A Fuzzy AHP and PROMETHEE method-based heuristic for disassembly line balancing problems

Nowadays, new products have been introduced in the market at an ever increasing pace due to rapid technology advancement. Consequently, products are becoming outdated and discarded faster than ever before. Since the demand for new solutions to economically deal with such outdated products begun to rise, the disassembly line has emerged as a viable solution to this problem. The disassembly line has been considered as a viable choice for automated disassembly of returned products. The problem of sequence generation in disassembly is complex due to its NP-Hard nature and therefore the heuristically solutions are most preferable for these types of problems. In this paper, a heuristic has been proposed to assign the disassembly tasks/parts to the work stations under its precedence constraints. It incorporates Fuzzy analytic hierarchy process (Fuzzy AHP) and PROMETHEE method for the selection of tasks for assignment to the disassembly line. The Fuzzy AHP has been used to find the relative importance of each criteria and PROMETHEE method has been used for prioritising the tasks for assignment. The proposed heuristics has been illustrated with an example and the results have been compared to the heuristic proposed by McGovern and Gupta. The proposed heuristic performs well and has shown improvements in terms of cycle time and idle time of the workstations.     

A Petri net-based heuristic for mixed-model assembly line balancing problem of Type-E

To effectively respond to the changing market demands, a manufacturer should produce variety of products with small lots. Thus, multiple products (models) are assembled simultaneously on a same line. However, it is very challenging to balance such an assembly line. This paper conducts a study on balancing a mixed-model assembly line of Type E. To solve this problem, a coloured-timed Petri net model is developed to describe the task precedence relationship. Also, the optimisation problem is formulated as a mathematical programming model. Then, with the models, a two-stage heuristic algorithm is proposed to solve the problem. At the first stage, based on the Petri net model, a P-invariant algorithm (PA) is presented to minimise the number of workstations. At the second stage, a heuristic is proposed to further minimise the cycle time by combining the PA with a binary search algorithm (BSA). Performance of the proposed method is evaluated by an illustrative example and numerical experiments. It is shown that it works well in terms of both solution accuracy and computational efficiency for large size problems.     

Decomposition heuristic for parallel-machine transfer line design with dual-uncertainties-based chance constraints

In this article, a new transfer line structure, the parallel-machine transfer line (PMTL), is studied. Compared to a traditional transfer line, PMTL is designed for high reliability due to its capacity for overdesign, by considering the uncertain failure of machines in the design. Dual uncertainties including the uncertain occurrence of machine failure and uncertain failure repair times are simultaneously considered in PMTL, and a chance-constrained programming (CCP) model is proposed for the uncertain PMTL design problem. A novel decomposition heuristic (DH) that is able to decompose the CCP into related sub-problems according to the relevance of the constraints is introduced, and linearization and transformation of the constraints are carried out to solve the sub-problems. Several experimental examples of uncertain PMTL design problems are prepared and solved by the DH and stochastic simulation, and the results show that DH outperforms the stochastic simulation with similar results but in a shorter computation time.     

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.     

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.     

A priority rule-based constructive heuristic and an improvement method for balancing assembly lines with parallel multi-manned workstations

Assembly lines of big-size products such as buses, trucks and helicopters are very different from the lines studied in the literature. These products’ manufacturing processes have a lot of tasks most of which have long task times. Since traditional assembly line models including only one worker in each station (i.e. simple assembly lines) or at most two workers (two-sided assembly lines) may not be suitable for manufacturing these type of products, they need much larger shop floor for a number of stations and long product flow times. In this study, an assembly line balancing problem (ALBP) with parallel multi-manned stations is considered. Following the problem definition, a mixed integer programming formulation is developed. A detailed study of priority rules for simple ALBPs is also presented, and a new efficient constructive heuristic algorithm based on priority rules is proposed. In order to improve solutions found by the constructive heuristic, a genetic algorithm-based solution procedure is also presented. Benchmark instances in the literature are solved by using the proposed mathematical programming formulation. It has been seen that only some of the small-size instances can be solved optimally by this way. So the efficiency of the proposed heuristic method is verified in small-size instances whose optimal solutions are found. For medium- and big-size instances, heuristics’ results and CPU times are demonstrated. A comparative evaluation with a branch and bound algorithm that can be found in the literature is also carried out, and results are presented.     

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.     

