Multi-product Manufacturing System Research Articles

A closed-loop model of a multi-station and multi-product manufacturing system using bond graphs and hybrid controllers

Production plans and production schedules are fundamentally dynamic and may be disturbed by several events. In the manufacturing research domain, however, dynamic models for scheduling and production control usually receive less attention as static models. In this paper, a bond graph model for depicting a multi-product production system with job shop configuration is proposed. A case study based on a real production system is presented to illustrate the modeling process. The state model derived from the pictorial representation (i.e., derived from the bond graphs) is simulated, in order to observe the dynamic response of the system. Also, a hybrid proportional controller (HPC) and a hybrid adaptive proportional controller (HAPC) are proposed. In this sense, this research extends the findings of a previous work reported in the literature, in which constant and proportional controllers were tested. The results demonstrated that the HAPC and the HPC outperforms the mentioned controllers, and that the bond graphs are a viable methodology to represent and study the dynamics of manufacturing systems. This approach is innovative since no other closed-loop model based on bond graphs for multiple products has been previously reported in the literature, nor its combination with a hybrid adaptive controller.

Economic and environmental value stream map (E2VSM) simulation for multi-product manufacturing systems

Value stream mapping (VSM) is a well-accepted tool within lean manufacturing concept which is often used for analysing and designing the flow of materials and information required to manufacture a product. However, the analysis is static and single product oriented, which fails to cope with either the variation of production plan or a multi-product environment. In addition, the environmental impact of a manufacturing system is highly associated with the dynamic consumption of energy and resources. Despite the recent integration of VSM with simulation or environmental studies (in the domain of energy efficiency), still neglected is the dynamic assessment of all the resources involved in a multi-product production environment. This paper presents a methodology for modelling multi-product manufacturing systems with dynamic material, energy and information flows with the aim to generate economic and environmental value stream maps (E2VSM). The proposed methodology is validated with an industrial case.

Analysis and Improvement of Multiproduct Bernoulli Serial Lines: Theory and Application

This paper is devoted to the performance analysis and continuous improvement of multiproduct manufacturing systems. First, a Bernoulli model of multiproduct serial line with unreliable machines and finite buffers is introduced. In such a model, each machine is capable of processing multiple product types, and each buffer is shared for all products. Closed formulas have been derived to evaluate the production rate of the line with one or two machines, and recursive procedures are used to analyze longer lines. Numerical studies indicate that such a method has a high precision in performance evaluation. The system-theoretic properties, such as asymptotic property, monotonicity, and reversibility, have been investigated. Second, in order to improve system performance, bottleneck (BN) analysis has been carried out to identify the machine and product whose improvement will lead to the largest improvement in the system production rate. Various types of BNs have been defined, and the BN indicators, based on the data collected on the factory floor, are proposed to identify the BNs without the complicated calculations of the production rate and its sensitivities. Numerical experiments have justified the practical usefulness of such indicators for BN identification in multiproduct manufacturing systems. Finally, a case study is introduced to illustrate the applicability of the model and the method.

A comparison of HK-CONWIP and BK-CONWIP control strategies in a multi-product manufacturing system

This paper evaluates the performance of the Hybrid Kanban Constant Work-In-Process control strategy and Basestock Kanban Constant Work-In-Process control strategy operating Shared Kanban Allocation Policy (S-KAP) and Dedicated Kanban Allocation Policy (D-KAP) in a multi-product serial flow line. We explored the effect of an increase of product types on the WIP inventory in the system. A simulation-based optimisation technique was used in determining the optimal settings for the strategies. The strategies were compared via pairwise comparison technique and Nelson’s ranking and selection procedure. S-KAP responds quicker to demand than D-KAP. BK-CONWIP outperforms HK-CONWIP in a serial manufacturing system. It was shown that an increase in the number of product-type increases the number of PAC and WIP inventory.

