Selection of a pull production control system in multi-stage production processes

In this paper, we conduct an analytical comparison of three pull production control systems: Kanban, CONWIP and Base-stock in multi-stage production processes. First, we compare the three control systems in a multi-stage serial production process. Then, we compare them in multi-stage assembly production processes, and present guidelines that allow us to select the best system. As a result, we show which structural parameters decide the superiority of one control scheme to the others, and how they are related. A key for superiority is a configuration of parameters, such as processing times and number of cards employed in the system. We show that there is no general superiority amongst the analysed concepts. Finally, we verify the effect of variability on the system performance, and generalise the analytical results of deterministic cases by conducting numerical experiments.     

Economic batch quantity in a multi-stage production system

A mathematical model has been developed in this paper for determining the total annual variable cost for a product which requires processing on a number of production stages. The demand for the product is assumed uniform over time and it is manufactured in equal lot sizes. In order to minimize the manufacturing cycle time of o production lot, the movement of the items between production stages is in sub-batches of equal sizes, As a result of manufacturing a production lot in sub-batches, the following additional costs are incurred: (1) additional cost of stock holding for process inventory; (2) cost of transporting sub-batches; (3) cost of multiple set-ups. A simple method is then adopted for minimizing the total annual variable cost of the multistage production system. An example has been solved to illustrate the method.     

Batch sizing with controllable production rates in a multi-stage production system

In this paper, a comprehensive model is presented for cell formation and layout design in cellular manufacturing systems (CMS). The proposed model incorporates an extensive coverage of important operational features and especially layout design aspects to determine optimal cell configuration and Intra and Inter-cell layout in CMS. Hence, proposed integrated approach attempts to design intra and inter-cell layout and material handling flow path structure simultaneously. We examine the great potential benefits of providing these features consist of routing flexibility, operation sequence, machine capacity, considering number of cells as a decision variable, un-equal dimension of machines, free machines and cells orientation, and considering pickup and drop off station for each cell. In order to show the effects and important of integrated design in the CMS, two approaches, sequentially and integrated, have been investigated and demonstrate the integrated approach improve the quality of obtained solution. The proposed model is a mixed integer non-linear programme. Linearisation procedures are proposed to transfer it into a linearised mixed integer programming formulation. Computational results are presented with the linearised formulation. We presented several enhancements in terms of valid inequalities and extensions to the proposed model in order to improve its computational performance. Finally, concluding remarks are provided.     

On the choice of a production control system

This paper deals with a problem to develop ‘factory automation systems’ which harmonize companies’ needs with workers’ desires. Two kinds of indices and methods of survey are proposed on the basis of the ‘fusion system concept’. One is that of the state of workers’ desires which are based on the ‘stimulus-response model’, the other is that of situations where ‘factory automation’ is implemented in a workshop. The effects of ‘factory automation’ on workers’ desires are clarified in a study of Japanese manufacturing industries in 1983. As a result of this investigation, some problems can be pointed out which should be solved in the future to develop ‘factory automation systems’.     

Lot-sizing in a multi-stage flow line production system with energy consideration

In this paper, a single-item capacitated lot-sizing problem in a flow-shop system with energy consideration is studied. The planning horizon is defined by a set of periods where each one is characterised by a length, an allowed maximal power, an electricity price, a power price and a demand. The objective is to determine the quantities to be produced by each machine at each period while minimising the production cost in terms of electrical, inventory, set-up and power required costs. For medium- and large-scale problems, lot-sizing problems are hard to solve. Therefore, in this study, two heuristics are developed to solve this problem in a reasonable time. To evaluate the performances of these heuristics, computational experiments are presented and numerical results are discussed and analysed.     

A bi-objective robust inspection planning model in a multi-stage serial production system

In this paper, we present a bi-objective mixed-integer linear programming (BOMILP) model for planning an inspection process used to detect nonconforming products and malfunctioning processors in a multi-stage serial production system. The model involves two inter-related decisions: (1) which quality characteristics need what kind of inspections (i.e. which-what decision) and (2) when the inspection of these characteristics should be performed (i.e. when decision). These decisions require a trade-off between the cost of manufacturing (i.e. production, inspection and scrap costs) and the customer satisfaction. Due to inevitable variations in manufacturing systems, a global robust BOMILP (RBOMILP) is developed to tackle the inherent uncertainty of the concerned parameters (i.e. production and inspection times, errors type I and II, misadjustment and dispersion of the process). In order to optimally solve the presented RBOMILP model, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is combined with the Taguchi and Monte Carlo methods. The proposed model and solution algorithm are validated through a real industrial case from a leading automotive industry in France.     

