Mixed-model Assembly Line Sequencing Research Articles

On Energy Consumption and Productivity in a Mixed-Model Assembly Line Sequencing Problem

Mixed and multi-model assembly line sequencing problems are more practical than single-product models. The methods and selection criteria used must keep up with the constantly increasing level of variability, synchronize flows between various—often very energy-intensive production departments—and cope with high dynamics resulting from interrupted supply chains. The requirements for conscious use of Earth’s limited natural resources and the need to limit energy consumption and interference in the environment force the inclusion of additional evaluation criteria focusing on the environmental aspect in optimization models. Effective sustainable solutions take into account productivity, timeliness, flow synchronization, and the reduction of energy consumption. In the paper, the problem of determining the sequence of vehicles for a selected class of multi-version assembly lines, in which the order restrictions were determined taking into account the above criteria, is presented. Original value of the paper is the development of the Grey Wolf Optimizer (GWO) for the mixed-model assembly lines sequencing problem. In the paper, a comparative analysis of the greedy heuristics, Simulated Annealing and GWO for a real case study of a mixed vehicle assembly line is presented. The GWO outperforms other algorithms. Overall research performance of the GWO on the sequencing problem is effective.

Hierarchical and two-stage framework for the paced mixed-model assembly line balancing and sequencing problem considering ergonomic risk

Paced mixed-model assembly lines are popular with various manufacturing enterprises. However, they face the weakness that they are at risk of line stoppages owing to the occurrence of workstation work overload situations. Moreover, the consideration of workers' health and ergonomic risk on manual assembly lines is a necessity required by legislation. Therefore, this article addresses mixed-model assembly line balancing and sequencing taking the problem of ergonomic risk into consideration and manages work overloads using a side-by-side policy with utility workers. To solve these problems, this article proposes an hierarchical and two-stage framework. The optimization goals are to minimize the number of workstations and utility workers. Furthermore, this article integrates an iterated greedy algorithm into a genetics algorithm to obtain global exploration and local exploitation ability. Finally, a divide-and-conquer strategy is proposed to meet the challenge of solving a large-scale problem. Experimental results show the effectiveness of the mechanism proposed in this article.

Robust mixed-model assembly line balancing and sequencing problem considering preventive maintenance scenarios with interval processing times

Robust mixed-model assembly line balancing and sequencing problem considering preventive maintenance scenarios with interval processing times

A mixed-model assembly line sequencing problem with parallel stations and walking workers: a case study in the automotive industry

ABSTRACT A newly emerging mass-individualisation concept has attracted increasing attention in recent years. However, this concept increases the complexity of manufacturing systems within organisations. In such systems, one of the main challenges is the sequencing problem, especially in dynamic environments where unpredictable events demand new constraints. In this context, the ability to use real-time data to make efficient, quick decisions has become one of the main priorities of managers. In this paper, based on a real-world case from Fiat Powertrain Technologies, we define a dynamic mixed-model assembly line sequencing problem with walking workers. In this context, each worker is assigned to a product for all assembly operations and moves from one station to another. A mathematical model is proposed to minimise production time. Since the problem is NP-hard, a hyper-heuristic is also developed to solve the problem. Moreover, a simulation-optimisation model is developed using FlexSim software to solve a real-world problem in a dynamic environment. Comparison of the results illustrates the effectiveness of using the simulation approach to dynamically solve such problems, especially in real-world cases. Finally, a thorough description of managerial insights is provided to indicate the applicability of the proposed approach.

Multi-objective metaheuristic algorithms for the mixed model assembly line sequencing problem with a bypass sub-line

ABSTRACT Nowadays, mixed-model assembly lines (MMALs) are extensively applied to manufacture different products with no need for the changeover of the whole lines to satisfy the diversified preferences of consumers. In some assembly lines, there is a considerable variation in cycle times, which reduces production efficiency. However, a bypass sub-line that undertakes a portion of product assembly operations reduces the variation in the assembly times. In the present paper, the following three objective functions are considered simultaneously: (1) minimizing the variation in the actual and required production capacity, (2) minimizing total utility work, and (3) minimizing total variation in production rate. The formulated sequencing problem is solved by two different Multi-Objective Evolutionary Algorithm (MOEAs) including the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). Then, some numerical examples are conducted, and the efficiency of the two proposed algorithms is measured based on some comparison metric.

