Iterated local search for single machine total weighted tardiness batch scheduling

Queiroga, Eduardo; Pinheiro, Rian G. S.; Christ, Quentin; Subramanian, Anand; Pessoa, Artur Alves

doi:10.1007/s10732-020-09461-x

Cited by 24 publications

(7 citation statements)

References 40 publications

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“…The problem considered in this work considers items with incompatible family types and a parallel machine environment. Besides various works on single and multi machines, i.e., Trindade, de Araújo, and Fampa (2021); Queiroga et al (2021);İbrahim Muter (2020), the literature also considers different kinds of flow shop and job shop environments, i.e., Shahnaghi et al (2016b,a); Wang and Luh (1997). For more information on alternative objective functions and proposed solution techniques, we refer to the survey from Fowler and Mönch (2021).…”

Section: Related Workmentioning

confidence: 99%

Parallel Batch Processing for the Coating Problem

Horn

Demirović

Yorke-Smith

2023

ICAPS

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We solve a challenging scheduling problem with parallel batch processing and two-dimensional shelf strip packing constraints that arises in the tool coating field. Tools are assembled on so-called planetaries (batches) before they are loaded into coating machines to get coated. The assembling is not trivial and must fulfil specific constraints, which we refer to as shelf strip packing constraints. Further, each tool is associated with a starting time window s.t. tools can only be put on the same planetary if their time window overlap. The objective is to minimise the makespan and the number of required planetaries. Since the problem naturally decomposes into scheduling and packing parts, we tackle the problem with a two-phase logic-based Benders decomposition approach. The master problem assigns items to batches. The first phase solves as subproblem the packing problem by checking if the assignment is feasible, whereas the second phase solves the scheduling subproblem. The approach is compared with a monolithic mixed integer linear programming approach as well as a monolithic constraint programming approach. Experimental evaluation shows that our proposed approach outperforms the state-of-the-art benchmarks by solving more instances to optimality in a shorter time.

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Section: Related Workmentioning

confidence: 99%

Parallel Batch Processing for the Coating Problem

Horn

Demirović

Yorke-Smith

2023

ICAPS

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“…Finally, in the work by Adhikari et al [14] the workload of cloud servers, makespan, resource usage, and stability were the goals of a workflow scheduling approach based on the firefly algorithm (FA). Concering the ILS algorithm, ILS has been used in many fields including, the travelling salesman problem [15], [16], vehicle routing problem [17], [18], flow-shop problem [19], [20], task scheduling on cloud computing [21], precedence-constraint task list scheduling [22], the parallel machine scheduling problems [23], and the quadratic assignment problem [24]. Several enhancements were proposed over time including, using clusters and a modified multi-restart [15], using multi types of neighborhoods moves [17], ILS memory-based [18], use of a biased-randomized [20], [25], and hybrid with other metaheuristics algorithms [21], [22].…”

Section: Related Workmentioning

confidence: 99%

Optimized scheduling of scientific workflows based on iterated local search

Jihad

Al-Janabi

Yassen

2022

IJEECS

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Recent years have witnessed a great interest in scientific applications with large data and processing-intensive, so cloud computing is used which provides the resources needed to implement and run these applications. One of the challenges in the management of scientific workflow applications is scheduling them to solve many combinatorial optimization problems, including reducing execution time, cost, resource utilization, and energy consumption. Due to the fact that the iterated local search algorithm (ILS) has been successfully applied to solve many combinatorial optimization problems, this paper investigates the performance of ILS in solving the scientific workflow scheduling problem which is a highly constrained problem. The main components that are different from one problem to others are the ILS parameters, local search, and perturbation, which must be carefully designed. The performance of the standard ILS has been examined and compared with the latest technology. The experimental results show that the proposed algorithm (ILS) obtained good results compared to the best-known results in the literature. This is due to the ILS being an adaptable metaheuristic, which can be simply adapted to diﬀerent search situations and instances.

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“…Iterated local search (ILS) is stochastic metaheuristics algorithm abel to generate new starting solutions by perturbation of the formerly establish solutions. The basic idea of ILS has been performed in different optimization problem such as travelling salesman problem (TSP) [12], the problem of single machine with total weighted tardiness [13], Single and parallel-machine scheduling [14], Flow shop scheduling [15], Graph bipartitioning [16], MAX-SAT, quadratic assignment problem [17], Job shop scheduling [18], and other optimization problems [19].…”

Section: General Iterated Local Searchmentioning

confidence: 99%

Rule Induction with Iterated Local Search

Jabba¹

2021

IJIES

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The wide amount of data in the modern applications available on the Internet make it very complicated to deal with the knowledge behind these data. The data classification task is a useful tool that used to deal with a huge amount of data by classify these data into coherent groups. The data size decreases the performace of the classification technique, especially when contain uninformative data (i.e., irrelevant, noisy). The stochastic local search algorithm is an optimization approaches employed to find the informative data to build the classification model. These methods are used to search for the optimal patterns to construct the classification algorithm. Thus, this research introduces a stochastic local search algorithm for rule induction called Iterated-Miner (Iterated local search-based rule induction). The purpose of the algorithm is to construct classification rules from data. This classification algorithm is inspired by the concepts and principles of stochastic local search and rule induction. The performance of the proposed classifier evaluated with a well-known and state of art classification algorithms called, Ant-Miner, ACO/PSO2, PART FURIA, and ACO/GA on 10 UCI datasets. The results demonstrate that our classifier is superior compare with all classifiers respect to classification accuracy; and the model sizes by our classifier are considerably competitive with those discovered by other classifier.

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Iterated local search for single machine total weighted tardiness batch scheduling

Cited by 24 publications

References 40 publications

Parallel Batch Processing for the Coating Problem

Parallel Batch Processing for the Coating Problem

Optimized scheduling of scientific workflows based on iterated local search

Rule Induction with Iterated Local Search

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