Multi-objective balancing of assembly lines by population heuristics

This paper is concerned with the solution of the multi-objective single-model deterministic assembly line balancing problem (ALBP). Two bi-criteria objectives are considered:

1. Minimising the cycle time of the assembly line and the balance delay time of the workstations.

2. Minimising the cycle time and the smoothness index of the workload of the line.

A new population heuristic is proposed to solve the problem based on the general differential evolution (DE) method. The main characteristics of the proposed multi-objective DE (MODE) heuristic are:

1. It formulates the cost function of each individual ALB solution as a weighted-sum of multiple objectives functions with self-adapted weights.

2. It maintains a separate population with diverse Pareto-optimal solutions.

3. It injects the actual evolving population with some Pareto-optimal solutions.

4. It uses a new modified scheme for the creation of the mutant vectors.

Moreover, special representation and encoding schemes are developed and discussed which adapt MODE on ALBPs. The efficiency of MODE is measured over known ALB benchmarks taken from the open literature and compared to that of two other previously proposed population heuristics, namely, a weighted-sum Pareto genetic algorithm (GA), and a Pareto-niched GA. The experimental comparisons showed a promising high quality performance for MODE approach.     

A multi-decision genetic approach for workload balancing of mixed-model U-shaped assembly line systems

A mixed-model assembly line is a type of production line where a variety of product models similar in product characteristics are produced. As a consequence of introducing the just-in-time (JIT) production principle, it has been recognised that a U-shaped assembly line system offers several benefits over the traditional straight line system. This paper proposes a new evolutionary approach to deal with workload balancing problems in mixed-model U-shaped lines. The proposed method is based on the multi-decision of an amelioration structure to improve a variation of the workload. This paper considers both the traditional straight line system and the U-shaped assembly line, and is thus an unbiased examination of line efficiency. The performance criteria considered are the number of workstations (the line efficiency) and the variation of workload, simultaneously. The results of experiments enhanced the decision process during multi-model assembly line system production; thus, it is therefore suitable for the augmentation of line efficiency in workstation integration and simultaneously enhancement of the variation of the workload. A case study is examined as a validity check in collaboration with a manufacturing company.     

Applying case-based reasoning in assembly sequence planning

Assembly sequence planning (ASP) is the foundation of the assembly process planning which plays a key role in the whole product life cycle. In this paper, a unique ASP reasoning method supported by the artificial intelligent technique of case-based reasoning (CBR) is proposed and developed. First, based on the previous ASP literatures review and the CBR characteristics analysis, the systematic architecture of the CBR based ASP is presented. Then, some key techniques including assembly case modelling, similar case retrieving, case based reasoning, and case base maintenance, etc., are explored thoroughly. To enhance the efficiency and quality of the reasoning process, genetic algorithm (GA) is designed and applied to automatically inferring of the reference assembly sequence. Finally, the corresponding software system with an engineering example is given to demonstrate the effectiveness of the CBR based ASP.     

Increasing production rate in U-type assembly lines with sequence-dependent set-up times

U-shaped assembly lines are commonly used in just-in-time production systems as they have some advantages over straight lines. Although maximizing production rates on these lines by assigning tasks to stations is common practice in industrial environments, studies on the stated assembly line balancing problem are limited. This article deals with maximizing the production rate on U-shaped assembly lines under sequence-dependent set-up times. Sequence-dependent set-up times mean that after a task is performed, a set-up time, the duration of which depends on adjacent tasks, is required to start the next task operation. These set-ups are considered by dividing them into two groups, named forward and backward set-ups, to make the problem more practical. Two heuristics based on simulated annealing and genetic algorithms are improved beside the mathematical model. Experimental results show that solving the stated problem using the mathematical model is nearly impossible, while heuristics may obtain solutions that have acceptable deviations from the lower bounds.     