Effect of product mix on multi-product pull control

Product mix influences the performance of pull production control strategy in multi-product manufacturing systems. The complexity of product mix on the performance of a manufacturing system is primarily based on the characteristics of the demand and production control strategies. Demands are mainly characterised by volume and product-type while production control strategy is characterised by material release time, part flow, inventory control and throughput times. In multi-product systems, pull production control strategy operates dedicated or shared Kanban allocation policy. This paper examines the performance of the Generalised Kanban Control Strategy (GKCS), Extended Kanban Control Strategy (EKCS) and Basestock Kanban-CONWIP (BK-CONWIP) control strategy operating Shared Kanban Allocation Policies (S-KAP) or Dedicated Kanban Allocation Policies (D-KAP) for a healthcare parallel/serial assembly line with setup times. A simulation based multi-objective optimisation technique was adopted to examine the effect of different product mixes on the strategies and policies. A ranking and selection technique for multiple systems was used to screen the performance of the strategies. It was shown that product mix variability in a system influence the inventory levels of the pull control strategies examined. However, the performances of the strategies vary with strategies operating S-KAP having better inventory control than strategies operating D-KAP. Similarly, BK-CONWIP outperformed its alternatives.

Modeling the dynamics of a multi-product manufacturing system: A real case application

Abstract In this paper, a continuous multi-product model is developed to represent the shop floor dynamics of a job shop, based on dynamic modeling and on analogies to electrical components. This approach allows the mathematical formulation of the model (state representation) and the analysis of its dynamic response via simulation. A real case application in the textile industry is presented. Thus, this research contributes in the following ways: first, proposing a model that is suitable for multi-product systems with intricate job shop configuration and that is generalizable to various manufacturing systems; second, presenting a real case application of the proposed model. As practical implications, it provides production managers and practitioners with a prescriptive decision model that considers the dynamics of the production systems and the interdependencies of the decisions made in the shop floor. From the academic perspective, it contributes to the existing literature by presenting the application of an alternative modeling methodology, and by extending this methodology to manufacturing systems with multiple products, instead of single-product systems. Continuous models such as the one proposed can benefit from a wide range of tools for system analysis and control design, come from control theory. Although these tools have been extensively applied to model the supply chain, applications devoted to the plant level seem to be neglected over the past years. This model also aims to contribute in this direction.

Applying Bond Graphs for Modelling the Manufacturing Dynamics

Supply chain dynamics and execution control seem not to receive as much attention as the domain of supply chain planning by means of static optimization models. The dynamics of manufacturing systems also deserves attention, since they are the fundamental pieces of a supply chain. In this paper, a dynamic model for the production control of a multi-product manufacturing system is developed using the Bond Graph modeling methodology. The proposed model is applied to depict the dynamics of a real job shop production system of propylene bags, where the control objective is to adjust the processing frequency of the machines to attend the demands of the products, while keeping the work in process at the desired levels. Exploratory simulations were carried out, and the preliminary results showed that the system could be autonomously controlled. Some advantages of the Bond Graph methodology are also highlighted.

Analysis of multi-product manufacturing systems with arbitrary processing times

Multi-product systems with finite buffers and sequence-dependent set-up times are quite common in modern manufacturing industry. In practice, the distribution of machine processing time could be arbitrary, while in existing literature it is often assumed to follow an exponential distribution. In this paper, we develop an analytical method to study the multi-product manufacturing systems with non-exponential processing times. An embedded Markov chain model is constructed and two approximation methods, Gamma estimation and linear approximation, are proposed. The model is validated with high accuracy by numerical experiments and practical data from an automotive assembly system.

Analysis of Multiproduct Manufacturing Systems With Homogeneous Exponential Machines

This paper is devoted to modeling and analysis of multiproduct manufacturing systems with homogeneous exponential machines and finite buffers. In such systems, each machine processes multiple product types with different speeds, but the processing time for each product type is the same on all machines. Buffers are finite, shared for all products. Analytical methods to evaluate the system performance are developed, and system-theoretic properties are investigated. It is shown from numerical experiments that such a method has a high accuracy in performance evaluation. To improve the performance of such systems, bottleneck analysis is carried out to identify the machine and/or product whose improvement will lead to the largest improvement in system throughput.

