混装线投产序列和工位任务的协同调度机理

柔性汽车混装线需根据订单和生产需求调整当月工位任务规划和车型投产序列.通过分析汽车行业装配生产现状,提出高效而精准的调度目标,指出面向车型投产序列规划和工位任务规划的协同调度思想;说明针对不同目标采用的投产序列和工位规划方法,陈述了协同调度的策略框架,并用实际事例递进演示了协同调度机理.实验 证明协同调度能达到混装生产作业的高效精准过程控制的目的.     

The type-II assembly line rebalancing problem considering stochastic task learning

Assembly lines with non-constant task time attribute are widely studied in the literature. For the SALBP-II assembly line balancing problem, we take account of stochastic task time changes, which is more practical than the deterministic times often assumed in industrial application. An algorithm – ENCORE, which leverages the traditional algorithm SALOME2, is proposed to address the assembly line balancing problem with stochastic task time attribute. Computational and statistical experiments are conducted to show the efficiency of proposed algorithms over traditional methods with regards to the improvement of total production times.     

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.     

A genetic algorithm for the stochastic mixed-model U-line balancing and sequencing problem

Mixed-model assembly lines are widely used to improve the flexibility to adapt to the changes in market demand, and U-lines have become popular in recent years as an important component of just-in-time production systems. As a consequence of adaptation of just-in-time production principles into the manufacturing environment, mixed-model production is performed on U-lines. This type of a production line is called a mixed-model U-line. In mixed-model U-lines, there are two interrelated problems called line balancing and model sequencing. In real life applications, especially in manual assembly lines, the tasks may have varying execution times defined as a probability distribution. In this paper, the mixed-model U-line balancing and sequencing problem with stochastic task times is considered. For this purpose, a genetic algorithm is developed to solve the problem. To assess the effectiveness of the proposed algorithm, a computational study is conducted for both deterministic and stochastic versions of the problem.     

Balancing and sequencing of stochastic mixed-model assembly U-lines to minimise the expectation of work overload time

A mixed-model assembly U-line is a flexible production system capable of manufacturing a variety of similar models, and it has become popular as an important component of the just-in-time production system. However, it poses new challenges for the optimal design of assembly lines because both the task assignment and the production sequence affect the workload variance among workstations. As a consequence, this paper addresses the line balancing problem and the model sequencing problem jointly and proposes a 0–1 stochastic programming model. In this model, task times are assumed to be stochastic variables independently distributed with normal distributions and the objective is to minimise the expectation of work overload time for a given combination of cycle time and number of workstations. To solve the problem, a simulated annealing-based algorithm is developed, which can also be used to minimise the absolute deviation of workloads in a deterministic environment. The experimental results for a set of benchmark problems show that the proposed algorithm outperforms the existing algorithms in terms of solution quality and running time.     

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.     

Robust optimization applied to uncertain production loading problems with import quota limits under the global supply chain management environment

Global supply chain management presents some special challenges and issues for manufacturing companies in planning production: these challenges are different from those discussed in domestic production plans. Globally loading production among different plants usually involves substantial uncertainty and great risk because of uncertain market demand, fluctuating quota costs incurred in the global manufacturing process, and shortening lead times. This study proposes a dual-response production loading strategy for two types of plants—company-owned and contracted—to hedge against the short lead time and uncertainty, and to be as responsive and flexible as possible to cope with the uncertainty and risk involved. Three types of robust optimization models are presented: the robust optimization model with solution robustness, the robust optimization model with model robustness, and the robust optimization model with the trade-off between solution robustness and model robustness. A series of experiments are designed to test the effectiveness of the proposed robust optimization models. Compared with the results of the two-stage stochastic recourse programming model, the robust optimization models provide a more responsive and flexible system with less risk, which is particularly important in the current context of global competitiveness.     

