Multi-objective group scheduling with learning effect in the cellular manufacturing system

An Immune Genetic algorithm for inter-cell layout problem in cellular manufacturing system

A simulated annealing algorithm for facility layout problems under variable demand in Cellular Manufacturing Systems

In this paper, a new mathematical model is proposed for inter- and intra-cell layout problem in cellular manufacturing system. A binary particle swarm optimization algorithm with a new heuristic approach for satisfying the constraints of model is implemented to solve the proposed model. Also, a variable neighborhood search is applied for finding better local optimal solutions. Some sensitivity analyses are carried out to find the best values of PSO parameters. To prove the efficiency of the proposed algorithm, various numerical examples in small, medium, and large sizes in both perfect geometric shapes and random placements are generated and solved.

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

Batch scheduling is a well-known topic that has been studied widely with various objectives, methods, and circumstances. Unfortunately, batch scheduling in a collaborative flow shop system is still unexplored. All studies about batch scheduling that are found were in a single flow shop system where all arriving jobs come from single door. In a collaborative flow shop system, every flow shop handles its own customers although joint production among flow shops to improve efficiency is possible. This work aims to develop a novel batch scheduling model for a collaborative multi-product flow shop system. Its objective is to minimize make-span and total production cost. This model is developed by using non-dominated sorting genetic algorithm (NSGA II) which is proven in many multi objective optimization models. This model is then compared with the non-collaborative models which use NSGA II and adjacent pairwise interchange algorithm. Due to the simulation result, the proposed model performs better than the existing models in minimizing the make-span and total production cost. The make-span of the proposed model is 10 to 17 percent lower than the existing non-collaborative models. The total production cost of the proposed model is 0.3 to 3.5 percent lower than the existing non-collaborative models.

The layout problem is one of important decision in cellular manufacturing systems (CMS). After deciding the cells and the intercellular movements in the cell formation stage, it is necessary to do proper layout of machines to get effective utilization of CMS. Proper layout will minimize material handling cost to a greater extent. In this paper, a mathematical formulation is presented. As it is NP-hard, a genetic algorithm is adopted as solution methodology. In order to validate the performance of the algorithm a well-known quadratic assignment problems (QAP) that deal with location are taken for testing, which shows that the proposed methodology provides promising results. The effectiveness of GA approach is evaluated with numerical examples. The cost performance is compared with other approaches. The intra cell layout problem is discussed.

This paper presents an integrated fuzzy data envelopment analysis (FDEA) and fuzzy computer simulation approach for optimization of operator allocation in multi product cellular manufacturing systems (CMS) with learning effects. Operator allocation with learning effects is a challenging issue in flexible manufacturing systems in general and in CMS in particular. The main contribution of this work is taking into consideration various operators layouts and learning effects using fuzzy simulation and fuzzy DEA. FDEA is utilized to assess simulation alternatives in various levels of uncertainty. Previous studies consider only one type of product with crisp inputs, whereas this study considers multi-products and fuzzy set up times and processing times for CMS modeling. In addition, this study considers and integrates learning effects for optimum operators’ allocation. Moreover, more robust CMS assessment indicators are used in the proposed model. A case study illustrates the practicability, effectiveness and superiority of the proposed methodology.

This paper presents a new strategy addressed as “service level-based smooth production strategy” for the cell formation decisions of a robust cellular manufacturing system (CMS) to handle varying demands. In this strategy, a service level concept is adapted to fix smooth production level estimated to meet the desired demand satisfaction rate (DSR) or fill rate of the customers. Besides, the model considers cross-flow and scheduling aspects, which are not often addressed in CMS designs. A non-linear integer program is formulated to evolve CMS decisions for the specified DSR under fluctuating demands. Two solution methodologies of mathematical programming using ILOG CPLEX solver and genetic algorithm (GA) with adaptive mutation and crossover operators addressed in as adaptive genetic algorithm (AGA) are proposed. The model analysis carried out under various DSR reveals that the cell formation decisions are highly sensitive to DSR. This paper also discusses how this model can be used to compare robust and reconfigurable approaches to decide the suitable approach to handle fluctuating demands with minimum cost. The performance study of solution methodologies reveals that CPLEX and AGA are efficient in finding optimal solutions to smaller-size problems. However, AGA is comparatively efficient for larger-size problems with a reasonable computational time.

