Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method

Nodes in the Wireless sensor network (WSN) have the limited power, memory and battery capacity. In this work, we propose an efficient routing algorithm called Cluster Based Wireless Sensor Network using Ant Colony Optimization (CBACO). The Cluster Heads (CH) are selected based on the cost derived from node parameters remaining energy, number of neighbours and distance to base station. The weighted average method is used to compute cost. The routing processes are established in two levels. The cluster member to cluster head data transmission is handled at level one and in the second level path finding process between cluster head to base station handled by Ant Colony Optimization (ACO) method which is the biologically inspired optimization technique. All the cluster head nodes are participate in the second level inter-cluster routing operation. The performance of the CBACO algorithm in terms of delay is minimized by using the clustering method and ACO method. The efficiency of the proposed algorithm is analysed by compare with existing routing algorithm which uses LEACH and ACO method. The results indicate that our proposed work achieves low energy consumption and high throughput.

Water delivery scheduling was investigated in this study using fuzzy state, action, reward, state, action (SARSA) learning (FSL) and ant colony optimization (ACO) methods to find the advantages of a new robust model (FSL) over a conventional model (ACO) in both normal and emergency conditions. The mathematical models of these methods were developed. Three water shortages of 10%, 20%, and 30% were considered in the East Aghili canal, Iran, for the simulation process. Water depth and delivery indicators were used for evaluating the performance of the developed models. The results revealed that the FSL and ACO methods offered almost the same performance for the normal operation condition with high and acceptable indicators. However, the FSL method outperformed the ACO method in terms of performance in three considered emergency operations. It can be concluded that the FSL, as a new method, can schedule water delivery efficiently, adequately, equitably, and dependably. Furthermore, the FSL method is likely to lead to less maximum absolute error (MAE) and integral of absolute magnitude of Error (IAE) in comparison to the ACO method and is therefore recommended.

A survey of scheduling problems with setup times or costs

The maintenance scheduling problem of thermal generators is a large-scale combinatorial optimization with constraints. In this paper an Ant Colony System (ACS) algorithm, one of the Ant Colony Optimization (ACO) algorithms, is proposed for the maintenance scheduling problem. This ant colony optimization method allows the “agents” of an ant colony to deposit a small amount of pheromone trail to every path that has been explored, thus passing on to the other agents the information concerning the best solution. With the iterations we construct the final solution. This method is called “positive feedback”. The basic optimization routine is reinforced with the introduction of elitist ants who make the best solution stronger. The algorithm is applied to a real-scale system, and further experimenting leads to results that are commented.

Mixed integer goal programming models for the flexible job-shop scheduling problems with separable and non-separable sequence dependent setup times

Ant Colony Optimization is the optimization method used for the analysis of the cluster. This method performs clustering data well. This method has limitations on computation time. This paper proposed a method of pattern reduction enhanced ant colony optimization to resolve the problem. Functions reduction pattern used to bypass loop system. This function ensures pheromone value if it meets certain iteration, so the next ant agent definitely choose the path that has been determined. The test is done through testing parameters and test accuracy. accuracy test compared with ant colony optimization method(ACO) with the results of the proposed method has the advantage of computing time. The test results show that the proposed method has 46 second faster than ACO method (73 second) for iris dataset, 141 second faster than ACO method (272 second) for wine dataset, and 150 second faster than ACO method (204 second) for synthetic dataset.

Ant colony optimization (ACO) methods, which imitate a mechanism of pheromone secretion when ants carry food to their nest, are one of efficient heuristic search methods for combinational optimization problems such as traveling salesman problems (TSPs) and so on. In this paper, we analyze the queen ant strategy AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> that is one of ACO methods more in detail by applying it to six kinds of city configurations included in the TSPLIB. Furthermore, in order to improve searching ability of the AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> , we propose a new method named "stimulative queen ant strategy AS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> ". As experimental results, we have clarified that the AS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> shows better performance than the conventional AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> in the viewpoint of both "discovery rate of optimal solution" and "average number of iterations"

Energy sources that are often used to generate electricity are non-renewable energy sources which, if used continuously, will run out, such as petroleum, natural gas and coal. So, renewable energy sources are needed which are in abundant supply and do not run out quickly, one of which is water energy. Picohydro power plants (PLTPH) are an alternative small-scale power plant that can be applied in rural areas where there is a river flow that has a continuous water discharge and a relatively low water fall to drive turbines that can produce electrical power. To be able to produce electrical power with such potential, a pico-hydro power plant is needed. To optimize the performance of a picohydro power plant, a controller called PID (Proportional Integral Derivative) is needed. Then this PID is combined with the ACO (Ant Colony Optimization) method. ACO is a method for optimizing PID control parameters in a system adapted from the ability of an ant colony to find the shortest path to a food source from its nest

