Line Balancing Problems Research Articles

Variable-depth local search heuristic for assembly line balancing problems

ABSTRACT Assembly lines are production flow systems wherein activities are organised around a line consisting of various workstations through which the product flows. At each station, the product is assembled through a subset of operations. The assembly line balancing problem (ALBP) consists of allocating operations between stations to maximise the system efficiency. In this study, a variable-depth local search algorithm is proposed for solving simple assembly line balancing problems (SALBPs), which are the most widely studied versions of the ALBP. Although the state-of-the-art techniques for solving the SALBP consist of exact enumeration-based methods or heuristics, this paper proposes a local search-based heuristic using variable-length sequences that allow the solution space to be efficiently explored. The proposed algorithm improves the best solution known for multiple instances reported in the literature, indicating that its efficiency is comparable to those of the state-of-the-art method for solving the SALBP. Moreover, the characteristics of the instances for which the proposed procedure provides a better solution than previously reported construction procedures are investigated.

A note on integrated disassembly line balancing and routing problem

This note comments on the study of [Diri Kenger, Zülal, Çağrı Koç, and Eren Özceylan. 2020. “Integrated Disassembly Line Balancing and Routing Problem.” International Journal of Production Research 58 (23): 7250–7268.] which studies an integrated disassembly line balancing and routing problem and develops two mixed integer linear programming (MILP) models and three mixed integer nonlinear programming (MINLP) models to handle five different scenarios, respectively. The purpose is twofold. First, we demonstrate that the two MILP models can be separated into two parallel subproblems whose optimal solutions can be combined to obtain the optimal solution of the original models. Second, we show that the three MINLP models can be linearised and propose two different linearisation techniques to reformulate them as equivalent MILP models. Computational results indicate that the linearised model outperforms the original nonlinear model in most cases.

Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

A digital-twin based worker's work allocation framework for a collaborative assembly system

Assigning the right task to the right worker remains a challenge to an assembly line manager. This work is proposed to use digital-twin technologies for Assembly line worker assignment and balancing problems (ALWABP) for a collaborative assembly system. Applications of the Internet of Things, Data analytics and Learning algorithms are proposed to estimate the worker's work skill and task performance. Assembly line Digital Twin and collaborative robot (CoBot) work simulation tools are proposed to estimate the CoBot's assembly task performing ability and execution time. ALWABP framework is proposed to optimize the work allocation between the worker and the CoBot. Assembly line digital twin is furthermore proposed to simulate the ALWABP output during the decision-making process of the assembly line manager by considering the Human-in-the-loop decision-making system.

A Review on Collaborative Robot Assembly Line Balancing Problems

Assembly line balancing problems have been the subject of numerous studies for decades. The recent advantages of technologies in the Industry 4.0 era and integrating collaborative robots into the assembly systems have created task allocation, workload balance, and scheduling challenges. This paper provides a brief literature review on the existing collaborative assembly line balancing problems comparing their main characteristics and suggests some research directions for future studies.

A genetic algorithm for heterogenous human-robot collaboration assembly line balancing problems

Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations.

Perancangan Efisiensi Lintasan Produksi dengan Menggunakan Metode Helgeson-Birnie (Ranked Positional Weight) Untuk Meningkatkan Kapasitas Produksi (Studi Kasus Pada Unit Produksi I Shift I PT. SUMBETRI MEGAH)

The problem of line balancing in industry, assembling, and manufacturing often occurs. This is indicated by a relatively high bottleneck of work in process at several stations, resulting in non-optimal line efficiency and non-smooth process flow. This study aims to calculate line balancing and determine the division of work stations that can increase production capacity in the production unit I shift I pt. Magnificent resource. This study used the Helgeson-Birnie (Ranked Positional Weight) method. From this research, the design results were obtained with line efficiency (line efficiency) 89.16%, idle time 2730.85, balance delay 10.84%, smoothness index (smoothness index) 781.24, and the number of work stations 15. Based on the calculation, it can be seen that there is an increase in line efficiency from 74.46% to 89.35%, which is 14.89%.

