A Petri net-based heuristic for mixed-model assembly line balancing problem of Type-E

To effectively respond to the changing market demands, a manufacturer should produce variety of products with small lots. Thus, multiple products (models) are assembled simultaneously on a same line. However, it is very challenging to balance such an assembly line. This paper conducts a study on balancing a mixed-model assembly line of Type E. To solve this problem, a coloured-timed Petri net model is developed to describe the task precedence relationship. Also, the optimisation problem is formulated as a mathematical programming model. Then, with the models, a two-stage heuristic algorithm is proposed to solve the problem. At the first stage, based on the Petri net model, a P-invariant algorithm (PA) is presented to minimise the number of workstations. At the second stage, a heuristic is proposed to further minimise the cycle time by combining the PA with a binary search algorithm (BSA). Performance of the proposed method is evaluated by an illustrative example and numerical experiments. It is shown that it works well in terms of both solution accuracy and computational efficiency for large size problems.     

Computational modelling of manufacturing choice complexity in a mixed-model assembly line

Manufacturing systems have evolved to adopt a mixed-model assembly line enabling the production of high product variety. Although the mixed-model assembly system with semi-automation (i.e. human involvement) can offer a wide range of advantages, the system becomes very complex as variety increases. Further, while the complexity from different options can worsen the system performance, there is a lack of quantifiable models for manufacturing complexity in the literature. Thus, in this paper, we propose a novel method to quantify manufacturing choice complexity for the effective management of semi-automated systems in a mixed-model assembly line. Based on the concept of information entropy, our model considers both the options mix and the similarities between options. The proposed model, along with an illustrative case study, not only serves as a tool to quantitatively assess the impact of choice complexity on total system performance, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.     

Minimising utility work and utility worker transfers for a mixed-model assembly line

Mixed-model assembly lines (MMALs) are types of production lines that are able to respond to diversified costumers’ demand for a variety of models without holding large inventories. The effective utilisation of a mixed-model assembly line requires the determination of the assembly sequence for different models. In this paper, two objectives are considered in a sequential manner, namely minimising: (i) total utility work, which means work from an additional worker to assist an operator for completion of an assembly task; and (ii) utility worker transfer which states the move of a utility worker to a different segment of the assembly line. First, due to the NP-hard nature of the problem, three heuristic methods are proposed with the aim of minimising total utility work. Then, the solutions which are obtained from the heuristics are improved in terms of the total number of utility worker transfers via a local search based method. Furthermore, the solution approach was applied in a real life mixed model tractor assembly line. Results validated the effectiveness of sequencing approach in terms of solution quality.     

Balancing and sequencing of parallel mixed-model assembly lines

In this paper, a simulated annealing approach is developed for the parallel mixed-model assembly line balancing and model sequencing (PMMAL/BS) problem which is an extension of the parallel assembly line balancing (PALB) problem introduced by Gökçen et al. (2006 Gökçen, H and A?pak, K. 2006. A goal programming approach to simple U-line balancing problem. European Journal of Operational Research, 171(2): 577–585. [Crossref], [Web of Science ®] , [Google Scholar]). In PALB, the aim is to balance more than one assembly line together. Balancing of the lines simultaneously with a common resource is very important in terms of resource minimisation. The proposed approach maximises the line efficiency and distributes the workloads smoothly across stations. The proposed approach is illustrated with two numerical examples and its performance is tested on a set of test problems. The computational results show that the proposed approach is very effective for PMMAL/BS.     

An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line that is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such lines involve several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). This paper proposes an adaptive genetic algorithm approach to solve MMALSP where multiple objectives such as variation in part consumption rates, total utility work and setup costs are considered simultaneously. The proposed approach integrates an adaptive parameter control (APC) mechanism into a multi-objective genetic algorithm in order to improve the exploration and exploitation capabilities of the algorithm. The APC mechanism decides the probability of mutation and the elites that will be preserved for succeeding generations, all based on the feedback obtained during the run of the algorithm. Experimental results show that the proposed adaptive GA-based approach outperforms the non-adaptive algorithm in both solution quantity and quality.     

