Scheduling manufacturing systems for delayed product differentiation in agile manufacturing

Production of customized products to respond to changing markets in a short time and at a low cost for agile manufacturing can be implemented with delayed product differentiation in a manufacturing system. The successful implementation of delayed product differentiation lies in efficient scheduling of the manufacturing system. Scheduling problems in implementing delayed product differentiation in a general flexible manufacturing system are defined, formulated and solved here. The manufacturing system consists of two stages: machining and assembly. At the machining stage, a single machine is used to produce standard component parts for assembly products. These parts are then assembled at the assembly stage by multiple identical assembly stations to form customized products. The products to be produced in the system are characterized by their assembly sequences represented by digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of assembly stations at the assembly stage. Based on the representation of assembly sequence of the products, three production modes are defined: production of a single product with a simple assembly sequence ; production of a single product with a complex assembly sequence ; and production of N products . According to the three defined production modes, the associated scheduling problems are defined as G s scheduling problems, G c scheduling problems and N-product scheduling problems, respectively. Optimal and heuristic methods for solving the scheduling problems are developed. The computational experiment shows that the heuristics provide good solutions to the scheduling problems.     

Systemic criterion of sustainability in agile manufacturing

Agile manufacturing systems work in a constantly changing global market, particularly assembly systems at the last stage of product differentiation. Meanwhile, sustainability is becoming a key issue for manufacturing strategy. This paper formulates a systemic criterion of sustainability in agile manufacturing and computes it through flexibility and complexity. It is defined as a ratio of utility and entropy as a sustainability measurement. Under a unified framework, utility allows one to quantify the contributions to agility, in particular system flexibility. Complexity is measured by entropy. Thus, an original complementary role of flexibility and the complexity of the system are proposed. Developed from the distribution of system states, the systemic approach to sustainability in terms of output evolution is enriched. Based on a simple assembly line integer model simulation, a first quantitative analysis illustrates the concepts introduced.     

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.     

Set-partitioning-based heuristic for balancing and configuration of automated flexible machining line

Flexible machining lines are used in a wide range of industries due to their ability of reconfiguration to meet high variety of customer demands. A novel problem is proposed in the current research to consider automated flexible machining line (AFML) with automated machining using computer numerical control machines and automated auxiliary operations using robots. A mixed-integer programming model for the current novel problem is developed. Moreover, a novel method named set-partitioning-based heuristic (SPH) is proposed to solve this new flexible machining line balancing problem to minimise the cycle time of the line and the performance is compared with both exact algorithm and random search algorithm. A set of benchmark instances based on different size of problems against different system parameters is made. Furthermore, sensitivity analysis of the system parameter in AFML is performed to know, how the number of machines and processing time can influence the cycle time and the utilisation of AFML. Computational experiments are performed to show the performance of the proposed method SPH against other methods and the results indicate that SPH performs best among all test methods in terms of solution quality and computation on both the proposed benchmark instances.     

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 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.     

A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems

This paper presents a new algorithm for the flexible manufacturing system (FMS) scheduling problem. The proposed algorithm is a heuristic based on filtered beam search. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and automated guided vehicle (AGV) schedules for a given scheduling period. A new deadlock resolution mechanism is also developed as an integral part of the proposed algorithm. The performance of the algorithm is compared with several machine and AGV dispatching rules using mean flow time, mean tardiness and makespan criteria. It is also used to examine the effects of scheduling factors (i.e., machine and AGV load levels, routing and sequence flexibilities, etc.) on the system performance. The results indicate that the proposed scheduling algorithm yields considerable improvements in system performance over dispatching rules under a wide variety of experimental conditions.     

