Using A Bee Colony Algorithm For Neighborhood Search In Job Shop Scheduling Problems

Chong, Chin Soon; Low, Malcolm Yoke Hean; Sivakumar, Appa Iyer

doi:10.7148/2007-0459

Cited by 19 publications

(12 citation statements)

References 20 publications

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“…CHONG used a bee colony optimization algorithm to Job Shop scheduling [12,13,14]. ALGORITHMS Wang and Zhen found a better solution than other methods available using GASA algorithm depending on advanges of GA and SA [15].CHONG etal solve JSSP using bee colony algorithm combining with neighborhood search [16]. Kacem I proposed an optimal methods based on multiobjective evolutionary combing with local search [17].…”

Section: Afinding Solution Of Jssp Using a Heuristics Algorithmsmentioning

confidence: 99%

Heuristics Algorithms for Job-Shop Scheduling Problem: Critical Element Analysis

Sun

Wang

Yun-Cai

2012

2012 International Conference on Computer Science and Service System

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Solving JSSP is an intractable subject. It has great significance in improving productive efficiencies for manufacture enterprise. In recent decades, designing algorithms for solving problem of JSSP problem become an important issue. Moreover, it is difficult to find an appropriate criteria to evaluate efficiency of algorithms available with respect to solving JSSP . Various algorithms for solving JSSP are listed , and their advantages and disadvantages are also indicated. At the same time, three critical element for designing algorithms for JSSP were proposed. Finally , several future research trends of solving jobshop problems and proposals for the future research work are summarized and presented respectively.

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Section: Afinding Solution Of Jssp Using a Heuristics Algorithmsmentioning

confidence: 99%

Heuristics Algorithms for Job-Shop Scheduling Problem: Critical Element Analysis

Sun

Wang

Yun-Cai

2012

2012 International Conference on Computer Science and Service System

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“…Vassiliadis and Dounias (2008) Modelling process and supply chain scheduling Banarjee et al (2008) Job shop scheduling Chong et al (2006) Job shop scheduling Chong et al (2007) Fuzzy bee system Teodorovic et al (2006) Reaction-diffusion model Pure model Tereshko (2000) Information-mapping patterns Tereshko and Lee (2002) Dynamic system model Tereshko and Loengarov (2005) Phase transitions and bistability model Tereshko (2008) Foraging model Ghosh andMarshall (2005) Honeybee search strategies Routing and congestion avoidance in Internet services Walker (2004) BeeAdhoc Routing in mobile ad hoc networks Wedde and Farooq (2005c); Wedde et al (2005) BeeSec Tackle with the disruptions of malicious nodes in an untrusted MANET Mazhar and Farooq (2007) BeeAIS Security in the challenging MANET Saleem and Farooq (2007) BeeAIS-DC Security of manets Mazhar and Farooq (2008) BeeAdhoc Two performance metrics for beeadhoc Saleem et al (2008) BeeSensor=beeAdhoc + beeHive Routing in networks Saleem and Farooq (2007) Artificial bee colony (ABC) algorithm Numerical problems Karaboga (2005) Ozturk and Karaboga (2008). TSP Fenglei et al (2007) Leaf-constrained minimum spanning tree (LCMST) problem…”

Section: Fig 1 Distribution Of Publications With Respect To Yearsmentioning

confidence: 99%

“…Chong et al (2006) described a bee colony optimization algorithm based on foraging and waggle dance and using dance durations to select a new path and the algorithm was applied to job shop scheduling. Chong et al (2007) utilized an efficient neighborhood structure to search for feasible solutions and iteratively improve on prior solutions. The initial solutions are generated using a set of priority dispatching rules.…”

Section: Collective Decision and Nest Site Selectionmentioning

confidence: 99%

A survey: algorithms simulating bee swarm intelligence

2009

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Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.

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“…In its basic version, the algorithm performs a kind of neighbourhood search combined with random search. Advanced mechanisms could be guided by genetics [57] or taboo operators [58]. The standard Bees Algorithm first developed in Pham and Karaboga in 2006 [59, 60] requires a set of parameters: no.…”

Section: Bees Algorithm (Ba) Approachmentioning

confidence: 99%

Production Scheduling Approaches for Operations Management

Fera¹,

Fruggiero²,

Lambiase³

et al. 2013

Operations Management

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This research examines the main characteristics of the most promising meta-heuristic approaches for the general process of a Job Shop Scheduling Problems (i.e., JSSP). Being a NP complete and highly constrained problem, the resolution of the JSSP is recognized as a key point for the factory optimization process [4]. The chapter examines the soundness and key contributions of the 7 meta-heuristics (i.e., Genetics Approaches, Ants Colony Optimization, Bees Algorithm, Electromagnetic Like Algorithm, Simulating Annealing, Tabu Search and Neural Networks), those that improved the production scheduling vision. It reviews their accomplishments and it discusses the perspectives of each meta approach. The work represents a practitioner guide to the implementation of these meta-heuristics in scheduling job shop processes. It focuses on the logic, the parameters, representation schemata and operators they need. 2. The job shop scheduling problem The two key problems in production scheduling are "priorities" and "capacity". Wight (1974) described scheduling as "establishing the timing for performing a task" and observes that, in 1 The etymology of the word heuristic derives from a Greek word heurìsco (єΰρισκω)-it means "to find"-and is considered the art of discovering new strategy rules to solve problems. Heuristics aims at a solution that is "good enough" in a computing time that is "small enough". 2 The term metaheuristc originates from union of prefix meta (μєτα)-it means "behind, in the sense upper level methodology"-and word heuristic-it means "to find". Metaheuristcs' search methods can be defined as upper level general methodologies guiding strategies in designing heuristics to obtain optimisation in problems.

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Using A Bee Colony Algorithm For Neighborhood Search In Job Shop Scheduling Problems

Cited by 19 publications

References 20 publications

Heuristics Algorithms for Job-Shop Scheduling Problem: Critical Element Analysis

Heuristics Algorithms for Job-Shop Scheduling Problem: Critical Element Analysis

A survey: algorithms simulating bee swarm intelligence

Production Scheduling Approaches for Operations Management

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