A reactive decision-making approach to reduce instability in a master production schedule

One of the primary factors that impact the master production scheduling performance is demand fluctuation, which leads to frequently updated decisions, thereby causing instability. Consequently, global cost deteriorates, and productivity decreases. A reactive approach based on parametric mixed-integer programming (MIP) is proposed that aims to provide a set of plans such that a compromise between production cost and production stability is ensured. Several stability measures and their corresponding MIP model are proposed. An experimental study is performed to highlight the effectiveness of the reactive approach with regard to the proposed performance measures. It is observed that an improvement in stability does not mean a significant increase in the total production cost. Furthermore, the procedure yields a set of plans that in practice would enable flexible management of production.     

Balancing mixed-model assembly lines: a computational evaluation of objectives to smoothen workload

Mixed-model assembly lines are widely used in a range of production settings, such as the final assembly of the automotive and electronics industries, where they are applied to mass-produce standardised commodities. One of the greatest challenges when installing and reconfiguring these lines is the vast product variety modern mixed-model assembly lines have to cope with. Traditionally, product variety is bypassed during mid-term assembly line balancing by applying a joint precedence graph, which represents an (artificial) average model and serves as the input data for a single model assembly line balancing procedure. However, this procedure might lead to considerable variations in the station times, so that serious sequencing problems emerge and work overload threatens. To avoid these difficulties, different extensions of assembly line balancing for workload smoothing, i.e. horizontal balancing, have been introduced in the literature. This paper presents a multitude of known and yet unknown objectives for workload smoothing and systematically tests these measures in a comprehensive computational study. The results suggest that workload smoothing is an essential task in mixed-model assembly lines and that some (of the newly introduced) objectives are superior to others.     

Framework for designing a flexible cellular assembly system

Modern manufacturing companies have to face competition in a turbulent world market that requires them to improve their operating performances according to increasingly differentiated products with shorter life cycles, low volumes and reduced customer delivery times. The aim of the present paper is to develop a conceptual framework for the simultaneous design and control of a flexible, agile reconfigurable and robust assembly system in conjunction with the analysis and optimization of product, process, system structure, material-handling devices and plant layout. An effective solution for reconfigurability and agility in assembly to order and batch-type systems is a modular semi-automatic approach based on cellular flexible facilities: modularity, automation and human skills are combined to gain the advantages of mass production. Families of parts are produced in flexible cells, i.e. groups of various machines and resources that are physically close together and process one or more families of similar parts. The proposed approach is based on a multistage iterative procedure that integrates different supporting decision techniques and tools such as design for assembly, group technologies and cellular manufacturing. An application to the optimization of a semi-automatic flexible assembly system is illustrated: the system performances of example alternative solutions are presented and compared.     

Robust optimisation approach applied to the analysis of production / logistics and crop planning in the tomato processing industry

The soluble solids content in the tomato fruit, also known as ‘brix’, and the crop yield are the most relevant uncertain parameters to determine technical and economic performance in the tomato processing industry. This paper presents a linear programming model and three robust optimisation models to deal with data uncertainty in the analysis of crop, logistics and industrial tactical planning in this industry. We focused the analysis on the production and logistics costs due to the impacts of unfavourable disturbances on the amount of soluble solids and the quantity of tomatoes processed in the system. A typical industry in this sector collaborated with this study by providing real data of its production, logistics and crop plans and with in-depth discussions. From the results, some general conclusions were outlined and we discuss the benefits of adopting the robust optimisation approach instead of a deterministic one. The robust approach proved to be a powerful tool for elaborating scenarios for uncertainty analysis in medium-term decisions, as described in this study, and clearly has potential to be employed in real contexts.     

Production control in a complex production system using approximate dynamic programming

Development of an efficient production and inventory control policy for a production system with multiple working stations, intermediate components and end products is difficult. In particular, uncertain demand and large changeover times at the work stations cause significant problems. In this paper, we consider an assembly line for dishwashers which require multiple types of wire racks that must be fabricated and coated at different work centres before supplying the assembly lines. An approximate dynamic programming (ADP) method is proposed to address the complexities associated with such a system. In addition, an Artificial Neural Network model is designed to approximate state values of the system, thus helping the system to make decisions at particular states. A near optimal production and inventory control policy is developed through an ADP algorithm. The proposed method can be extended to any similar system.     