Tool path optimisation method for large thin-wall part of spacecraft

Roughing tool path of panel machining, which is a bottleneck of spacecraft production, should be optimised rapidly to shorten process time. This problem has a large solution space, and surface quality should be taken into account. The decision variables are cavity machining order, feed point and cutting direction of each cavity. Our problem is presented as an asymmetric general travelling salesman problem (AGTSP). A cluster optimisation-based hybrid max–tmin ant system (CO-HMMAS) is proposed, which solves two sub-problems as a whole. The oriented pheromone and dynamic heuristic information calculating methods are designed. We analyse the differences between one-stage and two-stage AGTSP local search heuristics and combine CO-HMMAS with them properly. An improved Global 3-opt heuristic suitable for both symmetric and asymmetric cases is proposed with sharply reduced time complexity. Comparison experiments verified that, two-stage local search heuristics decrease solution error significantly and rapidly when the error is great, and one-stage ones improve a near-optimal solution costing much more computing time. Benchmarks tests show that, CO-HMMAS outperforms the state-of-the-art algorithm on several technical indexes. Experiments on typical panels reveal that all algorithm improvements are effective, and CO-HMMAS can obtain a better tool path than the best algorithm within less CPU time.     

Minimising makespan on a single batch processing machine with dynamic job arrivals and non-identical job sizes

Batch scheduling is a prevalent policy in many industries such as burn-in operations in semiconductor manufacturing and heat treatment operations in metalworking. In this paper, we consider the problem of minimising makespan on a single batch processing machine in the presence of dynamic job arrivals and non-identical job sizes. The problem under study is NP-hard. Consequently, we develop a number of efficient construction heuristics. The performance of the proposed heuristics is evaluated by comparing their results to two lower bounds, and other solution approaches published in the literature, namely the first-fit longest processing time-earliest release time (FFLPT-ERT) heuristic, hybrid genetic algorithm (HGA), joint genetic algorithm and dynamic programming (GA+DP) approach and ant colony optimisation (ACO) algorithm. The computational experiments demonstrate the superiority of the proposed heuristics with respect to solution quality, especially for the problems with small size jobs. Moreover, the computational costs of the proposed heuristics are very low.     

An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises

Increasing global competition drives enterprises, especially small and medium-sized enterprises, to collaborate in order to respond faster to customers’ needs, reduce operating costs, increase capacity, and produce customised products to reach the market quicker. A virtual enterprise (VE) is an important manufacturing paradigm to address this trend in the dynamic global economy. Partner selection is a key issue tightly coupled to the success of a VE coalition, and because of its complexity, it is considered a multi-attribute optimisation problem. In this paper, an enhanced ant colony optimiser (ACO) is proposed to address the partner selection problem. Five attributes (namely, cost, time, quality, reputation, and risk) considering both qualitative and quantitative aspects have been investigated to evaluate the candidate partners. Experiments have been conducted to validate the performance of the enhanced ACO algorithm, and the results show that the enhanced ACO algorithm can produce better results in terms of search accuracy and computing time.     

Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem

This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.     

Integrated scheduling and self-reconfiguration for assembly job shop in knowledgeable manufacturing

The problems of integrated assembly job shop (AJS) scheduling and self-reconfiguration in knowledgeable manufacturing are studied with the objective of minimising the weighted sum of completion cost of products, the earliness penalty of operations and the training cost of workers. In AJS, each workstation consists of a certain number of teams of workers. A product is assumed to have a tree structure consisting of components and subassemblies. The assembly of components, subassemblies and final products are optimised with the capacity of workstations simultaneously. A heuristic algorithm is developed to solve the problem. Dominance relations of operations are derived and applied in the development of the heuristic. A backward insertion search strategy is designed to locally optimise the operation sequence. Once the optimal schedule is acquired, the teams are reconfigured by transferring them from workstations of lower utilisation to those of higher utilisation. Effectiveness of the proposed algorithm is tested by a number of numerical experiments. The results show that the proposed algorithm promises lower total cost and desirable simultaneous self-reconfiguration in accordance with scheduling.     

