Ant colony optimisation with random selection for block transportation scheduling with heterogeneous transporters in a shipyard

This paper considers a scheduling problem of heterogeneous transporters for pickup and delivery blocks in a shipyard assuming a static environment where all transportation requirements for blocks are predetermined. In the block transportation scheduling problem, the important issue is to determine which transporter delivers the block from one plant to the other plant and when, in order to minimise total logistic times. Therefore, the objective of the problem is to simultaneously determine the allocation policy of blocks and the sequence policy of transporters to minimise the weighted sum of empty transporter travel times, delay times, and tardy times. A mathematical model for the optimal solution is derived and an ant colony optimisation algorithm with random selection (ACO_RS) is proposed. To demonstrate the performance of ACO_RS, computational experiments are implemented in comparing the solution with the optimal solutions obtained by CPLEX in small-sized problems and the solutions obtained by conventional ACO in large-sized problems.     

A mathematical formulation and anarchic society optimisation algorithms for integrated scheduling of processing and transportation operations in a flow-shop environment

This paper addresses a permutation flow-shop scheduling problem where there are a finite number of transporters to carry jobs from each machine to its subsequent machine. The problem is first formulated as a mixed-integer linear programme, and then two anarchic society optimisation (ASO) algorithms are developed to solve large-sized instances of the problem. The numerical experience shows that the ASO algorithms are considerably effective and efficient. Finally, a sensitivity analysis is carried out to study the performance of the manufacturing system versus the transportation times and the number of transporters.     

Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand

This paper presents an algorithm portfolio methodology based on evolutionary algorithms to solve complex dynamic optimisation problems. These problems are known to have computationally complex objective functions, which make their solutions computationally hard to find, when problem instances of large dimensions are considered. This is due to the inability of the algorithms to provide an optimal or near-optimal solution within an allocated time interval. Therefore, this paper employs a bundle of evolutionary algorithms (EAs) tied together with several processors, known as an algorithm portfolio, to solve a complex optimisation problem such as the inventory routing problem (IRP) with stochastic demands. EAs considered for algorithm portfolios are the genetic algorithm and its four variants such as the memetic algorithm, genetic algorithm with chromosome differentiation, age-genetic algorithm, and gender-specific genetic algorithm. In order to illustrate the applicability of the proposed methodology, a generic method for algorithm portfolios design, evaluation, and analysis is discussed in detail. Experiments were performed on varying dimensions of IRP instances to validate different properties of algorithm portfolio. A case study was conducted to illustrate that the set of EAs allocated to a certain number of processors performed better than their individual counterparts.     

A novel hybrid meta-heuristic algorithm for a no-wait flexible flow shop scheduling problem with sequence dependent setup times

In this paper, we contemplate the problem of scheduling a set of n jobs in a no-wait flexible flow shop manufacturing system with sequence dependent setup times to minimising the maximum completion time. With respect to NP-hardness of the considered problem, there seems to be no avoiding application of metaheuristic approaches to achieve near-optimal solutions for this problem. For this reason, three novel metaheuristic algorithms, namely population based simulated annealing (PBSA), adapted imperialist competitive algorithm (AICA) and hybridisation of adapted imperialist competitive algorithm and population based simulated annealing (AICA?+?PBSA), are developed to solve the addressed problem. Because of the sensitivity of our proposed algorithm to parameter's values, we employed the Taguchi method as an optimisation technique to extensively tune different parameters of our algorithm to enhance solutions accuracy. These proposed algorithms were coded and tested on randomly generated instances, then to validate the effectiveness of them computational results are examined in terms of relative percentage deviation. Moreover, some sensitive analyses are carried out for appraising the behaviour of algorithms versus different conditions. The computational evaluations manifestly support the high performance of our proposed novel hybrid algorithm against other algorithms which were applied in literature for related production scheduling problems.     

Block spatial scheduling modelling and application in shipbuilding

With the wide application of module-shipbuilding technology, problems related to block spatial scheduling occur in various working areas, and this restricts the productivity of shipbuilding. To address the problems and to obtain the optimum block sequence and spatial layout, typical block features and work plates were investigated. A heuristic spatial scheduling model was established based on the investigation and proposed strategies with the objective to minimise makespan. With the heuristic algorithm, a block spatial scheduling system was developed and implemented with real data from a large ship. Through the spatial scheduling system, visual results of daily block layouts and progress charts for all blocks can be easily obtained and work orders can also be created for site workers. Several other spatial scheduling methods are described and compared with the above-mentioned heuristic algorithm. The result shows that the heuristic algorithm is better than Cplex and a genetic algorithm in solving large-scale block scheduling, and the heuristic algorithm is better than a grid algorithm and manual scheduling in all aspects such as makespan, utilisation of work plates, runtime of scheduling and on-time delivery. The developed block spatial scheduling system is applied in a block production shop of a modern shipyard and shows good performance.     