Balancing and scheduling assembly lines with human-robot collaboration tasks

In light of the Industry 5.0 trend towards human-centric and resilient industries, human-robot collaboration (HRC) assembly lines can be used to enhance productivity and workers’ well-being, provided that the optimal allocation of tasks and available resources can be determined. This study investigates the assembly line balancing problem (ALBP), considering HRC. This problem, abbreviated ALBP-HRC, arises in advanced manufacturing systems, where humans and collaborative robots share the same workplace and can simultaneously perform tasks in parallel or in collaboration. Driven by the need to solve the more complex assembly line-balancing problems found in the automotive industry, this study aims to address the ALBP-HRC with the cycle time and the number of operators (humans and robots) as the primary and secondary objective, respectively. In addition to the traditional ALBP constraints, the human and robot characteristics, in terms of task times, allowing multiple humans and robots at stations, and their joint/collaborative tasks are formulated into a new mixed-integer linear programming (MILP) model. A neighborhood-search simulated annealing (SA) is proposed with customized solution representation and neighborhood search operators designed to fit into the problem characteristics. Furthermore, the proposed SA features an adaptive neighborhood selection mechanism that enables the SA to utilize its exploration history to dynamically choose appropriate neighborhood operators as the search evolves. The proposed MILP and SA are implemented on real cases taken from the automotive industry where stations are designed for HRC. The computational results over different problems show that the adaptive SA produces promising solutions compared to the MILP and other swarm intelligence algorithms, namely genetic algorithm, particle swarm optimization, and artificial bee colony. The comparisons of human/robot versus HRC settings in the case study indicate significant improvement in the productivity of the assembly line when multiple humans and robots with collaborative tasks are permissible at stations.

A novel bi-level multi-objective genetic algorithm for integrated assembly line balancing and part feeding problem

The manufacturing industry has been pursuing an efficient and economical assembly system. By considering assembly line balancing (ALB) and part feeding (PF) as an integrated problem and programming them simultaneously opens additional opportunities to improve the performance of the entire assembly system. However, the integrated ALB and PF problem is a non-deterministic polynomial (NP) hard problem. This implies that exact solutions cannot be obtained in a reasonable computation time and its near-optimal solutions can only be realised by meta-heuristics. In this study, we propose a novel bi-level multi-objective genetic algorithm (NBMGA) to solve the integrated ALB and PF problem. First, a bi-level mathematical model is established to simultaneously minimise the number of stations and workload smoothness of ALB in the upper level as well as the number of supermarkets of PF in the lower level. Second, the NBMGA with two modified strategies, including extending fitness evaluation and adaptive termination condition, is designed for problem solving. Finally, a series of computational experiments are conducted to demonstrate the efficacy of the proposed algorithm. The computational results indicate that the proposed algorithm outperforms the bi-level nondominated sorting genetic algorithm (NSGA) II in terms of the approximation to the true frontier without sacrificing computational efficiency.

A linear physical programming model for assembly line balancing problem

This paper deals with the mixed-model assembly line balancing problem. This type of line is applied to more than one similar model of a product in an intermixed order. Despite their widespread use, these lines have received little attention in the literature. Metaheuristics, heuristics and mathematical programming techniques are developed to solve these types of assembly line balancing problems. However, linear physical programming method has never used. In this paper, a linear physical programming model is proposed for balancing a mixed-model assembly line. The performance of the proposed model is applied to a numerical example to analyze the usage of the methodology. Five objectives are considered in the model and the outperformance of the methodology is demonstrated by comparing it to a different approach. According to the results, it has been seen that the proposed linear physical programming model is practical and useful approach for mixed-model assembly line balancing problems.

A risk-averse two-stage stochastic programming model for a joint multi-item capacitated line balancing and lot-sizing problem

In this paper, a comprehensive production planning problem under uncertain demand is investigated. The problem intertwines two NP-hard optimization problems: an assembly line balancing problem and a capacitated lot-sizing problem. The problem is modelled as a two-stage stochastic program assuming a risk-averse decision maker. Efficient solution procedures are proposed for tackling the problem. A case study related to mask production is presented. Several insights are provided stemming from the COVID-19 pandemic. Finally, the results of a series of computational tests are reported.

