Balancing two-sided assembly lines with sequence-dependent setup times

Two-sided assembly lines are often designed to produce large-sized products, such as automobiles, trucks and buses. In this type of production line, both left-side and right-side of the line are used in parallel. In all studies on two-sided assembly lines, sequence-dependent setup times have not yet been considered. However, in real life applications, setups may exist between tasks. Performing a task directly before another task may influence the latter task inside the same station, because a setup for performing the latter task may be required. Furthermore, if a task is assigned to a station as the last one, then it may cause a setup for performing the first task assigned to that station since the tasks are performed cyclically. In this paper, the problem of balancing two-sided assembly lines with setups (TALBPS) is considered. A mixed integer program (MIP) is proposed to model and solve the problem. The proposed MIP minimises the number of mated-stations (i.e., the line length) as the primary objective and it minimises the number of stations (i.e., the number of operators) as a secondary objective for a given cycle time. A heuristic approach (2-COMSOAL/S) for especially solving large-size problems based on COMSOAL (computer method of sequencing operations for assembly lines) method is also presented. An illustrative example problem is solved using 2-COMSOAL/S. To assess the effectiveness of MIP and 2-COMSOAL/S, a set of test problems are solved. The computational results show that 2-COMSOAL/S is very effective for the problem.     

Scheduling algorithms for remanufacturing systems with parallel flow-shop-type reprocessing lines

This study considers a scheduling problem for remanufacturing systems in which end-of-life products are separated into their major components at a disassembly workstation, each of them is reprocessed at its dedicated flow-shop-type reprocessing line with serial workstations, and finally, the reprocessed components, together with new components if required, are reassembled into remanufactured products at a reassembly workstation. Among various system configurations, we focus on the one with parallel flow-shop-type reprocessing lines since it is a typical remanufacturing configuration. The problem is to determine the sequence of products to be disassembled, the sequence of components to be reprocessed at each workstation of flow-shop-type reprocessing lines and the sequence of products to be reassembled for the objective of minimising the total flow time. An integer programming model is developed to represent the problem mathematically, and then, three types of heuristics, i.e. priority rule-based heuristic, Nawaz–Enscore–Ham-based heuristic and iterated greedy algorithm, are proposed due to the problem complexity. To show the performances of the heuristics, a series of computational experiments were done on various test instances, and the results are reported.     

Approximate analysis of serial flow lines with multiple parallel-machine stations

This paper studies serial flow lines, in which each station consists of multiple identical reliable parallel machines. The parallel machines of different work stations are not necessarily identical, viz., station processing times are assumed to be exponentially distributed with non-identical mean service rates. Initially, a model consisting of two stations with multiple parallel machines and an intermediate buffer is solved analytically, by developing a recursive algorithm that generates the transition matrix for any value of the intermediate buffer capacity. This model is used as a decomposition block for solving larger lines. More specifically, the decomposition block is solved via exact Markovian analysis and then the decomposition equations and an algorithm that simultaneously solves them are derived in order to evaluate the performance measures of large production systems with multiple parallel-machine stations. Numerical results are provided for large production lines with up to 1000 workstations. These results are compared against simulation and the average percentage error is found to be very small.     

Capacitated lot sizing with parallel machines,sequence-dependent setups,and a common setup operator

In this paper, we consider a special variant of a capacitated dynamic lot sizing problem which, despite its practical relevance, has not been treated sufficiently in the literature. In addition to the significant complexity introduced by the sequence dependency of setup times, the problem is further complicated by the fact that there is only one single setup operator which is responsible for all setups on all machines. Hence, the multi-machine problem cannot be decomposed into multiple single machine problems, as the setup operations must be coordinated among the different machines. In addition, lots are produced in batches whereby the processing time of a lot is a step-wise function of the number of batches per lot. Due to perishability and quarantine issues, time windows for the production are given. We formulate a big-bucket lot sizing model and apply MIP-based heuristics to two industrial data sets.     

