Balancing assembly lines: an industrial case study

This paper presents a case study where a practical balancing problem for an assembly line of appliances with two sides and two different heights is solved with an enhanced priority-based heuristic. We show how to adapt such heuristic to account for the practical aspects of industrial applications. We also show that a good use of logic and randomness in the algorithm is the key to allow the heuristic to find good solutions. In order to speed up its implementation and to facilitate software maintenance, we have implemented the heuristic on MsAccess97. It takes less than 1 min on a standard PC to solve a problem having 248 tasks, 400 precedence constraints, and four task attributes. Although this is slower than if it would have been implemented on a compilable language, the benefits of such computer environment largely outweigh its low speed.     

Balancing and scheduling tasks in assembly lines with sequence-dependent setup times

The classical Simple Assembly Line Balancing Problem (SALBP) has been widely enriched over the past few years with many realistic approaches and much effort has been made to reduce the distance between the academic theory and the industrial reality. Despite this effort, the scheduling of the execution of tasks assigned to every workstation following the balancing of the assembly line has been scarcely reported in the scientific literature. This is supposed to be an operational concern that the worker should solve himself, but in several real environments, setups between tasks exist and optimal or near-optimal tasks schedules should be provided inside each workstation. The problem presented in this paper adds sequence-dependent setup time considerations to the classical SALBP in the following way: whenever a task is assigned next to another at the same workstation, a setup time must be added to compute the global workstation time. After formulating a mathematical model for this innovative problem and showing the high combinatorial nature of the problem, eight different heuristic rules and a GRASP algorithm are designed and tested for solving the problem in reasonable computational time.     

Hashing vectors for tabu search

Tabu search as proposed by Glover [3,4] has proven to be a very effective metaheuristic for hard problems. In this paper we propose that hash functions be used to record the solutions encountered during recent iterations of the search in a long list. Hash values of potential solutions can be compared to the values on the list for the purpose of avoiding cycling. This frees the algorithm designer of the need to consider cycling when creating tabu restrictions based on move attributes. We suggest specific functions that result in very good performance.     

Balancing assembly lines with variable parallel workplaces: Problem definition and effective solution procedure

Assembly line balancing problems (ALBP) arise whenever an assembly line is configured, redesigned or adjusted. An ALBP consists of distributing the total workload for manufacturing any unit of the products to be assembled among the work stations along the line subject to a strict or average cycle time. Traditionally, stations are considered to be manned by one operator, respectively, or duplicated in form of identical parallel stations, each also manned by a single operator. In practice, this assumption is usually too restrictive. This is particularly true for large products like cars, trucks, busses and machines, which can be handled by several operators performing different tasks at the same time. Only restricted research has been done on such parallel workplaces within the same station though they have significant relevance in real-world assembly line settings.     

Balancing of simple assembly lines under variations of task processing times

One of the simple assembly line balancing problems (SALBPs), known as SALBP-E, is considered. It consists in assigning a given set V={1,2,??,n} of elementary tasks to linearly ordered workstations with respect to precedence and capacity restrictions while minimizing the following product: number of used workstations × working time on the most loaded one. The stability of feasible and optimal solutions for this problem with regard to possible variations of the processing time of certain tasks is investigated. Two heuristic procedures finding a compromise between the efficiency and the considered stability measure of studied solutions are suggested and evaluated on known benchmarks.     

Balancing two-sided U-type assembly lines using modified particle swarm optimization algorithm

In this paper, a new two-sided U-type assembly line balancing (TUALB) procedure and a new algorithm based on the particle swarm optimization algorithm to solve the TUALB problem are proposed. The proposed approach minimizes the number of stations for a given cycle time as the primary objective and it minimizes the number of positions as a secondary objective. The proposed approach is illustrated with an example problem. In order to evaluate the efficiency of the proposed algorithm, the test problems available in the literature are used. The experimental results show that the proposed approach performs well.     

Heuristic reliability optimization by tabu search

A new heuristic algorithm, based on the tabu search methodology, is proposed for constrained redundancy optimization in series and in complex systems. It has the advantage of not being blocked as soon as a local optimum is found. Results given by the new method are compared with those of previous heuristics on a series of examples.We are grateful to R. Bulfin for making the code for reliability optimization of series systems he wrote with C.-Y. Liu available to us.Work of the first author was supported by NSERC Grant No. GP0105574, FCAR Grant No. 92EQ1048 and AFOSR Grant No. 90-0008 to Rutgers University.Work of the second author was partly supported by AFOSR Grant No. 90-0008 to Rutgers University while he was a graduate student.     

