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Design, balancing, and sequencing are the key issues associated with assembly lines (ALs). The purpose of this paper is to identify AL design issues and to develop an integrated methodology for mixed‐model assembly line balancing (MMALB) and sequencing. Primarily, mixed‐model lines are utilized for high‐variety, low‐volume job shop or batch production. Variation of a generic product is important for the manufacturers as the demand is mostly customer driven in the present global market.

Different AL design norms, performance indexes, and AL workstation indexes have been identified in the initial stage of this work. As the paper progresses, it has focused towards an integrated approach for MMALB and sequencing addressed for small‐ and medium‐scale assembly plants. A small‐scale practical problem has been justified with this integrated methodology implemented by MATLAB.

ALs execution in the production floor require many important factors to be considered. Different line orientations, production approaches, line characteristics, performance and workstation indexes, problem definitions, balancing and product sequencing in accordance with the objective functions are needed to be taken into account by the line designer.

This paper has highlighted the important AL design characteristics and also provided an integrated approach for balancing mixed‐model assembly lines (MMALs) combined with sequencing heuristic. The findings of this paper can be helpful for the designers while designing an AL. The integrated approach for balancing and sequencing of MMALs can be used as a functional tool for assembly‐based contemporary industries.

This paper aims to review and discuss four aspects of mixed-model assembly line balancing (MMALB) problem mainly on the optimization angle. MMALB is a non-deterministic polynomial-time hard problem which requires an effective algorithm for solution. This problem has attracted a number of research fields: manufacturing, mathematics and computer science.

This paper review 59 published research works on MMALB from indexed journal. The review includes MMALB problem varieties, optimization algorithm, objective function and constraints in the problem.

Based on research trend, this topic is still growing with the highest publication number observed in 2016 and 2017. The review indicated that the future research direction should focus on human factors and sustainable issues in the problem modeling. As the assembly cost becomes crucial, resource utilization in the assembly line should also be considered. Apart from that, the growth of new optimization algorithms is predicted to influence the MMALB optimization, which currently relies on well-established algorithms.

The originality of this paper is on the research trend in MMALB. It provides the future direction for the researchers in this field.

This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line.

To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time.

The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy.

This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm.

This paper briefly reviews the assembly line balancing techniques developed over the last 30 years. It attempts to establish the direction of research, to identify unexplored areas with potential for study and recommends future courses of action.

The increasing energy shortage leads to worldwide attentions. This paper aims to develop a mathematical model and optimization algorithm to solve the energy-oriented U-shaped assembly line balancing problem. Different from most existing works, the energy consumption is set as a major objective.

An improved flower pollination algorithm (IFPA) is designed to solve the problem. The random key encoding mechanism is used to map the continuous algorithm into discrete problem. The pollination rules are modified to enhance the information exchange between individuals. Variable neighborhood search (VNS) is used to improve the algorithm performance.

The experimental results show that the two objectives are in conflict with each other. The proposed methodology can help manager obtain the counterbalance between them, for the larger size balancing problems, and the reduction in objectives is even more significant. Besides, the experiment results also show the high efficiency of the proposed IFPA and VNS.

The main contributions of this work are twofold. First, a mathematical model for the U-shaped assembly line balancing problem is developed and the model is dual foci including minimized SI and energy consumption. Second, an IFPA is proposed to solve the problem.

The recent shift towards accommodating flexibility in manufacturing companies and the complexity resulting from product variety highlight the significance of flexible assembly systems and designing products for them. The purpose of this paper is to provide insight into the requirements of a flexible assembly system for product design from the assembly system’s standpoint.

To fulfil the purpose of the paper, a literature review and a case study were performed. The case study was conducted with an interactive research approach in a global market leader company within the heavy vehicle manufacturing industry.

The findings indicate that common assembly sequence, similar assembly interfaces, and common parts are the main requirements of a flexible assembly system for product design which reduce complexity and facilitate various flexibility dimensions. Accordingly, a model is proposed to broaden the understanding of these requirements from the assembly system’s standpoint.

This study contributes to the overlapping research area of flexible assembly systems and product design.

The proposed model is largely based on practical data and clarifies the role of product design in facilitating flexibility in an assembly system. It can be used by assembly managers, assembly engineers, and product designers.