Robust disassembly line balancing with ambiguous task processing times

Disassembly line balancing problem (DLBP), which is to select disassembly process, open workstations and assign selected tasks to opened workstations, plays an important role in the recycling of End Of Life products. In real-world disassembly operations, task processing times are usually stochastic due to various factors. Most related works address the uncertain processing times by assuming that the probability distribution is known and the task processing times are independent of each other. In practice, however, it is difficult to get the complete distributional information and there is always underlying correlation between the uncertain processing times. This paper investigates the DLBP with partial uncertain knowledge, i.e. the mean and covariance matrix of task processing times. A new distributionally robust formulation with a joint chance constraint is proposed. To solve the problem, an approximated mixed integer second-order cone programming (MI-SOCP) model is proposed, and a two-stage parameter-adjusting heuristic is further developed. Numerical experiments are conducted, to evaluate the performance of the proposed method. We also draw some managerial insights and consider an extension problem.     

A scheduling approach for a flexible manufacturing system

This paper discusses the scheduling problem of a particular flexible manufacturing system (FMS). The two main components of the FMS are a CNC turret lathe and a CNC machining centre. In the system a wide range of different jobs has to be processed. Each job consists of one or more processing operations on one or both machines. Important characteristics of the scheduling problem are sequence-dependent change-over times (on the turret lathe) and transfer times (on both machines and between the machines). The change-over times are caused by the need to exchange tools in the turret when a new part is going to be processed. The transfer times reflect the time needed to perform manual transportation and clamping activities between two subsequent processing (machining) operations of a part. In this paper a branch and bound algorithm is described based on an active schedule strategy. Solutions are compared to results obtained by a simple dispatching rule     

Worker allocation in lean U-shaped production lines

U-shaped lines are widely used in lean systems. In U-shaped production lines, each worker handles one or more machines on the line: the worker allocation problem is to establish which machines are handled by which worker. This differs from the widely-investigated U-line assembly line balancing problem in that the assignment of tasks to line locations is fixed. This paper address the worker allocation problem for lean U-shaped production lines where the objectives are to minimize the quantity of workers and maximize full work: such allocations provide the opportunity to eliminate the least-utilized worker by improving processes accordingly. A mathematical model is developed: the model allows for any allocation of machines to workers so long as workers do not cross paths. Walking times are considered, where workers follow circular paths and walk around other worker(s) on the line if necessary. A heuristic algorithm for tackling the problem is developed, along with a procedure representing the ‘traditional’ approach of constructing standard operations routines. Computational experiments considering three line sizes (up to 20 machines) and three takt time levels are performed. The results show that the proposed algorithm both improves upon the traditional approach and is more likely to provide optimal solutions.     

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.     

A GA-based solution approach for balancing printed circuit board assembly lines

Printed circuit board (PCB) assembly lines consist of a number of different machines for mounting electronic components onto PCBs. While high-speed placement machines are employed to assemble standard components, so-called fine-pitch placement machines are used to mount complex electronic components with high precision and by use of specific nozzles. In this paper, we investigate a typical mass production environment where a single type of PCB is assembled in a line comprising high-speed as well as high-precision placement machines. The PCB assembly line balancing problem consists of assigning component feeders, each holding a specific electronic component type, and the corresponding placement operations to machines in the line so as to minimize the assembly cycle time. To solve this problem, a two-stage solution procedure based on genetic algorithm (GA) is proposed. In the first stage, component feeders are assigned to the placement machines with the objective of balancing the workload within the assembly line. A number of candidate solutions are then transmitted to the second stage, where specific machine optimization algorithms are applied to determine the feeder-slot assignment in the component magazine of the machines and the placement sequence of the various components. As a result, fine-tuned placement operation times are achieved which reflect the individual operation mode and the actual component setup of the placement machines. Finally, from the candidate solutions the one which minimizes the actual PCB assembly time is selected.     

Component allocation and job sequencing for two series-connected SMD placement machines

The problem of component allocation and sequencing PCB assembly jobs for two series-connected SMD placement machines is addressed. With the allocation of components, a balancing of the workload per PCB for the two machines should be achieved. Furthermore, when switching from one type of PCB to another, setup times must be considered. Two heuristic procedures are developed and tested in extensive numeric analyses of a number of realistic case models. The problem examined and the database used represent a concrete case of an industrial application in which very diversified small jobs are to be processed.     

