Designing a manufacturing cell system by assigning workforce

Purpose: In this paper, we have proposed a new model for designing a Cellular Manufacturing System (CMS) for minimizing the costs regarding a limited number of cells to be formed by assigning workforce. Design/methodology/approach: Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used. A small randomly generated test problem with real-world dimensions has been solved using simulated annealing and particle swarm algorithms. Findings: The quality of the two algorithms has been compared. The results showed that PSO algorithm provides more satisfactory solutions than SA algorithm in designing a CMS under uncertainty demands regarding the workforce allocation. Originality/value: In the most of the previous research, cell production has been considered under certainty production or demand conditions, while in practice production and demand are in a dynamic situations and in the real settings, cell production problems require variables and active constraints for each different time periods to achieve better design, so modeling such a problem in dynamic structure leads to more complexity while getting more applicability. The contribution of this paper is providing a new model by considering dynamic production times and uncertainty demands in designing cells.Peer Reviewe

How to Make Lean Cellular Manufacturing Work? Integrating Human Factors in the Design and Improvement Process

There are three components involved in lean implementation at any institute, and only when considered cooperatively, they can guarantee a sustainable deployment: technical components, human factors, and the organizational elements. In this paper, we propose a comprehensive model of these components to assist managers in transitioning from a traditional manufacturing facility to a cellular lean manufacturing unit. This model can also be employed to enhance the performance of a lean cell. We integrate the mechanical factors including the number of processes, types of tasks, and demand levels with the human behavioral factors including learning, forgetting, and motivation levels, to enhance productivity. The role of organizational and cultural change is also discussed within this transformation process

Designing a manufacturing cell system by assigning workforce

Purpose: In this paper, we have proposed a new model for designing a Cellular Manufacturing System (CMS) for minimizing the costs regarding a limited number of cells to be formed by assigning workforce. Design/methodology/approach: Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used. A small randomly generated test problem with real-world dimensions has been solved using simulated annealing and particle swarm algorithms. Findings: The quality of the two algorithms has been compared. The results showed that PSO algorithm provides more satisfactory solutions than SA algorithm in designing a CMS under uncertainty demands regarding the workforce allocation. Originality/value: In the most of the previous research, cell production has been considered under certainty production or demand conditions, while in practice production and demand are in a dynamic situations and in the real settings, cell production problems require variables and active constraints for each different time periods to achieve better design, so modeling such a problem in dynamic structure leads to more complexity while getting more applicability. The contribution of this paper is providing a new model by considering dynamic production times and uncertainty demands in designing cells.Peer Reviewe

Presentation and Solution of Critical Chain Project Scheduling Problem (CCPSP) model with consideration of feeding buffer

During the recent years, extensive research has been done on the field of project scheduling. There is always uncertainty in the area of project scheduling that causes a deviation in the real plan from the scheduled plan. One of the solutions to deal with this uncertainty is using the critical chain method (CCM) in project scheduling. This method which is derived from the theory of constraints (TOC) is a new method in project control which was first proposed by Goldartt in 1997.In this research we attempt to use the principals of critical chain in resource-constrained project scheduling problem. The main innovation in this research is presentation of critical chain project scheduling problem model with consideration of feeding buffer and using float as a supplement for feeding buffer. For this matter, the project scheduling under resources constraints with critical chain approach was first written and its reliability was evaluated using the Lingo software. In the next step the solution algorithm of this model was developed using the genetic algorithm and finally different sample issues were investigated. The results of this research show the efficiency of the presented genetic algorith

The job rotation scheduling problem considering human cognitive effects: an integrated approach

Purpose: This paper aims to unfold the role that job rotation plays in a lean cell. Unlike many studies, the authors consider heterogeneous operators with dynamic performance factor that is impacted by the assignment and scheduling decisions. The purpose is to derive an understanding of the underlying effects of job rotations on performance metrics in a lean cell. The authors use an optimization framework and an experimental design methodology for sensitivity analysis of the input parameters. Design/methodology/approach: The approach is an integration of three stages. The authors propose a set-based optimization model that considers human behavior parameters. They also solve the problem with two meta-heuristic algorithms and an efficient local search algorithm. Further, the authors run a post-optimality analysis by conducting a design of experiments using the response surface methodology (RSM). Findings: The results of the optimization model reveal that the job rotation schedules and the human cognitive metrics influence the performance of the lean cell. The results of the sensitivity analysis further show that the objective function and the job rotation frequencies are highly sensitive to the other input parameters. Based on the findings from the RSM, the authors derive general rules for the job rotations in a lean cell given the ranges in other input variables. Originality/value: The authors integrate the job rotation scheduling model with human behavioral and cognitive parameters and formulate the problem in a lean cell for the first time in the literature. In addition, they use the RSM for the first time in this context and offer a post-optimality analysis that reveals important information about the impact of the job rotations on the performance of operators and the entire working cell

Balancing, sequencing, and job rotation scheduling of a U-shaped lean cell with dynamic operator performance

Performance of a manufacturing cell is dependent on an efficient layout design, and optimal work schedules. However, the operator-dependent factors such as learning, forgetting, motivation, and boredom, can considerably impact the output of the system. In this study, we consider heterogeneous operators with dynamic performance metrics and integrate the job assignment, and job rotation scheduling problems, with the balancing and production sequencing in a U-shaped lean manufacturing cell. We present a novel multi-period nonlinear mixed-integer model to minimize the deviations from takt time, and the number of operators, in a finite planning horizon. An efficient meta-heuristic approach is developed to solve the problem and the results are compared to a static case where no human factor is included. Our computational results demonstrate that including the operator-dependent metrics can improve the performance of the cell design. We conduct a sensitivity analysis of the scheduling parameters including, rotation frequencies, takt time, cell size, and task types, and derive that the obtained solutions with the static settings, are not sufficient for an efficient lean cell design in the presence of dynamic human factors

Designing GA and ICA approaches to solve an originative job rotation scheduling problem regarding bordem costs

In this paper we develop the concept of boring caused by doing the same jobs to two types of boring, negative or undesirable and positive or desirable, which are felt by operators because of doing similar jobs and not only due to doing the same ones. Based on this new concept, the flexible model has been proposed by which jobs will be scheduled to minimize the total cost of assignment including the cost of doing the jobs by operators and the boring cost so that job scheduled with respect to their similarities in the smallest time period as well as dissimilarities in the biggest given time period. For the reason that the proposed job rotation scheduling model has a multi-­period assignment structure and formulated as an integer non-linear model, it is recognized as a combinatorial optimization problem. So applying the metaheuristic algorithms to overcome the complexity of such a problem is required. We use the genetic and imperialist competitive algorithms to do that and verify their efficiency in comparison to that of Lingo software which solves the small integer nonlinear problems. It is also shown that the quality of imperialist competitive algorithm solutions is better than those of genetic algorithm for the proposed model

GENETIC ALGORITHM FOR OBSTACLE LOCATION-ALLOCATION PROBLEMS WITH CUSTOMER

In this paper we propose a metaheuristic approach to solve a customer priority based location-allocation problem in presence of obstacles and location-dependent supplier capacities. In many network optimization problems presence of obstacles prohibits feasibility of a regular network design. This includes a wide range of applications including disaster relief and pandemic disease containment problems in healthcare management. We focus on this application since fast and efficient allocation of suppliers to demand nodes is a critical process that impacts the results of the containment strategy. In this study, we propose an integrated mixed-integer program with location-based capacity decisions that considers customer priorities in the network design. We propose an efficient multi-stage genetic algorithm that solves the problem in continuous space. The computational findings show the best allocation strategies derived from proposed algorithms