Scheduling job shop associated with multiple routings with genetic and ant colony heuristics

The general job shop problem is one of the well known machine scheduling problems, in which the operation sequence of jobs are fixed that correspond to their optimal process plans and/or resource availability. Scheduling and sequencing problems, in general, are very difficult to solve to optimality and are well known as combinatorial optimisation problems. The existence of multiple job routings makes such problems more cumbersome and complicated. This paper addresses a job shop scheduling problem associated with multiple job routings, which belongs to the class of NP hard problems. To solve such NP-hard problems, metaheuristics have emerged as a promising alternative to the traditional mathematical approaches. Two metaheuristic approaches, a genetic algorithm and an ant colony algorithm are proposed for the optimal allocation of operations to the machines for minimum makespan time criterion. ILOG Solver, a scheduler package, is used to evaluate the performance of the proposed algorithms. The comparison reveals that both the algorithms are capable of providing solutions better than the solution obtained with ILOG Solver.     

Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing

With the increasing prosperity of additive manufacturing, the 3D-printing shop scheduling problem has presented growing importance. The scheduling of such a shop is imperative for saving time and cost, but the problem is hard to solve, especially for simultaneous multi-part assignment and placement. This paper develops an improved evolutionary algorithm for application to additive manufacturing, by combining a genetic algorithm with a heuristic placement strategy to take into account the allocation and placement of parts integrally. The algorithm is designed also to enhance the optimisation efficiency by introducing an initialisation method based on the characteristics of the 3D printing process through the development of corresponding time calculation model. Experiments show that the developed algorithm can find better solutions compared with state-of-the-art algorithms such as simple genetic algorithm, particle swarm optimisation and heuristic algorithms.     

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.     

An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

Recent years witness a great deal of interest in artificial intelligence (AI) tools in the area of optimization. AI has developed a large number of tools to solve the most difficult search-and-optimization problems in computer science and operations research. Indeed, metaheuristic-based algorithms are a sub-field of AI. This study presents the use of the metaheuristic algorithm, that is, water cycle algorithm (WCA), in the transportation problem. A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution. Since the parameters are stochastic, the corresponding constraints are probabilistic. They are converted into deterministic constraints using the stochastic programming approach. In this study, we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems. WCA is influenced by the water cycle process of how streams and rivers flow toward the sea (optimal solution). WCA is applied to the stochastic transportation problem, and obtained results are compared with that of the new metaheuristic optimization algorithm, namely the neural network algorithm which is inspired by the biological nervous system. It is concluded that WCA presents better results when compared with the neural network algorithm.     

Biogeography-based optimisation for flexible manufacturing system scheduling problem

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time.     

Optimisation of block erection scheduling based on a Petri net and discrete PSO

The shipyard block erection system (SBES) is a typical discrete-event dynamic system. To model multiprocessing paths and a concurrent assembly procedure, a timed Petri net (TPN) is proposed. The definition of a Petri net is extended to accord with the real-world SBES organisation. The basic TPN modules are presented to model the corresponding variable structures in the SBES, and then the scheduling model of the whole SBES is easily constructed. A modified discrete particle swarm optimisation (PSO) based on the reachability analysis of Petri nets is developed for scheduling of the SBES. In the proposed algorithm, particles are coded by welding transitions and selecting places of the TPN model, and then the collaboration and competition of particle individuals is simulated by crossover and mutation operators in a genetic algorithm. Numerical simulation suggests that the proposed TPN–PSO scheduler can provide an improvement over the conventional scheduling method. Finally, a case study of the optimisation of a back block erection process is provided to illustrate the effectiveness of the method.     

基于优先规则的船舶分段空间调度算法

为有效解决船舶分段的空间调度问题,提出了一种基于优先规则的求解算法。首先利用优先规则和禁忌搜索算法产生可行的分段调度序列,再采用一种启发式定位策略——最下最左填满策略对产生的调度序列进行解码,以评估调度序列的优劣。算法不断迭代,最终可得到近似最优解。对船厂的实际生产数据进行了实证分析,并与现有的算法进行了对比,验证了所提出的算法在空间调度问题上的有效性和优越性。     

Scheduling dynamic block assembly in shipbuilding through hybrid simulation and spatial optimisation

Scheduling block assembly in shipyard production poses great difficulties regarding the accurate prediction of the required spatial resource and effective production control for achieving managerial objectives due to the dynamic spatial layout and the stochastic nature of the production system. In this study, this dynamic space-constrained problem is viewed as two sequential decisions, namely rule-based dispatching and a static spatial configuration. A novel hybrid planning method is developed to employ discrete-event simulation as look-ahead scheduling to evaluate the system performance under various control policies. To rationalise block placement and improve long-term area utilisation, a discrete spatial optimisation problem is formulated and solved using an enumeration-based search algorithm, followed by the application of a series of heuristic positioning strategies. By imitation of the dynamic dispatching and spatial operation, a statistical analysis of the resultant performance can be conducted to select the best-performing priority rules. A case study with an experimental investigation is performed for a local shipyard to demonstrate the applicability of the proposed method.     