A comparison of make-to-stock and make-to-order in multi-product manufacturing systems with variable due dates

This article models a single-stage hybrid production system, which can be regarded as a Make To Order (MTO) production system with safety stocks or a Make To Stock (MTS) production system with advance demand information. In an environment with multiple products and variable customer due dates, optimality conditions for safety stocks (base stocks) and safety lead times (work-ahead window) that minimize inventory and backorder costs are derived. For a simplified M/M/1 system with exponentially distributed customer required lead time, an explicit comparison between MTO and MTS is conducted. A pure MTO policy gets relatively more favorable to a pure MTS policy if inventory holding costs increase, backorder costs decrease, the mean customer required lead time increases, or the processing rate increases. In a numerical study, the influence of variance, the behavior of optimal parameters, and the cost reduction potential of this hybrid policy are shown.

A Bernoulli Model of Multi-Product Manufacturing Systems

Modeling and analysis of multiple products manufacturing systems are of significant importance. In this paper, a Bernoulli model of multi-product manufacturing system with unreliable machines and finite buffers is introduced. In such a model, each machine is capable of processing multiple product types, and each buffer is shared for all types of products. All machines process the parts following the same sequence of different product types. By aggregating different product types into a single one, closed formulas have been derived to evaluate the production rate of the line with one or two machines, and recursive procedures are used to analyze longer lines. Numerical studies indicate that such a method has a high precision in performance evaluation. In addition, system-theoretic properties, such as asymptotic property, monotonicity and reversibility, have been investigated.

Scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

Multi-product production systems with sequence-dependent setup times are typical in the manufacturing of semiconductor chips and other electronic products. In such systems, the scheduling policies coordinating the production of multiple product types play an important role. In this paper, we study a multi-product manufacturing system with finite buffers, sequence-dependent setup times and various scheduling policies. Using continuous-time Markov chain models, we evaluate the performance of such systems under seven scheduling policies, i.e. cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing time), longest queue, longest processing time, and longest overall time. The impacts of these policies on system throughput are compared, and the conditions characterising the superiority of each policy are investigated. The results of this work can provide production engineers and supervisors practical guidance to operate multi-product manufacturing systems with sequence-dependent setups.

The robustness of scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

In this paper, a continuous time Markov chain model is introduced to study multi-product manufacturing systems with sequence-dependent setup times and finite buffers under seven scheduling policies, i.e., cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing times), longest queue, longest processing time, and longest overall time. In manufacturing environments, optimal solution may not be applicable due to uncertainty and variation in system parameters. Therefore, in this paper, in addition to comparing the system throughput under different policies, we introduce the notion of robustness of scheduling policies. Specifically, a policy that can deliver good and stable performance resilient to variations in system parameters (such as buffer sizes, processing rates, and setup times) is viewed as a “robust” policy. Numerical studies indicate that the cyclic and longest queue policies exhibit robustness in subject to parameter changes. This could provide production engineers a guideline in operation management.

Optimal production plan for a multi-products manufacturing system with production rate dependent failure rate

This study deals with the problem of dependence between production and failure rates in the context of a multi-product manufacturing system. It provides an answer about how to produce (i.e. the production rates) and what to produce (i.e. which product) over a finite horizon of H periods of equal length. We consider a single randomly failing and repairable manufacturing system producing two products Pa and Pb . The product Pa is produced to supply the strategic demand d(k) of the principal customer via a buffer stock S over k periods (k = 1, 2, … , H). The second product Pb is produced to meet a secondary but very profitable demand. It is produced during a given interval at the end of each period k. We develop a genetic algorithm to determine simultaneously the optimal production rate of the first product during each period k and the optimal duration of the production interval of the second product, maximising the total expected profit.

Simulation-based optimisation of inspection stations allocation in multi-product manufacturing systems

In this paper a multi-product production systems (MPPS) with in-line quality control is examined. The problem of determining the optimal inspection strategy that results in lowest total inspection cost, while assuring required outgoing quality level is modelled discrete-event simulation is used to model the production system. The optimal allocation is determined by using a tabu search algorithm.