Feedback method of production ordering system in multi-stage production and inventory systems

This paper deals with the problem of developing a production ordering system which maintains high reliability and productivity of multi-stage production and inventory systems with capacity-constraint and machine breakdown. In this paper, two ‘push type’ production ordering systems (Matsuura and Soshiroda 1981) are proposed according to information feedback methods. The influence of forecasting error, the difference between forecasted demands, down-time, and the backlog caused by the restriction of production capacity, upon the production and inventory levels is analysed by each production ordering system. The results indicate that, in designing an effective production ordering system, a feedback method must be chosen according to the conditions of the average loading ratio, the down-time, and the autocorrelation coefficient of demand.     

Protective stocks in multi-stage production systems

Uncertainty in MRP systems does exist in several forms, variability in demand from period to period, uncertainty in the supply from stage to stage due to the variability in the yields from each production batch, and uncertainty in the lead times. The goals of the paper are to develop theoretical models for determining optimal decisions in this uncertain environment, to develop a computationally tractable heuristic and to examine how safety stock and safety time can arise. Finally, our methodology is compared with MRP practice.     

Route selection and flow control in a multi-stage manufacturing system with heterogeneous machines within stages

With increasing market competition and the need to serve diverse market segments, many companies are responding to customer needs by manufacturing parts in small batches. Unless properly planned, batch production can lead to high material handling time. In this paper, a methodology for selecting the best routeing for a batch in a multi-stage production system with material handling considerations is presented. Each stage consists of multiple, functionally identical machines that have non-identical characteristics. The model determines the optimal routeing for the batch and generates the number of parts to handle or transport at a time as a unit load between machines in order to minimize total batch manufacturing time. The result from the model provides the optimal routeing (i.e., the machine to use at each production stage) and the number of parts to move at a time as a sub-lot. The solution procedure developed is easy to use and can be incorporated as a daily shop management tool.     

On the stability and bullwhip effect of a production and inventory control system

This paper focuses on the discrete-time automatic pipeline, inventory and order-based production control system (APIOBPCS), a well-established production and inventory control model. The feedback mechanism within the replenishment rule enables the model to mitigate the bullwhip effect, but introduces a stability problem. In this research, a comprehensive stability analysis is conducted for arbitrary lead times using difference equation theory. On the basis of stability, a state space approach is advocated to analyse the impact of replenishment parameters, demand processes, and lead times on the robustness of the bullwhip effect. The stability results demonstrate that the production control system can easily be destabilised without incorporating the work-in-progress (WIP) feedback loop. Furthermore, it reveals that the stability problem for long lead times can be simplified with the stability condition independent of the lead time. The results obtained in this study provide useful guidelines for the selection of replenishment parameters to guarantee stability and mitigate the bullwhip effect.     

Integrated maintenance and production control of a deteriorating production system

We consider a make-to-stock production/inventory system consisting of a single deteriorating machine which produces a single item. We formulate the integrated decisions of maintenance and production using a Markov Decision Process. The optimal dynamic policy is shown to have a rather complex structure which leads us to consider more implementable policies. We present a double-threshold policy and derive exact and approximate methods for evaluating the performance of this policy and computing its optimal parameters. A detailed numerical study demonstrates that the proposed policy and our approximate method for computing its parameters perform extremely well. Finally, we show that policies which do not address maintenance and production control decisions in an integrated manner can perform rather badly.     

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.     

The effects of routing flexibility in a multi-stage pull-type system

The pull-type manufacturing system is known for its need for a frozen schedule, balanced work load and inability to handle fluctuations in demand. But today research and development are addressing ways to handle a pull-type system to accommodate greater fluctuations. One approach is to design the system with machines that have a greater routing flexibility in what work they can perform. However, most of the existing literature dealing with routing flexibility has primarily been devoted to the push-type production systems. This research examined the effects of routing flexibility for a multi-stage kanban-controlled pull-type system.     

Investigating control strategies for a domestic low-temperature heat pump heating system

Despite energy conservation regulations and efforts for improving HVAC operations, numerous domestic buildings do not perform energy efficiently and many times the indoor environment is far away from specified comfort levels. Especially in houses served from low-temperature heating systems the low ability of the heating system to respond to fast changing thermal loads is common. In such cases, the implementation of new, sophisticated controls is an important issue. In this study, we use a reference model of a domestic low temperature heat pump heating system developed in TRNSYS–EES and analyse its operation. Several methods of control strategies have been applied for specified time periods in order to keep the comfort within reasonable ranges. Prognostic climatic control and increased ventilation rates when required are some of these methods. The results depict the influence of the control method on the indoor temperature and the comfort indexes of PMV and PPD. The highest indoor temperature difference for a chosen day reaches 4 °C when there is no shading and when there is internal shading with the option of applying prognostic climatic control. Generally, the findings highlight the importance of dynamics in controlling functions and the difficulty of incorporating in models unpredictable factors as the solar radiation.     

