Machine Workload Research Articles

Comparative analysis of two hierarchical methods of assembly planning for producers of electric and electronic equipment

The two two-level methods of assembly planning for producers of electric and electronic equipment are compared. The methods are constructed for flexible assembly lines with parallel machines. At the upper level of the prepared methods, a task is solved for balancing machine workload (in the method I) or a task for balancing assembly stages workload (in the method II). Simultaneously, only one assembly plan is selected for each product type. At the lower level, assembly tasks are scheduling. The results of calculation experiments are presented.

Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem

Hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO) and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP). Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.

Multiobjective Flexible Job Shop Scheduling Using A Modified Invasive Weed Optimization

Recently, many studies are carried out with inspirations from ecological phenomena for developing optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems

Firefly algorithm (FA) is a nature-inspired optimisation algorithm that can be successfully applied to continuous optimisation problems. However, lot of practical problems are formulated as discrete optimisation problems. In this paper a hybrid discrete firefly algorithm (HDFA) is proposed to solve the multi-objective flexible job shop scheduling problem (FJSP). FJSP is an extension of the classical job shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. Three minimisation objectives - the maximum completion time, the workload of the critical machine and the total workload of all machines are considered simultaneously. This paper also proposes firefly algorithms discretisation which consists of constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. In the proposed algorithm discrete firefly algorithm (DFA) is combined with local search (LS) method to enhance the searching accuracy and information sharing among fireflies. The experimental results on the well-known benchmark instances and comparison with other recently published algorithms shows that the proposed algorithm is feasible and an effective approach for the multi-objective flexible job shop scheduling problems.

New Scheduling Algorithm for Shipping Operation in Steel Works

This paper proposes a new scheduling algorithm for shipping operations in steel works. In the shipping operation, steel products are handled by various kinds of machines, for example, overhead cranes in warehouses, special vehicles, forklifts and quay cranes. The operations of these machines affect each other and the situation makes the scheduling problem quite complicated. The proposed algorithm deals with shipping facilities which include warehouse buildings with plural cranes. Because some areas are accessible by plural cranes in the buildings, the movement of each crane is strictly restricted by others to prevent the collisions of cranes. The new algorithm is composed of two main steps to obtain efficient schedules and achieve short computation time. The first step assigns shipping jobs to time windows which are created by dividing the scheduling horizon, and then levels the workload of machines. The next step determines the order and operating time of jobs and develops a feasible schedule. The scheduling algorithm was applied to practical data and compared with real operations in this paper. The result shows the high performance of the new algorithm.

An Effective Hybrid Particle Swarm Optimization for Flexible Job Shop Scheduling Problem

The job shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. This paper proposed a new effective approach based on the hy- bridization of the particle swarm optimization (PSO) and local search algorithm of variable neighborhood search (VNS) to solve the FJSP for minimizing the makespan, the maximal machine workload, and the total workload of machines. PSO is a highly efficient evolutionary computation technique inspired by bird's flight and communication behaviors. PSO inte- grating the local search and global search has highly search ability. VNS has the strong local search ability. Benchmark problems are used to evaluate and study the performance of the proposed algorithm. Computational results show that the proposed hybrid algorithm is an efficient and effective approach.

An Improved Immune Algorithm for Multi-objective Flexible Job-shop Scheduling

Flexible job-shop scheduling problem derived from the job-shop scheduling problem, and is more realistic than the job-shop scheduling problem in the manufacturing enterprise. It should be utilized with high efficiency. But it is a multi-objective optimal problem. Three minimization objectives (Maximum completion time (makespan), Total workload of machines and Workload of the critical machine) are considered simultaneously. This multi-objective flexible job-shop scheduling problem is one of the hardest combinatorial optimization problems, and is a strongly NP-hard problem. To solve the multi-objective flexible job-shop scheduling problem, a quantum immune algorithm based on the quantum and immune principles is proposed. According to the analysis of the experimental results, this algorithm is feasible and effective to solve the multi-objective flexible job-shop scheduling problem. It effectively relieves some disadvantages of the quantum and immune optimization

A Novel Quantum Immune Algorithm for Multiobjective Flexible Job Shop Scheduling

In this study, a hybrid of Quantum Evolutionary and Artificial Immune Algorithms (QIA) is proposed for solving Multiobjective Flexible Job Shop Scheduling Problem (MFJSSP). This problem is formulated as three-objective problem which minimizes completion time (makespan), critical machine workload and total work load of all machines. The quantum coding is shown to improve the immune strategy. The proposed algorithm overcomes the problem by increasing the speed of convergence and diversity of population. Three benchmarks of Kacem and Brandimart are examined to evaluate the performance of the proposed algorithm. The experimental results show a better performance in comparison to other approaches.