Constructing covering codes by tabu search

The problem of finding good covering codes in Hamming spaces is considered. Many different local search methods have been used to find packing codes (the dual problem), whereas practically all published results on searches for covering codes are based on simulated annealing. In this article tabu search is evaluated and compared against the simulated annealing method. A novel neighborhood function is also presented. The combination of a new optimization method and a new neighborhood function turns out to speed up the search for covering codes remarkably compared to the traditional simulated annealing approach. Using the new approach, the best known upper bound for the football pool problem for 9 matches is improved to 1341. © 1997 John Wiley & Sons, Inc.     

Absalom: Balancing assembly lines with assignment restrictions

Assembly line balancing problems (ALBPs) arise whenever an assembly line is configured, redesigned or adjusted. An ALBP consists of distributing the total workload for manufacturing products among the work stations along the line. On the one hand, research has focussed on developing effective and fast solution methods for exactly solving the simple assembly line balancing problem (SALBP). On the other hand, a number of real-world extensions of SALBP have been introduced but solved with straight-forward and simple heuristics in many cases. Therefore, there is a lack of procedures for exactly solving such generalized ALBP.In this paper, we show how to extend the well-known solution procedure Salome [Scholl, A., Klein, R., 1997. Salome: A bidirectional branch-and-bound procedure for assembly line balancing. Informs J. Comput. 9 319–334], which is able to solve even large SALBP instances in a very effective manner, to a problem extension with different types of assignment restrictions (called ARALBP). The extended procedure, referred to as Absalom, employs a favorable branching scheme, an arsenal of bounding rules and a variety of logical tests using ideas from constraint programming.Computational experiments show that Absalom is a very promising exact solution approach although the additional assignment restrictions complicate the problem considerably and necessitate a relaxation of some components of Salome.     

Large-scale controlled rounding using tabu search with strategic oscillation

When publishing tabular data, the United States Bureau of the Census must sometimes round fractional data to integer values or round integer data to multiples of a prespecified base. Data integrity can be maintained by rounding tabular data subject to additivity constraints while minimizing the overall perturbation of the data. In this paper, we describe a heuristic based on tabu search with strategic oscillation for solving this NP-hard problem. A lower-bounding technique is developed in order to evaluate the quality of the solutions and provide a starting solution for the tabu search. Numerical results demonstrate the effectiveness of the procedure when applied to extremely large tables with up to 27,000 randomly generated entries. Additionally, the algorithm is shown to perform extremely well when applied to actual data obtained from the United States Bureau of the Census.     

A user's guide to tabu search

We describe the main features of tabu search, emphasizing a perspective for guiding a user to understand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability to adapt a rudimentary prototype implementation to encompass additional model elements, such as new types of constraints and objective functions. Similarly, the method itself can be evolved to varying levels of sophistication. We provide several examples of discrete optimization problems to illustrate the strategic concerns of tabu search, and to show how they may be exploited in various contexts. Our presentation is motivated by the emergence of an extensive literature of computational results, which demonstrates that a well-tuned implementation makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.The research of this paper has been supported in part by the joint Air Force Office of Scientific Research and Office of Naval Research Contract No. F49620-90-C-0033, at the University of Colorado and by the Swiss National Research Council Grant No. 20-27926.89.     

Parallel asynchronous tabu search for multicommodity location-allocation with balancing requirements

We study and compare asynchronous parallelization strategies for tabu search, and evaluate the impact on performance and solution quality of some important algorithmic design parameters: number of processors, handling of exchanged information, etc. Parallelization approaches are implemented and compared by using a tabu search algorithm for multicommodity location-allocation problems with balancing requirements.     

Reducing the bandwidth of a sparse matrix with tabu search

The bandwidth of a matrix A={a_ij} is defined as the maximum absolute difference between i and j for which a_ij≠0. The problem of reducing the bandwidth of a matrix consists of finding a permutation of the rows and columns that keeps the nonzero elements in a band that is as close as possible to the main diagonal of the matrix. This NP-complete problem can also be formulated as a labeling of vertices on a graph, where edges are the nonzero elements of the corresponding symmetrical matrix. Many bandwidth reduction algorithms have been developed since the 1960s and applied to structural engineering, fluid dynamics and network analysis. For the most part, these procedures do not incorporate metaheuristic elements, which is one of the main goals of our current development. Another equally important goal is to design and test a special type of candidate list strategy and a move mechanism to be embedded in a tabu search procedure for the bandwidth reduction problem. This candidate list strategy accelerates the selection of a move in the neighborhood of the current solution in any given iteration. Our extensive experimentation shows that the proposed procedure outperforms the best-known algorithms in terms of solution quality consuming a reasonable computational effort.     

