Modeling and Solving Scheduling Problem with m Uniform Parallel Machines Subject to Unavailability Constraints

Kaabi, Jihene

doi:10.3390/a12120247

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…The paper [4] deals with scheduling jobs on a set of uniform parallel machines subject to given unavailability intervals with the objective to minimize the makespan. For this NP-hard problem, a new quadratic model is developed.…”

Section: Main Results Of the Special Issuementioning

confidence: 99%

Special Issue on Exact and Heuristic Scheduling Algorithms

2019

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This special issue of Algorithms is a follow-up issue of an earlier one, entitled ‘Algorithms for Scheduling Problems’. In particular, the new issue is devoted to the development of exact and heuristic scheduling algorithms. Submissions were welcome both for traditional scheduling problems as well as for new practical applications. In the Call for Papers, we mentioned topics such as single-criterion and multi-criteria scheduling problems with additional constraints including setup times (costs), precedence constraints, batching (lot sizing), resource constraints as well as scheduling problems arising in emerging applications.

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Section: Main Results Of the Special Issuementioning

confidence: 99%

Special Issue on Exact and Heuristic Scheduling Algorithms

2019

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“…The works of Senthilkumar and Narayanan [29,30] proposed four different genetic algorithms and three variations of simulated annealing algorithms to minimize the makespan in the uniform parallel machine scheduling problem. Kaabi [31] investigated scheduling on uniform parallel machines with machine unavailability, aiming to minimize the makespan. The author proposed a quadratic model for optimal solutions to small-and medium-sized problems and a novel two-phase algorithm for large-sized problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Variable Neighborhood Search for Minimizing the Makespan in a Uniform Parallel Machine Scheduling

Bamatraf,

Gharbi

2024

Systems

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This paper investigates a uniform parallel machine scheduling problem for makespan minimization. Due to the problem’s NP-hardness, much effort from researchers has been directed toward proposing heuristic and metaheuristic algorithms that can find an optimal or a near-optimal solution in a reasonable amount of time. This work proposes two versions of a variable neighborhood search (VNS) algorithm with five neighborhood structures, differing in their initial solution generation strategy. The first uses the longest processing time (LPT) rule, while the second introduces a novel element by utilizing a randomized longest processing time (RLPT) rule. The neighborhood structures for both versions were modified from the literature to account for the variable processing times in uniform parallel machines. We evaluated the performance of both VNS versions using a numerical example, comparing them against a genetic algorithm and a tabu search from existing literature. Results showed that the proposed VNS algorithms were competitive and obtained the optimal solution with much less effort. Additionally, we assessed the performance of the VNS algorithms on randomly generated instances. For small-sized instances, we compared their performance against the optimal solution obtained from a mathematical formulation, and against lower bounds derived from the literature for larger instances. Computational results showed that the VNS version with the randomized LPT rule (RLPT) as the initial solution (RVNS) outperformed that with the LPT rule as the initial solution (LVNS). Moreover, RVNS found the optimal solution in 90.19% of the small instances and yielded an average relative gap of about 0.15% for all cases.

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“…(2016), Benttaleb et al. (2018, 2019), Huo and Zhao (2018), Kaabi (2019), Krim et al. (2019a, 2019b, 2020), Luo and Liu (2017), Najat et al.…”

Section: Introductionmentioning

confidence: 99%

Scheduling with day shifts and breaks

2020

Int Trans Operational Res

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Traditional scheduling assumes that production can continue around the clock unless machines are not available. This is not without question for labor-intensive industries where operations are performed manually. Motivated by operation management problems arising in the assembly stage of aeroengines and in the paintspraying stage of wooden furniture, we consider a scheduling problem with day shifts and breaks. We transform the problem into a new bin-packing problem and present some theoretical analyses. To meet the needs of practical applications, we propose a binary integer programming (BIP) formulation and a hybrid genetic algorithm (HGA) that is based on five heuristic algorithms and two postoptimization procedures. Computational results indicate that the BIP formulation is suitable for some data sets while the HGA is the most promising in that it saves about 37% of day shifts compared to the best of the five heuristic algorithms in the context of scheduling.

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Modeling and Solving Scheduling Problem with m Uniform Parallel Machines Subject to Unavailability Constraints

Cited by 4 publications

References 17 publications

Special Issue on Exact and Heuristic Scheduling Algorithms

Special Issue on Exact and Heuristic Scheduling Algorithms

Variable Neighborhood Search for Minimizing the Makespan in a Uniform Parallel Machine Scheduling

Scheduling with day shifts and breaks

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