Multi-level Assembly Systems Research Articles

A hybrid genetic algorithm for a multilevel assembly replenishment planning problem with stochastic lead times

This paper on replenishment planning for multi-level assembly systems with several components at each level deals with the problem of calculating planned lead-times when the real lead-times for all components are assumed to be stochastic. This problem is already treated in the literature by using a recursive procedure and a Branch and Bound algorithm. Here, in order to decrease the computation time, a novel generalized probabilistic model based on an iterative approach is developed. The proposed model calculates the expected total cost, which is composed of the inventory holding cost for components and the backlogging and inventory holding costs for the finished product. An iterative approach and a hybrid genetic algorithm are introduced to determine the planned order release dates for components at the last level of the bill of materials that minimizes the expected total cost. Experimental results show that the proposed optimization algorithm efficiently finds good-quality approximate solutions regardless of the type of assembly system, the number of components at the last level and the variability of the finished product-related costs.

Planned lead times optimization for multi-level assembly systems under uncertainties

Planned lead times are crucial parameters in management of supply networks that continue to be more and more extended with multiple levels of inventory of components and uncertainties. The object of this study is the problem of determining planned lead times in multi-level assembly systems with stochastic lead times of different partners of supply chains. A general probabilistic model with a recursive procedure to calculate all the necessary distributions of probability is proposed. A Branch and Bound algorithm is developed for this model to determine planned order release dates for components at the last level of a BOM which minimize the sum of inventory holding and backlogging costs. Experimental results show the behavior of the proposed model and optimisation algorithm for different numbers of components at the last level of the BOM and for different numbers of levels and values of holding and backlogging costs. The model and algorithm can be used for assembly contracting in an assembly to order environment under lead time uncertainty.

Multilevel Production Systems with Dependent Demand with Uncertainty of Lead Times

This study considers a multilevel assembly system with several components in each sublevel. It is assumed that actual lead time for all components is probabilistic; and periodic order quantity (POQ) policy for ordering is utilized. If at a certain level a job is not received at the expected time, a delay is incurred at the delivery of production at this level and this may result in backorders of the finished product. It is assumed in this case that a fixed percentage of the shortage is backlogged and other sales are lost. In the real situation, some but not all customers will wait for backlogged components during a period of shortage, such as for fashionable commodities or high-tech products with the short product life cycle. The objective of this study is to find the planned lead time and periodicity for the total components in order to minimize the expected fixed ordering, holding, and partial backlogging costs for the finished product. In this study, it is assumed that a percentage of components at each level are scrap. A general mathematical model is suggested and the method developed can be used for optimization planned lead time and periodicity for such an MRP system under lead time uncertainties.

Location and lead-time perturbations in multi-level assembly systems of perishable goods in Spanish baby food logistics

The current economic crisis is reflected in lower capacity expansion or even capacity contraction, closing of distant activities and reduction of lead time. However, the locations of suppliers are strongly connected with the quality of agricultural products. The transportation from distant locations can largely affect the food production business in all aspects. The article presents how the Extended Material Requirements Planning (EMRP) model enables to evaluate perturbations in lead time and temperature, and shows how distant growing areas of agricultural products, and, as a result, transportation lead times play a crucial role in the net present value calculation. In this paper, we show the impact of choosing a less distant site for growing of agricultural products, and overlapping the transportation and quarantine lead times on decreasing the perishability, hence increasing the added value in multi-level food assembly systems. Also, a case study of Spanish baby food industry is presented, using the principles of the well-developed EMRP Theory.

Genetic algorithm for multi-level assembly systems under stochastic lead times

The aim of this paper is to propose tools to adapt and parameterize the Material Requirement Planning (MRP) method under lead time uncertainty. We study multi-level assembly systems with one type of finished products and several types of components. We consider that each component has a fixed unit inventory cost and the finished product has a backlogging cost per unit of time. The lead times of components are discrete random variables, and the costumer's demand of the finished product is known. A general mathematical model for supply planning of multi-level assembly systems is presented. A Genetic Algorithm (GA) method is proposed to minimize the sum of the average inventory holding cost for components and the average backlogging and inventory holding costs for the finished product.

Mathematical Model for Supply Planning of Multi-level Assembly Systems with Stochastic Lead Times

The deal for industrial companies is to satisfy their customers with the best quality, the most reliable lead times, and the lowest costs. One of the keys to achieve it is in the inventory control in the Supply Chain. Moreover, it is mandatory to possess necessary components in order to produce the requested products by the due date. But, as the components lead time is an unpredictable parameter, it is difficult to take it into account correctly. The paper deals with this question considering that each component has a fixed unit inventory cost; the finished product has an inventory cost and a backlogging cost per unit of time. Then, a general mathematical model for supply planning of multilevel assembly system is presented to calculate the expected value of the total cost which equals to the sum of the inventory holding costs for the components, the backlogging and the inventory holding costs for the finished product.