Sequencing mixed-model assembly lines with risk-averse stochastic mixed-integer programming

Sequencing decisions in mixed-model assembly lines are complicated by various uncertainty factors. This paper addresses a real-life uncertainty factor identified in a manufacturer of large vehicles, by modelling unreliable part delivery and quality. Stochastic optimisation is applied to find sequencing policies that improve the on-time performance of its mixed-model assembly lines. As schedulers have different levels of risk aversion, a risk-averse programme is further presented to protect against the decision maker’s chosen fraction of worst scenarios. Computational studies with Progressive Hedging as the solution method, and its lower bounding approach, demonstrate the high quality of resulting sequencing decisions and the time efficiency of the solution method.

Using variable neighbourhood descent and genetic algorithms for sequencing mixed-model assembly systems in the footwear industry

This paper addresses a new Mixed-model Assembly Line Sequencing Problem in the Footwear industry. This problem emerges in a large company, which benefits from advanced automated stitching systems. However, these systems need to be managed and optimised. Operators with varied abilities operate machines of various types, placed throughout the stitching lines. In different quantities, the components of the various shoe models, placed in boxes, move along the lines in either direction. The work assumes that the associated balancing problems have already been solved, thus solely concentrating on the sequencing procedures to minimise the makespan.An optimisation model is presented, but it has just been useful to structure the problems and test small instances due to the practical problems’ complexity and dimension. Consequently, two methods were developed, one based on Variable Neighbourhood Descent, named VND-MSeq, and the other based on Genetic Algorithms, referred to as GA-MSeq.Computational results are included, referring to diverse instances and real large-size problems. These results allow for a comparison of the novel methods and to ascertain their effectiveness. We obtained better solutions than those available in the company.

Sequencing mixed-model assembly lines with demand management: problem development and efficient multi-objective algorithms

As a kind of production line, the mixed-model assembly line (MMAL) has been progressively adopted by industries to satisfy the diversification of customer demands. This study applied an MMAL sequencing problem for a make-to-order (MTO) strategy. Using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach, this study sorted customer orders, satisfying these by a desired due date being the main feature in MTO systems. This MMAL problem was investigated to achieve three minimization objectives: total set-up cost, number of work overload situations, and total earliness and tardiness costs, according to the specified priority of orders. Accordingly, a multi-objective particle swarm optimization (MOPSO) algorithm was developed for the proposed NP-hard model and the algorithm parameters were tuned by applying the Taguchi technique to the experimental design. The computational results of a comparison between the proposed MOPSO model and four multi-objective algorithms confirmed its advantages.

MULTI-OBJECTIVE ENERGY EFFICIENT MIXED MODEL ASSEMBLY LINE SEQUENCING FOR SUSTAINABLE MANUFACTURING

The sustainability of manufacturing systems is not thoroughly investigated in the existing literature despite that fact that it affects service-oriented organisations. This paper addresses this gap by incorporating the criterion of energy consumption in mixed model assembly line sequencing (MMALS) to explore the potential for energy saving. Energy consumption was integrated with makespan and sequence dependent setup time of mixed models to achieve the sustainability in sequencing. A mathematical model was developed for the optimisation of energy efficient MMAL (EEMMAL). The multi-objective intelligent genetic algorithm (MOIGA) was proposed for minimisation of three conflicting objectives. A case study was conducted using centrifugal pump assembly line to test the performance of proposed MOIGA and the results were compared with those obtained from multi-objective genetic algorithm (MOGA). Finally, a trade-off analysis was conducted between total setup time, makespan (a service level measure on shop floor) and energy consumption (a factor of environmental sustainability). This analysis indicated the effectiveness of MOIGA (compared to MOGA) for EEMMALS problems.

Applying available-to-promise (ATP) concept in mixed-model assembly line sequencing problems in a Make-To-Order (MTO) environment: problem extension, model formulation and Lagrangian relaxation algorithm

