ASRS sizing for recreating automotive assembly sequences

Since a mixed model assembly line's efficiency depends on the sequence of jobs moving down the line, manufacturers spend considerable effort optimizing the sequence of jobs entering the plant. In automotive assembly plants however, repair loops and parallel stations scramble the sequence before it reaches the final assembly stage. Many automotive assembly plants use an automatic storage and retrieval system to revamp the scrambled sequence before final assembly. One plant even goes so far as to reconstruct the original sequence by completely undoing the sequence scrambling. We derive a relationship between the sequence scrambling information, the variety of model-colour configurations, and the size of the automatic storage and retrieval system needed to reconstruct the initial sequence. We enunciate this new ASRS sizing problem actually facing industry, show how to model it, present a solution approach, and demonstrate the approach on actual sequence scrambling data from an automotive assembly plant.     

A simulation study of part sequencing in a flexible assembly cell

A discrete modular simulation program has been developed for the assessment of real-time part-sequencing systems for flexible assembly cells. The program is used with different sequencing strategies to study the cell performance and decision lead time.

A combined index is used as a measure of the cell performance. The index is a function of throughput, throughput time and cell utilization. Time response of the part-sequencing rule is assessed by a decision lead-time index which is a function of the average computation time required by the computer module to generate a decision.     

Lot-to-order matching for a semiconductor assembly andtest facility

This paper is motivated by the problem of assigning semiconductor fabrication wafer lots to customer orders of various sizes. The goal of this research is to develop a method for deciding, on a given day, which orders to fill and the assignment of available lots to orders. This decision should be made in order to effectively utilize the capacity of the assembly/test facility, to minimize excess product that must be sent to a storage facility, and to maximize on-time delivery of customer orders. This problem can be formulated as an integer program with a nonlinear objective and nonlinear constraints. Because of the complexity of this formulation we decompose the problem into two integer linear programs and solve them in sequence by heuristic methods. The performance of the heuristic is analyzed using a representative data set. Based on this analysis, it is shown that our greedy heuristic performs significantly better than current practice.     

Mixed-model assembly line sequencing heuristics for smoothing component parts usage: A comparative analysis

This paper addresses the mixed-model sequencing problem by providing a controlled comparison of six sequencing heuristics. The sequencing heuristics are evaluated for several test problems on the basis of their ability to develop a sequence which smoothes out the rate of use of each component part as much as possible. The test problems were designed to determine whether problem size, product range, product structure, or product mix affects the relative ranking of the performance of the heuristics.     

A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.     

A genetic search algorithm to optimize job sequencing under a technological constraint in a rolling-mill facility

This article presents some results from the application of a genetic search algorithm to solve a job scheduling problem where setup costs depend on the order of the jobs. An empirical study shows that, for small problems, the solutions given by the genetic algorithm are as good as those obtained with a mixed-integer linear program. For larger problems that are computationally infeasible, we benchmark the genetic solutions against traditional scheduling heuristics. We also study different population management strategies that can improve the performance of the algorithm. Finally, future research avenues are discussed.     

A comparative study of priority dispatching rules in a hybrid assembly/job shop

This paper reports on research conducted on the use of priority dispatching rules in a hybrid assembly/job shop which manufactures both single-component and multiple-component products. A simulation model was constructed and a large stale experiment performed. Statistical analysis of the simulation results indicated significant impact of both the priority rules tested, and the product-mix considered on shop performance.

Among the 12 priority rules tested, the SPT (shortest processing time) rule and the ASMF-SPT (assembly jobs first with SPT as tie-breaker) rule performed very well with respect to measures like lateness, flow time, tardiness, staging time, and percent of jobs tardy. These findings lead to further investigation of a combined priority rule, MIXED, which implements the ASMF-SPT rule at all machine centres that process components of assembly jobs, and the SPT rule at the remaining machine centres which process non-component jobs. The additional research results yielded evidence that the MIXED rule can reduce the staging time of the SPT rule, and yielded betrer results than the ASMF-SPT rule with regard to other performance measures.

The most interesting finding, however, was the small variation in flow time distribution resulting from use of the MIXED rule when there were more assembly jobs. In an MRP environment, it is especially desirable to have a priority dispatching rule resulting in minimum variation in individual flow times which allows the replenishment lead times to be estimated with greater accuracy.     

