An ant colony optimisation algorithm for scheduling in agile manufacturing

Producing customised products in a short time at low cost is one of the goals of agile manufacturing. To achieve this goal an assembly-driven differentiation strategy has been proposed in the agile manufacturing literature. In this paper, we address a manufacturing system that applies the assembly-driven differentiation strategy. The system consists of machining and assembly stages, where there is a single machine at the machining stage and multiple identical assembly stations at the assembly stage. An ant colony optimisation (ACO) algorithm is developed for solving the scheduling problem of determining the sequence of parts to be produced in the system so as to minimise the maximum completion time (or makespan). The ACO algorithm uses a new dispatching rule as the heuristic desirability and variable neighbourhood search as the local search to make it more efficient and effective. To evaluate the performance of heuristic algorithms, a branch-and-bound procedure is proposed for deriving the optimal solution to the problem. Computational results show that the proposed ACO algorithm is superior to the existing algorithm, not only improving the performance but also decreasing the computation time.     

Fractional factorial analysis to the configuration of simulated annealing applied to the multi-objective optimization of master production scheduling problems

Searching for the global optimal solution in a Master Production Scheduling problem usually demands an effort most industries are not willing to pay. Therefore, the use of meta-heuristics that generates good solutions in reasonable computer time becomes an attractive alternative. However, such strategies are usually complex to implement and configuring their parameters is not a trivial task because of the number of usually conflicting objectives involved. The use of statistical methods that facilitate the set-up of the heuristic's parameters becomes therefore necessary. Knowing which parameters are more important, that is, the ones that really affect the solution quality, and those that are irrelevant, is very important for chosen technique performance. This work presents how fractional factorial analysis can be applied to the configuration of simulated annealing used for optimization of Master Production Scheduling problems. Two scheduling scenarios illustrate the use of the proposed method.     

一种混合优化策略在工步排序中的应用

结合创成式ＣＡＰＰ系统中工步优化问题，介绍了由遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍｓ，ＧＡ）和模拟退火算法（ＳｉｍｕｌａｔｅｄＡｎｎｅａｌｉｎｇＡｌｇｏｒｉｔｈｍｓ，ＳＡ）构成的混合寻优策略。最后以一箱体的工艺规划过程为例，将基于混合寻优策略的工步排序融入以加工中心为主要加工设备的ＣＡＰＰ工艺决策过程。     

Optimization of transit network layout and headway with a combined genetic algorithm and simulated annealing method

This article proposes a methodology for optimizing transit networks, including both route structures and headways. Given information on transit demand, transit fleet size and street network in the transit service area, the methodology seeks to minimize transfers and total user cost while maximizing service coverage. The goal is to provide an effective mathematical solution procedure with minimal reliance on heuristics to solve large-scale transit network optimization problems. This article describes the representation of the transit route network and the associated network search spaces, the representation of route network headways and the associated search spaces, the total user cost objective functions, and a stochastic global search scheme based on a combined genetic algorithm and simulated annealing search method. The methodology has been tested with published benchmark problems and applied to a large-scale realistic network optimization problem. The results show that the methodology is capable of producing improved solutions to large-scale transit network design problems.     

A simulated annealing algorithm based on a closed loop layout for facility layout design in flexible manufacturing systems

Facility layout design problems in flexible manufacturing systems (FMS) differ from traditional facility design problems and are more difficult to solve because there are more constraints that must be considered (i.e., cell shape, cell orientation, pick-up and drop-off point positions). The focus of this paper is on the closed loop type layout, which is based on a predetermined layout pattern. This layout pattern is commonly found in manufacturing settings since it requires a simplified material handling system configuration and since it facilitates a modular and expandable layout structure. The open-field type layout problem, where there is no predetermined layout pattern, may potentially have a more efficient configuration, since there are fewer restrictions. However, this problem is more difficult to solve and may result in configurations that are not desirable due to the lack of structure or modularity. The procedure developed in this paper improves the efficiency of the closed loop configuration by changing the rectangular shape of the loop to different sizes. In many cases, the resulting closed loop layout proves to be as efficient as the open field layout. A simulated annealing procedure (SA-CL) is used to search for the configuration that minimizes the total material handling costs. A comparison of the results with existing methods indicates that, based on solution quality and computational time, the SA-CL offers a favourable alternative for efficient layout design.     

Petri nets and genetic algorithms for complex manufacturing systems scheduling

In this paper we propose the GAPN (genetic algorithms and Petri nets) approach, which combines the modelling power of Petri nets with the optimisation capability of genetic algorithms (GAs) for manufacturing systems scheduling. This approach uses both Petri nets to formulate the scheduling problem and GAs for scheduling. Its primary advantage is its ability to model a wide variety of manufacturing systems with no modifications either in the net structure or in the chromosomal representation. In this paper we tested the performance on both classical scheduling problems and on a real life setting of a manufacturer of car seat covers. In particular, such a manufacturing system involves features such as complex project-like routings, assembly operations, and workstations with unrelated parallel machines. The implementation of the algorithm at the company is also discussed. Experiments show the validity of the proposed approach.     