Mixed-model assembly line is known to be a special case of production lines where variety of product models similar to product characteristics are assembled. This article addresses available-to-promise (ATP) in mixed-model assembly line sequencing problems in a Make-To-Order environment in two stages. First, the customers are prioritized based on their corresponding profit values and a decision support system for order acceptance/rejection based on ATP is developed. By implementing this concept and developing a mathematical model, delivery quantity and date in a planning horizon are determined based on the inventories in the stock. The second stage is solving a mixed binary mathematical model to sequence accepted orders suitably according to demands due dates that guarantees the orders are not released too late or too early. The problem simultaneously considers following objectives: minimizing the total tardiness and earliness costs based on the determined priority of orders and minimizing the utility work and idle time of workers in the production line. An algorithm based on Lagrangian relaxation is developed for the problem, and tested in terms of solution quality and computational efficiency. To validate the performance of the proposed algorithm, various test problems in small size are solved using the CPLEX solver, and compared with the Lagrangian relaxation method. Finally, the proposed model is solved in large size problems to analyze the model performance. The drawback of the CPLEX is that it could not solve large problem instances in reasonable time. For the small sized problem, there is approximately 1% duality gap for the Lagrangian relaxation method. The maximum duality gap in the Lagrangian relaxation method for the large sized problem is always kept below 4% while the average computing time is very reasonable. Therefore, according to the results obtained from test problems, the developed Lagrangian relaxation method proved to be the suitable method for this problem.

Fuzzy activity time-based model predictive control of open-station assembly lines

The sequencing and line balancing of manual mixed-model assembly lines are challenging tasks due to the complexity and uncertainty of operator activities. The control of cycle time and the sequencing of production can mitigate the losses due to non-optimal line balancing in the case of open-station production where the operators can work ahead of schedule and try to reduce their backlog. The objective of this paper is to provide a cycle time control algorithm that can improve the efficiency of assembly lines in such situations based on a specially mixed sequencing strategy. To handle the uncertainty of activity times, a fuzzy model-based solution has been developed. As the production process is modular, the fuzzy sets represent the uncertainty of the elementary activity times related to the processing of the modules. The optimistic and pessimistic estimates of the completion of activity times extracted from the fuzzy model are incorporated into a model predictive control algorithm to ensure the constrained optimization of the cycle time. The applicability of the proposed method is demonstrated based on a wire-harness manufacturing process with a paced conveyor, but the proposed algorithm can handle continuous conveyors as well. The results confirm that the application of the proposed algorithm is widely applicable in cases where a production line of a supply chain is not well balanced and the activity times are uncertain.

Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics

This paper presents a mixed-model sequencing problem with stochastic processing times (MMSPSP) in a multi-station assembly line. A new mixed-integer nonlinear programing model is developed to minimize weighted sum of expected total work-overload and idleness, which is converted into a mixed-integer linear programming model to deal with small-sized instances optimally. Due to the NP-hardness of the problem, this paper develops a novel hyper simulated annealing (HSA). The HSA employs a Q-learning algorithm to select appropriate heuristics through its search process. Numerical results are presented on several test instances and benchmark problems from the related literature. The results of statistical analysis indicate that the HSA is quite competitive in comparison with optimization software packages, and is significantly superior to several SA-based algorithms. The results highlight the advantages of the MMSPSP in comparison with traditional deterministic approaches in mixed-model sequencing contexts.

Parallel Optimization of the Balancing and Sequencing for Mixed-model Assembly Lines

The traditional Mixed-model Assembly Lines (MMALS) balancing and sequencing serial design methods are difficult to adapt to rapidly changing requirements. From the perspective of the parallel design of balancing and sequencing, a mixed integer linear programming model for MMALS balancing and sequencing is proposed. An improved particle swarm optimization (PSO) algo-rithm was proposed, in the process of updating the optimal solution, the simulated annealing (SA) algorithm is added to make it possible to jump out of the local optimum with a certain probability and expand the solution selection to the entire population. Based on the algorithm, random coding and ascending decoding methods are proposed, the number of products and the number of tasks are coded and decoded at the same time. Verify the effectiveness of the algorithm by an example.

A Multi-Objective Mixed-Model Assembly Line Sequencing Problem With Stochastic Operation Time

In today’s competitive market, those producers who can quickly adapt themselves todiverse demands of customers are successful. Therefore, in order to satisfy these demands of market, Mixed-model assembly line (MMAL) has an increasing growth in industry. A mixed-model assembly line (MMAL) is a type of production line in which varieties of products with common base characteristics are assembled on. This paper focuses on this type of production line in a stochastic environment with three objective functions: 1) total utility work cost, 2) total idle cost, and 3) total production rate variation cost that are simultaneously considered. In real life, especially in manual assembly lines, because of some inevitable human mistakes, breakdown of machines, lack of motivation in workers and the things alike, events are notdeterministic, sowe consideroperation time as a stochastic variable independently distributed with normal distributions; for dealing with it, chance constraint optimization is used to model the problem. At first, because of NP-hard nature of the problem, multi-objective harmony search (MOHS) algorithm is proposed to solve it. Then, for evaluating the performance of the proposed algorithm, it is compared with NSGA-II that is a powerful and famous algorithm in this area. At last, numerical examples for comparing these two algorithms with some comparing metrics are presented. The results have shown that MOHS algorithm has a good performance in our proposed model.

