Single-model Assembly Line Balancing Research Articles

A GRA-based approach for optimal selection of SALBP-1 assembly line balancing heuristics

Assembly line balancing is a compulsory approach which enables mass production of similar parts with proper efficacy. Multiple heuristics are proposed in literature to solve single model assembly line balancing problems (SALBP-1). Previous work has focused on developing and evaluating assembly line balancing heuristics and apply them to different fields. Other work investigates the factors affecting the performance of particular assembly line balancing heuristic. However, there is still a need for a guideline for selecting the most suitable heuristic for a particular application with insights into multiple performance measures. This research presents a grey relational analysis (GRA)-based approach to aid process designers in selecting the heuristic generating that yields the best line balancing. Specifically, combinations of factors of cycle time, order strength, number of tasks, and time variability are considered as operational setups of the line balancing process. The solution of each setup is solved by each heuristic, and the performance of the solution is evaluated. Then, GRA is applied to rank the heuristics on basis of the resulting grey rational grades. The proposed approach provides useful insights into the combinations suitable for a particular heuristic. In this study, time variability and cycle time are shown to have the greatest impact on the overall performance of the considered heuristics.

Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach

In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems.

Balancing mixed-model assembly lines: a computational evaluation of objectives to smoothen workload

Mixed-model assembly lines are widely used in a range of production settings, such as the final assembly of the automotive and electronics industries, where they are applied to mass-produce standardised commodities. One of the greatest challenges when installing and reconfiguring these lines is the vast product variety modern mixed-model assembly lines have to cope with. Traditionally, product variety is bypassed during mid-term assembly line balancing by applying a joint precedence graph, which represents an (artificial) average model and serves as the input data for a single model assembly line balancing procedure. However, this procedure might lead to considerable variations in the station times, so that serious sequencing problems emerge and work overload threatens. To avoid these difficulties, different extensions of assembly line balancing for workload smoothing, i.e. horizontal balancing, have been introduced in the literature. This paper presents a multitude of known and yet unknown objectives for workload smoothing and systematically tests these measures in a comprehensive computational study. The results suggest that workload smoothing is an essential task in mixed-model assembly lines and that some (of the newly introduced) objectives are superior to others.

A goal programming approach to simple U-line balancing problem

In this paper, a goal programming model for the simple U-line balancing (ULB) problem is developed. The model is based on the integer programming formulation developed by Urban [Urban, Note: Optimal balancing of U-shaped assembly lines, Management Science 44(5) (1998) 738–741] for the ULB problem and the goal model of Deckro and Rangachari [Deckro, Rangachari, A goal approach to assembly line balancing, Computers and Operations Research 17 (1990) 509–521] developed for the traditional single model assembly line balancing (ALB) problem. The proposed model which is the first multi-criteria decision making approach to the U-line version provides increased flexibility to the decision maker since several conflicting goals can be simultaneously considered.

Integration of Process Planning and Scheduling for Outsourcing in the Apparel Industry

Although outsourcing is a popular method for reducing manufacturing costs, many firms fail to take notice of the hidden pitfalls associated with delays in the delivery system. The objective of the paper is to develop a computer-aided process planning tool with a software called PPSGAR (Process Planning and Scheduling For Garment Manufacturing Unit), which takes into account the concept of integration of process planning and scheduling using an object-oriented paradigm for the apparel industry. It provides the management, prior to accepting an order, with a suggestion for the production cost and the due date combination by considering the shop-floor conditions. Also, it can provide suggestions for possible changes in the design of the garment in order to reduce the cost or shorten the delivery date. Process planning is achieved by adopting the Hybrid Variant-generative process planning method. Scheduling is done by Stochastic Assembly Line Balancing (SALB) methodology for the solution of a single-model assembly line balancing, in response to the stochastic environment. For validation of the system, the Software PPSGAR has been implemented successfully in a firm in the apparel industry in North Cyprus.