A two-stage AIS approach for grid scheduling problems

Grid workflow scheduling problem has been a research focus in grid computing in recent years. Various deterministic or meta-heuristic scheduling approaches have been proposed to solve this NP-complete problem. A perusal of published papers on the artificial immune system (AIS) reveals that most researchers use the clonal selection of B cells during the evolving processes and the affinity function of B cells to solve various optimisation problems. This research takes a different approach to the subject – firstly by applying a modified algorithm (Hu, T.C., 1961. Parallel sequencing and assembly line problem. Operations Research, 9 (6), 841–848) to sequence the job and this sequence is applied for further application. Secondly, the derived sequence is then used for machine allocations using the AIS approach. The proposed AIS apply B cells to reduce the antigens and then combining T helper cells and T suppressor cells to solve the grid scheduling problems. Our proposed methodology differs from other earlier approaches as follows: 1. A two-stage approach is applied using a fixed sequence derived from heuristic to allocate machine. 2. AIS apply B cells as bases and then T cells are employed next. T helper cells are used to help improve the solution and then T suppressor cells are generated to increase the diversity of the population. A new formula is proposed to calculate the affinity of the antibody with the antigen. The total difference of completion time of each job is applied instead of the difference of makespan of the schedule. This new AIS method can supplement the flaw of genetic algorithms (GA) using fitness as the basis and a new lifespan which will keep good diversified chromosomes within the population to extend the searching spaces. The experimental tests show that this novel AIS method is very effective when compared with other meta-heuristics such as GA, simulated annealing (SA), and ant colony optimisation (ACO).     

Optimal generator maintenance scheduling using a modified discrete PSO

A modified discrete particle swarm optimisation (MDPSO) algorithm to generate optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system, while satisfying system load demand and crew constraints, is presented. Discrete particle swarm optimisation (DPSO) is known to effectively solve large-scale multi-objective optimisation problems and has been widely applied in power system. The MDPSO proposed for the generator maintenance scheduling optimisation problem generates optimal and feasible solutions and overcomes the limitations of the conventional methods, such as extensive computational effort, which increases exponentially as the size of the problem increases. The efficacy of the proposed algorithm is illustrated and compared with the genetic algorithm (GA) and DPSO in two case studies ? a 21-unit test system and a 49-unit system feeding the Nigerian national grid. The MDPSO algorithm is found to generate schedules with comparatively higher system reliability indices than those obtained with GA and DPSO.     

A modified ant-colony optimisation algorithm to minimise the completion time variance of jobs in flowshops

In this work, the flowshop scheduling problem is considered with the objective of minimising the completion-time variance (CTV) of jobs, and an Ant Colony Optimisation (ACO) algorithm is presented. Two implementations of the Modified Ant Colony Optimisation algorithm (MACO-I and MACO-II) are proposed to solve the permutation flowshop scheduling problem. The proposed ant-colony-algorithm implementations have been tested on 90 benchmark flowshop scheduling problems. The solutions yielded by the proposed MACO implementations are compared with various algorithms and with the best CTV of jobs reported in the literature. The proposed MACO implementations are found to perform very well in minimising the chosen performance measure.     

Multi-objective sequence optimization of PCB component assembly with GA based on the discrete Fréchet distance

A new mechanism,namely a combination of curve matching method based on the discrete Fréchet distance and evolutionary algorithms,is proposed to solve pick-and-place sequence optimisation problems as a multi-objective optimisation problem. The essence of the mechanism is to accomplish the comparison of objective vectors with curve matching method. The objective vector is mapped into the array of points with a binary mapping operator and the discrete Fréchet distance is utilised to measure the similarity between the reference array of points and the comparison array of points. The genetic algorithm based on the discrete Fréchet distance (FGA) is proposed. To test the new mechanism, together with FGA, three other test algorithms are selected to solve the sequence optimisation problem. The simulation results indicate that FGA outperforms other algorithms. This new mechanism is rational and feasible for multi-objective pick-and-place sequence optimisation problems.     

Design of high-performing hybrid meta-heuristics for unrelated parallel machine scheduling with machine eligibility and precedence constraints

This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.