A bi-objective mixed-model assembly line sequencing problem considering customer satisfaction and customer buying behaviour

ABSTRACTCustomer satisfaction and focusing on the most profitable customers are the key elements to achieving a sustainable competitive advantage in the current business environment. The mixed-model assembly line (MMAL) is an efficient type of manufacturing system that has been used increasingly over recent years to satisfy diverse customer demands. Product sequencing in an MMAL system has a huge effect on the performance of the firm in meeting customer demand and, therefore, on customer satisfaction. This study aims to address a sequencing problem with respect to recency, frequency and monetary factors. A two-stage framework is developed to achieve this goal. First, the prioritized orders based on the preference ranking organization method for enrichment of evaluations (PROMETHEE) are categorized into high-priority and normal-priority orders. Then, a bi-objective mathematical programming model is developed to determine the optimum sequence of products to minimize the total cost, as well as to maximize levels of customer satisfaction. After validation of the proposed model, owing to the NP-hard nature of this problem, two multi-objective metaheuristics are applied to tackle the second stage of this study. Several test problems are presented to evaluate the performance of these algorithms. The results show the superiority of one solution approach over the other.

An imperialist competitive algorithm in mixed-model assembly line sequencing problem to minimise unfinished works

ABSTRACTMixed-model assembly lines (MMALs) are a type of production lines where a variety of product models similar to product characteristics are assembled in a just-in-time production system. Usually, MMAL consists of a number of stations linked by a conveyor belt and each station has a work zone limited by upstream and downstream boundaries. To avoid improper interference between operators in the adjacent stations and excess of machine moving range, operators are forced to complete their operations within their predetermined work zone. There is a set of criteria on which to judge sequences of product models in terms of the effective utilisation of these lines. In this paper, the sequence of models for minimising the total unfinished work within their work zone is discussed. A novel imperialist competitive algorithm (ICA) is developed for solving this problem in small-, medium- and large-scale problems. The solutions obtained via ICA are compared against solutions obtained via B&B, a heuristic procedure, GRASP and BDP-2 in small problems, and also against a proposed genetic algorithm and a simulated annealing in small, medium and large problems. Experimental results show that this algorithm provides reasonably good solutions with low computational costs.

Minimising work overload in mixed-model assembly lines with different types of operators: a case study from the truck industry

This paper considers the problem of sequencing mixed-model assembly lines (MMALs). Our goal is to determine the sequence of products to minimise work overload. This problem is known as the MMAL sequencing problem with work overload minimisation: we explicitly use task operation times to find the product sequence. This paper is based on an industrial case study of a truck assembly line. In this industrial context, as a reaction to work overloads, operators at the workstations finish their tasks before the product reaches the next workstation, but at the expense of fatigue. Furthermore, there are different types of operators, each with different task responsibilities. The originality of this work is to model this new way of reacting against work overloads, to integrate three operator types in the sequencing model and to apply the developed methods in a real industrial context. To solve this problem, we propose three meta-heuristic procedures: genetic algorithm, simulated annealing and a combination of these two meta-heuristics. All the methods proposed are tested on industrial data and compared to the solutions obtained using a mixed-integer linear programme. The results show that the proposed methods considerably improve the results of the current procedure used in the case study.

MILP models and metaheuristic for balancing and sequencing of mixed-model two-sided assembly lines

Mixed-model assembly lines are becoming increasingly popular due to flexibility of producing customised products. In a mixed-model assembly line, line balancing and model sequencing problems are tightly interrelated and very important for efficiency. This paper proposes a new assembly line configuration based on paced mixed-model two-sided assembly lines where balancing and sequencing problems are considered simultaneously. Minimal work has been reported considering both problems simultaneously for this type of assembly line configuration. Two mixed-integer linear programming (MILP) models are developed and a restarted SA algorithm with new encoding, decoding and neighbourhood procedures is developed. The parameters of the proposed algorithm are selected based on a statistical technique and the performance of it is tested on a set of new benchmark problems. The computational results demonstrate the effectiveness of the MILP models and the high efficiency of the proposed algorithm. The proposed algorithm outperforms the comparative original SA algorithm. [Received 22 July 2016; Revised 1 December 2016; Accepted 14 February 2017]

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.

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.