Sequencing of parts on single-stage multifunctional machining systems using a chaos-embedded simulated annealing algorithm

This paper deals with the objective of determining an optimal part sequence on a single-stage multifunctional machining system (SSMS) with a view to achieve the broad objectives of cost minimization and time minimization. SSMS has become a preferred alternative for manufacturers to use the resource efficiently, owing to the flexibility and process variety offered by it. This paper formulates a mathematical model that considers the minimization of both set-up cost and time simultaneously. The option of hiring an additional fixture has also been considered that enables the reduction in tool magazine replenishment and re-fixturing operations, which in turn offers economic advantage by way of reducing set-up cost. This study has proposed a new heuristic by modifying the simulated annealing concept to solve the underlying problem. The conventional simulated annealing search scheme is replaced by a chaotic search that takes into account the ergodic and stochastic properties of chaotic systems. In order to restrict the premature convergence and to diversify the search space, a modified perturbation scheme has been employed. The performance of the proposed algorithm was tested on a simulated case study adopted from the literature and the results obtained reveal the effectiveness and scalability of the proposed algorithm. The results establish that the proposed approach is effective and reactive to severe disturbances and must take place in the manufacturing environment.     

Real-time scheduling in manufacturing system with machining and assembly operations: a state of art

Manufacturing systems producing multiple products are common in many industries, where products are made from several parts and/or sub-assemblies that require machining operations in first stage and assembly operations at later stage. Several scheduling techniques are proposed in the literature for such manufacturing system to develop near optimal schedule. A disruption in the manufacturing necessitates adjusting previously planned schedule which is known as real-time scheduling. This paper presents a comparative evaluation of different scheduling methods proposed by different investigators for dealing such situations. The literature indicates that real-time scheduling of manufacturing system with machining and assembly operations is hardly attempted. The paper offers a framework for developing rescheduling methodologies for such manufacturing situations.     

蚁群算法在复杂系统可靠性优化中的应用

复杂系统可靠性优化问题为典型的NP-难问题.不考虑系统的具体连接形式,将每个部件视为一级,产生随机数作为网络节点, 把蚁群优化算法成功应用到复杂系统可靠性优化中,搜索到其他算法未能得到的最优解.仿真结果表明,蚁群优化算法可以在相对短的时间内较快地找到问题的最优解.蚁群优化算法与其他元启发式算法一样,可以有效克服求解组合优化的计算复杂度问题.     

Optimum drilling path planning for a rectangular matrix of holes using ant colony optimisation

This paper investigates optimum path planning for CNC drilling machines for a special class of products that involve a large number of holes arranged in a rectangular matrix. Examples of such products include boiler plates, drum and trammel screens, connection flanges in steel structures, food-processing separators, as well as certain portions of printed circuit boards. While most commercial CAD software packages include modules that allow for automated generation of the CNC code, the tool path planning generated from the commercial CAD software is often not fully optimised in terms of the tool travel distance, and ultimately, the total machining time. This is mainly due to the fact that minimisation of the tool travel distance is a travelling salesman problem (TSP). The TSP is a hard problem in the discrete programming context with no known general solution that can be obtained in polynomial time. Several heuristic optimisation algorithms have been applied in the literature to the TSP, with varying levels of success. Among the most successful algorithms for TSP is the ant colony optimisation (ACO) algorithm, which mimics the behaviour of ants in nature. The research in this paper applies the ACO algorithm to the path planning of a CNC drilling tool between holes in a rectangular matrix. In order to take advantage of the rectangular layout of the holes, two modifications to the basic ACO algorithm are proposed. Simulation case studies show that the average discovered path via the modified ACO algorithms exhibit significant reduction in the total tool travel distance compared to the basic ACO algorithm or a typical genetic algorithm.     