Genetic algorithm for assembly line balancing

Research on single-model assembly line balancing has produced several good algorithms for solving large problems. The majority of these algorithms generate just one solution to the problem, whereas the real line design faces the need to investigate alternative solutions, where preferences for work allocation to stations are considered, or constraints other than technological precedence are taken into account. The MUST algorithm suggested by Dar-El and Rubinovitch is one of the few algorithms that provides such diversity of solutions. Heuristics enable MUST to solve relatively large line-balancing problems. However, when the initial problem has relatively few constraints, the time and memory requirements needed by MUST may become excessive. This paper describes the development and testing of a Genetic Algorithm for the generation of multiple solutions to the assembly line balancing (ALB) problem. The results are compared with MUST results for different classes of problems. Good results are achieved by combining the genetic approach with a simple local optimization procedure. This procedure performs much faster than MUST for problems with large number of stations and high flexibility ratio. Different crossover and mutation procedures are tested and evaluated, in order to recommend these which are most effective for solving ALB problems.

A stochastic approach to assembly line balancing

Abstract This paper propounds a stochastic methodology—SALB—for the solution of a single-model assembly line balancing, which has been developed in response to the problems associated with the allocation of tasks to work stations, especially where uncertainties (and hence risks) abound. The study adopts the methodology of stochastic modelling, whereby various probability distributions are integrated within a modified COMSOAL algorithm, as a means of addressing the uncertainties associated with key assembly line balancing variables, such as cycle time and task times. The proposed methodology incorporates four objective criteria options—minimizing the number of work stations, minimizing the balance delay, minimizing the cycle time, or a combination of two or more thereof. These are designed and equipped with the capability to generate a range of possible solutions to the assembly line balancing problem, from which an ‘optimal’ solution can be ascertained. The SALB methodology is demonstrated with a case stu...

Complexity of single model assembly line balancing problems

This paper describes a method for generating assembly line balancing problems randomly and measuring their complexities. In this present study, a measure for complexity of assembly line balancing problem, TF-ratio (ratio of the sum of total number of followers of each element and maximum possible number of ordering relations among them) is used. A complex problem will need more CPU time for its solution. Some of the factors, such as, work content (W), cycle time (C), standard deviation (SD) of elemental times, TF-ratio, etc., which are responsible for the complexity of the line balancing problem, are identified and their effect on the complexity of the problem is discussed.

Must—A Multiple Solutions Technique for Balancing Single Model Assembly Lines

Research on single-model assembly line balancing has produced several good algorithms for solving large problems. All these algorithms, however, generate just one solution to the balancing problem. With these, line designers wishing to investigate alternative station combinations of work elements are forced to do so manually. This paper concerns an algorithm (MUST) which generates alternative solutions of equal quality for single-model assembly line balancing problems. The method is based on the Mansoor-Yadin algorithm and in its optimum seeking form, MUST generates all existing optimal balances in a single pass. Three heuristics, used singularly or together, are introduced for solving the larger balancing problems. These are designed to produce about 100 different balance solutions of equal quality at each pass. MUST has been successfully used in balancing assembly lines having up to 140 work elements with widely differing precedence structures. A comparison of this method with MALB, one of the most efficient known heuristic methods, results in MUST dominating or equalling MALB in every case. Reasonable computation times (average 125.4 sec.—IBM 370/168) and core usage are achieved through the use of advanced computation methods. The need to explore alternative station combinations may arise in several ways—for example, the line designer may prefer (but not require) that certain work elements be allocated to a common station either because of: access to materials handling and storage facilities, service availability (e.g., compressed air lines), use of common tools or operator skills, etc. Alternatively, minor adjustments to the line may be required between adjacent stations in order to alleviate conditions at one station which exhibits excessive variability in its performance times. The generation of multiple solutions of equal efficiency adds a new dimension to the quality of balancing effectiveness, enabling the line designer to select the alternative that best suits his requirements.

MALB—A Heuristic Technique for Balancing Large Single-Model Assembly Lines

Abstract This paper is concerned with the development of a heuristic algorithm (MALB) for the general solution of large single-model assembly line balancing problems. It is based on the optimum-seeking backtracking method proposed by Mansoor (1964). Excellent results were obtained from tests taken over a wide range of large ALB problems. Over a third of the tests yielded a 100% line efficiency and the overall computation time was 22 sec on an IBM 7040/7044.