An improved ant colony optimisation algorithm for three-tier supply chain scheduling based on networked manufacturing

This paper presents a study on supply chain scheduling from the perspective of networked manufacturing (NM). According to feature analysis of supply chain scheduling based on NM, we comprehensively consider the combined benefits of cost, time, and satisfaction level for customised services. In order to derive a scheduling strategy among supply chain members based on NM, we formulate a three-tier supply chain scheduling model composed of manufacturer, collaborative design enterprise and customer. Three objective functions – time function, cost function and delay punishment function – are employed for model development. We also take into account multi-objective optimisation under the constraint of product capacity. By using an improved ant colony optimisation algorithm, we add different pheromone concentrations to selected nodes that are obtained from feasible solutions and we confine pheromone concentrations τ within the minimum value τ _min and the maximum value τ _max, thus obtaining optimal results. The results obtained by applying the proposed algorithm to a real-life example show that the presented scheduling optimisation algorithm has better convergence, efficiency, and stability than conventional ant colony optimisation. In addition, by comparing with other methods, the output results indicate that the proposed algorithm also has better solutions.     

Applying ant colony system algorithm in the navigation process for plastic injection mould manufacturing scheduling optimisation

Under the computer-aided design (CAD) software architecture, this study aims to develop navigation processes for plastic injection mould manufacturing scheduling optimisation. Mould manufacturing is a job-shop scheduling problem, with components processing sequence under limited conditions. This study uses the search capabilities of the ant colony system (ACS) to determine a set of optimal schedules, under the condition of not violating the processing sequences, in order to minimise the total processing time and realise makespan minimisation. As the test results suggest, it can save up to 52% of manufacturing time, and also substantially shorten the processing time of the production plan. This study completes the algorithm steps and manufacturing process time estimation by operations on the navigation interface, and uses mould manufacturing scheduling to make optimised arrangements of finished components. The method can comply with the on-site manufacturing processes, improve scheduling prediction accuracy and consistently and efficiently integrate the optimisation scheduling system and mould manufacturing system. Visualised information of the scheduling results can be provided, thus allowing production management personnel to ensure smooth scheduling.     

Genetic algorithms and simulated annealing for scheduling in agile manufacturing

In this paper, genetic algorithms and simulated annealing are applied to scheduling in agile manufacturing. The system addressed consists of a single flexible machine followed by multiple identical assembly stations, and the scheduling objective is to minimize the makespan. Both genetic algorithms and simulated annealing are investigated based on random starting solutions and based on starting solutions obtained from existing heuristics in the literature. Overall, four new algorithms are developed and their performance is compared to the existing heuristics. A 2³ factorial experiment, replicated twice, is used to compare the performance of the various approaches, and identify the significant factors that affect the frequency of resulting in the best solution and the average percentage deviation from a lower bound. The results show that both genetic algorithms and simulated annealing outperform the existing heuristics in many instances. In addition, simulated annealing outperforms genetic algorithms with a more robust performance. In some instances, existing heuristics provide comparable results to those of genetic algorithms and simulated annealing with the added advantage of being simpler. Significant factors and interactions affecting the performance of the various approaches are also investigated.     

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.     

Reactive manufacturing control using the ant colony approach

As a reaction to the volatile market demands with regards to the number and variants of products offered, ever more complex procedures for manufacturing control are being developed. Most recently, self-organising procedures, which often mimic the behaviour of natural systems, have arisen. The method of ant colony optimisation (ACO), which was inspired by ants, can provide the necessary fundamentals in order to realise self-organising manufacturing control. In this context, the ifab-Institute has developed the AntControl tool for self-organising manufacturing control based on ACO. In order to investigate the potential of ACO, several concepts have been developed and integrated into the existing OSim simulation tool to create the new OSim-Ant tool. An exemplary simulation study within a manufacturing system has been carried out to evaluate the behaviour of AntControl. This paper presents this tool as well as the results of the simulation study.     

Multiprocessor task scheduling in multistage hybrid flow-shops: an ant colony system approach

The hybrid flow-shop scheduling problem (HFSP) has been of continuing interest for researchers and practitioners since its advent. This paper considers the multistage HFSP with multiprocessor tasks, a core topic for numerous industrial applications. A novel ant colony system (ACS) heuristic is proposed to solve the problem. To verify the developed heuristic, computational experiments are conducted on two well-known benchmark problem sets and the results are compared with genetic algorithm (GA) and tabu search (TS) from the relevant literature. Computational results demonstrate that the proposed ACS heuristic outperforms the existing GA and TS algorithms for the current problem. Since the proposed ACS heuristic is comprehensible and effective, this study successfully develops a near-optimal approach which will hopefully encourage practitioners to apply it to real-world problems.     

