Mixed-model Two-sided Assembly Line Research Articles

Multi-objective simulated annealing algorithm for robotic mixed-model two-sided assembly line balancing with setup times and multiple constraints

Robotic mixed-model two-sided assembly lines (RMTALs) have become increasingly prevalent in manufacturing industries for productivity enhancement. However, only limited attention has been paid to the RMTAL balancing problems that contain both setup times and multiple constraints such as positional constraints, zoning constraints, and synchronism constraints. Given the coexistence of these aspects in real-world scenarios, this study aims to address the RMTAL balancing problem with setup times and the mentioned constraints. To formulate the problem, a mixed integer programming model is proposed to optimize cycle time and total energy consumption. To handle multiple constraints and setup times, a new method based on four-vector encoding is introduced. This method addresses the three constraints during the encoding phase using four intervals and five rules, while setup times are processed during the decoding phase. To solve the problem, a Pareto entropy-based two-mode multi-objective simulated annealing algorithm is developed. The algorithm employs variable neighborhood descent algorithm with switchable objectives to generate the initial Pareto archive. Subsequently, it selects the initial solution with minimal potential in the Pareto archive, alternating between two search modes of exploration and exploitation based on the entropy difference of Pareto solutions. Comparative experiments with four state-of-the-art algorithms on benchmark instances of varying scales demonstrate that the proposed algorithm outperforms the others in over 93% of instances for the hypervolume ratio and inverted generational distance metrics.

Solving human-robot collaborative mixed-model two-sided assembly line balancing using multi-objective discrete artificial bee colony algorithm

Human-robot collaboration (HRC) is an emerging technology that aims to optimize production efficiency and reduce ergonomic risks by enabling humans and collaborative robots to work together in a shared workspace. This paper investigates HRC mixed-model two-sided assembly line balancing problem, which is a challenging real-world problem in manufacturing systems. The problem is formulated using a mixed-integer programming model with the objectives of minimizing ergonomic risk, energy consumption, and cycle time. To solve the problem efficiently, a multi-objective discrete artificial bee colony algorithm with specialist bees (MDABCSB) is proposed. MDABCSB utilizes a new five-vector encoding and decoding scheme and incorporates two types of neighbor structures for each vector. Additionally, the algorithm introduces specialist bees to search for the optimal solution for each objective, combines a tabu list to help them jump out of local optima, and employs variable neighborhood descent to enhance their exploration capabilities. Furthermore, the algorithm adopts a new temperature-based food sources update mechanism to probabilistically update the food sources of employed bees to reinforce exploitation. By comparing the proposed algorithm with other algorithms, the results demonstrate that MDABCSB performs better in terms of solution performance.

A genetic algorithm for balancing and sequencing of mixed-model two-sided assembly line with unpaced synchronous transfer

Research on the assembly line balancing problem has been continuously incorporated the characteristics of actual production lines without considering the mixed-model two-sided assembly line with unpaced synchronous transfer, which is common in body shops of cars or trucks. This study attempts to model a mixed-model two-sided assembly line balancing and sequencing problem with unpaced synchronous transfer, and designs a genetic algorithm to solve this problem. First, to describe the process whereby model sequence and its execution, station and its number, and the process time caused by task allocation lead to the variable output of an unpaced synchronous line, a model without an observation point restriction is proposed. Based on this, a mathematical model of a balancing and sequencing problem of a mixed-model two-sided assembly line with an unpaced synchronous transfer is established, in which the balancing problem is aimed at Type-II. Then, an improved genetic algorithm adapted to the complexity of the problem is proposed. The algorithm designs a combination and evaluation mechanism to ensure that the solution populations of the balancing and sequencing problems are sufficiently combined and evaluated in each iteration to improve efficiency. The proposed algorithm is illustrated using a small numerical example. Finally, the algorithm is experimentally investigated using a set of test cases and several comparison algorithms. The experimental results show that the combination and evaluation mechanism is suitable for these problems, especially for large-sized cases, and that the proposed algorithm is effective and has better performance than the comparison algorithms.

