Multiple-product Manufacturing System Research Articles

Joint Optimization Approach of Maintenance and Production Planning for a Multiple-Product Manufacturing System

This paper deals with the problem of maintenance and production planning for randomly failing multiple-product manufacturing system. The latter consists of one machine which produces several types of products in order to satisfy random demands corresponding to every type of product. At any given time, the machine can only produce one type of product and then switches to another one. The purpose of this study is to establish sequentially an economical production plan and an optimal maintenance strategy, taking into account the influence of the production rate on the system’s degradation. Analytical models are developed in order to find the production plan and the preventive maintenance strategy which minimizes sequentially the total production/inventory cost and then the total maintenance cost. Finally, a numerical example is presented to illustrate the usefulness of the proposed approach.

Joint optimization approach of maintenance planning and production Scheduling for a multiple-product manufacturing system

In this paper, we deal with the problem of maintenance planning and production planning for a multiple-product manufacturing system. The manufacturing system under consideration consists of one machine which is subject to random failures and produces several products in order to satisfy some random demands. At any given time, the machine can only produce one type of product. The purpose of this study is to establish an economical production planning followed by an optimal maintenance strategy, taking into account the influence of production rate on the system degradation. Analytical models are developed in order to minimize sequentially the production/storage costs and the total maintenance cost. Finally, a numerical example is presented to illustrate the usefulness of the proposed approach.

An optimal production planning and maintenance policy for a multiple-product and single machine under failure rate dependency

This paper addresses the production and maintenance problem of multiple-product manufacturing system satisfying several random demands corresponding to every product. The goal of this study is to establish an economical production planning followed by an optimal maintenance strategy, taking into account the influence of the multiple-product production rate on the materiel degradation. Analytical models are developed in order to minimize sequentially the production/holding costs and the total maintenance cost. A numerical example is presented to prove the developed approach.

Joint production control and product quality decision making in a failure prone multiple-product manufacturing system

This paper considers joint production control and product quality specifications decision making in unreliable multiple-product manufacturing system. This is with the knowledge that an optimum compromise should guide the decision making process. In fact, tight process specifications will generally lead to products with good quality and higher market values, but at the same time associated with a higher rate of non-conforming parts rejection leading to higher non quality costs and lower plant productivity. Moreover, in unreliable manufacturing context the decision maker should adopt an adequate production policy to hedge against future capacity shortages caused by machine failures in order to meet customer demand. This paper intends to extend previous findings to tackle this problem and study the overall decision making process aiming to guide the production and quality specification decisions in multiple-product context. The overall optimal decision policy is defined here as one that maximises the long term average per unit time profit of a combined measure of quality and quantity dependent sales revenue, minus inventory and backlog costs, in the presence of random plant failures and random repair durations.

Stochastic Flow Model using Kalman Filters for parameter estimation

Attaining high quality from manufacturing systems requires utilizing appropriate system-level quality performance modeling and analysis tools. This paper describes the application of the stochastic-flow-modeling (SFM) approach to represent the quality output behavior of a manufacturing system. To do this, a basic one-product type SFM is extended to that of a multiple-product manufacturing system. This work also provides a novel addition to the SFM approach through the use of a Kalman filter to estimate quality parameters. After a presentation of the reference manufacturing system, results are given for different examples and the effectiveness of the SFM model is examined in terms of accuracy and convergence.

Operational level-based policies in production rate control of unreliable manufacturing systems with set-ups

This paper deals with the control of the production rates and set-up actions of an unreliable multiple-machine, multiple-product manufacturing system. Each part type can be processed for a specified period on one of the involved machines. When switching the production from one type to another, each machine requires both set-up time and set-up cost. Our objective is to determine the production rates and a sequence of set-ups in order to minimize the total set-up and surplus cost. As an analytical or even a numerical solution of the problem is very difficult to find, a combined approach is presented. The proposed approach is based on stochastic optimal control theory, discrete event simulation, experimental design and response surface methodology. It is proved experimentally that an extended version of the Hedging Corridor Policy is more realistic and guarantees better performance for two study cases. The first consists of the unreliable one-machine case facing exponential failure and repair time distribution. The second, which is more complex and where the optimal control theory may not be easily used to obtain the optimal control policy, consists of five machines facing non-exponential failure and repair time distributions. To illustrate the contribution of the paper and the robustness of the obtained control policy, numerical examples and sensitivity analysis are presented.

