Zhao Xiaobo

A stochastic model of a reconfigurable manufacturing system Part 1: A framework

Products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) manages to satisfy customers, with each family corresponding to one configuration of the RMS. Then the products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system possesses the reconfigurable function for different families. In an RMS there are three important issues: the optimal configurations in the design, the optimal selection policy in the utilization, and the performance measure in the improvement. This paper proposes a framework for a stochastic model of an RMS, which involves the above issues. Two optimization problems and the performance measure stemmed from the issues are formulated. An example is given for illustration. Some discussions are presented for future research work.

Algorithms for sequencing mixed models on an assembly line in a JIT production system

In many mixed-model assembly lines in just-in-time (JIT) production systems, workers have the power and the responsibility to stop the conveyor whenever they fail to complete their operations within their work zones. Therefore, the conveyor stoppage in a sequencing problem should be taken into consideration. In this paper, two algorithms are proposed for finding an optimal or sub-optimal sequence of mixed models that minimizes the total conveyor stoppage time. The branch-and-bound method is devoted to find an optimal solution for small sized problems, while the simulated annealing method is used to cope with large scale problems to obtain a good sub-optimal solution. A numerical example of a 14 model problem shows that the simulated annealing algorithm is about 100 times faster than the branch-and-bound algorithm to find an optimal solution.

Properties of a sequencing problem for a mixed model assembly line with conveyor stoppages

Abstract The concept of Autonomation in the Toyota production system admits workers to stop the conveyor whenever they fail to complete the operations within their work stations in a mixed-model assembly line. Therefore, the conveyor stoppage becomes a crucial criterion in sequencing problems for mixed-model assembly lines in the Toyota production system. In this paper, we consider a goal of minimizing a total conveyor stoppage time. A sequencing problem with this goal is formulated. Several useful properties, such as, the computational complexity of the sequencing problem, necessary and sufficient conditions that a conveyor stoppage occurs, lower and upper bounds of the objective function, and sufficient conditions for the optimality of a solution, are characterized. Based on the properties, a heuristic algorithm is designed. A numerical example is given to illustrate the methodology.

A note on Toyota's goal of sequencing mixed models on an assembly line

Abstract Toyotas goal of sequencing mixed models on an assembly line is to keep the constant usage of every part used in the assembly line. This goal is a good way of fitting the just-in-time concept in Toyota’s production system. In all of Toyotas goal oriented studies a consideration which has not been explained explicitly in the literature is that all the parts of a given product are assumed to be used at the epoch of just this unit into the assembly line. This treatment is equivalent to an assumption of a single workstation assembly line with zero length. For an assembly line with multiple workstations, however, it is clear that the parts of a given product are used at different epochs subsequent to originally feeding this unit into it. This note discusses Toyotas goal of sequencing mixed models on an assembly line with multiple workstations. The sequencing problem is formulated based on defining the ideal usage rate of a part as the requirement for the part per time period. A modified goal chasing algorithm is proposed for solving this sequencing problem. An example is given to illustrate the methodology.

A stochastic model of a reconfigurable manufacturing system Part 2: Optimal configurations

Various products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) manages to satisfy customers, with each family corresponding to one configuration of the RMS. Then, the products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system possesses the reconfigurable function for different families. In the design period of a RMS, there may exist several feasible configurations for each family. Then, an important issue in a RMS is the optimal configurations for the families. Based on a stochastic model, an optimization problem stemmed from the issue is formulated. Two algorithms are devised to solve the optimization problem. Numerical examples are presented for evaluating the efficiency of the algorithms.

A stochastic model of a reconfigurable manufacturing system Part 3: Optimal selection policy

Products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) satisfies customer requirements by ensuring that each family corresponds to one configuration of the RMS. Products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system is reconfigurable for different families. To utilize the RMS, a selection policy that is an action rule is needed, by which the manufacturer selects a family to produce ordered products belonging to the selected family. Thus, an important issue for an RMS is the optimal selection policy. Based on a stochastic model, an optimization problem stemmed from the issue is formulated. Two solution procedures are devised to solve the optimization problem. Numerical examples are presented for evaluating the efficiency of the algorithms.

A sequencing problem for a mixed-model assembly line in a JIT production system

Abstract In an assembly line of a just-in-time (JIT) production system, workers have the power and the responsibility to stop the line when they fail to complete their operations within their work zones. This paper deals with a sequencing problem for the mixed-model assembly conveyor line in the JIT production system. In some environment, the most important criterion is the line stoppage rather than the variation of production rates. The problem is to find an optimal sequence of units that minimizes the total line stoppage time. Lower and upper bounds of the total line stoppage time are derived and the branch-and-bound method is applied to the problem. A numerical example is given.

A stochastic model of a reconfigurable manufacturing system - Part 4: Performance measure

Various products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) manages to satisfy customers, with each family corresponding to one configuration of the RMS. Then, the products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system possesses the reconfigurable function for different families. A performance measure is defined as service levels for the families. A semi-Markov process is formulated for obtaining the performance measure. When a larger fluctuation in the market happens, the manufacturer can adjust the system to improve the performance measure. An optimization of a reassigning problem is discussed, which reassigns the maximum numbers of orders to the families. Two solution approaches are proposed to solve the problem. Numerical examples are given for illustrating the methodologies.

Modeling and analysis of a supply-assembly-store chain

Abstract We consider a supply–assembly–store chain with produce-to-stock strategy, which comprises a set of component suppliers, a mixed-model assembly line with a constantly moving conveyor linking a set of workstations in series, and a set of product storehouses. Each supplier provides components of a specified family, which are assembled at a corresponding workstation. Units belonging to different models of products are sequentially fed onto the conveyor, and pass through the workstations to generate finished products. Each storehouse stores finished products belonging to a specific model for satisfying customer demands. The suppliers deliver components according to a just-in-time supply policy with stochastic leadtimes. Customer demands for a particular model of products arrive at the corresponding storehouse according to a Poisson stream. The paper conducts a modeling and performance analysis in the design stage of the system in the sense of “long-term-behavior”. A rolling technique is constructed for analyzing stationary probability distributions of the numbers of components. A two-dimensional Markov chain with infinite states is introduced for analyzing stationary probability distributions of inventories of finished products. Based on these distributions, performance measures of the system, such as work-in-process of components, inventory amounts of finished products, as well as service levels for customers, can be easily obtained. Managerial insights are obtained from both analytical and numerical results.

On control strategies in a multi-stage production system

A multi-stage production system is composed of a set of stations, each station performing a given task, and a set of vehicles, each vehicle moving between two successive stations. A station can choose a buffer or a kanban mechanism for controlling the work-in-process (WIP) in the station. A vehicle can choose a push or a pull policy for carrying parts from its upstream station to its downstream station. A control strategy is formed by combining the WIP mechanisms adopted in all stations and the carrying policies employed by all vehicles. The production system is modelled as a queuing system. Some structural properties of performance measures are characterized. We develop a decomposition approach for large systems, which performs very well. We determine the optimal numbers of buffers or kanbans at all stations in the design period, and the optimal control strategy during operation. Many numerical computations are given for evaluating the efficiencies of the decomposition approach and optimization methods, and further providing some intuitions and insights.