scholarly journals Parallel machines scheduling with time-dependent deterioration, using meta-heuristic algorithms

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Jaber Kalaki Juybari ◽  
Somayyeh Kalaki Juybari ◽  
Reza Hasanzadeh
Keyword(s):  
Large Scale ◽  
Processing Time ◽  
Parallel Machines ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Time Dependent ◽  
Mixed Integer ◽  
Small Scale ◽  
Simulating Annealing ◽  
Parallel Machines Scheduling

AbstractIn this paper, we consider the identical parallel machines scheduling problem with exponential time-dependent deterioration. The meaning of time-dependent deterioration is that the processing time of a job is not a constant and depends on the scheduled activities. In other words, when a job is processed later, it needs more processing time compared to the jobs processed earlier. The main purpose is to minimize the makespan. To reach this aim, we developed a mixed integer programming formulation for the problem. We solved problem in small scale using GAMS software, while due to the fact that in larger scales the aforesaid case is a complex and intricate optimized problem which is NP-hard, it is not possible to solve it by standard calculating techniques (in logical calculating times); we applied the meta-heuristic genetic algorithm, simulating annealing and artificial immune system, and their performance has been evaluated. In the end, we showed that solving the problem in small scale, with the meta-heuristic algorithms (GA, SA, and AIS) equals the optimal solution (GAMS), And on a large scale, at a time of approximately equal solution, meta-heuristic algorithm simulating annealing, provides a more optimal solution.

scholarly journals Fairness Time-Slot Allocation and Scheduling with QoS Guarantees in Multihop WiMAX Mesh Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Chien-Yu Wu ◽  
Hann-Jang Ho ◽  
Sing-Ling Lee ◽  
Liang Lung Chen
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Mesh Networks ◽  
Time Slot ◽  
Optimal Solution ◽  
Small Scale ◽  
Scheduling Problem ◽  
Slot Allocation ◽  
Long Distance ◽  
Time Slot Allocation

The WiMAX technology has been defined to provide high throughput over long distance communications and support the quality of service (QoS) control applied on different applications. This paper studies the fairness time-slot allocation and scheduling problem for enhancing throughput and guaranteeing QoS in multihop WiMAX mesh networks. For allocating time slots to multiple subscribe stations (SSs), fairness is a key concern. The notion of max-min fairness is applied as our metric to define the QoS-based max-min fair scheduling problem for maximizing the minimum satisfaction ratio of each SS. We formulate an integer linear programming (ILP) model to provide an optimal solution on small-scale networks. For large-scale networks, several heuristic algorithms are proposed for better running time and scalability. The performance of heuristic algorithms is compared with previous methods in the literatures. Experimental results show that the proposed algorithms are better in terms of QoS satisfaction ratio and throughput.

scholarly journals Two meta-heuristic algorithms for optimizing a multi-objective supply chain scheduling problem in an identical parallel machines environment

2021 ◽  
Vol 12 (3) ◽  
pp. 249-272 ◽  
Author(s):  
Nima Farmand ◽  
Hamid Zarei ◽  
Morteza Rasti-Barzoki
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Parallel Machines ◽  
Heuristic Algorithms ◽  
Small Scale ◽  
Scheduling Problem ◽  
Identical Parallel Machines ◽  
Nsga Ii ◽  
Multi Objective ◽  
Large Scale Problems

Optimizing the trade-off between crucial decisions has been a prominent issue to help decision-makers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a non-dominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria.

