Multi Objective Simulated Annealing algorithm) and Evolutionary Strategy of type (µ/ρ + λ)-ES. Initially, appropriate metrics are used to compare the proposed methods in a simplified problem with only two objective functions. Then a complete optimization is performed, in order to underline the degradation of the proposed optimization methods as the size of the problem increases and to define the best method according to the number of objective functions.

A well-planned arrangement of manufacturing departments on a two-dimensional planar region considerably increases the efficiency of its production systems, which is termed facility layout problem (FLP). Conventional layout design approach often designs intercell layout (determining exact location of each department on shop floor area) and flow path layout design of material handling system (MHS) step by step in a sequential manner. This results in suboptimal solutions for FLP. In this paper, an integrated approach is adopted to design the intercell layout and the flow path layout of MHS simultaneously. The quality of the final layout is evaluated by minimizing total material handling cost. Sequence pair (SP) representation is used for layout encoding. The translation from SP to layout is efficiently made by longest common subsequence (LCS) methodology. An elitist strategy genetic algorithm using simulated annealing (E-GASAA) as a local search mechanism is developed and tested with four test problem instances available in the literature. Elitist strategy is incorporated to enhance convergence characteristic of the proposed algorithm. It is found that the proposed E-GASAA is able to produce best solutions consistently for the test problem instance of different sizes within acceptable computational effort. In addition to that, we tried to reduce the computational load with the help of adopted LCS computation methodology and achieved a good improvement.

In this era of globalization, product demand and product mix disparate frequently. In addition, entry of new product and deletion of existing product due to changing market scenarios make the manufacturing environment uncertain. In the uncertain manufacturing environment, a facility layout design must be capable to handle all these changes while keeping minimal material handling cost (MHC) and re-configuration cost. This paper proposed a novel modified simulated annealing (modified SA) approach to solving bi-objective robust stochastic cellular facility layout problem (RSCFLP). The RSCFLP minimizes material handling distance (MHD) and thus MHC and maximizes similarity score for multi-periods and provides a robust layout design considering stochastic demand for multi-periods. The proposed robust layout design suits for all time periods and avoids re-configuration cost. The proposed modified SA is tested using twenty-five data sets with a varying number of machines, products, cells, time periods, and product demand. Lastly, results are compared to show the significance of proposed approach.

Facility layout design problems in flexible manufacturing systems (FMS) differ from traditional facility design problems and are more difficult to solve because there are more constraints that must be considered (i.e., cell shape, cell orientation, pick-up and drop-off point positions). The focus of this paper is on the closed loop type layout, which is based on a predetermined layout pattern. This layout pattern is commonly found in manufacturing settings since it requires a simplified material handling system configuration and since it facilitates a modular and expandable layout structure. The open-field type layout problem, where there is no predetermined layout pattern, may potentially have a more efficient configuration, since there are fewer restrictions. However, this problem is more difficult to solve and may result in configurations that are not desirable due to the lack of structure or modularity. The procedure developed in this paper improves the efficiency of the closed loop configuration by changing the rectangular shape of the loop to different sizes. In many cases, the resulting closed loop layout proves to be as efficient as the open field layout. A simulated annealing procedure (SA-CL) is used to search for the configuration that minimizes the total material handling costs. A comparison of the results with existing methods indicates that, based on solution quality and computational time, the SA-CL offers a favourable alternative for efficient layout design.

Offline optimization of sensor configuration and integration architecture for efficient cooperative navigation

Freight villages reflect a modern way of organizing logistics, transport, and goods distribution activities. Warehouses are a basic element in such building complexes, and their efficient planning and operation is essential for the viability of the system. Warehouse design requires a mixture of analytical skills and creativity. Although effort has been dedicated to defining a global methodological design framework, the most scientific papers on the subject address well-defined, isolated problems. A practitioner may notice, however, that many warehouses offering efficient, cost-effective services are in operation and that several warehouse design manuals exist that tackle aspects of design and operation. Investigating academic and pragmatic points of view, this paper is an attempt to present an overall view of the facility layout and warehouse design problem, and it introduces the analysis and results of the preliminary design phase of a new freight village where issues related to facilities layout, warehouse sizing, and rail connections have been considered.

The most desirable characteristic of a facility layout is its ability to maintain its efficiency over time while coping with the uncertainty in product demand. In the traditional facility layout design method, the facility layout is governed by the flow intensity between departments, which is the product flow quantity between departments. Hence, an error in the product demand assessment can render the layout inefficient with respect to material handling costs. Most of the research integrates uncertainty in the form of probability of occurrence of different from-to charts. In an environment where the variability of each product demand is independent, the derivation of ‘probabilistic from-to chart’ based scenario cannot be used to address uncertainty of individual demands. This paper presents an FLP (facility layout problem) approach to deal with the uncertainty of each product demand in the design of a facility layout. Two procedures are presented: the first procedure is utilised to assess the risk associated with the layout, while the second procedure is used to develop the layout that minimises the risk. Results from case studies have shown that the procedure produces a reduction of risk as high as 68%.