Modelling and analysis of a multiple product manufacturing system with split and merge

To satisfy a rapidly changing market and various requirements of customer demand, many automotive assembly systems are designed to increase flexibility. One way to achieve this is to have a system making multiple products through split and merge. This paper presents an iterative approach to model such a kind of flexible manufacturing system with common lines and dedicated branches to process different products. The mathematical procedures, associated with the justification of convergence and accuracy, are provided. A case study is presented to illustrate the applicability of the method.

Allocating work in process in a multiple-product CONWIP system with lost sales

To operate a multiple-product manufacturing system under a CONWIP control policy, one must decide how to assign kanbans to products. With a fixed total number of kanbans in a competitive environment, the goal is to determine their allocation to product types in order to minimize lost sales equitably. In particular, we consider systems in which the products may make multiple visits to the same station with a different processing time distribution on each repeat visit. With a fixed number of kanbans dedicated to each product, the system is modeled as a multiple-chain multiple-class closed queuing network. A nonlinear program simultaneously provides an approximate performance evaluation and optimizes the allocation of kanbans to product types. In numerical examples, the allocations identified are similar to those obtained by exhaustive enumeration with simulation, but frequently differ significantly from a naïve allocation according to demand rates. A variant of the model that minimizes the total work-in-process to achieve specified throughput targets yields results similar to a previous heuristic method.

Throughput of Multiple Part-Type Systems: A Subjective Linear Measure

The term throughput, which is commonly used as a performance measure of various production systems, has not been uniquely defined for multiple part-type systems. In some cases, the analytical procedures that were developed to maximize throughput of multiple part-type systems are difficult to evaluate and justify. Moreover, in some cases an inaccurate definition of this term motivated incorrect operational concepts. This paper discusses some of the problems in the traditional definition of throughput and suggests a new, more suitable definition for a multiple-product manufacturing system.

Optimal production control problem in stochastic multiple-product multiple-machine manufacturing systems

This paper deals with the issue of production control in a manufacturing system with multiple machines which are subject to breakdowns and repairs. The control variables considered are the production rates for different products on the machines. Our objective is to minimize the expected total discounted cost due to the finished good inventories and backlogs. Based on the structure of the hedging point policy, a parameterized near-optimal production policy for a multiple-product manufacturing system is proposed. The analytical formalism is combined with simulation-based statistical tools, such as experimental design and response surface methodology. The aim of such a combination is to provide an approximation of the optimal control policy. In the proposed approach, the parameterized near-optimal control policy is used as an input for the simulation model. For each entry consisting of a combination of parameters, the cost incurred is obtained. The significant effects of the control variables are determined by the experimental design. The relationship between the cost and these input factors is obtained through a response surface model. It is from the obtained relationship that the best values of control factors are determined. Extensive computational experience is reported for two-part-type and five-part-type production systems. Finally, simulation experiments on several examples are concentrated on the sensitivities of the control policy obtained.

Control of production and corrective maintenance rates in a multiple-machine, multiple-product manufacturing system

This paper presents the analysis of the optimal production control and corrective maintenance planning problem for a failure prone manufacturing system consisting of several identical machines. Machines are subject to breakdowns and repairs and can produce several parts of products. At any given time, each machine can only produce one type of product. The introduction of the corrective maintenance will increase the availability of the production system which guarantees the improvement of the system's productivity if the production planning is well done. The decision variables are the production and the machine repair rates which influence the inventory levels and the system capacity, respectively. The objective of the work is to minimize the cost of surplus and repair activities. A computational algorithm, based on numerical methods, is given for solving the optimal control problem. Finally, a numerical example is presented to illustrate the usefulness of the proposed approach and extensions to more complex manufacturing systems are discussed.

A simulation optimization approach in production planning of failure prone manufacturing systems

In this paper, the implementation of a new method to control the production rate of manufacturing systems, based on the combination of stochastic optimal control theory, discrete event simulation, experimental design and response surface methodology is outlined. The system under study consists of several parallel machines, multiple-product manufacturing system. Machines are subject to failures and repairs and their capacity process is assumed to be a finite state Markov chain throughout the analytical control model. The problem is to choose the production rates so as to minimize the expected discounted cost of inventory/backlog over an infinite horizon. We first show that, for constant demand rates and exponential failure and repair times distributions of the machines, the hedging point policy is optimal. The structure of the hedging point policy is then parameterized by factors representing the thresholds of involved products. With such a policy, simulation experiments are combined to experimental design and response surface methodology to estimate the optimal control policy. We obtain that the hedging point policy is also applicable to a wide variety of complex problems including non-exponential failure and repair times distributions and random demand rates. Analytical solutions may not be easily obtained for such complex situations.