Uniform Parallel Machines Scheduling with Setup Time, Learning Effect, Machine Idle Time, and Processing Set Restrictions to Minimize Earliness/Tardiness Costs

2021 ◽  
pp. 107-133
Author(s):  
Javad Rezaeian ◽  
Keyvan Shokoufi ◽  
Reza Alizadeh Foroutan
Keyword(s):  
Parallel Machines ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Setup Time ◽  
Mixed Integer ◽  
Computational Time ◽  
Just In Time ◽  
Large Size ◽  
Parallel Machines Scheduling ◽  
Dependent Setup Times

Inspired by a real industrial case, this study deals with the problem of scheduling jobs on uniform parallel machines with past-sequence-dependent setup times to minimize the total earliness and tardiness costs. The paper contributes to the existing literature of uniform parallel machines problems by the novel idea of considering position-based learning effects along with processing set restrictions. The presented problem is formulated as a Mixed Integer linear programming (MILP) model. Then, an exact method is introduced to calculate the accurate objective function in the just-in-time (JIT) environments for a given sequence of jobs. Furthermore, three meta-heuristic approaches, (1) a genetic algorithm (GA), (2) a simulated annealing algorithm (SA), and (3) a particle swarm optimization algorithm (PSO) are proposed to solve large size problems in reasonable computational time. Finally, computational results of the proposed meta-heuristic algorithms are evaluated through extensive experiments and tested using ANOVA followed by t-tests to identify the most effective meta-heuristic.

scholarly journals Multidepot Heterogeneous Vehicle Routing Problem for a Variety of Hazardous Materials with Risk Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bochen Wang ◽  
Qiyuan Qian ◽  
Zheyi Tan ◽  
Peng Zhang ◽  
Aizhi Wu ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Programming Model ◽  
Hazardous Materials ◽  
Mixed Integer ◽  
Small Scale ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem

This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

scholarly journals Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing

2020 ◽  
Vol 12 (6) ◽  
pp. 2177
Author(s):  
Jun-Ho Huh ◽  
Jimin Hwa ◽  
Yeong-Seok Seo
Keyword(s):  
Genetic Algorithm ◽  
Software Architecture ◽  
Large Scale ◽  
Structural Characteristics ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Software Systems ◽  
Small Scale ◽  
Hill Climbing Algorithm ◽  
Architecture Management

A Hierarchical Subsystem Decomposition (HSD) is of great help in understanding large-scale software systems from the software architecture level. However, due to the lack of software architecture management, HSD documentations are often outdated, or they disappear in the course of repeated changes of a software system. Thus, in this paper, we propose a new approach for recovering HSD according to the intended design criteria based on a genetic algorithm to find an optimal solution. Experiments are performed to evaluate the proposed approach using two open source software systems with the 14 fitness functions of the genetic algorithm (GA). The HSDs recovered by our approach have different structural characteristics according to objectives. In the analysis on our GA operators, crossover contributes to a relatively large improvement in the early phase of a search. Mutation renders small-scale improvement in the whole search. Our GA is compared with a Hill-Climbing algorithm (HC) implemented by our GA operators. Although it is still in the primitive stage, our GA leads to higher-quality HSDs than HC. The experimental results indicate that the proposed approach delivers better performance than the existing approach.

scholarly journals Environmental Surveillance Can Dynamically Track Ecological Changes in Enteroviruses

2019 ◽  
Vol 85 (24) ◽  
Author(s):  
Hiroki Ozawa ◽  
Hiromu Yoshida ◽  
Shuzo Usuku
Keyword(s):  
Multidimensional Scaling ◽  
Large Scale ◽  
Local Community ◽  
Amino Acid Sequences ◽  
Time Dependent ◽  
Small Scale ◽  
Dependent Manner ◽  
Environmental Surveillance ◽  
Ecological Changes ◽  
Over Time