Combinatorial Benders decomposition for mixed-model two-sided assembly line balancing problem

In this work, we consider a mixed-model two-sided assembly line balancing problem for a given cycle time (MTALBP-I), in which the primary objective is to minimise the number of mated-stations (i.e. the length of the two-sided assembly line), while the number of stations, which describes the total number of operators, is also concerned. To solve this problem, we propose a combinatorial Benders decomposition-based exact algorithm, and develop a sequence-based enumerative search method to calculate effective combinatorial Benders cuts. To evaluate the performance of our proposed solution approach, we conduct extensive computational experiments on a set of benchmark instances, and the results demonstrate its efficiency of finding exact solutions even for large-sized instances.

Local search methods for type I mixed-model two-sided assembly line balancing problems

Two-sided assembly lines are widely utilized to assemble large-sized products such as cars and trucks. Recently, these types of assembly lines have been applied to assemble different types of products due to a large variety of customer demands and strong market competition. This paper presents two simple local search methods, the iterated greedy algorithm and iterated local search algorithm, to deal with type I mixed-model two-sided assembly line balancing problems. These two algorithms utilize new precedence-based local search functions with referenced permutation and two neighborhood structures to emphasize intensification while preserving high search speed. Additionally, these local search methods are enhanced by utilizing the best decoding scheme amongst nine candidates and a new station-oriented evaluation to guide the search direction. New lower bound calculations are also presented to check the optimality of the achieved solutions. Eleven recent and high-performing metaheuristic algorithms are re-implemented to test the performance of the proposed algorithms. A comprehensive study on a set of benchmark problems demonstrates the advantages of the improvements and the superiority of the two proposed methods. Experimental results show that the proposed algorithms obtain 23 new upper bounds compared with two recently published algorithms, among which 19 cases are proven to be optimal for the first time.

Multi-Objective Two-Sided Robotic Mixed-Model Assembly Line Balancing Problem Considering Energy Consumption and Smoothing Workload

In this paper, a type II robotic mixed-model two-sided assembly line balancing problem is considered. This paper presents a new mixed-integer programming model for type II robotic mixed-model two-sided assembly line balancing to minimize the cycle time, energy consumption, and purchased cost of robots for a given number of workstations. We provided an effective framework for optimizing the multi-objective robotic mixed-model two-sided assembly line balancing problem considering energy consumption and smoothing workload in the make to order environment to help the decision makers make the right decisions under stochastic demand. An augmented epsilon constraint and Lp-metric methods applied to solve the problem, and then, with the help of defining two vertical and horizontal criteria, we attempt to help the decision maker to choose a more efficient solution to make the production line more smooth workload. We demonstrate the efficiency of the proposed method by designing the numerical experiments. ZZ ZZ ZZ ZZ ZZ

Mixed model two sided assembly line balancing problem: an exact solution approach

Mixed model two-sided assembly line balancing problem (MTALBP) is realized in plants delivering a high volume of big sizes products such as cars or trucks to increase space utilization. In MTALBP there is a procedure of introducing two single stations in each position left and right of the assembly line for the combined product model. In this paper, the proposed objective function is to maximize the workload at each station such that the number of stations is minimized. Since the problem is well known as NP hard, benchmark problems of MTALBP are solved using branch and bound algorithm on Lingo 16 solver. The proposed mathematical model is solved with benchmark test problems mentioned in research papers and applied to solve the case study problem of a turbocharger assembly line plant. The experimental results of the case study problem show that line efficiency is obtained 86.50% for model A and 80.75% for model B and the number of single and mated stations of the assembly line is close to the theoretical minimum number of stations. Results indicate that applying boundary conditions reduce the computational time to solve the case study problem as well as minimizes the number of stations, reduces idle time and reduces the length of the assembly line for the MTALBP.

Modelling and solving mixed-model two-sided assembly line balancing problem with sequence-dependent setup time

Two-sided assembly lines balancing problem has been extensively studied in recent years. However, only limited attention has been paid to balancing mixed-model two-sided assembly lines problem (MTALBP). Moreover, the majority of balancing research assumes the setup as negligible, although it is ubiquitous in the assembly process. As the non-increment activities, setups occur in two ways: forward and backward setups. According to our best knowledge, no published work in literature on MTALBP has simultaneously considered forward and backward setups. In this paper, the problem of balancing mixed-model two-sided assembly lines with setups (MTALBPS) is considered. The purpose of this paper is twofold. The primary objective is to develop a mixed-integer programming (MIP) mathematical model to formulate the type-I problem of MTALBPS. The secondary objective is to propose an effective variable neighbourhood search (VNS) algorithm to solve it, especially for the large-sized problems. In addition, to test the effectiveness of the proposed approaches, a number of test problems from the literature with up to 148 tasks are solved and compared with the lower bound. The results demonstrate that the proposed algorithm is effective and produces very close results to the lower bound in a reasonable time.