Balancing of mixed-model parallel U-shaped assembly lines considering model sequences

As a consequence of increasing interests in customised products, mixed-model lines have become the most significant components of today’s manufacturing systems to meet surging consumer demand. Also, U-shaped assembly lines have been shown as the intelligent way of producing homogeneous products in large quantities by reducing the workforce need thanks to the crossover workstations. As an innovative idea, we address the mixed-model parallel U-shaped assembly line design which combines the flexibility of mixed-model lines with the efficiency of U-shaped lines and parallel lines. The multi-line stations utilised in between two adjacent lines provide extra efficiency with the opportunity of assigning tasks into workstations in different combinations. The new line configuration is defined and characterised in details and its advantages are explained. A heuristic solution approach is proposed for solving the problem. The proposed approach considers the model sequences on the lines and seeks efficient balancing solutions for their different combinations. An explanatory example is also provided to show the sophisticated structure of the studied problem and explain the running mechanism of the proposed approach. The results of the experimental tests and their statistical analysis indicated that the proposed line design requires fewer number of workstations in comparison with independently balanced mixed-model U-lines.     

A case study in productivity-cost trade-off in the design of paced parallel production systems

This paper examines the investment and operational cost differences between high-volume serial CNC-based machining lines and parallel CNC-based machining lines. With the progress of CNC technology and their descending cost, more CNC machines have been used in high-volume production systems. CNC machines increase the flexibility and machining capability of production lines, greatly increasing the number of line configurations. Parallel configurations improve system throughput and have the same effect as adding buffers to a pure serial line but without additional work-in-process inventory. This analysis is performed through a case study of a CNC-based automotive cylinder head machining line. Examining machine reliability, line balance, configuration throughput, and cost yields insight into the cost-benefit tradeoff of implementing parallelism. It is found that even with large increases in investment in automated material handling, parallel configurations can yield significant annual cost savings over pure serial lines through reductions in capital investment, especially in CNC machines, and improvements in efficiency, and on a per unit capacity basis, parallel configurations are the least expensive.     

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.     

An ant colony optimisation algorithm for balancing two-sided U-type assembly lines with sequence-dependent set-up times

Some practical arrangements in assembly lines necessitate set-up times between consecutive tasks. To create more realistic models of operations, set-up times must be considered. In this study, a sequence-dependent set-up times approach for two-sided u-type assembly line (TUAL) structures is proposed for the first time. Previous studies on TUAL have not included set-up times in their analyses. Furthermore, an algorithm based on the Ant Colony Optimization (ACO) algorithm, which is using a heuristic priority rule based procedure has been proposed in order to solve this new approach. In this paper, we look at the sequence-dependent set-up times between consecutive tasks and consecutive cycles, called the “forward set-up time” and the “backward set-up time”, respectively. Additionally, we examine the “crossover set-up time”, which arises from a new sequence of tasks in a crossover station. In order to model more realistic assembly line configurations, it is necessary to include sequence-dependent set-up times when computing all of the operational times such as task starting times and finishing times as well as the total workstation time. In this study, the proposed approach aims to minimize the number of mated-stations as the primary objective and to minimize the number of total workstations as a secondary objective. In order to evaluate the efficiency of the proposed algorithm, a computational study is performed. As can be seen from the experimental results the proposed approach finds promising results for all literature-test problems.     

Scheduling preventive maintenance on a single CNC machine

In this study we attempt to deal with process planning, scheduling and preventive maintenance (PM) decisions, simultaneously. The objective is to minimize the total completion time of a set of jobs on a CNC machine. During the process planning, we decide on the processing times of the jobs which are controllable (i.e. they can be easily changed) on CNC machines. Using shorter processing times (higher production rates) would result in greater deterioration of the machine, and we would need to plan more frequent PM visits to the machine, during which it would not be available. Therefore, the selected processing times determine not only the completion times but also the PM visit times. We first provide optimality properties for the joint problem. We propose a new heuristic search algorithm to determine simultaneously the processing times of the jobs, their sequence and the PM schedule.     