Minimising total cost for training and assigning multiskilled workers in seru production systems

This paper investigates the multiskilled worker training and assignment (MWT&A) problem of the seru production system (SPS), which is a new type of assembly line configured as multiple assembly cells, or so-called serus. The configuration of the SPS emphasises production efficiency and flexibility, achieved by multiskilled workers (MWs) able to cope with the demand of high-variety and low-volume manufacturing. Well-arranged and trained MWs are viewed as a critical factor when it comes to enhancing the performance of SPSs. This paper studies the MWT&A problem in the SPS with the aim of minimising the total cost, specifically, the workers’ training cost and the balance cost of processing times of the MWs in serus. This study provides an applicable mathematical programming model and designs a two-phase heuristic, named the SAIG algorithm, to effectively and efficiently solve this problem. The performance of the proposed algorithm is demonstrated by a comparison with the state-of-the-art heuristic through a series of computational experiments.     

求解考虑顺序相关调整时间的双边装配线平衡问题的变邻域搜索算法

为有效解决带有顺序相关调整时间的双边装配线平衡问题,提出了一种简单高效的变邻域搜索算法。该算法通过将优先关系约束融入到交换、插入、交叉、变异等算子中,分别得到4个不同的邻域结构来保证搜索过程中解的可行性,避免过多重复邻域解的生成。4个邻域结构的搜索空间依次变大,以增强算法搜索能力。同时,结合装配线的特点,提出基于作业序列的编码和解码方式,在解码过程中,优先选择空闲时间较多的边,引入启发式目标加快算法收敛。分配结束后,对装配线末端的工作站组进行局部调整。通过将该算法先后用于求解无/有顺序相关调整时间的双边装配线平衡第一类问题,并与已有的算法进行对比,验证了所提的变邻域搜索算法的优越性和有效性。     

Improved imperialist competitive algorithms for rebalancing multi-objective two-sided assembly lines with space and resource constraints

In this paper, a mathematical model and an improved imperial competition algorithm (IICA) are proposed to solve the multi-objective two-sided assembly line rebalancing problem with space and resource restrictions (MTALRBP-SR). The aim is to find lines’ rebalance with the trade-off between efficiency, rebalancing cost and smoothing after reconfiguration. IICA utilises a new initialisation heuristic procedure based on classic heuristic rules to generate feasible initial solutions. A novel heuristic assimilation method is developed to vigorously conduct local search. In addition, a group-based decoding heuristic procedure is developed to fulfil the final task reassignment with the additional restrictions. To investigate the performance of the proposed algorithm, it is first tested on MTALRBP of benchmark problems and compared with some existing algorithms such as genetic algorithm, variable neighbourhood search algorithm, discrete artificial bee colony algorithm, and two iterated greedy algorithms. Next, the efficiency of the proposed IICA for solving MTALRBP-SR is revealed by comparison with a non-dominated sorting genetic algorithm (NSGA-II) and two versions of original ICA. Computational results and comparisons show the efficiency and effectiveness of IICA. Furthermore, a real-world case study is conducted to validate the proposed algorithm.     

Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry

Improving performance of global supply chains requires careful consideration of various factors including distance from markets, access to resources, exchange and tax rates, import tariffs, and trade regulations. In this paper, a comprehensive optimization model is proposed to maximise the after-tax profit of a closed-loop global supply chain for medical devices under uncertainty. The uncertainty of the decision-making environment is modelled using the budget of uncertainty concept in interval robust optimization. International financial issues due to the Economic Cooperation Organisation Trade Agreement as well as national regulations including transfer pricing limitations, exchange rates, tax rates, and import tariffs are considered. The proposed model considers various realistic assumptions pertaining to medical device supply chains such as multiple products, multiple periods, multiple echelons, and limited warehousing lifetime. In addition, reverse flows of perished and defective products are considered to address environmental concerns and customers’ requirements as well as to gain economic advantages. To tackle this problem, an efficient memetic algorithm is developed that incorporates adaptive variable neighbourhood search as its local search heuristic. Computational results demonstrate the efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context. In addition, several managerial insights are discussed based on the results.