Bicriteria sequencing for just-in-time mixed-model assembly lines

Under a Just-In-Time (JIT) pull system the sequencing of products requires the satisfaction of two main goals: (1) keeping a constant rate of usage of parts, and (2) smoothing the workload at work stations to avoid line stoppages. By using a practical observation related to JIT delivery systems we propose a two-step approach, where in the first step we consider only goal (1) by applying a benchmark heuristic. In the second step we focus on goal (2), by investigating the effectiveness of a spacing-constraint based approach, commonly used in the automotive industry, in comparison with a more general time-based one. We designed and conducted a simulation experiment based on the practical situation of final assembly lines and we found that the benchmark heuristic represents an appropriate choice for step one (based on a new performance measure that represents a lower bound on variation in parts utilization). For the second step, related to workload smoothing, the spacing-constraint based method presents better achievement than the time-based one.     

A simulation study on the performance of task-determination rules and delivery-dispatching rules for multiple-load AGVs

In this paper, the control problem of multiple-load automated guided vehicles (AGVs) is studied. A control process that identifies four problems faced by multiple-load AGVs is proposed. The first problem is the task-determination problem, in which a multiple-load AGV determines whether its next task is a pickup task or a delivery task. The second problem is the delivery-dispatching problem, in which a multiple-load AGV determines which delivery point it should visit next if its next task is a delivery task. The third problem is the pickup-dispatching problem, in which a multiple-load AGV determines which pickup point it should visit next if its next task is a pickup task. Finally, the fourth problem is the load-selection problem, which requires a multiple-load AGV to determine which load it should pick up from the output queue of a pickup point. This paper focuses on the first and second problems. Different task-determination rules and delivery-dispatching rules are proposed for these two problems. For the problems that are not the main focus of this study, rules found in the literature or real systems are adopted in this study. The objective of this study is twofold. First, we need to understand how well the proposed rules will perform in different performance measures, e.g. the system's throughput and the mean lateness of parts. Second, we need to understand the mutual effects that different types of rules have on each other, so that the best combination of rules can be identified. Computer simulations were conducted to test the performance of the proposed rules. It is hoped the knowledge learned from this study can be beneficial to real multiple-load AGV systems similar to the one studied here.     

A comparison of sequencing rules in static and dynamic hybrid flow systems

It is well known that efficient scheduling of jobs is essential for improving the economics of production in manufacturing organizations. As a result, extensive research has been conducted on scheduling, especially in job shop and flow shop settings. In contrast, little research has been done on hybrid flow systems, even though they are found in many industries, including beer processing, glass container production, pertroleum refining, plastic-coated cable production, and fertilizer production. Furthermore, the few studies that have dealt with hybrid systems have been limited by the assumptions made about their operating environments. Therefore, we conducted a study that extends the previous work on hybrid systems in two significant ways: (1) it included financially oriented scheduling rules and a new, related performance measure; and (2) the new rules were compared with the existing ones in a large simulation experiment under both static and dynamic (generally encountered in practice) hybrid flow shop environments. To date such comparisons have been made only under static environments. The results show that the relative performances of the scheduling rules differ as the assumptions regarding the operating environment are changed.     

Dispatching rules for scheduling in assembly jobshops - Part 1

Research on jobshop scheduling has tended to concentrate on the development of dispatching rules for jobs that are independent, i.e. single-component jobs. However, in real-life situations, many jobs involve assembly operations that require scheduling of multiple components through the jobshop where both serial and parallel operations take place. In this two-part paper, we consider the problem of scheduling in assembly jobshops, i.e. jobshops that manufacture multi-level assembly jobs. The development of new and efficient dispatching rules with a view to address various measures of performance related to flowtime and staging delay of jobs is first undertaken. A new concept, called 'operation synchronization date' is introduced and made use of in the new dispatching rules. The best existing dispatching rules and the proposed dispatching rules are relatively evaluated by an exhaustive simulation study. The results indicate that the proposed rules emerge to be superior to the existing ones for most measures of performance.     

Dispatching rules for scheduling in assembly jobshops - Part 2

In this part of the paper, we present the development and evaluation of dispatching rules for scheduling in jobshops manufacturing multi-level assembly jobs with the performance measures reated to tardiness. We present a new definition of 'operation due date' in the context of assembly jobs and use it in the development of dispatching rules. A simulation study is carried out to evaluate the performances of the existing and the proposed dispatching rules with respect to different measures of tardiness. We also measure their performances with respect to different measures of flowtime and staging delays. The results of the study indicate that the proposed rules perform better than the existing rules.     

A heuristic based on Vogel's approximation method for sequencing mixed-model assembly lines

Sequencing mixed-model assembly lines is a well researched topic in the literature. However, many methods that have been developed to solve this problem fail to cope with either the large size or the specific characteristics of real-life problems. In this paper, a heuristic is proposed that is derived from Vogel's approximation method for transportation planning. The heuristic is able to handle large and supposedly difficult problem instances. Sophisticated test scenarios considering real-life aspects were generated to evaluate the performance of the heuristic for realistic problem instances. It is shown that the proposed heuristic significantly outperforms priority rule-based methods and requires only reasonable computational effort.     