Workload balancing and manufacturing complexity levelling in mixed-model assembly lines

To effectively react and meet the current ever growing demand for individualised motor vehicles, built to customer specific requirements, automotive industry has accelerated its transition towards mass-customisation. As a result, the number of new model introductions has drastically increased over the past three decades. To cope with this intensified customisation, the current automotive assembly platforms are designed to assemble a wide range of relatively different models, and are turned into mixed-model assembly lines (MMALs). This implies that the set of tasks to be performed on each workstation is no longer stable but varies highly with the model-mix. As a consequence, the manufacturing complexity increases at the workstations and throughout the whole assembly system. This paper proposes a method to monitor manufacturing complexity at each workstation while the MMAL is being balanced. An entropy-based quantitative measure of complexity, which incorporates the variability of each task duration, is developed. This measure is used to monitor the manufacturing complexity level at each workstation. An integrated mixed-line balancing and complexity monitoring heuristic is proposed, to determine workload balance solutions, in which manufacturing complexity is levelled throughout the workstations composing the line. This procedure is tested on a real data-set provided by an automotive manufacturer. The results are reported and thoroughly discussed.     

Balancing of mixed-model parallel U-shaped assembly lines considering model sequences

As a consequence of increasing interests in customised products, mixed-model lines have become the most significant components of today’s manufacturing systems to meet surging consumer demand. Also, U-shaped assembly lines have been shown as the intelligent way of producing homogeneous products in large quantities by reducing the workforce need thanks to the crossover workstations. As an innovative idea, we address the mixed-model parallel U-shaped assembly line design which combines the flexibility of mixed-model lines with the efficiency of U-shaped lines and parallel lines. The multi-line stations utilised in between two adjacent lines provide extra efficiency with the opportunity of assigning tasks into workstations in different combinations. The new line configuration is defined and characterised in details and its advantages are explained. A heuristic solution approach is proposed for solving the problem. The proposed approach considers the model sequences on the lines and seeks efficient balancing solutions for their different combinations. An explanatory example is also provided to show the sophisticated structure of the studied problem and explain the running mechanism of the proposed approach. The results of the experimental tests and their statistical analysis indicated that the proposed line design requires fewer number of workstations in comparison with independently balanced mixed-model U-lines.     

Measuring complexity in mixed-model assembly workstations

In an effort to maintain or increase their market share and at the same time prevent costs from escalating, manufacturing organisations are increasingly using their current manufacturing system to produce custom output. As a consequence, the large number of product variants increases significantly the complexity of manufacturing systems, both for the operators as for the support services. This is especially true in automotive industry, where customisation is increasing at a rapid pace. To counter the ensuing loss of productivity, a more fundamental approach to dealing with this complexity in manufacturing processes is required. In order to investigate the impact of complexity on production performance, one must first delineate the concept and then identify as unambiguously as possible highly complex workstations. This article defines complexity at the workstation level and proposes a complexity measure for mixed-model assembly workstations. Based on data from several leading automotive companies from Belgium and Sweden, some statistical models are proposed to characterise workstations complexity. The models are described and their validity and accuracy are discussed.     

Hierarchical approach for paced mixed-model assembly line balancing and sequencing with jolly operators

In order to increase flexibility and reduce costs, several companies adopt mixed-model assembly lines whose output products are variations of the same basic model with specific and distinctive attributes. Unfortunately, such attributes typically lead to variations in the task process times. In the case of un-paced buffered assembly lines, these variations are smoothed by buffers with consequences in terms of work-in-progress, costs, space utilisation and lower productivity control. To face such weaknesses, some companies adopt paced un-buffered assembly lines where the cycle time is controlled by the continuous/synchronous moving of the products from the first to the last assembly station. In such contexts, the possibility of assembling different models with different assembly times can be managed through the use of supplementary flexible workforce. This article introduces an innovative balancing and sequencing hierarchical approach for paced mixed-model assembly lines using supplementary flexible workforce called ‘jolly operators’. The goals are to minimise the number of jolly operators and to limit the occurrence of work-overloads, which typically result in out-of-the-line assembly completion. The proposed approach is preliminary validated and applied to a case study from an Italian company assembling industrial air-dryers.     