Production control in a complex production system using approximate dynamic programming

Development of an efficient production and inventory control policy for a production system with multiple working stations, intermediate components and end products is difficult. In particular, uncertain demand and large changeover times at the work stations cause significant problems. In this paper, we consider an assembly line for dishwashers which require multiple types of wire racks that must be fabricated and coated at different work centres before supplying the assembly lines. An approximate dynamic programming (ADP) method is proposed to address the complexities associated with such a system. In addition, an Artificial Neural Network model is designed to approximate state values of the system, thus helping the system to make decisions at particular states. A near optimal production and inventory control policy is developed through an ADP algorithm. The proposed method can be extended to any similar system.     

Economic acceptance sampling plans in complex multi-stage production systems

Specification of economic acceptance sampling schemes in complex multi-stage production systems is considered. The analysis is based on approximations of the distribution of lot fractions defective at all production stations of the system. A heuristic algorithm is developed that combines individual optimal solutions to subproblems corresponding to the production stations, to arrive at a near-optimal solution for the original problem. Numerical examples illustrate the application of the method to serial and non-serial manufacturing-assembly processes.     

Operating an FMC by a decision-tree-based adaptive production control system

In previous works decision tree mechanisms were suggested for real-time production control of flexible manufacturing systems operating in a constant mix environment. The current paper tests the performance of a decision-treebased adaptive production control system operating in a flexible manufacturing cell in more random environments. A decision-tree-based production control system, which periodically chooses, on the basis of the current state of the system, the most appropriate scheduling rule, out of several predetermined ones, is presented. The performance of the decision-tree-based production control system was tested by simulating a flexible manufacturing cell operating in two different operational environments, distinct by the variability of their demand. The decision tree-based-production control system has performed rather well in the produce-to-order environment. However, it does not outperform the alternative scheduling rules in a highly random produce-to-order environment.     

Algorithms for the design of a multi-stage adaptive kanban system

The traditional kanban system with a fixed number of cards does not work satisfactorily in an unstable environment. With the adaptive kanban-type pull control mechanism, the number of kanbans is allowed to change with respect to the inventory and backorder level. It is required to set the threshold values at which cards are added or deleted, which is part of the design. Previous studies used local search and meta-heuristic methods to design an adaptive kanban system for a single stage. In a multi-stage system the cards are circulated within the stage and their presence at designated positions signals to the neighbouring stages details concerning the inventory. In this work, a model of a multi-stage system adapted from a traditional and adaptive kanban system is developed. A genetic and simulated annealing algorithm based search is employed to set the parameters of the system. The results are compared with a traditional kanban system and signs of improvement are found. The numerical results also indicate that the use of a simulated annealing algorithm produces a better solution.     

Design of product specifications and control policies in a single-stage production system

Screening (100%-inspection) is a common practice in quality engineering. Traditional screening procedures seek upper and lower specification limits for a quality characteristic associated with some product, such that the total quality cost per item is minimized. This practice ignores the effect of parts rejection on product lead times, inventory carrying costs, shortage costs, throughput, and, eventually, total profit rate of the system. In this paper, a single-stage production system is considered and two control policies arc determined jointly with the inspection plans. Numerical experiments indicate that such co-ordinated policies achieve a better performance than independently determined quality and production control policies     

Production scheduling and sequencing for multi-stage production systems

Summary This paper focuses on multistage, multiproduct dynamic production systems. Up to now little work has been done on such realistic problems, the reasons being that multiproduct models are subject to capacity considerations and to the general sequencing problem. Optimal and heuristic solution methods are presented for this combined sequencing/scheduling problem. Our computational experience indicates that the proposed heuristic is efficient and differs marginally from the optimal solution.

---

Zusammenfassung In diesem Beitrag werden mehrstufige Mehrprodukt-Produktionssysteme mit dynamischem Zeitablauf betrachtet. Da die entsprechenden Modelle es erfordern, gleichzeitig Seriengröße und -folge zu bestimmen derart, daß ein zulässiger Produktionsplan erhalten wird, hat man sich bisher wenig mit solchen realistischen Problemen beschäftigt. Optimale und heuristische Lösungsverfahren für dieses kombinierte Entscheidungsproblem werden vorgestellt. Die Rechenerfahrungen deuten darauf hin, daß das vorgeschlagene heuristische Lösungsverfahren effektiv ist und die erreichten Lösungen sich von einer Optimallösung nur wenig unterscheiden.