Exact, Heuristic and Meta-heuristic Algorithms for Solving Shop Scheduling Problems

Zobolas, G. I.; Tarantilis, Christos D.; Ioannou, George

doi:10.1007/978-3-540-78985-7_1

Cited by 24 publications

(18 citation statements)

References 143 publications

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“…Various works have been reported in the literature where different metaheuristics are used to solve specific types of scheduling problems (e.g. job shop, flow shop, and cyclic scheduling problems) [39,51] due to the NP-hard nature of these problems. Concept of job-shop scheduling problem (where jobs may be assigned to different sets of processors at particular times) and parallel machine scheduling (where tasks are assigned to a number of processors) can be classified as a part of UAV scheduling problem [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Task scheduling system for UAV operations in indoor environment

Khosiawan

Park

Moon

et al. 2018

Neural Comput & Applic

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The application of unmanned aerial vehicle (UAV) in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space flexibility in an occupied or hardly accessible indoor environment, e.g. shop floor of manufacturing industry, greenhouse, and nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in a time-efficient manner. Consequently, a scheduler is an essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a mathematical model of the problem and a heuristic-based methodology to solve it. To suit near real-time operations, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with particle swarm optimization algorithm to find a near optimal schedule in a short computation time. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on real flight demonstrations. Performance evaluation of scheduler is discussed in detail, and the best solution obtained from a selected set of parameters is reported.

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Section: Literature Reviewmentioning

confidence: 99%

Task scheduling system for UAV operations in indoor environment

Khosiawan

Park

Moon

et al. 2018

Neural Comput & Applic

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“…The methods used range from exact methods [1], to heuristic [2] [3], and finally to metaheuristics such as simulated annealing (SA) [4], tabu search (TS) [5], ant colony optimization (ACO) [6], parallel artificial bee colony optimization (ABCO) [7], discrete particle swarm optimization (PSO) [8], modified clonal selection algorithm (CSA) [9], and parallel bat algorithm (BA) [10]. An overview of JSSP techniques can be found in Zobolas et al [11], while an outdated but comprehensive survey of them can be found in Jain and Meeran [12]. Genetic algorithm (GA) is a well-known global search method that has a wide range of applications for solving combinatorial optimization problems.…”

Section: Introductionmentioning

confidence: 99%

An effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem

Kurdi¹

2019

ijisae

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This work presents an effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem with the objective of makespan minimization (GA-CPG-GT). Even though passing important traits from parents to offspring is known to be an important feature of any uniform crossover operator, most of the proposed operators adopt random exchange of genetic materials between parents; this is probably due to the fact that it is tricky to identify the genetic materials that hold the important traits. For that reason, in this work, a new selective exchange of genetic materials is proposed. In the proposed approach, at first, the genetic materials that hold the important traits are identified according to some domain specific information provided by the critical paths of the parents, and then the exchange is made on favor of them. The properties of critical path are usually utilized by the local search methods such as tabu search and simulating annealing; however, in this work, they are utilized in the global search method GA during the crossover operator. The implications of the proposed approach are investigated using the Giffler and Thompson crossover operator, which is a uniform crossover combined with the G&T algorithm. The proposed approach is tested on 55 benchmark instances, with the proposed selective exchange, and without it using the random one, and also compared with other 5 similar works reported in the literature. The computational results validate the enhancements accomplished by the proposed selective exchange, and show the superiority of the proposed algorithm over the compared works in terms of solution quality, and validate its effectiveness.

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“…The problem Jm//C max proved to be an archetype of hard combinatorial optimization problems and also served as a testbed for many algorithmic advances in the field (Jain and Meeran, 1999;Zobolas et al, 2008). Interestingly, this high research activity around Jm//C max did not extend to other important objectivesin particular to those involving due dates until relatively recently.…”

Section: Introductionmentioning

confidence: 99%

The job shop scheduling problem with convex costs

Bürgy

Bülbül

2018

European Journal of Operational Research

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The job shop scheduling literature has been dominated by a focus on regular objective functionsin particular the makespan -in its half a century long history. The last twenty years have encountered a spike of interest in other objectives, such as the total weighted tardiness, but research on non-regular objective functions has always been isolated and scattered. Motivated by this observation, we present a tabu search heuristic for a large class of job shop scheduling problems, where the objective is non-regular in general and minimizes a sum of separable convex cost functions attached to the operation start times and the differences between the start times of arbitrary pairs of operations. This problem definition generalizes a number of problems considered earlier in the literature. A particular notion of "critical paths" derived from the so-called timing problem is at the core of the proposed neighborhood definition exploited successfully in a tabu search algorithm. The computational results attest to the promise of our work.

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Exact, Heuristic and Meta-heuristic Algorithms for Solving Shop Scheduling Problems

Cited by 24 publications

References 143 publications

Task scheduling system for UAV operations in indoor environment

Task scheduling system for UAV operations in indoor environment

An effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem

The job shop scheduling problem with convex costs

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