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 novel bi-level hierarchy towards available-to-promise in mixed-model assembly line sequencing problems

This article addresses advanced available-to-promise (AATP) in mixed-model assembly line sequencing problems. In the developed framework, customers are prioritized with respect to 11 defined criteria using the technique for order of preference by similarity to ideal solution (TOPSIS) method, and order quantities are calculated using a nonlinear mathematical program. Next, a mixed binary nonlinear mathematical program is developed to determine the optimum sequence of the optimized order quantities to minimize the total lateness. Since the proposed models are intractable, a hybrid genetic algorithm–simulated annealing method is also developed. Finally, an industrial case study is reported, the results of which validate the developed AATP framework.     

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.     

Integrated procedure of balancing and sequencing for mixed-model assembly lines: a multi-objective evolutionary approach

A mixed-model assembly line is a type of production line which is used to assemble a variety of product models with a certain level of similarity in operational characteristics. This variety causes workload variance among other problems resulting in low efficiency and line stops. To cope with these problems, a hierarchical design procedure for line balancing and model sequencing is proposed. It is structured in terms of an amelioration procedure. On the basis of our evolutionary algorithm, a genetic encoding procedure entitled priority-based multi-chromosome (PMC) is proposed. It features a multi-functional chromosome and provides efficient representation of task assignment to workstations and model sequencing. The lean production perspective recognises the U-shape assembly line system as more advanced and beneficial compared to the traditional straight line system. To assure the effectiveness of the proposed procedure, both straight and U-shape assembly lines are examined under two major performance criteria, i.e., number of workstations (or line efficiency) as static criterion and variance of workload (line and models) as dynamic criterion. The results of simulation experiments suggest that the proposed procedure is an effective management tool of a mixed-model assembly line system.     

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed in the literature. The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent-based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent-based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles are explained and discussed.     

Level scheduling of mixed-model assembly lines under storage constraints

In a mixed-model assembly line, different models of a common base product can be manufactured in intermixed production sequences. A famous solution approach for the resulting short-term sequencing problem is the so-called level scheduling problem, which aims at evenly smoothing the material requirements over time in order to facilitate a just-in-time supply. However, if materials are delivered in discrete quantities, the resulting spread of material usages implies that issued cargo carriers of a respective material remain at a station for a longer period of time. In practical applications with many materials required per station, this procedure might lead to bottlenecks with respect to the scarce storage space at stations. This paper investigates level scheduling under the constraint that the induced part usage patterns may not violate given storage constraints. The resulting sequencing problem is formalised and solved by suitable exact and heuristic solution approaches.     

Sequencing mixed-model assembly lines operating with a heterogeneous workforce

We study the problem of sequencing mixed-model assembly lines operating with a heterogeneous workforce. The practical motivation for this study comes from the context of managing assembly lines in sheltered work centres for the disabled. We propose a general framework in which task execution times are both worker and model dependent. Within this framework, the problem is defined and mathematical mixed-integer models and heuristic procedures are proposed. These include a set of fast constructive heuristics, two local search procedures based on approximate measures using either a solution upper bound or the solution of a linear program and a GRASP metaheuristic. Computational tests with instances adapted from commonly used literature databases are used to validate the proposed approaches. These tests give insight on the quality of the different techniques, which prove to be very efficient both in terms of computational effort and solution quality when compared to other strategies such as a random sampling or the solution of the MIP models using a commercial solver.     