Tolerance chart balancing for machining process planning

This paper provides a mathematical model for tolerance chart balancing for machining process planning. The formulation of the proposed model is based on the graphic ‘rooted tree’ representation technique in describing the sequence of the machining process. The criteria considered in the presented study are based on the combined effects of manufacturing cost and quality loss under constraints such as process capability limits, design functionality restrictions, and product quality requirements. Applications of these models include minimizing the total cost of manufacturing activities and quality related issues with process selection and tolerance allocation in machining process planning, particularly in the early stage of planning.     

Disassembly line design with multi-manned workstations: a novel heuristic optimisation approach

As the first and the most time consuming step of product recovery, disassembly is described as the systematic separation of constituent parts from end-of-life products through a series of operations. In this context, designing and balancing disassembly lines are critical in terms of the efficiency of product recovery. Recent research on disassembly line balancing (DLB) has focused on classical stations where only one worker is allocated. However, such a line results in larger space requirement and longer disassembly lead time. In this paper, disassembly line balancing problem (DLBP) with multi-manned stations is introduced to the relevant literature as a solution to overcome these disadvantages. A mixed integer linear programming (MILP) model and two novel framework heuristic algorithms are developed to minimise the number of workers and workstations. MILP model has been applied to a dishwasher disassembly system. The application results indicate the superiority of establishing multi-manned stations over classical disassembly system design with single-worker stations with shorter disassembly lead time (80.9%) and line length (60.2%). Moreover, the proposed heuristics have been compared on newly generated test problems (instances) for DLBP. The results validate that the heuristics provide acceptable solutions in a reasonable amount of time even for large-sized problems.     

A network-based shortest route model for parallel disassembly line balancing problem

Disassembly lines should be balanced efficiently to increase productivity of the line and to reduce disassembly costs. This problem is called disassembly line balancing problem (DLBP). The objective of the DLBP is usually to find the minimum number of disassembly workstations required. This study introduces parallel DLBP (PDLBP) with single-product and proposes a network model based on the shortest route model (SRM) for solving PDLBP. The proposed model is illustrated via numerical examples. A comprehensive experiment is also conducted to evaluate problem-specific features of disassembly lines. To the best of our knowledge, this is the first study dealing with PDLBP. This paper will present a different point of view regarding DLBP.     

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.     

Scheduling of deadlock and failure-prone automated manufacturing systems via hybrid heuristic search

This work focuses on the scheduling problem of deadlock and failure-prone automated manufacturing systems, and presents a new scheduling method by combining a robust supervisory control policy and hybrid heuristic search. It aims to minimise makespan, i.e. the completion time of the last part. Based on the extended reach ability graph of the system, it establishes a new heuristic function and two dispatching rules to guide the search process for a schedule. By embedding a robust supervisory control policy into the search process, it develops a polynomial robust dynamic window search algorithm. Failure and repair events of unreliable resources may occur during the execution of a schedule obtained by the proposed algorithm and may make the schedule infeasible. To reduce the influence caused by them and ensure all parts to be finished, this work proposes two event-driven strategies. The first one suspends the execution of the parts requiring failed resources and those to be started until all failed resources are repaired and permits only those parts that have already been processed on working machines to be completed. The second one invokes the proposed algorithm to obtain a new schedule at the vertex generated after a resource failure or repair event and executes the new schedule. Both strategies are effective while the latter performs better at the expense of more computation.     

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

To achieve a significant improvement in the overall performance of a flexible manufacturing system, the scheduling process must consider the interdependencies that exist between the machining and transport systems. However, most works have addressed the scheduling problem as two independent decision making problems, assuming sufficient capacity in the transport system. In this paper, we study the simultaneous scheduling (SS) problem of machines and automated guided vehicles using a timed coloured Petri net (TCPN) approach under two performance objectives; makespan and exit time of the last job. The modelling approach allows the evaluation of all the feasible vehicle assignments as opposed to the traditional dispatching rules and demonstrates the benefits of vehicle-controlled assignments over machine-controlled for certain production scenarios. In contrast with the hierarchical decomposition technique of existing approaches, TCPN is capable of describing the dynamics and evaluating the performance of the SS problem in a single model. Based on TCPN modelling, SS is performed using a hybrid heuristic search algorithm to find optimal or near-optimal schedules by searching through the reachability graph of the TCPN with heuristic functions. Large-sized instances are solved in relatively short computation times, which were a priori unsolvable with conventional search algorithms. The algorithm’s performance is evaluated on a benchmark of 82 test problems. Experimental results indicate that the proposed algorithm performs better than the conventional ones and compares favourably with other approaches.     

