A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems

Lu, Po Hsiang; Wu, Muh Cherng; Tan, Hao; Peng, Yong; Chen, Chen Fu

doi:10.1007/s10845-015-1083-z

Cited by 78 publications

(25 citation statements)

References 32 publications

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“…There is a growing interest in the literature to study scheduling problems in cellular environment. In recent years, some researchers have studied the distributed and flexible job shop scheduling (DFJS), which involves the scheduling of jobs in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop (Giovanni and Pezzella, ; Ziaee, ; Chang and Liu, ; Lu et al, ). In this paper, the term FJCS is preferred to DFJS in introducing the cell scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

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In this paper, we propose novel mathematical models involving both single‐ and biobjective functions that deal with a flexible job shop scheduling problem in cellular manufacturing environment by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence‐dependent family setup times, and recirculation. The problem has been known as NP‐hard. The proposed models have been tested and solved using Lingo 11.0 with minimization of makespan for the problems involving about 4 cells, 4 part families, 15 parts, and 12 machines. The most suitable model among the proposed single‐objective models is determined using the test results. Then, another objective function as total tardiness is added to this model. The obtained biobjective model is solved using the scalarization methods, the weighted sum method, ɛ‐constraint method, and conic scalarization method (CSM), in order to convert the mathematical model's objectives into a single‐objective function. By utilizing these scalarization methods, the Pareto effective solutions are generated for a specific test problem. The advantages of the CSM are demonstrated by considering the Pareto effective solutions.

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Section: Introductionmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

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“…Similar to the classic flexible job shop problem [29], [30], the formulation of our VNF scheduling model can be decomposed into four subproblems. We first solve a virtual link bandwidth allocation sub-problem to assign for each virtual link a feasible data rate.…”

Section: Ga-based Heuristic Methods For Vnf Schedulingmentioning

confidence: 99%

“…(26)- (29) determine a relationship between the link bandwidth (z m ) and routing variable y p ijmr , for capacity allocation. (30) and (31) enforce that incoming flows and outgoing flows to a VM do no exceed the capacity limit of the available input and output capacities of the VM.…”

Section: Vnf Scheduling With Dynamic Bandwidth Allocationmentioning

confidence: 99%

Delay-Aware Scheduling and Resource Optimization With Network Function Virtualization

Assi

Shaban

2016

IEEE Trans. Commun.

207

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To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently, the concept of Network Function Virtualization (NFV) has emerged and became a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a softwareoriented approach for Virtual Network Functions (VNFs) creates more flexible and dynamic network services to meet a more diversified demand. Software-oriented network functions bring along a series of research challenges, such as, VNF management and orchestration, service chaining, VNF scheduling for low latency and efficient virtual network resource allocation with Network Function Virtualization Infrastructure (NFVI), among others. In this paper, we study the VNF scheduling problem and the corresponding resource optimization solutions. Here, the VNF scheduling problem is defined as a series of scheduling decisions for network network services on network functions and activating the various VNFs to process the arriving traffic. We consider VNF transmission and processing delays, and formulate the joint problem of VNF scheduling and traffic steering as a Mixed Integer Linear Program (MILP). Our objective is to minimize the makespan/latency of the overall VNFs' schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, and cater to services with stringent delay requirements, thereby increasing operators' revenues. Owing to the complexity of the problem, we develop a Genetic Algorithm (GA) based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical evaluation. We show that dynamically adjusting the bandwidths on virtual links connecting virtual machines, hosting the network functions, reduces the schedule makespan by 15%-20% in the simulated scenarios.

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“…According to the value of rand, the LMOX cross operation is carried out between the current i-th grey wolf individual and the α wolf, β wolf, and δ wolf, which is an arbitrarily selected ω wolves individual, respectively. The formula is shown in Equation (18). The pseudocode for the core part of the LMOX cross operation is outlined in Algorithm 4.…”

Section: Predatory Behavior Search Strategymentioning

confidence: 99%

Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory

Dong

2019

Applied Sciences

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Production scheduling of semiconductor wafer manufacturing is a challenging research topic in the field of industrial engineering. Based on this, the green manufacturing collaborative optimization problem of the semiconductor wafer distributed heterogeneous factory is first proposed, which is also a typical NP-hard problem with practical application value and significance. To solve this problem, it is very important to find an effective algorithm for rational allocation of jobs among various factories and the production scheduling of allocated jobs within each factory, so as to realize the collaborative optimization of the manufacturing process. In this paper, a scheduling model for green manufacturing collaborative optimization of the semiconductor wafer distributed heterogeneous factory is constructed. By designing a new learning strategy of initial population and leadership level, designing a new search strategy of the predatory behavior for the grey wolf algorithm, which is a new swarm intelligence optimization algorithm proposed in recent years, the diversity of the population is expanded and the local optimum of the algorithm is avoided. In the experimental stage, two factories’ and three factories’ test cases are generated, respectively. The effectiveness and feasibility of the algorithm proposed in this paper are verified through the comparative study with the improved Grey Wolf Algorithms—MODGWO, MOGWO, the fast and elitist multi-objective genetic algorithm—NSGA-II.

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A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems

Cited by 78 publications

References 32 publications

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Delay-Aware Scheduling and Resource Optimization With Network Function Virtualization

Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory

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