Discrete electromagnetism-like mechanism algorithm for assembly sequences planning

Assembly sequence planning (ASP) plays an important role in digital manufacturing. It is a combinatorial optimisation problem with strong constraints aiming to work out a specific sequence to assemble together all components of a product. The connector-based ASP, which uses the connector to simplify the complex assembly problem, is one of the most important and hardest types. In order to solve this problem effectively, a discrete electromagnetism-like mechanism (DEM) algorithm is proposed. A charge formula and a force formula are redefined in DEM algorithm. An adjacency list is applied to handle the precedence relationship and prevent infeasible solutions. Two movements based on path relinking are employed. Moreover, with two different guided mutations, the population diversity can be guaranteed. Five examples are used to test and evaluate the performance of DEM. The comparisons among the proposed DEM, traditional genetic algorithms (GAs), guided GAs, memetic algorithms and artificial immune systems show that DEM outperforms among these algorithms in terms of running time, computation accuracy, convergence speed and parameter robustness.     

ASALBP: the alternative subgraphs assembly line balancing problem

Assembly line balancing problems basically consist of assigning a set of tasks to a group of workstations while maintaining the tasks’ precedence relations, which are represented by a predetermined precedence graph. However, one or more parts of a product's assembly process may admit alternative precedence subgraphs, which represent possible assembly variants. In general, because of the great difficulty of the problem and the impossibility of representing alternative subgraphs in a precedence graph, the system designer will decide to select, a priori, one of such alternative subgraphs. This paper presents, characterizes and formulates a new general assembly line balancing problem with practical relevance: the alternative subgraphs assembly line balancing problem (ASALBP). Its novel characteristic is that it considers the possibility of having alternative assembly subgraphs, with the processing times and/or the precedence relations of certain tasks dependent on the assembly subgraph selected. Therefore, solving this problem implies simultaneously selecting an assembly subgraph for each part of the assembly that allows alternatives and balancing the line. The potentially positive effects of this on the solution of the problem are shown in a numerical example. Finally, a simple mathematical programming model is described and the results of a brief computational experiment are presented.     

Task proximity index: a novel measure for assessing the work-efficiency of assembly line balancing configurations

Assembly line balancing (ALB) aims at optimally partitioning the total work required to assemble a product to workstations. In this problem, precedence constraints must be observed on the sequence with which the distinct tasks of the assembly process may be carried out. In contrast to the strict mathematical posture employed in conventional ALB approaches, this paper provides a deep insight on the origin of precedence diagram which illustrates precedence constraints among the tasks. Our review on assembly planning methods reveals that these constraints are deduced from the geometry of the components to be attached, their position in the assembled product and even planner's expertise on the rational order with which the tasks are to be performed. Accordingly, it is explicitly shown through a case study of a real industrial product that the topology of tasks on the precedence diagram may be utilised to obtain more work-efficient ALB configurations. To this end, a metric system is developed to quantify the proximity of the tasks on the precedence diagram and a novel measure, namely the task proximity index, is proposed for assessing ALB solutions from a practical viewpoint.     

A knowledge-based system for solving multi-objective assembly line balancing problems

This paper presents design-and-development details of a knowledge-based system that solve multi-objective assembly line balancing problems to obtain an optimal assignment of a set of assembly tasks to a sequence of workstations. Assembly line balancing problems arise in high-volume production systems with a significant regularity. The formulation and solutions currently employed by managers and practitioners usually aims at optimizing one objective (e.g., number of work stations or cycle time), thus ignoring the multi-dimensional nature of the overall objectives of the manager. Furthermore, in practice ALBPs are ill-defined and ill-structured, making it difficult to formulate and solve them by mere mathematical approaches. The knowledge-based system multi-objective assembly line balancing approach, presented in this paper, addresses these needs. This paper presents a knowledge-base multi-objective approach to assembly line balancing problems. It demonstrates how such a system can be constructed and how a variety of assembly line balancing methods can be used in a uniform structure to support the decision maker (DM) to formulate, validate the formulation, generate alternatives, and choose the best alternative. Its capabilities include: (1) Elimination of inconsistencies in the problem structure. (2) The use of multi-objective formulation of the problem, (3) A well-designed user-interface, (4) Pursuance of the overall objectives of the manager via a new mechanism, (5) Development of several efficient alternatives, consistent with the user-specified constraint structure, providing the decision maker with a larger number of choices, and, (6) An approach for ranking and prioritizing alternatives consistent with the decision-maker's preferences.     