A new 0-1 linear programming approach and genetic algorithm for solving assignment problem in flexible manufacturing system

Flexible manufacturing system (FMS) consists of some multi-functional machines that are linked together through a material-handling system and the whole of the system is controlled by a central computer. One of the most important problems in production planning of FMS is machine–tool selection and operation allocation problem that directly influences the production costs and time. This paper presents a 0-1 integer linear programming model for the multi-objective machine–tool selection and operation allocation problem in FMS. The proposed model attempts to simultaneously minimize machining cost, material-handling cost, setup cost, and maximum machine work load time and tool life. We also consider the constraints of tool magazine capacity, tool life, and machine capacity in the problem. Due to the nondeterministic polynomial (NP)-hard nature of the derived problem, a genetic algorithm is developed to solve the model. Computational results indicate the effectiveness of the proposed algorithm for solving the problem.

Towards Automated Provisioning and Emergency Handling in Renewable Energy Powered Datacenters

Designing eco-friendly system has been at the forefront of computing research. Faced with a growing concern about the server energy expenditure and the climate change, both industry and academia start to show high interest in computing systems powered by renewable energy sources. Existing proposals on this issue mainly focus on optimizing resource utilization or workload performance. The key supporting hardware structures for cross-layer power management and emergency handling mechanisms are often left unexplored. This paper presents GreenPod, a research framework for exploring scalable and dependable renewable power management in datacenters. An important feature of GreenPod is that it enables joint management of server power supplies and virtualized server workloads. Its interactive communication portal between servers and power supplies allows datacenter operators to perform real-time renewable energy driven load migration and power emergency handling. Based on our system prototype, we discuss an important topic: virtual machine (VM) workloads survival when facing extended utility outage and insufficient onsite renewable power budget. We show that whether a VM can survive depends on the operating frequencies and workload characteristics. The proposed framework can greatly encourage and facilitate innovative research in dependable green computing.

A Hybrid Discrete Firefly Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems with Maintenance Activity

This paper presents a novel hybrid discrete firefly algorithm (HDFA) for solving the multi-objective flexible job shop scheduling problem with non fixed availability constraints (FJSP-nfa) due to maintenance activity. Three minimization objectives-the maximum completion time, the workload of the critical machine and the total workload of all machines are considered simultaneously. In this study, the discrete firefly algorithm is adopted to solve the problem, in which the machine assignment and operation sequence are processed by constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. In addition the decoding mechanism considering the maintenance activity is presented. A neighbourhood based local search is hybridized to enhance the exploitation capability. Representative benchmark problems are solved in order to evaluate and study the performance of the proposed algorithm.

A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints

In this paper, a hybrid discrete firefly algorithm is presented to solve the multi-objective flexible job shop scheduling problem with limited resource constraints. The main constraint of this scheduling problem is that each operation of a job must follow a process sequence and each operation must be processed on an assigned machine. These constraints are used to balance between the resource limitation and machine flexibility. Three minimisation objectives—the maximum completion time, the workload of the critical machine and the total workload of all machines—are considered simultaneously. In this study, discrete firefly algorithm is adopted to solve the problem, in which the machine assignment and operation sequence are processed by constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. Meanwhile, local search method with neighbourhood structures is hybridised to enhance the exploitation capability. Benchmark problems are used to evaluate and study the performance of the proposed algorithm. The computational result shows that the proposed algorithm produced better results than other authors’ algorithms.

Low-carbon scheduling and estimating for a flexible job shop based on carbon footprint and carbon efficiency of multi-job processing

With the enhancement of people’s environmental awareness, low-carbon and energy efficiency in manufacturing industry have been drawing much attention due to the huge consumption of raw materials and energy during machining processes. But as one of the approaches to reduce carbon emission, manufacturing shop scheduling strategies have historically emphasized the makespan, machine workload, and so on and neglected energy and environmental factors in most cases. This article presents a model of low-carbon scheduling of the flexible job shop, which considers both factors of production (i.e. makespan and machine workload) and environmental influence (i.e. carbon emission). A carbon footprint model of multi-job processing is established to quantify the carbon emission of different scheduling plans, and three carbon efficiency indicators are put forward to estimate the carbon emission of parts and machine tools, that is, processing carbon efficiency, part carbon efficiency, and machine tool carbon efficiency. To solve the proposed model, a hybrid non-dominated sorting genetic algorithm II which combines the original non-dominated sorting genetic algorithm II with a local search algorithm based on neighborhood search is proposed. Finally, test of some well-known benchmark instances is carried out to verify the effectiveness of the proposed algorithm, and an actual case is studied to demonstrate the feasibility and applicability of the proposed model.

Workload balancing in identical parallel machine scheduling using a mathematical programming method

AbstractThis paper addresses the workload balancing problem in identical parallel machines context. The problem consists of assigning n different jobs to m identical parallel machines in order to minimize the workload imbalance among the different machines. This problem is formulated as a linear mixed integer program to minimize the difference between the greatest and smallest workload assigned to each machine. Based on some numerical examples reported in the literature, we establish that the classical formulation which consists of minimizing the greatest machine completion time does not provide the optimal workload repartition. That is why we consider a new mathematical formulation based on the minimization of the difference between the workload of the bottleneck machine and the workload of the fastest machine. The proposed programming method is also used to provide optimal solutions in reasonable computational times for different test problems presented in the literature by Raghavendra and Murthy 10 to ...