Cellular manufacturing is a lean manufacturing approach that involves grouping machines, processes, and workstations into self-contained cells to reduce material handling, waiting times, and inventory. The layout design of cellular manufacturing facilities has a significant impact on their efficiency, productivity, and throughput. However, traditional trial-and-error methods for facility layout design can be time-consuming, costly, and risky. Digital twin simulation offers a powerful alternative by allowing designers to create virtual models of the facility and test different layout configurations before physically implementing them. Our study shows that digital twin simulation can help identify the optimal layout design that maximizes productivity, minimizes waste, and ensures the smooth flow of materials and products. Additionally, it enables us to evaluate the impact of changes in the production system on the facility's performance, helping decision-makers make informed decisions. Overall, this paper highlights the potential of digital twin simulations as a powerful tool for optimizing cellular manufacturing facility layout design and improving operational performance.

This paper proposes to develop a layout model for a diagnostic clinic in Metro Manila that caters the pre-employment medical examination of applicants from different companies serviced by this diagnostic clinic. The assessment of the current diagnostic clinic focused on the current layouts within Metro Manila in coordination with the patient's perspective. The assessment was done through survey for the three diagnostic clinics for both doctors/staffs and patients using different tools and technique in facilities layout and design to come up with the best possible combination of departments that should be close together and add possible facility that help utilize and maximize space while reducing the queue. The researchers used the systematic layout planning for the design and simulation software (ProModel) to evaluate if the proposed layout design is effective. Moreover, visual representation is needed in order to appreciate the new layout design and this was done using Auto-Cad or Google Sketch-Up. This paper were able to show the design of 3 layouts that cater to a capacity and inflow of 40, 60 and 80 patients in a day.

The research focus is redesigning the facility layout of Piedmont Plywood Industry, Port Harcourt, Rivers State, Nigeria. Optimal approach to productivity and cost to yielding in facility layout design modifications is the trend in this research work. The facility layout modification central idea is the plant layout factor. Plant layout factor deals path modification. That is arrangement of machine, equipment and work stations as close as possible for optimal utilization of available space in the work environment. The productivity index for the current layout or the existing layout is 0.000275m3/Naira. The Alternative 1 modification productivity index is 0.000289m3/Naira and that of Alternative 2 modification is 0.000296m3/Naira. This is an indication of higher rate of return on investment for the two alternative layout modifications. It is interesting to note that the results of the productivity index impacts positively on labor and capital cost of investment in such manner that there is tremendous cut back. The Alternative 1 modification resulted in total cost savings of N1,015,411.00/month while that of Alternative 2 is N1,436,545.00/month. These constitutes tremendous cut back on investments when accumulated over years. The Piedmont Plywood Product Industry and other wood product industries are strongly advised to inject the findings of the alternative layout modifications in the existing factory layout designs.

A modification of the simulated annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulation is proposed. In this modification, the Metropolis criterion depends on whether the objective function values indicate a statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, the proposed method uses a constant temperature. It is shown that the configuration that has been visited most often in the first k iterations converges almost surely to a global optimizer. Computational results and comparisons with other SA algorithms are presented to demonstrate the performance of the proposed SA algorithm.

The Generalized Gamma Distribution (GGD) is one of the most popular distributions in analyzing real lifetime datasets. Estimating the parameters of a high dimensional probability distribution is challenging due to the complexities associated with the resulting objectives function. When traditional estimation techniques fail due to complexity in the model objectives function, other powerful computational approaches are employed. In this study, a Bayesian approach to Generalized Gamma Distribution (GGD) based on Markov Chain Monte-Carlo (MCMC) has been employed to estimate model parameters. This study considers the Bayesian approach to GGD parameters using the Adaptive Rejection Metropolis Sampling (ARMS) technique of random variable generation within the Gibbs sampler. The MCMC approach has been used for estimating the multi-dimensional objectives function distribution. The results of the ARMS were compared to the existing Simulated annealing (SA) algorithm and Method of Moment (MM) based on modified internal rate of return data (MIRR). The performances of various derived estimators were recorded using the Markov chain Monte Carlo simulation technique for different sample sizes. The study reveals that ARMS's performance is marginally better than the existing SA and MA approaches. The efficiency of ARMS does not require a larger sample size as the SA does, in the case of simulated data. The performances of ARMS and SA are similar comparing them to the MM as an initial assumption in the case of real MIRR data. However, ARMS gives an acceptable estimated parameter for the different sample sizes due to its ability to evaluate the conditional distributions easily and sample from them exactly.