Assembly Line Problem Research Articles

An analytic framework for sequencing mixed model assembly lines

The problem of sequencing mixed model assembly lines is characterized by a set of parameters whose values are dictated by the actual manufacturing environment. In some cases, it may be desirable to minimize the size of the facility, while in others, the throughput time is paramount. Important design considerations include operator schedules, the product mix, station boundaries, and the launching discipline. The intent of this paper is to present a common mathematical framework in which each possible variant can be addressed. By implication, a solution technique developed for one can be readily adopted for the others. Virtually all of the previous work on mixed model sequencing has focused on the development of heuristics. While these may work well in specific instances, it is difficult to assess their performance without a frame of reference. Moreover, they cannot be universally applied. In this paper, we show that it is possible to obtain optimal solutions at very little cost. The presentation is concerned with sequencing the minimum part set; however, experience indicates that much larger problems can be solved.

A Stochastic Scheduling Problem with Intree Precedence Constraints

We consider n jobs to be scheduled on two parallel machines. The jobs are subject to intree precedence constraints, that is, each job, except one which is the root of the tree, has one successor. The job of the root is at level 0 and a job is at level l if its predecessor is at level l − 1. A job can start to be processed only after all its predecessors have finished. Job processing times are independent random variables with those at the same level being identically distributed. The objective is to minimize the expected completion time of the last job. Such a model can be applied to assembly line and computational problems. This paper generalizes a result of Pinedo and Weiss.

Analysis of a Preference Order Assembly Line Problem

An analysis is made of a preference order dynamic programming procedure proposed in the literature for stochastic assembly line balancing problems. It is shown that in general the procedure does not satisfy the monotonicity condition and that therefore there is no guarantee that the solutions will be optimal. It is also shown that for a certain class of problems for which the procedure does yield optimal solutions, the proposed preference order model can be reformulated as a regular dynamic programming model.

Analysis of production facilities- a network simulation approach

The existing analytic and heuristic techniques for the assembly-line problem are generally limited in scope and applicability. The restricted and often unrealistic assumptions of these procedures handicap them as a practical tool for modern manufacturing problems. This article presents a comprehensive model for the design and analysis of production-line facilities. The Generalized Network Simulator (GNS), a network-based simulation model, integrates the handling of time, queue, resource, and cost character istics of realistic situations in a single package and offers a "total system" tool for systems analysis and research. A companion Assembly Line Information System (ALIS) facilitates both the construction of the data base through interactive data input entry and the specification of desired statistical outputs. An example illustrates the application of the model in a fictitious manufacturing environment involving the cost and resource features of the GNS/ALIS programs.

Assembly Line Balancing with Multiple Stations

A case is stated for extending the techniques of assembly line balancing to provide for the parallel operation of identical stations, where this leads to a reduction in idle time. The practical implications of operating with this type of system are discussed, both for the stations themselves and the line as a whole, with reference to various classifications of assembly line, and ways in which balancing can be made to fit into an overall strategy for production line design are touched upon. Two distinct types of computer program have been developed to enable multiple stations to become a recognised feature incorporated into “heuristic” line balancing, rather than an appendage to be applied ad hoc by industrial engineers when current techniques have proven inadequate. One approach is based on a more sophisticated version of the ‘positional weight’ method while the other relies on the contrasting philosophy of the “random generation” method, and a comparison is made of their relative success in solving two assembly line problems, and their potential from an industrial viewpoint.

Some analytical approaches to assembly line problems

Some analytical approaches to assembly line problems

Some analytical approaches to assembly line problems

Some analytical approaches to assembly line problems

Parallel Sequencing and Assembly Line Problems

This paper deals with a new sequencing problem in which n jobs with ordering restrictions have to be done by men of equal ability. Assume every man can do any of the n jobs. The two questions considered in this paper are (1) How to arrange a schedule that requires the minimum number of men so that all jobs are completed within a prescribed time T, and (2) if m men are available, arrange a schedule that completes all jobs at the earliest time.