Improving short-term conflict alert via tabu search

In this paper we describe some work carried out by National Air Traffic Services in the UK into developing optimisation tools to help improve the effectiveness of one of their air traffic control safety systems—short-term conflict alert. In short-term conflict alert a computer system continually monitors radar data and alerts air traffic controllers if it detects a situation where two aircraft are in danger of approaching too close to one other. Within the computer program that makes up the short-term conflict alert system is a large number of parameters. Choosing appropriate values for these parameters is a task that is currently done via extensive human intervention. In this paper we describe how a modern heuristic technique, tabu search, can be used to make parameter choices.     

Scatter tabu search for multiobjective clustering problems

We propose a hybrid heuristic procedure based on scatter search and tabu search for the problem of clustering objects to optimize multiple criteria. Our goal is to search for good approximations of the efficient frontier for this class of problems and provide a means for improving decision making in multiple application areas. Our procedure can be viewed as an extension of SSPMO (a scatter search application to nonlinear multiobjective optimization) to which we add new elements and strategies specially suited for combinatorial optimization problems. Clustering problems have been the subject of numerous studies; however, most of the work has focused on single-objective problems. Clustering using multiple criteria and/or multiple data sources has received limited attention in the operational research literature. Our scatter tabu search implementation is general and tackles several problems classes within this area of combinatorial data analysis. We conduct extensive experimentation to show that our method is capable of delivering good approximations of the efficient frontier for improved analysis and decision making.     

Goal programming using multiple objective tabu search

Goal programming (GP) is one of the most commonly used mathematical programming tools to model multiple objective optimisation (MOO) problems. There are numerous MOO problems of various complexity modelled using GP in the literature. One of the main difficulties in the GP is to solve their mathematical formulations optimally. Due to difficulties imposed by the classical solution techniques there is a trend in the literature to solve mathematical programming formulations including goal programmes, using the modern heuristics optimisation techniques, namely genetic algorithms (GA), tabu search (TS) and simulated annealing (SA). This paper uses the multiple objective tabu search (MOTS) algorithm, which was proposed previously by the author to solve GP models. In the proposed approach, GP models are first converted to their classical MOO equivalent by using some simple conversion procedures. Then the problem is solved using the MOTS algorithm. The results obtained from the computational experiment show that MOTS can be considered as a promising candidate tool for solving GP models.     

A tabu search experience in production scheduling

During the last four years, tabu search has been shown to be a remarkably effective method in solving difficult combinatorial optimization problems. Nowhere has this success been more marked than in the timely and very important area of production scheduling. In this paper, we review some of the research that has contributed to that success. We also give a synthesis of the various tabu search mechanisms that have been employed, giving special attention to advances that have led to major improvements. In the final section of the paper, we suggest directions for future research.     

A tabu search algorithm for frequency assignment

This paper presents the application of a tabu search algorithm for solving the frequency assignment problem. This problem, known to be NP-hard, is to find an assignment of frequencies for a number of communication links, which satisfy various constraints. We report on our computational experiments in terms of computational efficiency and quality of the solutions obtained for realistic, computer-generated problem instances. The method is efficient, robust and stable and gives solutions which compare more favourably than ones obtained using a genetic algorithm.     

Enhancing case-based reasoning for personnel rostering with selected tabu search concepts

In this paper, we investigate the advantages of using case-based reasoning (CBR) to solve personnel rostering problems. Constraints for personnel rostering problems are commonly categorized as either ‘hard’ or ‘soft’. Hard constraints are those that must be satisfied and a roster that violates none of these constraints is considered to be ‘feasible’. Soft constraints are more flexible and are often used to measure roster quality in terms of staff satisfaction. We introduce a method for repairing hard constraint violations using CBR. CBR is an artificial intelligence paradigm whereby new problems are solved by considering the solutions to previous similar problems. A history of hard constraint violations and their corresponding repairs, which is captured from human rostering experts, is stored and used to solve similar violations in new rosters. The soft constraints are not defined explicitly. Their treatment is captured implicitly during the repair of hard constraint violations. The knowledge in the case-base is combined with selected tabu search concepts in a hybrid meta-heuristic algorithm. Experiments on real-world data from a UK hospital are presented. The results show that CBR can guide a meta-heuristic algorithm towards feasible solutions with high staff satisfaction, without the need to explicitly define soft constraint objectives.     

Iterated tabu search for the car sequencing problem

This paper introduces an iterated tabu search heuristic for the daily car sequencing problem in which a set of cars must be sequenced so as to satisfy requirements from the paint shop and the assembly line. The iterated tabu search heuristic combines a classical tabu search with perturbation operators that help escape from local optima. The resulting heuristic is flexible, easy to implement, and fast. It has produced very good results on a set of test instances provided by the French car manufacturer Renault.