Synergy between evolutionary optimization and induction graphs learning for simulated manufacturing systems

The performance of manufacturing systems is strongly dependent on a set of numerical and non-numerical decision variables, such as transport lot sizes and priority rules, for example. Configuring such systems requires determining a value for each decision variable to optimize a performance criterion. Recent works have shown that simulation optimization of manufacturing systems can be efficiently addressed using evolutionary algorithms. Nevertheless, these algorithms do not provide any understanding on the system's behaviour, which could be crucial regarding the investment amounts. Existing methods based on learning strategies try to provide knowledge on the system, but relevant information could be scattered. To optimize the system, characterize good solutions and highlight critical decision variables, an ‘intelligent optimization’ method is proposed based on the synergy between evolutionary optimization and induction graphs learning. These approaches work together to find good solutions with associated knowledge. The method is illustrated by the configuration of an assembly kanban system constituted of five machines that process three types of product.     

THE ROLE OF EVOLUTIONARY AND ADAPTIVE SEARCH DURING WHOLE SYSTEM,CONSTRAINED AND DETAILED DESIGN OPTIMIZATION

The paper introduces various strategies which incorporate evolutionary and adaptive search techniques. These strategies incorporate genetic algorithms (GA) and ant colony models combined within co-operating frameworks that provide a capability for decision support and optimization during whole system design and constraint satisfaction/ constrained optimization during the engineering design process. The objective during whole system design is to determine an optimum initial configuration for large engineering systems. Strategies for the efficient integration of evolutionary techniques with detailed design are also introduced. Each of these areas presents specific problems to the evolutionary/adaptive search processes and the overall objective here is to identify the main areas of difficulty and provide solutions that will lead to successful integration. The paper illustrates the flexibility and utility of the various techniques when applied across the various stages of the design process, i.e. from providing decision support during the high-risk stages of preliminary design to the identification of definitive optimal solutions during the more deterministic stages of detailed design.     

Design of stochastic assembly lines considering line balancing and part feeding with supermarkets

This article aims to address the assembly line balancing problem (ALBP) and supermarket location problem (SLP) as two long-term interrelated decision problems considering the stochastic nature of the task times and demands. These problems arise in real-world assembly lines during the strategic decision-making phase of configuring new assembly lines from both line balancing and part-feeding (PF) aspects. A hierarchical mathematical programming model is developed, in which the first level resolves the stochastic ALBP by minimizing the workstation numbers and the second level deals with the stochastic SLP while optimizing the PF shipment, inventory and installation costs. The results of case data from an automotive parts manufacturer and a set of standard test problems verified that the proposed model can optimize the configuration of assembly lines considering both ALBP and SLP performance measures. This study also validates the effect of the stochastic ALBP on the resulting SLP solutions.     