Cloud Model Evolutionary Multi-objective Flexible Job-shop Scheduling Based on Improved Non-dominated Sorting

A multi-objective evolutionary algorithm is applied to research the flexible job-shop scheduling problem, which sets maximum makespan, the total workload of machines and maximum workload of machine as optimization goals. Aiming at improving performance of NSGA- at distinguishing and comparing nonⅡ-dominated individuals, a modified non-dominated sorting algorithm is designed so that it can distinguish non-dominated individuals rapidly, eliminate dominated individuals and enhance the conformation efficiency of non-dominated sets. Combining the characteristics of flexible job-shop scheduling problem and properties of evolutionary algorithm, and introducing evolutionary strategy based on cloud model, a multi-objective flexible job-shop scheduling algorithm based on improved non-dominated sorting is proposed. Applying the excellent characteristics of cloud model in both fuzziness and randomness to maintain evolutionary populations and ameliorate the distribution breadth and uniformity of non-dominated solution. Making use of multi-attribute decision model based on weighted grey target to select the most satisfied schedule. Using the proposed algorithm to test the benchmark problems and analyze the results, and testified its feasibility and effectiveness. The proposed algorithm is applied to a real production case study, and the most satisfied schedule is generated.

A Comparison of Artificial Bee Colony algorithm and Genetic Algorithm to Minimize the Makespan for Job Shop Scheduling

Job shop scheduling is predominantly an Non deterministic polynomial (NP)- complete challenge which is successfully tackled by the ABC algorithm by elucidating its convergence. The Job Shop Scheduling Problem (JSSP) is one of the most popular scheduling models existing in practice which is among the hardest combinatorial optimization problems. The ABC (Artificial Bee Colony) technique is concerned, it is observed that the entire specific artificial bees move about in a search space and select food sources by suitably adapting their location, know-how and having a full awareness of their nest inhabitants. Moreover, several scout bees soar and select the food sources discretely without making use of any skills. In the event of the quantity of the nectar in the fresh source becoming larger than the nectar quantity of an available source, they remember the fresh location and conveniently disregard the earlier position. In this way, the ABC system integrates local search techniques, executed by employed and onlooker bees, with universal search approaches, administered by onlookers and scouts. In our ambitious approach we have employed these three bees to furnish optimization in makespan, machine work load and the whole run period in an optimized method. In this way, with the efficient employment of our effective technique we make an earnest effort to minimize the makespan and number of machines. This paper compares GA to minimize the make span of the job scheduling process with ABC and proved that ABC algorithm produces the better result.

An Effective Genetic Algorithm for Multi-objective Integrated Process Planning and Scheduling with Various Flexibilities in Process Planning

Process planning and scheduling are two of the most important functions in modern manufacturing system. Considering their complementarity, integrating them more tightly can improve the performance and productivity of the whole manufacturing system. Meanwhile, the multi-objective optimization problem is widespread existing in manufacturing system. In this paper, an effective genetic algorithm is proposed to optimize the multi-objective integrated process planning and scheduling (IPPS) problem with various flexibilities in process planning. Three types of flexibilities related to process, sequence and machine are considered. And three objectives including makespan, total machine workload and maximal machine workload are taken into account simultaneously. According to the model and characteristics of multi- objective IPPS, the framework of the proposed algorithm is designed to optimize three objectives simultaneously. Effective genetic operations are employed in the proposed algorithm. Pareto set is set to store and maintain the solutions obtained during the searching procedure, the proposed algorithm could get several Pareto optimal solutions during one searching process. Two experiments are employed to test the performance of the proposed algorithm. The experiment results show that the proposed algorithm can solve multi-objective IPPS problem with various flexibilities in process planning effectively and obtain good solutions.

A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities

This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.

Black box scheduling for resource intensive virtual machine workloads with interference models

Modern datacenters consist of increasingly powerful hardware. Achieving high levels of utilization on this hardware often requires the execution of multiple concurrent workloads. Virtualization has emerged as an efficient means to isolate workloads by partitioning large physical resources using self-contained virtual machine images. Despite the many advantages, some challenges regarding performance isolation still need to be addressed. Unmanaged multiplexing of resource intensive workloads has the potential to cause unexpected variances in workload performance.In this paper, we address this issue using performance models based on the runtime characteristics of virtualized workloads. A set of resource intensive workloads is benchmarked with increasing degrees of multiplexing. Resource usage profiles are constructed using the metrics made available by the Xen hypervisor. Based on these profiles, performance degradation is predicted using several existing modeling techniques. In addition, we propose a novel approach using both the classification and regression capabilities of support vector machines. Application clustering is used to identify several application types with distinct performance profiles. Finally, we evaluate the developed performance models by introducing several new scheduling techniques. We demonstrate that the integration of these models in the scheduling logic can significantly improve the overall performance of multiplexed workloads.

An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

In this paper, we present a new Imperialist Competitive Algorithm (ICA) to solve a bi-objective unrelated parallel machine scheduling problem where setup times are sequence dependent. The objectives include mean completion time of jobs and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA) method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO), original version of imperialist competitive algorithm (OICA) and genetic algorithm (GA) in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better.