Solving structural optimization problems with genetic algorithms and simulated annealing

In this paper we study the performance of two stochastic search methods: Genetic Algorithms and Simulated Annealing, applied to the optimization of pin‐jointed steel bar structures. We show that it is possible to embed these two schemes into a single parametric family of algorithms, and that optimal performance (in a parallel machine) is obtained by a hybrid scheme. Examples of applications to the optimization of several real steel bar structures are presented. Copyright © 1999 John Wiley & Sons, Ltd.     

Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems

Multi-factory production networks have increased in recent years. With the factories located in different geographic areas, companies can benefit from various advantages, such as closeness to their customers, and can respond faster to market changes. Products (jobs) in the network can usually be produced in more than one factory. However, each factory has its operations efficiency, capacity, and utilization level. Allocation of jobs inappropriately in a factory will produce high cost, long lead time, overloading or idling resources, etc. This makes distributed scheduling more complicated than classical production scheduling problems because it has to determine how to allocate the jobs into suitable factories, and simultaneously determine the production scheduling in each factory as well. The problem is even more complicated when alternative production routing is allowed in the factories. This paper proposed a genetic algorithm with dominant genes to deal with distributed scheduling problems, especially in a flexible manufacturing system (FMS) environment. The idea of dominant genes is to identify and record the critical genes in the chromosome and to enhance the performance of genetic search. To testify and benchmark the optimization reliability, the proposed algorithm has been compared with other approaches on several distributed scheduling problems. These comparisons demonstrate the importance of distributed scheduling and indicate the optimization reliability of the proposed algorithm.     

Genetic algorithms for layout optimization in crossdocking operations of a manufacturing plant

In this study, we use genetic algorithms to optimize the lane layout associated with the crossdocking operation at the Toyota Motor Manufacturing plant in Georgetown, Kentucky, USA. A genetic algorithm solution can be obtained within seconds, whereas an exhaustive search would require a computing time of over five days on a 1?GHz Intel Pentium III. The results of this study show that a simple rearrangement of the lanes will lead to a decrease in workload of nearly 34% in the crossdocking area and ultimately result in an overall reduced lead time.     

Improved simulated annealing with step width adaptation for Langmuir probe tuning

In a previous investigation, a simulated annealing (SA) method was developed to optimize 14 Fourier terms in a radio-frequency waveform for active compensation of a Langmuir probe system. This approach was shown to find better solutions in less time than skilled human operators. However, variations in fitness indicated that the SA algorithm did not always find the precise global solution, although it came consistently close to it. This variability was caused by the limited number of fitness evaluations available due to time constraints. In this research, the chosen maximum step width has been shown to have a significant effect on the overall performance of the algorithm. A scaling function has been developed to adapt the maximum step width of the SA algorithm, on-line, as a function of the number of elapsed iterations. The modified algorithm has been shown to find fitter solutions with reduced variability in fitness.     

Two-level modified simulated annealing based approach for solving facility layout problem

In this paper, we are considering the quadratic assignment model (QAP) of the facility layout problem (FLP) which is known to be NP-hard. We relax the integer constraints of the QAP and solve it on a commercially available package called LINGO 8. In the optimal solution so obtained, X_ij s take real values between zero and one. We identify promising X_ij s having a value strictly greater than 0.5 in the optimal solution and set them to one. We add the constraints (X_ij ?=?1) associated with promising X_ij s into the QAP (with integer restrictions) and resolve using LINGO 8. In all the cases attempted we obtained a superior feasible solution to the QAP which was further improved by the proposed modified simulated annealing (MSA) procedure. An encouraging comparative performance of this procedure is thus reported.     

Acoustic behavior design with simulated annealing

The present work extends the matrix method formalism, by using a supplementary computational method based on a simulated annealing algorithm, with the aim to design acoustical structures, especially acoustic filters. The algorithm introduces a cost function, which is minimized by the simulated annealing algorithm. Also, some numerical computations have been carried out to design some special acoustic filters and an experimental analysis of the designed acoustic filters is provided to test the validity of the method.     

Using simulated annealing to schedule a flowshop manufacturing cell with sequence-dependent family setup times

This paper proposes a simulated annealing-based meta-heuristic to minimise makespan in a flowshop manufacturing cell with sequence-dependent family setup times. To escape from local minima, Cauchy function?–?rather than the Boltzmann function?–?is used during the annealing process. The effectiveness and efficiency of the proposed simulated annealing-based meta-heuristic is compared against the existing heuristics on a benchmark problem dataset used in earlier studies. These computational results show that the proposed simulated annealing-based meta-heuristic is highly effective as compared to the state-of-the-art meta-heuristics for this problem on the same benchmark instances.     

Using queuing theory and simulated annealing to design the facility layout in an AGV-based modular manufacturing system

An automated guided vehicle-based flow production system is used for manufacturing prefabricated bathroom units. One unit can occupy a space of more than 10?m². Due to large time deviations in sequential processes, queues are formed and greater plant space is needed. Reducing work-in-progress helps to save plant space but renders manufacture less efficient. The research explores better workstation arrangements. An open queuing network (OQN) model was used to approximate the flow production system. Since the problem of workstation arrangement is a combinatorial optimisation problem, simulated annealing (SA) was applied to search for a good solution. The combination of an OQN model and SA provides a powerful tool to solve the facility layout problem for a stochastic flow production system. The experimental results show that the proposed approach has the potential to guide industrial layout design and practice.     

