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.     

Resequencing orders on mixed-model assembly lines: Heuristic approaches to minimise the number of overload situations

In production systems that are based on mixed-model assembly lines, the product sequence has a crucial impact on the load distribution of the workstations, which constitutes one of the main cost drivers. Due to disturbances that occur during the production process, the carefully planned production sequence often cannot be performed as intended because certain orders are blocked and must be resequenced later. In this paper, we consider a realistic resequencing setting of a mixed-model assembly line in the automotive industry. A basic mixed-integer programming model is presented and extended to consider multi-station workplaces and to prevent the premature deployment of utility workers. We devise and evaluate several local search procedures that combine ideas from simulated annealing, variable neighbourhood search and tabu search. We demonstrate that the best procedure produces optimal or near-optimal results for small and medium-sized problem instances quickly and outperforms the other heuristics on large test instances.     

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.     

Advanced scatter search approach and its application in a sequencing problem of mixed-model assembly lines in a case company

Mixed-model assembly line sequencing is significant in reducing the production time and overall cost of production. To improve production efficiency, a mathematical model aiming simultaneously to minimize overtime, idle time and total set-up costs is developed. To obtain high-quality and stable solutions, an advanced scatter search approach is proposed. In the proposed algorithm, a new diversification generation method based on a genetic algorithm is presented to generate a set of potentially diverse and high-quality initial solutions. Many methods, including reference set update, subset generation, solution combination and improvement methods, are designed to maintain the diversification of populations and to obtain high-quality ideal solutions. The proposed model and algorithm are applied and validated in a case company. The results indicate that the proposed advanced scatter search approach is significant for mixed-model assembly line sequencing in this company.     

Sequencing mixed-model assembly lines to minimise the number of work overload situations

The mixed-model sequencing problem is to sequence different product models launched down an assembly line, so that work overload at the stations induced by direct succession of multiple labour-intensive models is avoided. As a concept of clearing overload situations, especially applied by Western automobile producers, a team of cross-trained utility workers stands by to support the regular workforce. Existing research assumes that regular and utility workers assemble side-by-side in an overload situation, so that the processing speed is doubled and the workpiece can be finished inside a station's boundaries. However, in many real-world assembly lines the application of utility workers is organised completely differently. Whenever it is foreseeable that a work overload will occur in a production cycle, a utility worker takes over to exclusively execute work, whereas the regular worker omits the respective cycle and starts processing the successive workpiece as soon as possible. This study investigates this more realistic sequencing problem and presents a binary linear program along with a complexity proof. Different exact and heuristic solution procedures are then introduced and tested. Additional experiments show that the new model is preferable from an economic point of view whenever utility work causes considerable setup activities, for example walking to the respective station.     

Balancing and sequencing of stochastic mixed-model assembly U-lines to minimise the expectation of work overload time

A mixed-model assembly U-line is a flexible production system capable of manufacturing a variety of similar models, and it has become popular as an important component of the just-in-time production system. However, it poses new challenges for the optimal design of assembly lines because both the task assignment and the production sequence affect the workload variance among workstations. As a consequence, this paper addresses the line balancing problem and the model sequencing problem jointly and proposes a 0–1 stochastic programming model. In this model, task times are assumed to be stochastic variables independently distributed with normal distributions and the objective is to minimise the expectation of work overload time for a given combination of cycle time and number of workstations. To solve the problem, a simulated annealing-based algorithm is developed, which can also be used to minimise the absolute deviation of workloads in a deterministic environment. The experimental results for a set of benchmark problems show that the proposed algorithm outperforms the existing algorithms in terms of solution quality and running time.     

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.     

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.     

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.     

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.     

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.     

Line balancing and model sequencing to reduce work overload in mixed-model U-line production environments

Mixed-model U-lines (MMULs) are important elements of just-in-time production systems. For successful implementation of MMULs, a smoothed workload distribution among workstations is important. This requires that line balancing and model sequencing problems are solved simultaneously. This article presents a mixed, zero–one, nonlinear mathematical programming formulation for balancing and sequencing MMULs simultaneously with the objective of reducing work overload. Since the problem is NP-hard, an effective simulated annealing approach is also presented and its performance compared with existing approaches. The results show that the proposed simulated annealing algorithm outperforms existing approaches.     