Planned lead time optimization in material requirement planning environment for multilevel production systems

This paper deals with the problem of planned lead time calculation in a Material Requirement Planning (MRP) environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging costs. The proposed approach is based on discrete time inventory control where the decision variables are integer. Two types of systems are considered: multi-level serial-production and assembly systems. For the serial production systems (one type of component at each level), a mathematical model is suggested. Then, it is proven that this model is equivalent to the well known discrete Newsboy Model. This directly provides the optimal values for the planned lead times. For multilevel assembly systems, a dedicated model is proposed and some properties of the decision variables and objective function are proven. These properties are used to calculate lower and upper limits on the decision variables and lower and upper bounds on the objective function. The obtained limits and bounds open the possibility to develop an efficient optimization algorithm using, for example, a Branch and Bound approach. The paper presents the proposed models in detail with corresponding proofs and several numerical examples. Some advantages of the suggested models and perspectives of this research are discussed.

The investment model in preventive maintenance in multi-level production systems

In this research, cost/benefit models for investments made in quality improvements are developed to measure the impact of quality programs and to predict the return of an investment in these programs in a multi-level assembly system. Using these models, the decision makers can decide whether and how much to invest in quality improvement projects. The relationship between the investment and return on investment can be developed based on the tangible variables. The investment model in preventive maintenance is developed in a multi-level assembly system. The investment in preventive maintenance is to reduce the variance and the deviation of the mean from the target value of the quality characteristic, and hence to reduce the proportion of defectives and also to increase reliability. The proportion of defectives can be linked to manufacturing cost, inventory cost, and profit loss. The reliability is linked to warranty cost. The total costs in this investment model include manufacturing cost, setup cost, holding cost, profit loss, and warranty cost.

Flow control of multi-level assembly systems

This paper addresses the flow control problem of multi-level assembly production systems in which a finished product is obtained from the initial components by successive assembly operations. The manufacturing process of the finished product can be represented as a tree-like graph. We assume that the production capacity of each machine is constant and that the demand is known over a finite horizon. Backlogging is not allowed. The flow control problem consists of adjusting the production of the machines in order to minimize the total cost incurred by holding components and finished products in inventories. Properties of optimal solutions are established. We then propose a simple analytical solution based on these properties.

Kitting process in a stochastic assembly system

In small-lot, multi-product, multi-level assembly systems, kitting (or accumulating) components required for assembly plays a crucial role in determining system performance, especially when the system operates in a stochastic environment. This paper analyzes the kitting process of a stochastic assembly system, treating it as an assembly-like queue. If components arrive according to Poisson processes, we show that the output stream departing the kitting operation is a Markov renewal process. The distribution of time between kit completions is also derived. Under the special condition of identical component arrival streams having the same Poisson parameter, we show that the output stream of kits approximates a Poisson process with parameter equal to that of the input stream. This approximately decouples assembly from kitting, allowing the assembly operation to be analyzed separately.

Combining items for lot sizing in multi-level assembly systems

Abstract Multi-level lot sizing problems are difficult to solve optimally when the product structure contains more than a few items. This paper identifies conditions on the cost parameters which, when applicable, allow items to be combined for purpose of optimization, thereby reducing the size of the problem.

Evaluating scheduling policies in a multi-level assembly system

This paper orders in preference various scheduling policies comprised of dispatching and regeneration rules in a multi-level assembly production system. Traditional expected-value statistical and second-degree stochastic dominance preference ordering rules are used to identify the most efficient scheduling policies for risk-averse managers using various measures of performance. The results indicate that selection of an efficient regeneration rule is contingent upon the selection of a dispatching rule and these rules must be selected jointly to develop efficient scheduling policies. For this study, simple intuitively appealing scheduling policies were found to be efficient.

The effect of engineering changes and demand uncertainty on MRP lot sizing: a case study

Much has been said on the various aspects of lot sizing for single-level and multi-level (assembly) systems in MRP. Numerous heuristics have been developed and tested on (rather small) problems with finite horizon and deterministic, time-varying demand. However, in practice, the hypothesis of a finite horizon and deterministic demand contains several inherent deficiencies. Indeed, the finite horizon assumption ignores that decisions are usually made on a rolling schedule basis. Moreover, lot-sizing decisions are based on uncertain information about future demand. Timing and quantity of future requirements are, at least partially, based on forecasts and hence imply forecast errors and the need for rescheduling. Finally, engineering changes can also lead to very high scrap costs and hence should be incorporated into the lot-sizing decisions. In this paper the influence of these factors on lot-sizing decisions is examined for a real life problem in a company that produces electronic components to he used in ...

Design and optimization of multi-echelon assembly networks: Savings and potentialities

In the last decade considerable attention has been paid to the analysis and optimization of multi-echelon assembly networks. Research contributions in this field are mainly focussed on the determination of optimal batch quantities. In this paper two other issues concerning multi-echelon assembly networks are also discussed. The first point concerns the notion of flexibility in assembly operations. Flexibility can be achieved by spending on assembly engineering to cut set-up costs. The second point has to do with design of multi-level assembly systems. Furthermore the notion of minimum network leadtime is introduced. This turns out to be (besides minimal average cost per period) a performance indicator of considerable importance. To conclude an existing Philips product (called medsys) is presented in order to illustrate the ideas discussed in this paper.