Order sequencing and capacity balancing in synchronous manufacturing

Synchronous manufacturing aims at achieving the benefits of intermittent production lines in production situations that operate without lines. Benefits such as short and constant throughput times and predictable capacity loading can be acquired through an appropriate design of the synchronous manufacturing system and its control system. The order release mechanism is an essential part of this control system. It determines the sequence in which orders are released to the shop floor. As orders may differ in the amount and distribution of their capacity requirements over subsequent production stages, total capacity load may vary over time. If the available capacity per period is not flexible, capacity balancing becomes an issue in the order release decision. In practice, heuristics or rules of thumb are used to solve this problem, but their effectiveness is questioned. This paper examines the effectiveness of some new heuristics that are based on insights from assembly system design and work load control, and compare their performance with an optimal solution approach. The approaches are evaluated in a rolling schedule environment, and under different levels of capacity fluctuations and problem sizes. The results show that the performance of the heuristic solutions deteriorates if capacity fluctuations between the stages increase. If we measure both the amount and frequency of shortages over a long period of time in a rolling schedule environment, a quite simple rule that only takes the available capacity during the first stage into account outperforms more intelligent rules.     

Strategic decisions in manufacturing— on the choice of investments in flexible production organizations†

The striving for market-oriented production has resulted in increased needs for flexibility in the production process. In many cases it is the final value-adding component (i.e. assembly) in the manufacturing chain that has the largest pressures for flexibility. However, the internal and external vendors can restrict the flexibility of a nominally flexible assembly plant. Without the necessary flexibility in earlier production stages a total flexibility will only be achieved through large inventories and other trade-offs. In this paper based on an empirical study carried out in cooperation with a large Swedish manufacturer, the authors show that total flexibility in a multi-stage production chain primarily depends on capacity levels and work organization. It is concluded in the case presented here that large investments must be made in labour education and organizational adaptation in order to exploit the full potentials of a highly mechanized final assembly department     

Sequencing procedure for balancing the workloads variations in case of mixed model assembly system with multiple secondary feeder lines

In order to increase flexibility and reduce costs, assembly systems are changing from simple mixed-model assembly lines, to multi-lines systems, where some components are assembled in secondary lines, called feeder lines, connected to the main line by a ‘pull philosophy’. The production of different models in such a complex multi-lines assembly system, where the tasks to perform could be very different from model to model, impacts the production with very high workload time variations, with the consequence of lack of productivity. If operators can perform tasks on different stations on both the feeder and the main lines these time variations can generally be absorbed. But if working across the stations is not possible (closed stations), the balancing of these workload time variations becomes critical. In such contexts the consolidated approach, that allows to configure mixed-model-multi lines assembly system with a single average model representative of the whole mix, can be very limited and is unable to bring substantial results. This paper aims to introduce an innovative sequencing model for mixed-model-multi-lines system, in the case of closed stations, in which it is possible to obtain, after a first long term configuration, a short term balancing, using an appropriate sequencing, for a given production mix and characteristics of the assembly system. The proposed procedure is applied to un-paced assembly lines and validated through simulation and industrial applications.     

A robust optimisation model for aggregate and detailed planning of a multi-site procurement-production-distribution system

The planning problem in the context of a multi-site procurement-production-distribution system (MSPPDS) considered in this paper is motivated from a real life case of a multinational consumer goods company. A robust optimisation model considering model robustness and solution robustness in the objective function is developed for integrated planning in three dimensions. Detailed production, procurement and distribution plans are integrated; countrywide aggregate production plan is integrated with a detailed plan. Similarly the detailed production plans from the previous planning cycle are integrated with current production plans. Constraints on storage space, production capacity and the time lag between procurement, production and distribution activities are captured in the model. Procurement and production plans are treated as ‘here-and-now’ decisions and the distribution plans are treated as ‘wait-and-see’ decisions to be implemented based on the realised demand scenario. The model is illustrated using an example problem and also successfully applied to the data of a consumer goods company involving 104,000 variables (with 832 integer variables) and 21,000 constraints.     

