Mixed-model Assembly Line Sequencing Problem 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.

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.

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.

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.

Integrated Approach for Flexible Mixed Model Assembly Lines Balancing and Model Sequencing Problem

The manufacturers nowadays are forced to respond very quickly to changes in the market conditions. To adopt flexible mixed model assembly lines (MMAL) is a preferred way for manufacturers to improve competitiveness. Managing a mixed model assembly line involves two problems: assigning assembly tasks to stations (balancing problem) and determining the sequence of products at each station (sequencing problem). In order to solve both balancing and sequencing problem in MMAL simultaneously, an integrated mathematical model based on mixed integer programming (MIP) is developed to describe the problem. In the model, general type precedence relations and task duplications are considered. Due to the NP-hardness of the balancing and sequencing problem of MMAL, GA is designed to search the optimal solution. The efficiency of the GA is demonstrated by a case study.

Fuzzy mixed assembly line sequencing and scheduling optimization model using multiobjective dynamic fuzzy GA.

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.

Multiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model

It can be deduced from previous studies that there exists a research gap in assembly line sequencing optimization model for mixed-model production lines. In particular, there is a lack of studies which focus on the integration between job shop and assembly lines using fuzzy techniques. Hence, this paper is aimed at addressing the multiobjective mixed-model assembly line sequencing problem by integrating job shop and assembly production lines for factories with modular layouts. The primary goal is to minimize the make-span, setup time, and cost simultaneously in mixed-model assembly lines. Such conflicting goals arise when switching between different products. A genetic algorithm (GA) approach is used to solve this problem, in which trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data.

Multi-Objective Optimization of Order Oriented Mixed-Model Assembly Line Sequencing Problem

Mixed-model assembly lines are widely used in many manufacturing firms to meet diversified demands of consumers without possessing large product inventories. In this paper, we posed order oriented assembly line sequencing as a multiple-objective optimization problem with the objectives to minimize material consumption waviness, the total setup cost, and finished product inventory cost. The multi-objective optimization algorithm based on non-dominated sorting particle swarm optimization (NSPSO) is designed. Computational experiment has been demonstrated to the applicability of using NSPSO to solve the problem and effectiveness of the proposed approach. By means of this research, the valid solutions for order oriented mixed-model assembly line sequence can be offered to the decision makers effectively.

A Non-Dominated Sorting Particle Swarm Optimization Algorithm For Mixed-Model Assembly Line Sequencing Problem

Mixed-model assembly line (MMAL) sequencing is a typical problem that various models of a common base product are assembled on the same line. In this paper, we proposed products assembly sequencing in mixed-model assembly lines as a multiple-objective optimization problem with the objectives to minimize material consumption waviness, the total setup cost, and total task overlapped time. These three objectives are typically inversely correlated with each other, and simultaneously optimization of the three objectives is challenging. The multi-objective optimization algorithm based on non-dominated sorting particle swarm optimization (NSPSO) is designed. We conduct an extensive experiment study in which the performance of the proposed NSPSO is compared against non-dominated genetic algorithm (NSGA II). The computational results show that the proposed NSPSO outperforms NSGA II, significantly in large-sized problems.

A multi-objective mixed-model assembly line sequencing problem in order to minimize total costs in a Make-To-Order environment, considering order priority

Nowadays, mixed-model assembly line is used increasingly as a result of customers’ demand diversification. An important problem in this field is determining the sequence of products for entering the line. Before determining the best sequence of products, a new procedure is introduced to choose important orders for entering the shop floor. Thus the orders are sorted using an analytical hierarchy process (AHP) approach based on three criteria: critical ratio of each order (CRo), Significance degree of customer and innovation in a product, while the last one is presented for the first time. In this research, six objective functions are presented: minimizing total utility work cost, total setup cost and total production rate variation cost are the objectives which were presented previously, another objective is minimizing total idle cost, meanwhile two other new objectives regarding minimizing total operator error cost and total tardiness cost are presented for the first time. The total tardiness cost tries to choose a sequence of products that minimizes the tardiness cost for customers with high priority. First, to check the feasibility of the model, GAMS software is used. In this case, GAMS software could not search all of the solution space, so it is tried in two stages and because this problem is NP-hard, particle swarm optimization (PSO) and simulated annealing (SA) algorithms are used. For small sized problems, to compare exact method with proposed algorithms, the problem must be solved using meta-heuristic algorithms in two stages as GAMS software, whereas for large sized problems, the problem can be solved in two ways (one stage and two stages) by using proposed algorithms; the computational results and pairwise comparisons (based on sign test) show GAMS is a proper software to solve small sized problems, whereas for a large sized problem the objective function is better when solved in one stage than two stages; therefore it is proposed to solve the problem in one stage for large sized problems. Also PSO algorithm is better than SA algorithm based on objective function and pairwise comparisons.

An imperialist competitive algorithm for a mixed-model assembly line sequencing problem

This paper presents a novel imperialist competitive algorithm (ICA) to a just-in-time (JIT) sequencing problem where variations of production rate are to be minimized. This type of problem is NP-hard. Up to now, some heuristic and meta-heuristic approaches are proposed to minimize the production rates variation. This paper presents a novel algorithm for optimization which inspired by imperialistic competition in real world. Sequences of products where minimize the production rates variation is desired. Performance of the proposed ICA was compared against a genetic algorithm (GA) in small, medium and large problems. Experimental results show the ICA performance against GA.

Multi-Objective Analysis Applied to Mixed-Model Assembly Line Sequencing Problem through Elite Induced Evolutionary Method

Multi-Objective Analysis Applied to Mixed-Model Assembly Line Sequencing Problem through Elite Induced Evolutionary Method

A review of the current applications of genetic algorithms in mixed-model assembly line sequencing

A mixed-model assembly line (MMAL) is a type of production line which is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such assembly lines involves several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from the researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). An important issue that complicates the sequencing problem is its combinatorial nature. Typically, an enormous number of possible production sequences exist, even for relatively small problems, so that finding the optimal solution is usually impractical. Due to the complexity of the problem, in recent years, a growing number of researchers have employed genetic algorithms (GAs). This paper reviews the genetic algorithm based MMAL sequencing approaches presented in the literature and provides two hierarchical classification schemes to classify academic efforts according to both specifications of MMALSP and specifications of GA-based approaches. Moreover, future research directions have been identified and are suggested.

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.