Augmented reality for assembly guidance using a virtual interactive tool

The application of augmented reality (AR) technology for assembly guidance is a novel approach in the traditional manufacturing domain. In this paper, we propose an AR approach for assembly guidance using a virtual interactive tool that is intuitive and easy to use. The virtual interactive tool, termed the virtual interaction panel (VirIP), is an easy-to-use tool that can be used to interactively control AR systems. The VirIP is composed of virtual buttons, which have meaningful assembly information that can be activated by an interaction pen during the assembly process. The interaction pen can be any general pen-like object with a certain colour distribution. It is tracked using a restricted coulomb energy (RCE) network in real-time and used to trigger the relevant buttons in the VirIPs for assembly guidance. Meanwhile, a visual assembly tree structure (VATS) is used for information management and assembly instructions retrieval in this AR environment. VATS is a hierarchical tree structure that can be easily maintained via a visual interface. It can be directly integrated into the AR system or it can alternatively act as an independent central control station on a remote computer to control the data flow of the assembly information. This paper describes a typical scenario for assembly guidance using VirIP and VATS. The main characteristic of the proposed AR system is the intuitive way in which an assembly operator can easily step through a pre-defined assembly plan/sequence without the need of any sensor schemes or markers attached on the assembly components. Several experiments were conducted to validate the performance of the proposed AR-based method using a monitor and a head-mounted display. The results show that the AR-based method can provide an efficient way for assembly guidance.     

The sequence-dependent assembly line balancing problem

Assembly line balancing problems (ALBP) arise whenever an assembly line is configured, redesigned or adjusted. An ALBP consists of distributing the total workload for manufacturing any unit of the products to be assembled among the work stations along the line. The sequence-dependent assembly line balancing problem (SDALBP) is an extension of the standard simple assembly line balancing problem (SALBP) which has significant relevance in real-world assembly line settings. SDALBP extends the basic problem by considering sequence-dependent task times. In this paper, we define this new problem, formulate several versions of a mixed-integer program, adapt solution approaches for SALBP to SDALBP, generate test data and perform some preliminary computational experiments. As a main result, we find that applying SALBP-based search procedures is very effective, whereas modelling and solving the problem with MIP standard software is not recommendable.     

Evaluation of performance measures for representing operational objectives of a mixed model assembly line balancing problem

Performance evaluation of a mixed model assembly line is complicated in nature. For some configurations of the mixed model line, operational objectives can directly be evaluated; however, in others some performance measure is required for evaluating the operational objective. This paper reports a comprehensive comparative study of performance measures as to their relevance in representing the operational objectives of a mixed model assembly line balancing problem of type II. Two performance measures are proposed in this work. A genetic algorithm has been developed for comparing the proposed measures with eight others in an effort to investigate their ability to equalize workload along with minimizing cycle time. Through statistical analysis, one of the proposed performance measures has been established to perform better than the others. Application of the performance measures to balance an assembly line taken as part of an industrial case study also corroborates the results obtained by statistical analysis.     

ANTBAL: an ant colony optimization algorithm for balancing mixed-model assembly lines with parallel workstations

This paper presents ANTBAL, an ant colony optimization algorithm for balancing mixed-model assembly lines. The proposed algorithm accounts for zoning constraints and parallel workstations and aims to minimize the number of operators in the assembly line for a given cycle time. In addition to this goal, ANTBAL looks for solutions that smooth the workload among workstations, which is an important aspect to account for in balancing mixed-model assembly lines. Computational experience shows the superior performance of the proposed algorithm.     

Optimal order release dates for two-level assembly systems with stochastic lead times at each level

In this paper, we examine an optimisation problem for component replenishment in two-level assembly systems under stochastic lead times. The Assembly-to-Order principle is applied. The demand for a finished product and its planned due date are known. The capacity of the assembly system at each level is considered infinite. At each level, the assembly process starts when all the required components or semi-finished items are available. At the second level, the components are ordered from external suppliers and order release dates are decision variables of the problem. A backlogging cost is incurred if the finished product demand is satisfied after the planned due date. If the finished product, a given component or a semi-finished product is available before the corresponding assembly date, an inventory holding cost is considered. Genetic algorithms (GA) reinforced with different techniques are developed to find order release dates that minimise the total expected cost. A Branch and Bound method is also developed to assess the effectiveness of the hybrid GA. Regardless of the number of components and the variability of the costs related to the finished product, the experimental results indicate that the proposed GA are highly efficient.     

