Assembly Lines with Overlapping Work Stations

Purpose – This study seeks to present a simple assembly line design and its balance for a low‐volume manufacturing company.Design/methodology/approach – The study presents experiences with the design and implementation of a simple assembly line. The implementation concerns three aspects; design and construction of the assembly line, the assembly analysis of the product, and then balancing of the line. It also discusses construction and implementation difficulties of this tactical tool in the case company.Findings – The study presents some outcomes from the design, implementation, and balancing of an assembly line for SMEs.Research limitations/implications – The study is limited by the case company and its experience.Practical implications – This study is not pure theoretical study, it has application stages for industry, and it provides some real interface for the people from SMEs.Originality/value – The approach has an original value in respect of implementation of assembly line for a small manufacturing...

Large scale manufacturing companies are growing along with ever-increasing demand from consumers, one of which is the heavy equipment industry. Excavators are the products of the heavy equipment division at XYZ Corp, where the Excavator assembly lines are single-model. Based on observations of existing conditions that occur at XYZ Corp, the production still cannot meet the demands of consumers. In the assembly process, the Excavator has a significant time difference from each work station, the distribution of work elements in the assembly process is uneven. In the Final assembly section (Zone C) there is a discrepancy between the takt time of Excavator products which is one reason for not achieving the demand. In resolving these problems, a line balancing process is needed, namely balancing workloads on each workstation. This study applies the Heuristic Priority Rules method as an initial solution and allocation constraint should be added as input from the Genetic Algorithms method to complete line balancing with a single-model system. Problems can be solved by increasing line efficiency from existing conditions by 60.4% to 91.74% and Smoothness index which decreases from 955.54 to 158.3977. The proposed result of balancing the final assembly of the excavator results in a better balance of assembly lines.

PurposeCycle time fluctuations in assembly lines are one of the important reasons of re‐balancing. As a result of re‐balancing of assembly lines, it will be necessary to change task sequences or equipment locations. The purpose of this paper is to find the task sequence which enables assembly line balancing (ALB) with minimum number of stations (NS) for different cycle times such that tasks and equipment or fixture locations remain unchanged.Design/methodology/approachIn this paper a heuristic which consist of two stages is proposed to find a common task sequence for different cycle times in assembly lines.FindingsIt is shown that optimal NS for different cycle times can be achieved with a fixed task sequence.Research limitations/implicationsThe approach is limited to a single model case. Model variety together with cycle time variety can be investigated in further studies.Practical implicationsAssembly lines which require less time and cost for re‐balancing can be easily designed by the proposed approach.Originality/valueALB problem is handled with a new viewpoint. Also, it is observed that the proposed approach serves as a bridge between assembly line design and balancing. In this regard, it is thought to have an important place in the ALB literature.

Assembly lines with fractional tasks increase the throughput with a better workload balancing among the stations. The possibility to share tasks between two consecutive stations can also support the design of assembly lines with redundancies of the tasks. The redundancy of the tasks allows reacting to short failures or variations in processing time. This paper proposes a framework to support the design and control of redundancy assembly lines. The first problem concerns a modified mixed-integer linear programming model used for the fractional allocation assembly line problem to design redundancy assembly lines. Then, this research proposes control policies to manage the allocation of the tasks shared between two consecutive stations. Finally, the simulation evaluates the performance of the proposed methods to handle short failures and uncertain processing times. The numerical results show the potential improvements of the proposed methods with a limited number of redundancies. The main improvements concern the reduction of throughput time and work in process.

This article presents a novel approach for the automated design of assembly lines that combines the assembly line balancing problem with resource selection and the positioning of the chosen resources into one single optimisation problem. Existing approaches for the automated planning of assembly plants either focus on one planning step or work through different planning steps sequentially. So far, no method exists that sufficiently takes into account the interdependency between the selection and positioning of resources. This article addresses this problem by presenting a bi-level optimisation approach for the automated design of assembly lines. A nested genetic algorithm is used to solve an assembly line balancing problem that includes the selection of production resources while simultaneously considering the layouting options for the chosen resources. Three examples for the evaluation and validation of the algorithm are presented. The presented approach is economically promising as the design of assembly lines requires a lot of expert knowledge and is still mostly done manually.

The most important factor affecting efficiency and ergonomic risk levels in an assembly line design is the problem of assigning certain tasks to certain stations, namely the assembly line balancing problem. In the literature, assembly line balancing problem has often been studied, but studies that consider ergonomic risks are deficient. Recently, it has been one of the issues that have started to attract great attention with the realization of health problems caused by assembly lines. To this end, in this study, a bi‐objective mathematical model is developed that considers balancing assembly line station time and ergonomic risk levels, simultaneously. It is aimed to minimize both station time and the total deviations of ergonomic risk scores for the stations. Weighted sum and conic scalarization methods are applied to solve the bi‐objective model. To analyze the outcomes of the developed model, an application is proposed and tested on a real industrial case, at a home appliance assembly line. The deployment of the OMAX method is a contribution to the literature since it shows an analysis tool which evaluates the results of assembly line balancing. This method evaluates the performance of the stations based on different criteria such as station time and ergonomic risk. The number of high‐risk stations is obtained as 13 in the single‐objective model aiming to minimize the station time, while it is found to be nine in the bi‐objective model solved with CSM, without an increase in the total number of stations.