Models for Component Commonality in Multistage Production

Use of common parts for different products (commonality) is important methods for managing product variety and preserving competitiveness in the age of mass customization and supply chain competition. In literature, the advantages of inclusion of common components in a product family are well established. Unfortunately, most of the works have been conducted via simulation or conceptual thinking. The mathematical models in the premises are not adequate for production, planning and control in multistage production. This paper focuses on the advancement of venerable manufacturing resources planning models by incorporating the part commonality concept in a multiproduct, multi-period and multistage manufacturing system under a deterministic situation. The models are validated with established MRPII models. The material requirement schedule for the basic MRP II and proposed models are compared. It is really a good matching shown between the two schedules. The later bearing additional information of the location where to be available the parts in a time frame. The effects of commonality on cost, capacity and requirement schedule are discussed based on the outcomes of the mathematical models executed with the available live data.

Continuous Modeling of Re-Entrant Manufacturing Systems

This paper presents a continuous modeling approach for multi-product re-entrant manufacturing systems. First, a generic continuous model for single-product re-entrant manufacturing systems is elaborated. Second, the concept of re-entrant factor is introduced to the model, which considers the influences of the re-entrant degree. Then, the continuous model is extended from single-product re-entrant manufacturing systems to multi-product re-entrant manufacturing systems in which the product priority is also taken into consideration. Finally, the proposed continuous model is validated through the Mini-Fab case study.

Analysis and Optimization of a Combined Make-to-Stock and Make-to-Order Multiproduct Manufacturing System

We consider a single-stage multiproduct manufacturing facility producing several end-products for delivery to customers with a required customer lead-time. The end-products can be split in two classes: few products with high volume demands and a large number of products with low-volume demands. In order to reduce inventory costs, it seems efficient to produce the high-volume products according to an MTS policy and the low volume products according to an MTO policy. The purpose of this paper is to analyze and compare the impact of the scheduling policy on the overall inventory costs, under customer lead-time service level constraints. We consider two policies: the classical FIFO policy and a priority policy (PR) which gives priority to low volume products over high volume products. We show that for some range of parameters, the PR rule can significantly outperform the FIFO rule. In these ranges, the service level constraints are satisfied by the PR rule with much lower inventory costs.

A single period inventory model with imperfect production and stochastic demand under chance and imprecise constraints

Abstract This paper develops a mathematical model for a single period multi-product manufacturing system of stochastically imperfect items with continuous stochastic demand under budget and shortage constraints. After calculating expected profit in general form in terms of density functions of the demand and percentage of imperfectness, particular expressions for those density functions are considered. Here the constraints are of three types: (i) both are stochastic, (ii) one stochastic and other one imprecise (fuzzy) and (iii) both imprecise. The stochastic constraints have been represented by chance constraints and fuzzy constraints in the form of possibility/necessity constraints. Stochastic and fuzzy constraints are transformed to equivalent deterministic ones using ‘here and now’ approach and fuzzy relations respectively. The deterministic problems are solved using a non-linear optimization technique-Generalized Reduced Gradient Method. The model is illustrated through numerical examples. Sensitivity analyses on profit functions due to different permitted ‘aspiration’ and ‘confidence’ levels are presented.

Performance evaluation of a multi-product system under CONWIP control

Analytical models of multi-product manufacturing systems operating under CONWIP control are composed of closed queuing networks with synchronization stations. Under general assumptions, these queuing networks are hard to analyze exactly and therefore approximation methods must be used for performance evaluation. This research proposes a new approach based on parametric decomposition. Two-moment approximations are used to estimate the performance measures at individual stations. Subsequently, the traffic process parameters at the different stations are linked using stochastic transformation equations. The resulting set of non-linear equations is solved using an iterative algorithm to obtain estimates of key performance measures such as throughput, and mean queue lengths. Numerical studies indicate that the proposed method is computationally efficient and yields fairly accurate results when compared to simulation.