A simple facility to measure the scalar neutron spectrum in an assembly

An inexpensive, simple and safe facility was constructed to measure the neutron spectrum in an assembly, by setting a neutron source outside the assembly. A normalization technique for the neutron spectrum and error analyses are mentioned. The error of the calculated spectrometer efficiency is cancelled at the final spectrum normalization. The error caused by the present method is within acceptable levels.     

Comparison and evaluation of lot-to-order matching policies for a semiconductor assembly and test facility

This paper is motivated by the problem of assigning semiconductor fabrication wafer lots to customer orders of various sizes. The goal of this research is to develop a method for deciding, on a given day, which orders to fill and the assignment of available lots to orders. This problem can be formulated as an integer program with a non-linear objective and non-linear constraints. Because of the complexity of this formulation, the problem is decomposed into two integer linear programs and solved in sequence by heuristic methods. In this paper, heuristic solutions are selected for the two subproblems and the performances of these heuristics are analysed in an experimental design using a representative data set. Based on this analysis, it is shown that the greedy heuristics selected perform significantly better than current practice. Finally, future research is discussed.     

A methodology for throughput capacity analysis of a production facility considering environment condition

Throughput capacity of a production facility plays an important role in supporting managers and engineers in the decision making processes related to the design and optimization of a production plant. System throughput capacity is affected in complex ways by the reliability, maintainability, and capacity of its components. These in turn are considerably affected by operational environment such as ambient temperature, icing, dust, wind, etc. Therefore, in order to have an effective throughput capacity analysis (TCA), all influence factors (covariates) on these terms need to be identified; furthermore, their effect must be modeled and quantified by an appropriate statistical approach. The aim of this paper is to develop a methodology for throughput capacity analysis considering environment condition. A simple case study is used to demonstrate how the methodology can be applied in a real case.     

Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem

The increasing market demand for product variety forces manufacturers to design mixed-model assembly lines (MMAL) on which a variety of product models similar to product characteristics are assembled. This paper presents a method combining the new ranked based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named non-dominated ranking genetic algorithm (NRGA) to a just-in-time (JIT) sequencing problem when two objectives are considered simultaneously. The two objectives are minimisation the number of setups and variation of production rates. This type of problem is NP-hard. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. The solutions obtained via NRGA are compared against solutions obtained via total enumeration (TE) scheme in small problems and also against four other search heuristics in small, medium and large problems. Experimental results show that the proposed algorithm is competitive with these other algorithms in terms of quality and diversity of solutions.     

GRASP with path relinking for a multiple objective sequencing problem for a mixed-model assembly line

This research presents a new application of greedy randomised adaptive search procedure (GRASP) to address a production sequencing problem for mixed-model assembly line in a just-in-time (JIT) production system in two different cases. In the former case, small size sequencing problems are considered and two objectives are presented; minimisation of setups and optimisation of stability of material usage rates. These two objectives are inversely correlated with each other, so simultaneous optimisation of both is challenging. This type of problem is NP-hard. The GRASP, with path relinking, searches for efficient frontier where simultaneous optimisation of number of setups and usage rates is desired. Several test problems are solved via GRASP and its performance is compared to solutions obtained via complete enumeration and simulated annealing (SA), tabu search (TS) and genetic algorithms (GA) approaches from the literature. Experimental results reveal that the GRASP with path relinking provides near-optimal solutions in terms of the two objectives and its ‘average inferiority%’ and ‘average percentile’ performances are superior to that of other heuristics. In the latter case, the goal is to explore varying the emphasis of these two conflicting objectives. Larger sequencing problems are considered and solved via GRASP with path relinking. Its objective function values are compared to the solutions obtained via a SA approach from the literature. Experimental results show that GRASP also provides good performance on large size problems and its percentage improvement is better than that of SA. Overall results also show, however, that the GRASP performs poorly with regard to CPU time.     

A real-time scheduling mechanism for a flexible manufacturing system: Using simulation and dispatching rules

This paper presents a real-time scheduling methodology which uses simulation and dispatching rules for flexible manufacturing systems. We develop a scheduling mechanism in which job dispatching rules vary dynamically based on information from discrete event simulation that is used for evaluating candidate dispatching rules. In this paper, we improve and extend a previous research on simulation-based real-time scheduling by suggesting a more systematic framework for the scheduling mechanism through refinement of functions of modules in the mechanism, and by presenting and analysing various scheduling strategies used to operate the mechanism. The strategies are formed by combining two factors that might influence the performance of the mechanism: type of simulation model which is used in the mechanism and points of time when new dispatching rules are selected. In order to compare performance of the scheduling strategies, computational experiments are performed and results are reported.