This paper deals with the NP-hard single-machine total weighted tardiness problem with sequence dependent setup times. Incorporating fuzzy sets and genetic operators, a novel ant colony optimization algorithm is developed for the problem. In the proposed algorithm, artificial ants construct solutions as orders of jobs based on the heuristic information as well as pheromone trails. To calculate the heuristic information, three well-known priority rules are adopted as fuzzy sets and then aggregated. When all artificial ants have terminated their constructions, genetic operators such as crossover and mutation are applied to generate new regions of the solution space. A local search is then performed to improve the performance quality of some of the solutions found. Moreover, at run-time the pheromone trails are locally as well as globally updated, and limited between lower and upper bounds. The proposed algorithm is experimented on a set of benchmark problems from the literature and compared with other metaheuristics.

Ant Colony Optimisation (ACO) is a meta-heuristic method that has drawn much interest in solving challenging optimisation issues. This paper examines the use of ACO to reduce the time spent on moving goods and people in Covenant University's transportation system. Covenant University is a private Christian university in Ota, Ogun State, Nigeria. Covenant University started on 21st October 2002, and since its opening, it has grown to be one of the world-class universities that have been ranked and recognised all around the globe. The data was collected from an article that computed the time per unit distance between origins and destinations within the university using Google Maps. The three traditional transportation techniques (Northwest corner method, least cost method, and Vogel's approximation method) alongside Ant colony meta-heuristics were used to get the initial basic feasible solution, which gave (361.3, 361.7, 359.8, 361.7) minutes, respectively. Then, the MODI approach was used to find the optimal solution (346.6 minutes). Also, the R programming language was used to run the Codes for the three traditional methods and the ACO method, which gave the result of solving manually. The optimal solution showed that if the time spent is reduced, the movement of people and goods would be enhanced, resulting in more time to handle important matters.

Having analyzed studies on the assembly line balancing problem considering setup times, a few papers are observed. However, researches show that setup time plays an important role in total production time. Besides, in these studies, the setup times are assumed to be deterministic. Considering setup times to be deterministic decreases proximity of problem to real life application. Because the operation and setup times may vary in real applications due to human factors, machine breakdowns, lack of equipment and environmental factors. Therefore, in this study, assembly line balancing problem is investigated considering the sequence-dependent stochastic setup times. A heuristic approach based on COMSOAL is presented for the problem and it is seen that algorithm is effective for the problem.

A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK(P)), the probability of assigning true alleles (AK(G)) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A(-1)). Comparisons of these two methods showed that ACO yielded an increase in AK(P) ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A(-1), with different pedigree sizes and structures.

This thesis considers the scheduling challenges encountered at a particular facility in the nuclear industry. The scheduling problem is modelled as a variant of the job shop scheduling problem. Important aspects of the considered problem include the scheduling of jobs with both soft and hard due dates, and the integration of maintenance planning with job scheduling. Two variants of the scheduling problem are considered: The first variant makes the classic job shop assumption of infinite queueing capacity at each machine, while such queueing capacity is non-existent in the second variant. Without queueing capacity, the scheduling problem is a variant of the blocking job shop problem. For the non-blocking variant of the problem, it is shown that good solutions can be obtained quickly by hybridising a novel Ant Colony Optimisation method with a novel Branch and Bound method. For the blocking variant of the problem, it is shown that a novel Branch and Bound method can rapidly find optimal solutions. This Branch and Bound method is shown to provide good performance due to, amongst other things, a novel search strategy and a novel branching strategy.

A hybrid heuristic to solve the parallel machines job-shop scheduling problem

Multi-product production systems with sequence-dependent setup times are typical in manufacturing of semiconductor chips and other electronic products. In such systems, the scheduling policies to coordinate the production of multiple product types play an important role. In this paper, we study a multi-product manufacturing system with finite buffers, sequence-dependent setup times and various scheduling policies. Using continuous time Markov chain models, we evaluate the performance of such systems under seven scheduling policies, i.e., cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing time), longest queue, longest processing time, and longest overall time. The impact of these policies on systemthroughput are compared, and the conditions characterizing the superiority of each policy are investigated. The results of this work can provide production engineers and supervisors practical guidance to operate multi-product manufacturing systems with sequence-dependent setups.