Hierarchical approach for paced mixed-model assembly line balancing and sequencing with jolly operators

In order to increase flexibility and reduce costs, several companies adopt mixed-model assembly lines whose output products are variations of the same basic model with specific and distinctive attributes. Unfortunately, such attributes typically lead to variations in the task process times. In the case of un-paced buffered assembly lines, these variations are smoothed by buffers with consequences in terms of work-in-progress, costs, space utilisation and lower productivity control. To face such weaknesses, some companies adopt paced un-buffered assembly lines where the cycle time is controlled by the continuous/synchronous moving of the products from the first to the last assembly station. In such contexts, the possibility of assembling different models with different assembly times can be managed through the use of supplementary flexible workforce. This article introduces an innovative balancing and sequencing hierarchical approach for paced mixed-model assembly lines using supplementary flexible workforce called ‘jolly operators’. The goals are to minimise the number of jolly operators and to limit the occurrence of work-overloads, which typically result in out-of-the-line assembly completion. The proposed approach is preliminary validated and applied to a case study from an Italian company assembling industrial air-dryers.     

Balancing and sequencing of parallel mixed-model assembly lines

In this paper, a simulated annealing approach is developed for the parallel mixed-model assembly line balancing and model sequencing (PMMAL/BS) problem which is an extension of the parallel assembly line balancing (PALB) problem introduced by Gökçen et al. (2006 Gökçen, H and A?pak, K. 2006. A goal programming approach to simple U-line balancing problem. European Journal of Operational Research, 171(2): 577–585. [Crossref], [Web of Science ®] , [Google Scholar]). In PALB, the aim is to balance more than one assembly line together. Balancing of the lines simultaneously with a common resource is very important in terms of resource minimisation. The proposed approach maximises the line efficiency and distributes the workloads smoothly across stations. The proposed approach is illustrated with two numerical examples and its performance is tested on a set of test problems. The computational results show that the proposed approach is very effective for PMMAL/BS.     

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

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

Integrated procedure of balancing and sequencing for mixed-model assembly lines: a multi-objective evolutionary approach

A mixed-model assembly line is a type of production line which is used to assemble a variety of product models with a certain level of similarity in operational characteristics. This variety causes workload variance among other problems resulting in low efficiency and line stops. To cope with these problems, a hierarchical design procedure for line balancing and model sequencing is proposed. It is structured in terms of an amelioration procedure. On the basis of our evolutionary algorithm, a genetic encoding procedure entitled priority-based multi-chromosome (PMC) is proposed. It features a multi-functional chromosome and provides efficient representation of task assignment to workstations and model sequencing. The lean production perspective recognises the U-shape assembly line system as more advanced and beneficial compared to the traditional straight line system. To assure the effectiveness of the proposed procedure, both straight and U-shape assembly lines are examined under two major performance criteria, i.e., number of workstations (or line efficiency) as static criterion and variance of workload (line and models) as dynamic criterion. The results of simulation experiments suggest that the proposed procedure is an effective management tool of a mixed-model assembly line system.     

Simultaneous solving of balancing and sequencing problems in mixed-model assembly line systems

Recently, the mixed-model assembly line (MMAL) has been widely studied by many researchers. In fact, there are two basic problems, namely balancing and sequencing problems, which have been investigated in a lot of studies separately, but few researchers have solved both problems simultaneously. Regarding this, the best results in minimising total utility work have been gained by developing a co-evolutionary genetic algorithm (Co-GA) so far. This paper provides a mixed-integer linear programming (MILP) model to jointly solve the problems. Because of NP-hardness, an evolution strategies (ES) algorithm is presented and evaluated by the same test problems in the literature. Two main hypotheses, namely simultaneous search and feasible search, are tested in the proposed algorithm to improve the quality of solutions. To calibrate the algorithm, a Taguchi design of experiments is employed. The proposed ES is compared with the modified version of Co-GA and the MILP model results. According to numerical experiments and statistical proving, the proposed ES outperformed the modified Co-GA from two points of view: the objective function and the computational time. Additionally, the meta-heuristic algorithms are examined in terms of other well-known criteria in MMAL. Finally, the contribution of each hypothesis in accounting for this superiority is analysed.     

Analysis of the type II robotic mixed-model assembly line balancing problem

In recent years, there has been an increasing trend towards using robots in production systems. Robots are used in different areas such as packaging, transportation, loading/unloading and especially assembly lines. One important step in taking advantage of robots on the assembly line is considering them while balancing the line. On the other hand, market conditions have increased the importance of mixed-model assembly lines. Therefore, in this article, the robotic mixed-model assembly line balancing problem is studied. The aim of this study is to develop a new efficient heuristic algorithm based on beam search in order to minimize the sum of cycle times over all models. In addition, mathematical models of the problem are presented for comparison. The proposed heuristic is tested on benchmark problems and compared with the optimal solutions. The results show that the algorithm is very competitive and is a promising tool for further research.     

Mathematical models for parallel two-sided assembly line balancing problems and extensions

In this study, a mixed integer programming model for the parallel two-sided assembly line balancing problem is developed. Several extensions such as a cost-oriented model, a model with time and space constraints and a model with assignment restrictions which considers characteristics of parallel lines are also presented. The model has been tested on a number of test problems from the literature. The results for different objective functions are analysed on the test problems.     