A Mathematical Formulation for Mixed Model Two Sided Assembly Line Balancing Problem to Consider Boundary Conditions

Purpose: The main aim of this paper is to develop a new mathematical model for the mixed model two-sided assembly line balancing problem (MTALBP) generally occurs in plants producing large-sized high-volume products such as buses or trucks. Methodology: In this paper, the proposed mathematical model is applied to solve two-sided mixed-model assembly line balancing problem with lower and upper bound. The proposed mathematical model is solved using a branch and bound algorithm on LINGO 17.0 solver. Findings: Based on the computational result, line efficiency that is obtained by reducing single and mated stations of the assembly line is good as compare to the theoretical minimum number of stations and reduces computational time by applying boundary conditions. Practical implications: Since the problem is well known as an NP-hard problem a benchmark study problem is solved,and the result of the study can be beneficial for assembly of the mixed model products in term of minimizing mated stations as well as computational time. Originality: By literature review, t his paper is first to address mixed-model two-sided assembly line balancing problem with bounds using the exact solution approach.

A Multi-objective Mixed Model Two-sided Assembly Line Sequencing Problem in a Make –To- Order Environment with Customer Order Prioritization

Mixed model two-sided assembly lines (MM2SAL) are applied to assemble large product models, which is produced in high-volume. So, the sequence planning of products to reduce cost and increase productivity in this kind of lines is imperative. The presented problem is tackled in two steps. In step 1, a framework is developed to select and prioritize customer orders under the finite capacity of the proposed production system. So, an Analytic Network Process (ANP) procedure is applied to sort customers’ order based on 11 assessment criteria. In step 2, a mathematical model is formulated to determine the best sequence of products to minimize the total utility work cost, total idle cost, tardiness/earliness cost, and total operator error cost. After validation of the presented model using GAMS software, according to the NP-hard nature of this problem, a genetic algorithm (GA) and particle swarm optimization (PSO) are used. The performance of these algorithms are evaluated using some different test problems. The results show that the GA algorithm is better than PSO algorithm. Finally, a sign test for the two metaheuristics and GAMS is designed to display the main statistical differences among them. The results of the sign test reveal GAMS is an appropriate software for solving small-sized problems. Also, GA is better than PSO algorithm for large sized problems in terms of objective function and run time.

Balancing of mixed-model two-sided assembly lines with underground workstations: A mathematical model and ant colony optimization algorithm

Mixed-model assembly lines allow the production of different product variants in mass quantities on the same assembly line. In studies addressing mixed-model assembly with two-sided lines, assembly line (work)stations are classified as left-side or right-side stations depending on the operation side to which they are allocated. However, underground stations are also utilized in industry to perform tasks that need to be done underneath the product being assembled on the line. This paper introduces and mathematically formulates a mixed-model, two-sided assembly line balancing problem considering underground stations. The precedence relationships between tasks being performed in the three types of stations are defined and considered in the model. A numerical example is solved in GAMS (with CPLEX solver) and the detailed balancing solution is provided. A new ant colony optimization algorithm, in which the parameters are optimized using response surface methodology, is also developed to solve real-world problems. A total of 78 test problems are derived from the literature and their lower bounds are calculated to test the performance of the ACO algorithm. ACO finds optimum solutions for the majority of small and medium-sized test problems. In comparing the ACO results to the lower bounds for the large-sized problems, ACO finds near -optimum solutions in majority of the test cases.

Multiobjective Program and Hybrid Imperialist Competitive Algorithm for the Mixed-Model Two-Sided Assembly Lines Subject to Multiple Constraints

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature.

Balancing of mixed-model two-sided assembly lines using teaching–learning based optimization algorithm

Balancing of mixed-model two-sided assembly lines using teaching–learning based optimization algorithm

MILP model for integrated line balancing and model sequencing problems for Mixed-Model Two-Sided Assembly Line

This research explores two interrelated problems in Mixed-Model Two-Sided Assembly Line (MMTSAL), which are line balancing and model sequencing. These two problems are solved simultaneously using Mixed Integer Linear Programming (MILP) with the objectives of minimizing total utility work and idle time by considering various practical constraints. The problem is analyzed using small-size to large-sized test cases using General Algebraic Modelling System (GAMS) with the solver CPLEX. Experimental results indicate that integrating the problems help to minimize the proposed objective function. Also, it is found that the feasible solution for model sequence with the assignment of tasks to assembly line is optimal.