A new approach for rebalancing of U-lines with stochastic task times using ant colony optimisation algorithm

In real world, line balancing involves existing lines in existing factories and the line typically needs to be rebalanced rather than balanced. Rebalancing of a U-line can be defined as a changeover process from its initial configuration to a new configuration for a while due to the reasons such as demand variations, changes in product design and changes in task times, etc. This study defines U-line rebalancing problem with stochastic task times and proposes a solution procedure based on ant colony optimisation. The objective of the proposed algorithm is to minimise total cost of rebalancing which is the sum of task transposition costs, workstation opening/closing costs and operating costs of workstations for a particular planning horizon. A comprehensive experiment is conducted to generate problem instances and to compare rebalancing costs of U-lines by means of several factors. A total of 6600 rebalancing solutions are obtained and several comparisons are performed.     

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.     

Open shop scheduling problem with a multi-skills resource constraint: a genetic algorithm and an ant colony optimisation approach

The continuous evolution of manufacturing environments leads to a more efficient production process that controls an increasing number of parameters. Production resources usually represent an important constraint in a manufacturing activity, specially talking about the management of human resources and their skills. In order to study the impact of this subject, this paper considers an open shop scheduling problem based on a mechanical production workshop to minimise the total flow time including a multi-skill resource constraint. Then, we count with a number of workers that have a versatility to carry out different tasks, and according to their assignment a schedule is generated. In that way, we have formulated the problem as a linear as and a non-linear mathematical model which applies the classic scheduling constraints, adding some different resources constraints related to personnel staff competences and their availability to execute one task. In addition, we introduce a genetic algorithm and an ant colony optimisation (ACO) method to solve large size problems. Finally, the best method (ACO) has been used to solve a real industrial case that is presented at the end.     

Quantum-inspired ant colony optimisation algorithm for a two-stage permutation flow shop with batch processing machines

This paper studied two-stage permutation flow shop problems with batch processing machines, considering different job sizes and arbitrary arrival times, with the optimisation objective of minimising the makespan. The quantum-inspired ant colony optimisation (QIACO) algorithm was proposed to solve the problem. In the QIACO algorithm, the ants are divided into two groups: one group selects the largest job in terms of job size as the initial job for each batch and the other group selects the smallest job as the initial job for each batch. Each group of ants has its own pheromone matrix. In the computational experiment, our novel algorithm was compared with the hybrid discrete differential evolution (HDDE) algorithm and the batch-based hybrid ant colony optimisation (BHACO) algorithm. Although the HDDE algorithm has a shorter run time, the quality of the solution for large-scale jobs is not good, while the BHACO algorithm always obtains a better solution but requires a longer run time. The computational results show that the QIACO algorithm embedded in the quantum information has advantages in terms of both solution quality and running time.     

Balancing transfer lines using Benders decomposition and ant colony optimisation techniques

In this paper, we investigate a transfer line balancing problem in order to find the line configuration that minimises the non-productive time. The problem is defined at an auto manufacturing company where the cylinder head is manufactured. Technological restrictions among design features and manufacturing operations are taken into consideration. The problem is represented by an integer programming model that assigns design features and cutting tools to machining stations, and specifies the number of machines and production sequence in each station. Three algorithms are developed to efficiently solve the problem under study. The first algorithm uses Benders decomposition approach that decomposes the proposed model into an assignment problem and a sequencing problem. The second algorithm is a hybrid algorithm that mixes Benders decomposition approach with the ant colony optimisation technique. The third algorithm solves the problem using two nested ant colonies. Using 15 different problem dimensions, we compare results of the three algorithms in a computational study. The first algorithm finds optimal solutions of small problem instances only. Second and third algorithms demonstrate optimality gaps less than 4.04 and 3.8%, respectively, when compared to the optimal results given by the first algorithm. Moreover, the second and third algorithms are very promising in solving medium and large-scale problem instances.     