A vibration damping optimization algorithm for the integrated problem of cell formation, cellular scheduling, and intercellular layout

In the competitive shoe manufacturing industry, efficient production scheduling often struggles to balance minimizing makespan (MS) and reducing the number of cells, leading to increased complexity. This study introduces a biobjective optimization model to minimize the MS and the number of cells in a cellular manufacturing (CM) system. The model includes three phases: (1) a capacity phase calculates the required number of cells to meet the production demand within a suitable production rate; (2) a scheduling phase calculates the start and finish times for each product within a machine; (3) an optimization phase uses genetic algorithm (GA) for generating a set of optimal solutions considering the workforce distribution and product sequencing. The performance of the developed model was evaluated using a case study discussed in the literature to demonstrate its capabilities and highlight its superiority over similar models. These results showed a significant reduction of 0.4% in MS while maintaining the same number of cells compared to other models. A sensitivity analysis is conducted by varying the crossover and mutation probabilities. The findings indicate that the model achieves the most stable and consistent performance for paired crossover–mutation settings with crossover probabilities between 70% and 90% (corresponding to mutation probabilities between 30% and 10%), resulting in the lowest average MS and the most balanced cell utilization. The capabilities of the developed model provide production managers with reliable insights for identifying the production requirements of the shoe manufacturing company. The algorithm was also benchmarked on 12 published instances using 30 independent runs per instance to assess solution quality and stability.

In this paper, cell loading and shipment method optimisation problem in a cellular manufacturing system are studied. A hierarchical methodology that consists of mathematical optimisation model, genetic algorithms (GAs) and artificial neural networks (ANNs) were proposed. The mathematical model is compared with the GA in terms of the optimisation performance. Next, ANN model was developed as decision support tool to study the impact of GA parameters on the solution quality. Several problem sizes were experimented with the proposed GA and the mathematical model, and compared. GA was run to make a total of 648 sample solutions for the 20-job problem. Next, ANN model was built based on the sample solutions' data and the optimal ANN model is identified out of 1,000 networks. The results were also coupled with sensitivity and statistical analyses, which indicated that type of crossover and mutation operators, had the greatest impact on the solution quality.

The current research seeks to present a proposed model for the design of a dynamic cellular manufacturing system in the Light Industries Company/Ashtar in Baghdad, one of the Iraqi industrial companies. With the aim of generating an industrial environment capable of accommodating rapid changes in the market to meet the changing and diverse needs and desires of the customer at the lowest possible cost, at the right time and the required quality. To achieve the current research relied on the methods of artificial intelligence, employing them and benefiting from them in the industrial field, especially the genetic algorithm. Accordingly, the current research included a theoretical framework for the dynamic cellular manufacturing system and the genetic algorithm, in light of what was mentioned in the Arab and foreign sources related to them. The research also adopted a field framework to design this system in the researched company using the genetic algorithm, and the researchers chose the freezers and models labs as a field for study, and in light of the results of the programming language program (MATLAB). It was reached to build a proposed model that reflects the virtual reality of this system. On the basis of that, the research reached a set of conclusions, the most important of which is the genetic algorithm that contributed significantly and effectively to finding the optimal cellular path for the dynamic cellular manufacturing system and reducing both the total time and the total cost of production. In light of this a set of proposals were presented showing the importance of shifting from the existing cellular manufacturing system in the field of study to the dynamic cellular manufacturing system.

A project scheduling problem investigates a set of activities that have to be scheduled due to precedence priority and resource constraints in order to optimize project-related objective functions. This paper focuses on the multi-mode project scheduling problem concerning resource constraints (MRCPSP). Resource allocation and leveling, renewable and non-renewable resources, and time-cost trade-off are some essential characteristics which are considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid algorithm is proposed based on non-dominated sorting ant colony optimization and genetic algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to improve the efficiency of the ant colony algorithm. The test problems are generated based on the project scheduling problem library (PSPLIB) to compare the efficiency of the proposed algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result verifies the efficiency of the proposed hybrid algorithm in comparison to the NSGA-II algorithm.

This paper presents a new integrated mathematical model for a cellular manufacturing system and production planning. The aim of this model is to minimize machine purchasing, intra-cell material handling, cell reconfiguration and setup costs. The presented model forms the manufacturing cells and determines the quantity of machines and movements at each period of time that minimizes the aforementioned costs. It is so difficult to find an optimal solution in a reasonable time. Thus, we design and develop a meta-heuristic algorithm based on a genetic algorithm (GA). This proposed algorithm is evaluated, and the related results confirm the efficiency and effectiveness of our proposed GA to provide good solutions, especially for medium and large-sized problems.

Design of layouts for Cellular Manufacturing Systems (CMSs) has not received attention by researchers as much as cell formation in cellular manufacturing. In this paper a mathematical model, formulated as a multicriteria Quadratic Assignment Problem (QAP) is proposed for the design of layouts for CMSs with the objective of minimising the material handling cost (quantitative) and maximising the closeness rating (qualitative). A Genetic Algorithm (GA)-based tool called FUGEN, which makes use of crossover, mutation (inversion) and direct entry (elitism) with embedded fuzzy logic and Analytic Hierarchy Process (AHP) models, is developed for the design of layouts for CMSs. An attempt is made to make the model practical by considering the production volume, transfer batch size and operational sequence of parts. The proposed tool is validated using an example from the literature.