Simultaneous solving of balancing and sequencing problems in mixed-model assembly line systems

Recently, the mixed-model assembly line (MMAL) has been widely studied by many researchers. In fact, there are two basic problems, namely balancing and sequencing problems, which have been investigated in a lot of studies separately, but few researchers have solved both problems simultaneously. Regarding this, the best results in minimising total utility work have been gained by developing a co-evolutionary genetic algorithm (Co-GA) so far. This paper provides a mixed-integer linear programming (MILP) model to jointly solve the problems. Because of NP-hardness, an evolution strategies (ES) algorithm is presented and evaluated by the same test problems in the literature. Two main hypotheses, namely simultaneous search and feasible search, are tested in the proposed algorithm to improve the quality of solutions. To calibrate the algorithm, a Taguchi design of experiments is employed. The proposed ES is compared with the modified version of Co-GA and the MILP model results. According to numerical experiments and statistical proving, the proposed ES outperformed the modified Co-GA from two points of view: the objective function and the computational time. Additionally, the meta-heuristic algorithms are examined in terms of other well-known criteria in MMAL. Finally, the contribution of each hypothesis in accounting for this superiority is analysed.     

Balancing manual mixed-model assembly lines using overtime work in a demand variation environment

This study deals with the balancing problem of a manual mixed-model assembly line, where the production volume or the product mix changes from shift to shift during the planning horizon. The unstable demand can be characterised by several representative scenarios, and the line uses overtime work to meet the demand variation. The balancing problem concerns how to assign assembly tasks to stations and determine the amount of overtime in each possible demand scenario. The objective is to satisfy the demand in each possible scenario with the minimum labour costs paid for both normal shifts and overtime work. A lower bound on the labour costs is proposed, and a heuristic algorithm is developed to quickly find a feasible solution. A branch, bound and remember (BB&;R) algorithm is then proposed to find better solutions. These solution methods are tested on 765 instances. The BB&;R algorithm obtains optimal solutions for 510 instances and gives high-quality solutions for the remaining 255 instances within 60?s. The experimental results show that the use of overtime work and adjustable cycle times significantly reduces the labour costs, especially when the demand or task processing time variations are large.     

A multi-decision genetic approach for workload balancing of mixed-model U-shaped assembly line systems

A mixed-model assembly line is a type of production line where a variety of product models similar in product characteristics are produced. As a consequence of introducing the just-in-time (JIT) production principle, it has been recognised that a U-shaped assembly line system offers several benefits over the traditional straight line system. This paper proposes a new evolutionary approach to deal with workload balancing problems in mixed-model U-shaped lines. The proposed method is based on the multi-decision of an amelioration structure to improve a variation of the workload. This paper considers both the traditional straight line system and the U-shaped assembly line, and is thus an unbiased examination of line efficiency. The performance criteria considered are the number of workstations (the line efficiency) and the variation of workload, simultaneously. The results of experiments enhanced the decision process during multi-model assembly line system production; thus, it is therefore suitable for the augmentation of line efficiency in workstation integration and simultaneously enhancement of the variation of the workload. A case study is examined as a validity check in collaboration with a manufacturing company.     

Sequence-to-customer goal with stochastic demands for a mixed-model assembly line

A mixed-model assembly line comprises a set of workstations in serial and a conveyor moving at a constant speed, which can assemble variety products in different models during a working shift or a working day. Initial-units that belong to different models are successively fed onto the conveyor at a given cycle time length to get into the assembling operations as semi-products. The conveyor moves semi-products to pass through the workstations gradually to complete the assembling operations for generating finished products. A set of warehouses stores finished products, and each model has a specified warehouse. Customers arrive at the warehouses to demand finished products with stochastic demand forms. A daily scheduling task is the determination of the sequence that specifies the feeding order of the models, which must be set out at the beginning of each day. This paper deals with a new goal, ‘sequence-to-customer’, with stochastic customer demands. An optimization problem is formulated with the objective of minimizing the system cost that includes the holding cost for finished products and the penalty cost for backordered customers during a decision horizon. A lower bound of the system cost is found, which is useful in verifying the optimality of any solution. A heuristic algorithm is proposed to solve the optimization problem, which can obtain optimal solutions or near-optimal solutions with almost ignorable relative errors to optimal solutions. By using the algorithm, the behaviour of the system cost with respect to the variation in customer demands is also investigated to provide insights into management of a mixed-model assembly line.