An improved branch and bound procedure for scheduling a flow line manufacturing cell

This paper presents a new branch and bound procedure for scheduling a flow-line manufacturing cell. This procedure and an existing procedure are tested on several problem sets with varying numbers of families, jobs and machines, and varying setup time distributions. The results show that the new procedure solves small problems dramatically faster than the existing procedure. Three heuristic procedures, based on the new branch and bound procedure, are developed. These heuristic procedures as well as a tabu search procedure are tested on problem sets with larger problem sizes. The results show that one of the new procedures generates solutions with improved makespans compared to the tabu search procedure.     

Multiple colony ant algorithm for job-shop scheduling problem

Ant colony optimization (ACO) is a metaheuristic that takes inspiration from the foraging behaviour of a real ant colony to solve the optimization problem. This paper presents a multiple colony ant algorithm to solve the Job-shop Scheduling Problem with the objective that minimizes the makespan. In a multiple colony ant algorithm, ants cooperate to find good solutions by exchanging information among colonies which are stored in a master pheromone matrix that serves the role of global memory. The exploration of the search space in each colony is guided by different heuristic information. Several specific features are introduced in the algorithm in order to improve the efficiency of the search. Among others is the local search method by which the ant can fine-tune their neighbourhood solutions. The proposed algorithm is tested over set of benchmark problems and the computational results demonstrate that the multiple colony ant algorithm performs well on the benchmark problems.     

Ant colony optimisation with parameterised search space for the job shop scheduling problem

The job-shop scheduling problem (JSSP) is known to be NP-hard. Due to its complexity, many metaheuristic algorithm approaches have arisen. Ant colony metaheuristic algorithm, lately proposed, has successful application to various combinatorial optimisation problems. In this study, an ant colony optimisation algorithm with parameterised search space is developed for JSSP with an objective of minimising makespan. The problem is modelled as a disjunctive graph where arcs connect only pairs of operations related rather than all operations are connected in pairs to mitigate the increase of the spatial complexity. The proposed algorithm is compared with a multiple colony ant algorithm using 20 benchmark problems. The results show that the proposed algorithm is very accurate by generating 12 optimal solutions out of 20 benchmark problems, and mean relative errors of the proposed and the multiple colony ant algorithms to the optimal solutions are 0.93% and 1.24%, respectively.     

Ant colony optimisation with elitist ant for sequencing problem in a mixed model assembly line

Optimised sequencing in the Mixed Model Assembly Line (MMAL) is a major factor to effectively balance the rate at which raw materials are used for production. In this paper we present an Ant Colony Optimisation with Elitist Ant (ACOEA) algorithm on the basis of the basic Ant Colony Optimisation (ACO) algorithm. An ACOEA algorithm with the taboo search and elitist strategy is proposed to form an optimal sequence of multi-product models which can minimise deviation between the ideal material usage rate and the practical material usage rate. In this paper we compare applications of the ACOEA, ACO, and two other commonly applied algorithms (Genetic Algorithm and Goal Chasing Algorithm) to benchmark, stochastic problems and practical problems, and demonstrate that the use of the ACOEA algorithm minimised the deviation between the ideal material consumption rate and the practical material consumption rate under various critical parameters about multi-product models. We also demonstrate that the convergence rate for the ACOEA algorithm is significantly more than that for all the others considered.     

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.     

ANTBAL: an ant colony optimization algorithm for balancing mixed-model assembly lines with parallel workstations

This paper presents ANTBAL, an ant colony optimization algorithm for balancing mixed-model assembly lines. The proposed algorithm accounts for zoning constraints and parallel workstations and aims to minimize the number of operators in the assembly line for a given cycle time. In addition to this goal, ANTBAL looks for solutions that smooth the workload among workstations, which is an important aspect to account for in balancing mixed-model assembly lines. Computational experience shows the superior performance of the proposed algorithm.     

Large neighbourhood search based on mixed integer programming and ant colony optimisation for car sequencing

We investigate the problem of scheduling a sequence of cars to be placed on an assembly line. Stations, along the assembly line install options (e.g. air conditioning), but have limited capacities, and hence cars requiring the same options need to be distributed far enough apart. The desired separation is not always feasible, leading to an optimisation problem that minimises the violation of the ideal separation requirements. In order to solve the problem, we use a large neighbourhood search (LNS) based on mixed integer programming (MIP). The search is implemented as a sliding window, by selecting overlapping subsequences of manageable sizes, which can be solved efficiently. Our experiments show that, with LNS, substantial improvements in solution quality can be found.     

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.