Empirical evaluation of lower bounding methods for the simple assembly line balancing problem

The simple assembly line balancing problem (SALBP) is a classical problem that arises in the design of assembly lines. The objective of the SALBP is to divide the assembly work among different workstations of the assembly line in order to maximise the efficiency of the entire line. In this paper, we: (a) put forward different families of lower bounds for the SALBP; (b) introduce new lower bounds; (c) generalise previous results; and (d) analyse the performance of these lower bounding techniques in terms of solution quality using a recently proposed set of instances. An analysis of the results of these bounds leads us to: (1) improve the best-known lower bound for 104 out of the 175 open instances from the benchmark set; (2) identify the areas of applicability of different sources of bounds; and (3) identify possible uses of these bounds in other assembly line balancing problems.     

Heuristic procedures for solving the general assembly line balancing problem with setups

The general assembly line balancing problem with setups (GALBPS) was recently defined in the literature. It adds sequence-dependent setup time considerations to the classical simple assembly line balancing problem (SALBP) as follows: whenever a task is assigned next to another at the same workstation, a setup time must be added to compute the global workstation time, thereby providing the task sequence inside each workstation. This paper proposes heuristic procedures, based on priority rules, for solving GALBPS, many of which are an improvement upon heuristic procedures published to date.     

A fast branch,bound and remember algorithm for disassembly line balancing problem

In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1?seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects.     

应用模态分析及傅里叶变换的柔性转子无试重动平衡方法

针对旋转机械不平衡导致的振动问题,提出一种的柔性转子现场动平衡方法。其通过有限元理论建立转子系统动力学模型,并将系统运动微分方程按模态振型展开,利用FFT及IFFT变换,获取频域下不平衡载荷谱,最终识别转子不平衡状态。仿真和试验结果均表明,该方法仅需在低于临界转速的状态下采集振动响应数据,并且无需停机试重,就能准确识别转子不平衡状态,添加配重后可使转子各阶不平衡振动得到有效抑制。     

Capacity-filtering algorithms for finite-capacity planning of a flexible flow line

This paper addresses finite-capacity planning (or medium-term scheduling) issues in a flexible flow line such as a semiconductor fabrication (Fab), liquid crystal display (LCD) Fab, or printed circuit board (PCB) Fab. In such a flexible flow line, medium-term schedules such as release schedules and output schedules are critical to achieving the goal of full-capacity and on-time production. However, existing finite-capacity planning methods do not adequately reflect the actual capacity profiles of the Fab. This paper presents capacity-filtering algorithms for generating a finite-capacity loading-profile from an infinite-capacity loading-profile at a processing stage in a Fab. In addition, two types of finite-capacity planning problems are described, and ways to use the algorithms in solving these problems presented. Performance analyses using a real-life case study show that the proposed method is superior to existing methods. It is postulated that the results presented can be used as a building block for obtaining medium-term schedules for the entire Fab.     

Least in-sequence probability heuristic for mixed-volume production lines

The paper focuses on the sequencing aspects of a stochastic hybrid flexible assembly system (FAS) operating in a build-to-order environment. In such a system, although the flow of parts is unidirectional, parallel paths can exist for accommodating different types of parts produced and potential rework of the parts that fail inspection at a given production stage. As a result, the original sequential order of parts can become distorted, resulting in an exit demand sequence which is at variance with the input sequence. To compensate for such sequence disturbances, an adequately sized buffer is installed at the exit end of the FAS. From a practical viewpoint, the study is relevant to the sequencing of upstream operations in an automotive assembly plant functioning in an in-line vehicle sequencing mode. An important feature of the FAS considered in this study is that the demand sequence of part types is known and fixed for a given period of time. Further, the different part types that constitute the demand sequence can have different frequencies of occurrence in a range specified from low to high. We exploit this property of the demand sequence in the development of the least in-sequence probability (LISP) algorithm. The development of LISP is based on the trade-off of pulling low-volume parts ahead in the input sequence while delaying the high-volume parts. We propose the use of the heuristic as a means to achieve both of the following: (a) to improve customer service levels in terms of the number of in-sequence parts output from the system, given a fixed size for the re-sequencing buffers; and (b) to reduce re-sequencing buffer sizes given target levels of customer service.