The key originality of this paper compared to the previous studies lies in presenting a novel assembly-oriented design model. The model enhances understanding of a flexible assembly system’s requirements for product design with regard to reducing complexity and managing variation in a flexible assembly system. These requirements can be applied to product design across various product families within a company’s product portfolio.

A comprehensive analysis of a flexible hybrid assembly system (FHAS) where automated devices and human operators interact is presented. A computer simulation programme and ANOVA were conducted to identify which factors significantly affect the measures of performance of the system. The results clearly indicated that certain factors have a more significant effect on the measures of performance than the others. A classification of the relative significance of input factors affecting such a system is made. In order to investigate the generality of the results, alternative station configurations and factor levels are examined. The outcome shows that the results are applicable to different configurations of FHAS.

The purpose of this paper is to introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes.

Scenario planning approach is used to represent the input data uncertainty in the decision model. Two kinds of robust criteria are provided: one is min‐max related; and the other is α‐worst scenario based. Corresponding optimization models are formulated, respectively. A genetic algorithm‐based robust optimization framework is designed. Comprehensive computational experiments are done to study the effect of these robust approaches.

With min‐max related robust criteria, the solutions can provide an optimal worst‐case hedge against uncertainties without a significant sacrifice in the long‐run performance; α‐worst scenario‐based criteria can generate flexible robust solutions: through rationally tuning the value of α, the decision maker can obtain a balance between robustness and conservatism of an assembly line task elements assignment.

This paper is an attempt to robust mixed model assembly line balancing. Some more efficient and effective robust approaches – including robust criteria and optimization algorithms – may be designed in the future.

In an assembly line with significant uncertainty, the robust approaches proposed in this paper can hedge against the risk of poor system performance in bad scenarios.

Using robust optimization approaches to balance mixed model assembly line.

This paper aims to propose a parallel automated assembly line system to produce multiple products in a semi-continuous system.

The control system developed in this research consists of a manufacturing system for two-level hierarchical dynamic decisions of autonomous/automated/automatic-guided vehicles (AGVs) dispatching/next station selection and machining schedules and a station control scheme for operational control of machines and components. In this proposed problem, the assignment of multiple AGVs to different assembly lines and the semi-continuous stations is a critical objective. AGVs and station scheduling decisions are made at the assembly line level. On the other hand, component and machining resource scheduling are made at the station level.

The proposed scheduler first decomposes the dynamic scheduling problems into a static AGV and machine assignment during each short-term rolling window. It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as a minimum cost flow problem during each short-term scheduling window. A comprehensive decision making process and heuristics are developed for efficient implementation. A simulation study is worked out for validation.

Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The sequence of stations is pre-specified in each assembly line and the components of a product are kept in machine magazine. The transportation between the stations in an assembly line (intra assembly line) and among stations in different assembly lines (inter assembly line) are performed using AGVs.

Robots are used in assembly lines because of their higher flexibility and lower costs. The purpose of this paper is to develop mathematical models and simulated annealing algorithms to solve the robotic assembly line balancing (RALB-II) to minimize the cycle time.

Four mixed-integer linear programming models are developed and encoded in CPLEX solver to find optimal solutions for small-sized problem instances. Two simulated annealing algorithms, original simulated annealing algorithm and restarted simulated annealing (RSA) algorithm, are proposed to tackle large-sized problems. The restart mechanism in the RSA methodology replaces the incumbent temperature with a new temperature. In addition, the proposed methods use iterative mechanisms for updating cycle time and a new objective to select the solution with fewer critical workstations.

The comparative study among the tested algorithms and other methods adapted verifies the effectiveness of the proposed methods. The results obtained by these algorithms on the benchmark instances show that 23 new upper bounds out of 32 tested cases are achieved. The RSA algorithm ranks first among the algorithms in the number of updated upper bounds.

Four models are developed for RALBP-II and their performance is evaluated for the first time. An RSA algorithm is developed to solve RALBP-II, where the restart mechanism is developed to replace the incumbent temperature with a new temperature. The proposed methods also use iterative mechanisms and a new objective to select the solution with fewer critical workstations.