A comprehensive survey of augmented reality assembly research

In the past two decades, augmented reality(AR) has received a growing amount of attention by researchers in the manufacturing technology community, because AR can be applied to address a wide range of problems throughout the assembly phase in the lifecycle of a product, e.g., planning, design, ergonomics assessment, operation guidance and training. However, to the best of authors' knowledge, there has not been any comprehensive review of AR-based assembly systems. This paper aims to provide a concise overview of the technical features, characteristics and broad range of applications of ARbased assembly systems published between 1990 and 2015. Among these selected articles, two thirds of them were published between 2005 and 2015, and they are considered as recent pertinent works which will be discussed in detail. In addition, the current limitation factors and future trends in the development will also be discussed.     

Two-stage assembly flow-shop scheduling problem with non-identical assembly machines considering setup times

In this paper, we address the two stage assembly flow-shop problem with multiple non-identical assembly machines in stage two to minimise weighted sum of makespan and mean completion time. Also, sequence dependent setup times are considered for the first stage. This problem is a generalisation of previously proposed two stage assembly flow-shop problems (TSAFSP). In many real world industrial and production systems, there is more than one assembly machine to assemble job components. After extending a mathematical mixed-integer linear programming model to solve the problem, we use GAMS software. The TSAFSP has been known as NP-hard. Therefore, our more general problem is NP-hard too and so for large sized problems the right way to proceed is with the use of heuristic algorithms. So in this paper a hybrid VNS heuristic, which is a combination of the variable neighbourhood search (VNS) algorithm and a novel heuristic is developed and its solutions compared with solutions obtained by GAMS. Computational experiments reveal that the hybrid VNS heuristic performs much better than GAMS with respect to the percentage errors and run times.     

Modelling the Pareto-optimal set using B-spline basis functions for continuous multi-objective optimization problems

In the past few years, multi-objective optimization algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a set of Pareto-optimal solutions, one is often interested in capturing knowledge about the variation of variable values over the Pareto-optimal front. Recent innovization approaches for knowledge discovery from Pareto-optimal solutions remain as a major activity in this direction. In this article, a different data-fitting approach for continuous parameterization of the Pareto-optimal front is presented. Cubic B-spline basis functions are used for fitting the data returned by an EMO procedure in a continuous variable space. No prior knowledge about the order in the data is assumed. An automatic procedure for detecting gaps in the Pareto-optimal front is also implemented. The algorithm takes points returned by the EMO as input and returns the control points of the B-spline manifold representing the Pareto-optimal set. Results for several standard and engineering, bi-objective and tri-objective optimization problems demonstrate the usefulness of the proposed procedure.     

A multiple criteria decision making approach for the assembly line balancing problem

In practice, measuring total profit for a given assembly line balancing (ALB) problem is an involved process that is sometimes impossible because of much uncertainty and unavailability of data. In this paper, ALB is formulated as a multiple criteria problem where several easily quantifiable criteria (objectives) and constraints are defined. Objective functions include number of stations, cycle time, and operations cost, all to be minimized. After a discussion of applications and an overview of multiple criteria decision making (MCDM) approaches for ALB, the MCDM-ALB problem is formulated. Basic definitions and properties of MCDM for ALB are outlined and then an interactive MCDM approach is developed for solving the MCDM-ALB problem. To solve the problem, the decision maker (DM) interactively responds to paired comparisons of multicriteria alternatives. Through a limited number of interactions with the DM, the most preferred alternative is obtained. Many unexplored alternatives are eliminated by using a one-dimensional multiple criteria search. To present the DM's preference, we use the most flexible and general class of utility functions; namely, either quasi-concave or quasi-convex utility functions. An example is solved and computational experiments are reported. It is demonstrated that the bicriteria ALB, cycle time versus number of stations, can be easily solved by using the developed procedure. For the case that there are different criteria, an improved goal programming is developed to solve the MCDM-ALB problem. The motivation for development of the method, based on a case study of a lamp-making plant of the General Electric Company, is discussed.