ABSTRACT Environmental surveillance can be used to trace enteroviruses shed from human stool using a sewer network that is independent of symptomatic or asymptomatic infection. In this study, the local transmission of enteroviruses was analyzed using two wastewater treatment plants, which were relatively close to each other (15 km), designated as sentinels. Influent was collected at both sentinels once a month from 2013 to 2016, and viruses were isolated. Using neutralizing tests with type-specific polyclonal antisera and molecular typing, 933 isolates were identified as enteroviruses. Our results showed that the frequency of virus isolation varied for each serotype at the two sentinels in a time-dependent manner. Because echovirus 11 (Echo11) and coxsackievirus B5 isolates showed a high frequency and were difficult to distinguish, they were further grouped into various lineages based on the VP1 amino acid sequences. The prevalence of each lineage was visualized using multidimensional scaling. The results showed that Echo11 isolates of the same lineage were isolated continuously, similar to coxsackievirus B5 isolates of three lineages. Conversely, Echo1, Echo13, Echo18, Echo19, Echo20, Echo29, and Echo33 were isolated only once each. Our findings suggested that if an enterovirus is imported into the population, it may result in small-scale transmission, whereas if there are initially many infected individuals, it may be possible for the virus to spread to a wide area, beyond the local community, over time. In addition, our findings could provide insights into risk assessment of transmission for importation of poliovirus in polio-free countries and regions. IMPORTANCE In this study, we showed that environmental enterovirus surveillance can be used to monitor the propagation of nonpolio enteroviruses in addition to poliovirus detection. Since epidemiological studies of virus transmission based on the past were performed using specimens from humans, there were limitations to research design, such as specimen collection for implementation on a large-scale target population. However, environmental monitoring can dynamically track the ecological changes in enteroviruses in the region by monitoring viruses in chronological order and targeting the population within the area by monitoring viruses over time. We observed differences in the transmission of echovirus 11 and coxsackievirus B5 in the region according to lineage in a time-dependent manner and with a multidimensional scaling pattern.

DECOMPOSITION ALGORITHMS FOR THE INTERVAL SCHEDULING PROBLEM

2010 ◽  
Vol 27 (04) ◽  
pp. 517-537 ◽  
Author(s):  
SHIDONG WANG ◽  
LI ZHENG ◽  
ZHIHAI ZHANG
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Integer Program ◽  
Computational Effort ◽  
Small Scale ◽  
Test Problems ◽  
Decomposition Algorithms ◽  
Scheduling Problem ◽  
Interval Scheduling ◽  
Average Percentage

Scheduling track lines at a marshalling station where the objective is to determine the maximal weighted number of trains on the track lines can be modeled as an interval scheduling problem: each job has a fixed starting and finishing time and can only be carried out by an arbitrarily given subset of machines. This scheduling problem is formulated as an integer program, which is NP-Complete when the number of machines and jobs are unfixed and the computational effort to solve large scale test problems is prohibitively large. Heuristic algorithms (HAs) based on the decomposition of original problem have been developed and the benefits lie in both conceptual simplicity and computational efficiency. Genetic algorithm (GA) to address the scheduling problem is also proposed. Computational experiments on low and high utilization rates of machines are carried out to compare the performance of the proposed algorithms with Cplex. Computational results show that the HAs and GA perform well in most condition, especially HA2 with the maximum of average percentage deviation on average 3.5% less than the optimal solutions found by Cplex in small-scale problem. Our methodologies are capable of producing improved solutions to large-scale problems with reasonable computing resources, too.

Application of Mixed-Integer Programming and Dispatching Rules on Parallel Machine Scheduling with Inserted Idle Time

2013 ◽  
Vol 437 ◽  
pp. 748-751
Author(s):  
Chi Yang Tsai ◽  
Yi Chen Wang
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Parallel Machines ◽  
Optimal Solution ◽  
Parallel Machine Scheduling ◽  
Idle Time ◽  
Dispatching Rules ◽  
Mixed Integer ◽  
Scheduling Problems

This research considers the problem of scheduling jobs on unrelated parallel machines with inserted idle times to minimize the earliness and tardiness. The aims at investigating how particular objective value can be improved by allowing machine idle time and how quality solutions can be more effectively obtained. Two mixed-integer programming formulations combining with three dispatching rules are developed to solve such scheduling problems. They can easy provide the optimal solution to problem involving about nine jobs and four machines. From the results of experiments, it is found that: (1) the inserted idle times decreases objective values more effectively; (2) three dispatching rules are very competitive in terms of efficiency and quality of solutions.

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