Scheduling distributed flowshops with flexible assembly and set-up time to minimise makespan

The scheduling problems under distributed production or flexible assembly settings have achieved increasing attention in recent years. This paper considers scheduling the integration of these two environments and proposes an original distributed flowshop scheduling problem with flexible assembly and set-up time. Distributed production stage is deployed several homogeneous flowshop factories that process the jobs to be assembled into final products in the flexible assembly stage. The objective is to find a schedule, including a production subschedule for jobs and an assembly subschedule for products, to minimise the makespan. Such a scheduling problem involves four successive decisions: assigning jobs to production factories, sequencing jobs at every factory, designating an assembly machine for each product and sequencing products on each assembly machine. The computational model is first established, and then a constructive heuristic (TPHS) and two hybrid metaheuristics (HVNS and HPSO) are proposed. Numerical experiments have been carried out and results validate the algorithmic feasibility and effectiveness. TPHS can obtain reasonable solutions in a shorter time, while metaheuristics can report better solutions using more yet acceptable time. HPSO is statistically comparable yet less robust compared with HVNS for small-scale instances. For the large-scale case, HPSO outperforms HVNS on both effectiveness and robustness.     

An integrated robust replenishment/production/distribution policy under inventory inaccuracy

In this paper, a centralised production/distribution system is studied, in which the manufacturer controls the processes of raw material ordering, production and final goods distribution. The inventory records of raw materials, work-in-process, final goods in the manufacturer and the retailers are inaccurate. The objective of this research is to develop an integrated policy that can hedge against the negative impact of inventory inaccuracy at all stages of a supply chain. To achieve this aim, a backward recursive integrated robust policy is constructed. First, the time, quantity and vehicle route of the next distribution are forecasted by a distribution sub-policy; then the forecasted distribution time and quantity are used as the objective of the production process control, which is implemented by a production control sub-policy. Raw materials are ordered by a replenishment sub-policy according to the forecasted raw materials consumption in the production process. Numerical experiments are conducted to verify the feasibility and robustness of the proposed policy, which shows that besides of deploying radio frequency identification (RFID) devices in inventory management systems for raw materials, WIPs, final goods of the manufacturer and the retailers, integrated robust policies also can be employed to hedge against the impact of inventory inaccuracy.     

Mixed model assembly system with multiple secondary feeder lines: layout design and balancing procedure for ATO environment

The constant research for efficiency and flexibility has forced assembly systems to change from simple/single assembly lines to mixed model assembly lines, while the necessity to reduce inventory has led the transition from single to multi-line systems, where some components are assembled in secondary lines, called feeder lines, connected to the main one by a ‘pull philosophy’. A possible approach to configure such an assembly system is to balance the main line first and use the retrieved cycle time to balance each feeder line separately, which is a questionable solution, especially if operators can perform tasks on both the feeder and the main line. Moreover for its complexity the mixed model balancing problem is usually solved transforming it into a single model by creating a single ‘virtual average model’, representative of the whole production mix. The use of a virtual average model assumes that the processing times of some models are higher or lower than the cycle time, which creates overload/idle time at the stations. This approach, especially in complex multi line production systems, largely reduces the assembly line productivity and increases the buffers dimensions. This paper faces the mixed model assembly line balancing problem in the presence of multiple feeder lines, introducing an innovative integrated main-feeder lines balancing procedure in case of unpaced assembly systems. The proposed approach is compared with the classical one and validated through simulation and industrial applications.     

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.