A novel bi-level hierarchy towards available-to-promise in mixed-model assembly line sequencing problems

This article addresses advanced available-to-promise (AATP) in mixed-model assembly line sequencing problems. In the developed framework, customers are prioritized with respect to 11 defined criteria using the technique for order of preference by similarity to ideal solution (TOPSIS) method, and order quantities are calculated using a nonlinear mathematical program. Next, a mixed binary nonlinear mathematical program is developed to determine the optimum sequence of the optimized order quantities to minimize the total lateness. Since the proposed models are intractable, a hybrid genetic algorithm–simulated annealing method is also developed. Finally, an industrial case study is reported, the results of which validate the developed AATP framework.     

An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line that is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such lines involve several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). This paper proposes an adaptive genetic algorithm approach to solve MMALSP where multiple objectives such as variation in part consumption rates, total utility work and setup costs are considered simultaneously. The proposed approach integrates an adaptive parameter control (APC) mechanism into a multi-objective genetic algorithm in order to improve the exploration and exploitation capabilities of the algorithm. The APC mechanism decides the probability of mutation and the elites that will be preserved for succeeding generations, all based on the feedback obtained during the run of the algorithm. Experimental results show that the proposed adaptive GA-based approach outperforms the non-adaptive algorithm in both solution quantity and quality.     

Modelling and optimisation of energy-efficient U-shaped robotic assembly line balancing problems

Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and productivity improvement on U-shaped robotic assembly lines. This paper first develops a nonlinear multi-objective mixed-integer programming model, reformulates it into a linear form by linearising the multiplication of two binary variables, and then refines the weight of multiple objectives so as to achieve a better approximation of true Pareto frontiers. In addition, Pareto artificial bee colony algorithm (PABC) is extended to tackle this new complex problem. This algorithm stores all the non-dominated solutions into a permanent archive set to keep all the good genes, and selects one solution from this set to overcome the strong local minima. Comparative experiments based on a set of newly generated benchmarks verify the superiority of the proposed PABC over four multi-objective algorithms in terms of generation distance, maximum spread, hypervolume ratio and the ratio of non-dominated solution.     

Stochastic assembly line balancing using beam search

This paper presents a beam search-based method for the stochastic assembly line balancing problem in U-lines. The proposed method minimizes total expected cost comprised of total labour cost and total expected incompletion cost. A beam search is an approximate branch and bound method that operates on a search tree. Even though beam search has been used in various problem domains, this is the first application to the assembly line balancing problem. The performance of the proposed method is measured on various test problems. The results of the computational experiments indicate that the average performance of the proposed method is better than the best-known heuristic in the literature for the traditional straight-line problem. Since the proposed method is the first heuristic for the stochastic U-type problem with the total expected cost criterion, we only report its results on the benchmark problems. Future research directions and the related bibliography are also provided in the paper.     

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed in the literature. The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent-based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent-based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles are explained and discussed.     

Robotic U-shaped assembly line balancing using particle swarm optimization

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.     

A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.     

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.     

Two-sided U-type assembly line balancing problem

U-type and two-sided assembly lines are two types of design having advantages over traditional straight assembly lines. In this paper, a new line design hybrid of U-type and two-sided lines is presented. A bi-objective 0-1 integer programming model is developed to solve the line balancing problem of the proposed design. Zoning constraints are also considered for the proposed design. A number of test problems from the literature with up to 65 tasks are solved. Benefits of two-sided U-type lines are discussed.     

A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.     

Simulated annealing algorithms for the multi-manned assembly line balancing problem: minimising cycle time

Multi-manned assembly lines are often designed to produce big-sized products, such as automobiles and trucks. In this type of production lines, there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones. In this paper, such a class of problems, namely, the multi-manned assembly line balancing problem is addressed, with the objective of minimising the cycle time. A mixed-integer mathematical programming formulation is proposed for the considered problem. This model has the primary objective of minimising the cycle time for a given number of workstations and the secondary objective of minimising the total number of workers. Since the addressed problem is NP-hard, two meta-heuristic approaches based on the simulated annealing algorithm have been developed: ISA and DSA. ISA solves the problem indirectly while DSA solves it directly. The performance of the two algorithms are tested and compared on a set of test problems taken from the literature. The results show that DSA outperforms ISA in term of solution quality and computational time.     

JIT mixed-model sequencing with batching and setup considerations via search heuristics

This research addresses the problem of sequencing items for production when it is desired that the production sequences result in minimal usage rates–surrogate measures for flexibility in a JIT environment. While seeking sequences with minimal usage rates, the number of required setups for the sequences is also considered, along with feasible batch-sizing combinations. The general intent is to find minimum usage-rate sequences for each associated number of setups and total batches. This multiple objective problem is addressed via a three-dimensional efficient frontier. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of ‘real world’ size, the search heuristics of simulated annealing and genetic algorithms are presented and used to find solutions for several problem sets from the literature. Experimentation shows that the simulated annealing approach outperforms the genetic algorithm approach in both objective function and CPU performance.     

Integrated assembly line balancing with resource restrictions

This paper documents a study carried out on the problem of designing an integrated assembly line when many workers with a variety of skills are employed. This study addresses the problem of selecting multi-functional workers with different salaries to match their skills and of assigning tasks to work stations when there are precedence restrictions among the tasks. The objective of this study is to minimise the total annual work station costs and the annual salary of the assigned workers within a predetermined cycle time. A mixed integer linear program is developed with a genetic algorithm in order to address the problem of resource restrictions related to integrated assembly line balancing. Numerical examples demonstrate the efficiency of the developed genetic algorithm.