Job rotation and human\u2013robot collaboration for enhancing ergonomics in assembly lines by a genetic algorithm

Currently, the largest percentage of the employed workforce in the manufacturing industry is involved in the assembly process, making ergonomics a key factor when dealing with assembly-related problems. During these processes, repetitive tasks and heavy component handling are frequent for workers, who may result overloaded from an energetic point of view, thus affecting several aspects not only relating to the human factor but also to potentially reduced productivity. Different organizational strategies and technological solutions could be adopted to overcome these drawbacks. For these purposes, the present paper proposes a genetic algorithm for solving the typical problem of assembly line balancing, taking into account job rotation and human–robot collaboration for enhancing ergonomics of workers. The objectives of the problem are related to both economic aspects and human factor: (i) the cost for implementing the assembly line is minimized, evaluated on the basis of the number of workers and differentiated by skill levels and on equipment installed on workstations, including collaborative robots, and (ii) the energy load variance among workers is also minimized, so as to smooth their energy expenditure in performing the assigned assembly operations, calculated according to their movements, physiological characteristics, job rotations and degree of collaboration with robots. The paper finally presents and discusses the application of the developed tool to an industrial assembly case.

Mixed integer programming approaches to partial disassembly line balancing and sequencing problem

Product recovery has received greater attention in recent years mainly due to increased environmental awareness of consumers and stricter environmental regulations imposed by governments. In product recovery, disassembly of the product into its constituent parts is the most significant activity and generally performed on a disassembly line. During disassembly, a complete or partial disassembly of the product may be preferred. In complete disassembly, all parts must be disassembled, while partial disassembly allows to disassemble a subset of parts (e.g., the ones with relatively high revenues). This study deals with a partial disassembly line balancing and sequencing (PDLBS) problem considering revenues of parts to be disassembled, general workstation cost, additional cost of workstation(s) with hazardous parts, and cost of direction changes. For the PDLBS problem, a generic mixed integer programming (MIP) model, with the aim of maximizing total profit, is developed. To strengthen the MIP formulation, two sets of valid inequalities are proposed. The computational results show that the MIP model with valid inequalities is able to provide optimal solutions for the PDLBS problems with up to 30 tasks. To obtain near-optimal solutions for large-sized problems, a MIP-based solution approach is proposed. The proposed approach decomposes the entire MIP model into selection and assignment (SA) and sequencing (SEQ) models. The SA model is an exact relaxation of the MIP model (with valid inequalities) obtained by removing all the sequencing variables and constraints. Hence, SA model also produces an efficient upper bound for the PDLBS problem. The SEQ model, accordingly, aims to find an optimal sequence of tasks subject to the fixed selection and assignment of tasks provided by the SA model. The computational results show that the proposed MIP-based solution approach provides efficient solutions with small optimality gaps for large-sized problems.

ANALISA KESEIMBANGAN LINTASAN PERBANDINGAN METODE RPW DENGAN REGION APPROACH DALAM PEMBUATAN BATIK PRINTING DI “SEKAR JATI STAR”

This Sekar Jati Star Written Batik is a type of company that is still an SME or Home Industry with a wholesale production type or every time you receive orders, and the Sekar Jati Star Batik UKM is a company that adheres to a job shop because of the variety of products or different motives with different flow patterns. different too, so this industry practice research carries the topic of line balancing using the RPW method and the Region Approach, namely to find out that the production line balance problem occurs most in the assembly process compared to the manufacturing process. Continuous movement is most likely achieved by manual assembly operations when several operations can be divided by a short duration in the batik printing process so that the distribution of tasks can be evenly distributed so that jams can be avoided. After calculating the RPW method, the results of the number of stations in the initial condition are 5 and the RPW method 5, the balance delay in the initial conditions is 39.2% with the RPW method to 18.0%, the efficiency of the initial condition system is 60.8% with the RPW method 82%, Output Production Initial conditions 16 meters / day with the RPW method 31 meters / day. Then the calculation of the Region Approach method results in the number of work stations 5, Balance Delay 96%, and for work efficiency 0.0376 or 4%.