MILP model for integrated balancing and sequencing mixed-model two-sided assembly line with variable launching interval and assignment restrictions

This research explores the Mixed-Model Two-Sided Assembly Line (MMTSAL). There are two interrelated problems in MMTSAL which are line balancing and model sequencing. In previous studies, many researchers considered these problems separately and only few studied them simultaneously for one-sided line. However in this study, these two problems are solved simultaneously to obtain more efficient solution. The Mixed Integer Linear Programming (MILP) model with objectives of minimizing total utility work and idle time is generated by considering variable launching interval and assignment restriction constraint. The problem is analysed using small-size test cases to validate the integrated model. Throughout this paper, numerical experiment was conducted by using General Algebraic Modelling System (GAMS) with the solver CPLEX. Experimental results indicate that integrating the problems of model sequencing and line balancing help to minimise the proposed objectives function.

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]

Simultaneous Multi-skilled Worker Assignment and Mixed-model Two-sided Assembly Line Balancing

This paper addresses a multi-objective mathematical model for the mixed-model two-sided assembly line balancing and worker assignment with different skills. In this problem, the operation time of each task is dependent on the skill of the worker. The following objective functions are considered in the mathematical model: (1) minimizing the number of mated-stations (2), minimizing the number of stations, and (3) minimizing the total human cost for a given cycle time. Furthermore, maximizing the weighted line efficiency and minimizing the weighted smoothness are two indices considered simultaneously in this paper. Since this problem is well-known as NP-hard class, a particle swarm optimization (PSO) algorithm is developed to solve it. The performance of the proposed PSO algorithm is evaluated with a simulated annealing (SA) algorithm existed in the literature over several benchmarked test problems for the conditions of the current problem in terms of running time and solution quality. The results show the proposed algorithm is an efficient algorithm

A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing

In this paper, a new modified particle swarm optimization algorithm with negative knowledge is proposed to solve the mixed-model two-sided assembly line balancing problem. The proposed approach includes new procedures such as generation procedure which is based on combined selection mechanism and decoding procedure. These new procedures enhance the solution capability of the algorithm while enabling it to search at different points of the solution space, efficiently. Performance of the proposed approach is tested on a set of test problem. The experimental results show that the proposed approach can be acquired distinguished results than the existing solution approaches.

Application of AMGA in the Mixed Model Two-Sided Assembly Line Type I Balancing Problem

This paper searches mixed model two-sided assembly line type I balancing problem based on Y automobile manufacturing company. According to the characteristics and balance constraints of mixed model two-sided assembly line, the actual problem is abstracted as the mathematical model. The three minimal objectives are the station number, the balance index and the wait time. In this study, an advanced multi-objective genetic algorithm is proposed for analyzing and solving the satisfactory solution set of multi-objective optimization problem and example application shows its validity.

An effective hybrid honey bee mating optimization algorithm for balancing mixed-model two-sided assembly lines

Mixed-model two-sided assembly lines are widely used in a range of industries for their abilities of increasing the flexibility to meet a high variety of customer demands. Balancing assembly lines is a vital design issue for industries. However, the mixed-model two-sided assembly line balancing (MTALB) problem is NP-hard and difficult to solve in a reasonable computational time. So it is necessary for researchers to find some efficient approaches to address this problem. Honey bee mating optimization (HBMO) algorithm is a population-based algorithm inspired by the mating process in the real colony and has been applied to solve many combinatorial optimization problems successfully. In this paper, a hybrid HBMO algorithm is presented to solve the MTALB problem with the objective of minimizing the number of mated-stations and total number of stations for a given cycle time. Compared with the conventional HBMO algorithm, the proposed algorithm employs the simulated annealing (SA) algorithm with three different neighborhood structures as workers to improve broods, which could achieve a good balance between intensification and diversification during the search. In addition, a new encoding and decoding scheme, including the adjustment of the final mated-station, is devised to fit the MTALB problem. The proposed algorithm is tested on several sets of instances and compared with Mixed Integer Programming (MIP) and SA. The superior results of these instances validate the effectiveness of the proposed algorithm.