MIP approach to balancing transfer lines with blocks of parallel operations

A novel line balancing problem is considered. It differs from assembly line balancing problems in that the operations of each workstation are partitioned into blocks of simultaneously executed (parallel) operations. The blocks of each workstation are executed sequentially. For the line design stage considered in this paper, the compatibility (inclusion and exclusion) constraints for grouping operations into blocks and workstations as well as precedence constraints are known. The goal is to minimize a weighted sum of the number of workstations and the number of blocks while achieving a desired cycle time and satisfying all the constraints. The developed exact and heuristic methods are based on a mixed-integer programming approach. Experimental results are reported.     

A scheduling problem in glass manufacturing

In this paper, a production scheduling problem in glass manufacturing is studied. The production facility consists of multiple identical production lines and each production line includes a number of serially arranged machines. The production is characterized by semi-ordered processing times in each product family, and the last machine in each production line is a bottleneck machine. Significant changeover times are required when products of different families are produced on a production line. The scheduling problem was modeled as a parallel no-delay flowshop scheduling problem (PNDFSP). The PNDFSP combines the parallel machine scheduling problem (PMSP) with the no-delay flowshop scheduling problem (NDFSP). While PMSP and NDFSP have received considerable attention in the literature, PNDFSP has not been well studied. A mixed-integer programming formulation is developed and an efficient heuristic algorithm is proposed. The sequential heuristic algorithm considers simultaneously the line changeover time, no-delay effect, and line utilization in assigning product families to the production lines. The computational results are reported.     

Heuristic approaches for mixed-model sequencing problem with stochastic processing times

Despite many pioneering efforts and works over the past decades, stochastic events have not been studied extensively in mixed-model assembly lines thus far. For a mixed-model sequencing problem with stochastic processing times, this paper aims to minimise expected total work overload. It also focuses on the most critical workstation of the line. In practice, this assumption is useful when the whole or a big portion of the assembly line is considered as a single station. In order to tackle the problem, a dynamic programming (DP) algorithm as well as two greedy heuristics from the literature is employed. However, it is realised that the DP cannot guarantee the optimal sequence neither for stochastic nor deterministic problems. It is because the calculation of work overload is involved in a recursive procedure that affects the states’ value functions. Therefore, by the use of network representation, the problem is modelled as a shortest path problem and a new heuristic, inspired by Dijkstra’s algorithm is developed to deal with it. Numerical results show that the proposed method outperforms other algorithms strongly. Finally, some discussion is provided about why one should consider stochastic parameters and why the proposed heuristic performs well in this regard.     

Scheduling flexible flow lines with no in-process buffers

An heuristic algorithm is proposed for scheduling a flexible flow line with no intermediate buffers. The line is made up of several processing stages in series, where each stage has one or more identical parallel machines. In the line different part types can be manufactured simultaneously, each of which is processed by at most one machine in every stage. Intermediate queues of parts waiting between the stages for their next operations are not allowed. The problem objective is to minimize the makespan of the schedule for a set of part types selected for processing. The algorithm proposed is a part-by-part heuristic, in which during every iteration a complete processing schedule is determined for one part type selected for loading into the line. The selection of the part type and its complete schedule are based on the cumulative partial schedule obtained for all parts selected so far. The decisions in every iteration are made using a local optimization procedure aimed at minimizing total blocking and waiting time of the machines along the route of the selected part type. The efficiency of the algorithm is tested on several groups of random test problems     

Optimal cost design of flow lines with reconfigurable machines for batch production

Modular reconfigurable machines offer the possibility to efficiently produce a family of different parts. This paper formalises a cost optimisation problem for flow lines equipped with reconfigurable machines which carry turrets, machining modules and single spindles. The proposed models take into account constraints related to: (i) design of machining modules, turrets, and machines, (ii) part locations, and (iii) precedence relations among operations. The goal is to minimise equipment cost while reaching a given output and satisfying all the constraints. A mixed integer programming model is developed for the considered optimisation problem. The approach is validated through an industrial case study and extensive numerical experiments.