A Joint Assembly Line Balancing and Feeding Problem (JALBFP) considering direct and indirect supply strategies

In an assembly system, balancing the line and designing the part feeding setting play a fundamental role in guaranteeing efficiency and effectiveness. Although these two aspects are interrelated, they are usually performed subsequent to each other, with the part feeding decisions only being made once the line has already been balanced. However, the simultaneous solving of the assembly line balancing and part feeding problems can lead to interesting benefits. In this paper, an innovative approach for solving the Joint Assembly Line Balancing and Feeding Problem (JALBFP) is proposed, to achieve an integrated balancing of assembly tasks and parts picking/kitting and a fully synchronised assembly-feeding system. The aim is to minimise the operational system cost, including workforce, space and transportation costs. The Mixed Integer Linear Programming (MILP) model is applied to several instances and two case studies and compared to a hierarchical approach. The proposed model leads to a significant improvement in reducing the number of assembly stations required and the inventory level at the line, where space is notoriously scarce, also avoiding workforce oversizing in the feeding system. The strength of the model relies on its formulation and on its versatility, which makes it applicable to different assembly sectors.

Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system

Assembly line balancing problems (ALBP) have plagued scholars and practitioners for decades. This paper investigates a new assembly system called flexible assembly line (FAL) derived from empirical observations in an air-conditioner assembly workshop. FAL can avoid the ALBP itself thanks to its structural flexibility and reconfigurability. However, field investigation highlights new challenges in the FAL - the mismatch between production (assembly) and intralogistics (material supply) leads to long waiting/idle time and workflow chaos, consequently lowers productivity and increases backorders. The production-intralogistics (PiL) processes are spatiotemporally coupled and interactional. Its complexity is much higher than considering the production or intralogistics optimization solely. And the PiL processes are further complicated by uncertain events such as new job arrivals, stochastic operational time, and equipment failures. The advent of Industry 4.0 technologies shows the tremendous potentials to revolutionize the contemporary notions of production management. Massive production data can be collected and analyzed in real-time. Nevertheless, there is little methodological research regarding utilizing real-time data to support production decisions under uncertainties. Thus, how to leverage real-time data collected in Industry 4.0 environments to support the decision-making of PiL processes for achieving a matched, coordinated, and synchronous operations management under various uncertainties, is a novel research problem. This paper develops a five-phase Graduation intelligent Manufacturing System (GiMS) to achieve PiL synchronization with flexibility and resilience. The underlying principles and rationale of GiMS are formulated as a synchronization mechanism, which includes a graph-theory based clustering for planning/scheduling and real-time decentralized ticketing for execution/control. Comprehensive numerical results validate the superiority of GiMS and the benefits of visibility and traceability in various scenarios. Moreover, the effects of uncertainties and trolley capacity are investigated in the sensitivity analysis.

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.

Sequence-dependent time- and cost-oriented assembly line balancing problems: a combinatorial Benders’ decomposition approach

This article deals with the cost-oriented assembly line balancing problem with sequence-dependent set-up times. To this end, a mixed-integer linear programming (MILP) model is proposed for time- and cost-oriented assembly line balancing problems with sequence-dependent set-up times between tasks. The problem is computationally intractable; therefore, a Benders’ decomposition algorithm is developed to solve it. The proposed decomposition yields a master problem that addresses the issue of assigning assembly tasks to workstations, as well as a set of subproblems that deal with sequencing tasks within each workstation owing to sequence-dependent set-up times. The algorithm is tested on a set of randomly generated test problems and numerically compared with a MILP formulation of the problem solved using a commercial optimizer. The computational results demonstrate that the proposed Benders’ decomposition approach outperforms the MILP model. The contribution of this article lies in the new models proposed and the decomposition-based exact algorithm developed.

Approximation algorithms for simple assembly line balancing problems

Approximation algorithms for simple assembly line balancing problems

An exact solution method for multi-manned disassembly line design with AND/OR precedence relations

Specific government regulations for waste products and increasing environmental awareness are concerns for companies. Disassembly operations have become essential tools for green manufacturing and for reducing ecological hazards from waste electrical and electronic equipment. An effective and efficient line design may be vital to encourage companies to establish a disassembly center. The line-balancing problem is a critical issue in a disassembly center. Large products may allow simultaneous disassembly of parts using more than one operator at a workstation. However, multi-manned disassembly line balancing creates a more complicated problem. Thus, an efficient solution technique based on a constraint programming (CP) approach is proposed for multi-manned disassembly line balancing with AND/OR precedence relations to minimize cycle time as a primary objective and the total number of workers as a secondary objective. A mixed-integer linear programming (MILP) model based on previous studies was proposed to define the problem. A CP approach was developed for the first time to solve this problem. A genetic algorithm was used to show the relative performance of the CP method for large problems. The proposed CP approach demonstrates superior performance.