A simulated annealing method for design of laminates with required stiffness properties

Designing a laminate based on its stiffness properties requires finding the optimum lamination stacking sequence to yield the required stiffness properties. The design variables to be considered are the number of layers and orientation angle of fibers in each layer group, which are treated as discrete-variables. The optimum lamination is then obtained by minimizing a cost function composed of the relative difference between the calculated effective stiffness properties and weight of trial laminate and the desired properties. This error minimization problem was solved using a modified simulated annealing heuristic method. The new simulated annealing implementation comprises a cooling procedure in which the temperature decrease relied adaptively on the objective function evolution. It is shown that the proposed method can give rise to an improvement in convergence speed. To achieve a further improvement in the performance of the method, simulated annealing parallelization implemented using the proposed cooling process. The main features of this algorithm are described and its encouraging results are presented.     

Multi-threaded simulated annealing for a bi-objective maintenance scheduling problem

A parallel Simulated Annealing algorithm with multi-threaded architecture is proposed to solve a real bi-objective maintenance scheduling problem with conflicting objectives: the minimisation of the total equipment downtime caused by maintenance jobs and the minimisation of the multi-skilled workforce requirements over the given horizon. The maintenance jobs have different priorities with some precedence relations between different skills. The total weighted flow time is used as a scheduling criterion to measure the equipment availability. The multi-threaded architecture is used to speed up a multi-objective Simulated Annealing algorithm to solve the considered problem. Multi-threading is a form of parallelism based on shared memory architecture where multiple logical processing units, so-called threads, run concurrently and communicate via shared memory. The performance of the parallel method compared to the exact method is verified using a number of test problems. The obtained results imply the high efficiency and robustness of the proposed heuristic for both solution quality and computational effort.     

A two-stage simulated annealing procedure for block layout problems

Rules for setting simulated annealing control parameters are proposed for block layout problems where different material-handling devices are dynamically assigned to individual material movements as layout solutions are perturbed. Recognizing the high cost of computing materials-handling cost in this type of problem, the rules are based on adapting an existing two-stage simulated annealing procedure to accelerate convergence. Experimental results suggest that the application of these rules yields solution quality comparable with other single and two-stage simulated annealing algorithms but with significantly fewer re-evaluations of the objective function.     

Controlling asynchronous team design environments by simulated annealing

An organizational strategy for design environments,asynchronous teams, is reviewed. Simulated annealing is used to implement the necessarycontracting search behavior of asynchronous teams. An example of an asynchronous team design environment controlled by simulated annealing is given from the building design domain. The simulated annealing algorithm used, which has been modified for distributed use and multi-criteria, non-preference objectives, is described.     

Effective hybrid genetic algorithm for minimizing makespan on a single-batch-processing machine with non-identical job sizes

The paper addresses minimizing makespan by a genetic algorithm (GA) for scheduling jobs with non-identical sizes on a single-batch-processing machine. A batch-processing machine can process up to B jobs simultaneously. The processing time of a batch is equal to the longest processing time among all jobs in the batch. Two different GAs are proposed based on different encoding schemes. The first is a sequence-based GA (SGA) that generates random sequences of jobs using GA operators and applies the batch first fit heuristic to group the jobs. The second is a batch-based hybrid GA (BHGA) that generates random batches of jobs using GA operators and ensures feasibility by using knowledge of the problem based on a heuristic procedure. A greedy local search heuristic based on the problem characteristics is hybridized with a BHGA that has the ability of steering efficiently the search toward the optimal or near-optimal schedules. The performance of proposed GAs is compared with a simulated annealing (SA) approach proposed by Melouk et al. (Melouk, S., Damodaran, P. and Chang, P.Y., Minimizing makespan for single machine batch processing with non-identical job sizes using simulated annealing. Int. J. Prod. Econ., 2004, 87, 141–147) and also against a modified lower bound proposed for the problem. Computational results show that BHGA performs considerably well compared with the modified lower bound and significantly outperforms the SGA and SA in terms of both quality of solutions and required runtimes.     

Transfer batch scheduling using genetic algorithms

This paper examines scheduling in a manufacturing system with transfer batches. Transfer batches are considered as different batches although they stem from the same job. Genetic algorithms determine the size of the transfer batches for each job and the final schedule with a makespan criterion. A novelty of the genetic algorithm developed is twin chromosome encoding, the first chromosome representing the relative size (participation ratio) of each transfer batch with respect to the whole batch; and the second chromosome applying in effect a dynamic heuristic dispatching rule representation for resolving operation antagonism. New crossover and mutation operators were employed for the first chromosome and standard operators for the second. The genetic algorithms were coded in C++ for better control. A 20 job?×?eight machine shop was used as a test case. Results favour genetic algorithms over heuristic procedures, but the latter close the gap with an increase in the number of transfer batches. Design of Experiments was used to focus on the most promising genetic algorithm parameter value combinations.