Multi-objective Early-stage Design of Automotive Hybrid Assembly Lines

In this chapter, a system to support the design of assembly and production lines used in the make-to-order production phase is presented. The relevance of the system is supported by the fact that current market dynamics often leads to frequent modifications in the allocation of manufacturing resources, and, as a result, decisions related to manufacturing systems design that used to belong to the strategic level are now taken at the tactical level, and thus require new tools to support them.The decision support system addresses two different categories of problems: (i) assembly line balancing and (ii) production line balancing. Due to the high complexity of these problems, it uses heuristic methods based on evolutionary computation approaches to tackle them. The system aggregates several modules to address the different problems, a database to handle both input and output data and an interface that enables a user-friendly interaction with the decision maker.

Enumerations and stability analysis of feasible and optimal line balances for simple assembly lines

The manufacturing industry has evolved in the past decades, due to the competition of the global economy, where the market demands high quality and customized products, and meeting lowest possible costs are paramount. One of the many processes in manufacturing is the assembly line operation, namely the assembly line balancing (ALB) problem. ALB problem is dependent on optimum resource utilization in order to improve production output, reduces costs, and shortens production lead times. In these recent years, various approaches have been proposed to solve the complexity of assembly line balancing operations, which composed of exact, heuristic, and meta-heuristic approaches. However, little work had been done to solve the type E assembly line balancing problem. This paper proposed an approach using artificial immune system (AIS) algorithm, namely as the artificial immune cell (AIC) approach, for solving type E assembly line balancing problem. An initialization mechanism through the bone marrow model and probabilistic clonal selection mechanism had encouraged efficient exploration and exploitation of the solution space. The computational results over 242 instances of 24 datasets had demonstrated the efficiency of the proposed AIC approach by achieving high-quality solutions (up to 85.94% optimum solutions was obtained). Also, the results were statistically justified by comparing with multi-rule multi-objective simulated annealing (MRMOSA), priority-based genetic approach (PriGA), two-phased genetic approach (2P-GA), and assignment genetic approach (MA-GA).  Â

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.

AutomationML as Seamless Data Exchange Format for Integration of Automated Planning Tools for Assembly Line Design

This paper presents a heuristic procedure for balancing production assembly lines and a computer program for carrying out that procedure. This research was undertaken to investigate the application of complex information processing techniques (as used in producing the Chess Machine and Logic Theorist) to a typical industrial problem. The assembly line balancing problem is stated as: Given an assembly process made up of elemental tasks, each with a time required per unit of product and an ordering with other tasks, what is the least number of work stations needed to attain a desired production rate? The heuristic procedure for assembly line balancing consists of three phases: repeated simplification of the initial problem by grouping adjacent elemental tasks into compound tasks; solution of the simpler problems thus created by assigning tasks to work stations at the least complex level possible, breaking up the compound tasks into their elements only when necessary for a solution; smoothing the resulting balance by transferring tasks among work stations until the distribution of assigned time is as even as possible. The heuristics used in each phase are considered in some detail. Appropriate means for mechanizing such a procedure are discussed, and operating results of the program on actual problems are presented.

Design of assembly lines is a knowledge intensive process relying significantly on experience and associated with high costs, long lead times and high probability of risks and reworks. This paper presents a methodology to support the early stage design of assembly lines through Knowledge Based Engineering (KBE). In this introductory paper the authors discuss the methodology to implement the KBE approach. A set of system engineering rules is extracted from direct interviews and domain best practices and knowledge about product and process is formalized. These rules will be implemented in a user-friendly platform allowing the design of first phase line layout by taking the defined system requirements (e.g. cycle time) as input. Then, the KBE approach is extended to a specific case study taken from the powertrain sector.

The assembly line balancing problem deals with the assignment of tasks to work stations. Mixed-model assembly line problem is a type of assembly line balancing problem at which two or more models of the same product are assembled sequentially at the same line. To achieve optimality and efficiency of solving this problem, tasks at each work station have to be well balanced satisfying all constraints. This paper deals with the mixed-model assembly line balancing problem (MALBP) in which the objective is to minimize the cycle time for a given number of work stations. The problem is solved by using a hybrid of an ant colony optimization and a greedy algorithm (Ant-Greedy). MATLAB Software is used to perform the proposed method. Then, the proposed method is applied to a real case problem found in the literature for the assembly line of automatic changeover in the Electronic Industries Company in Iraq. The results of the proposed method are compared with the performance of the Merging Shortest and Longest Operation (MMSLO) method. The comparison shows that the Ant-Greedy optimization method is more efficient, where the efficiency increased from 93.53% for MMSLO method to 97.26% for the Ant-Greedy method.