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.     

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.     

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.     

A control theoretical modelling for velocity tuning of the conveyor belt in a dynamic mixed-model assembly line

This paper proposes a control theoretical modelling to study dynamic behaviour of a mixed-model assembly line. First, an open-loop model is developed for the system, then examined via different conveyor’s velocity values. It is realised that the performance of the system is very sensitive to the velocity; therefore, a closed-loop (CL) model is developed taking feedback from the system. By the use of proportional-integral-derivative (PID) controller and SIMULINK, some interesting results are obtained applying CL model: regardless of the sequence of the products in the line, the total work-overload and idleness always equals to zero. Moreover, less working area within the workstation is required. Based on the statistical analysis, it is found that no significant increase in makespan is imposed by CL model. It is also shown that PID controller is robust not only to the disturbances of the velocity, also to the uncertainties in the assembly operation times. These results are supported by many numerical experiments dealing with different test problems, line configurations and sequences. Finally, using a discrete event simulation model, the proposed approach is applied into a seru production mode. Simulation results show that the feedback PID controller can deal with real-world assembly line problems, successfully.     

Performance evaluation of ant colony optimization-based solution strategies on the mixed-model assembly line balancing problem

The aim of this article is to compare the performances of iterative ant colony optimization (ACO)-based solution strategies on a mixed-model assembly line balancing problem of type II (MMALBP-II) by addressing some particular features of real-world assembly line balancing problems such as parallel workstations and zoning constraints. To solve the problem, where the objective is to minimize the cycle time (i.e. maximize the production rate) for a predefined number of workstations in an existing assembly line, two ACO-based approaches which differ in the mission assigned to artificial ants are used. Furthermore, each ACO-based approach is conducted with two different pheromone release strategies: global and local pheromone updating rules. The four ACO-based approaches are used for solving 20 representative MMALBP-II to compare their performance in terms of computational time and solution quality. Detailed comparison results are presented.     

Reactive scheduling in a make-to-order flexible job shop with re-entrant process and assembly: a mathematical programming approach

A mixed-integer linear programming model is presented for the scheduling of flexible job shops, a production mode characteristic of make-to-order industries. Re-entrant process (multiple visits to the same machine group) and a final assembly stage are simultaneously considered in the model. The formulation uses a continuous time representation and optimises an objective function that is a weighted sum of order earliness, order tardiness and in-process inventory. An algorithm for predictive-reactive scheduling is derived from the proposed model to deal with the arrival of new orders. This is illustrated with a realistic example based on data from the mould making industry. Different reactive scheduling scenarios, ranging from unchanged schedule to full re-scheduling, are optimally generated for order insertion in a predictive schedule. Since choosing the most suitable scenario requires balancing criteria of scheduling efficiency and stability, measures of schedule changes were computed for each re-scheduling solution. The short computational times obtained are promising regarding future application of this approach in the manufacturing environment studied.     

An optimisation support for the design of hybrid production lines including assembly and disassembly tasks

The optimisation problems related to the assignment of tasks to workstations in assembly and disassembly lines have been largely discussed in the literature. They are known, respectively, as Assembly Line Balancing and Disassembly Line Balancing Problems. In this study, both types of task performed on the identical product are integrated in a common hybrid production system. Therefore, the logistic process is simplified and disassembly tasks can supply easier the assembly tasks with the required components. The considered production system has the layout of two parallel lines with common workstations. The product flow is conventional in the assembly line and reverse in the disassembly line. The paper provides a new mathematical model for designing such a hybrid system and an approximate approach based on ant colony optimisation for solving large-scale instances. The solution method is tested in a case study. The obtained results are compared with the solution provided by the design of two independent lines. The analysis of the results highlights the potential benefits of the hybrid production system.