Analysis of stochastic local search methods for the unrelated parallel machine scheduling problem

Santos, Haroldo Gambini; Toffolo, Túlio A. M.; Silva, Cristiano Luís Turbino de França e; Berghe, Greet Vanden

doi:10.1111/itor.12316

Cited by 40 publications

(39 citation statements)

References 24 publications

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“…In the second step, the savings list obtained in the CWS heuristic is randomized using a geometric probability distribution (Juan et al., , ; Dominguez et al., ). There are different approaches for randomizing a heuristic with the goal of better exploring the solutions space (Hemmati and Hvattum, ; Santos et al., ). One of these is the use of GRASP (Resende and Ribeiro, ).…”

Section: Overview Of Our Solution Methodologymentioning

confidence: 99%

An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls

Belloso

Juan

Faulín

2017

Int Trans Operational Res

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This paper analyzes the fleet mixed vehicle-routing problem with backhauls, a rich and realistic variant of the popular vehicle-routing problem in which both delivery and pick-up customers are served from a central depot using a heterogeneous and configurable fleet of vehicles. After a literature review on the issue and a detailed description of the problem, a solution based on a multistart biased-randomized heuristic is proposed. Our algorithm uses an iterative method that relies on solving a series of smaller instances of the homogeneous-fleet version of the problem and then using these subsolutions as partial solutions for the original heterogeneous instance. In order to better guide the exploration of the solutions space, the algorithm employs several biased-randomized processes: a first one for selecting a vehicle type; a second one for sorting the savings list; and a third one to define the number of routes that must be selected from the homogenousfleet subsolution. The computational experiments show that our approach is competitive and able to provide 20 new best-known solutions for a 36-instance benchmark recently proposed in the literature.

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Section: Overview Of Our Solution Methodologymentioning

confidence: 99%

An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls

Belloso

Juan

Faulín

2017

Int Trans Operational Res

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“…So far, the SA algorithm is still receiving great attention due to its special strategy. e detail procedure of the standard SA is summarized in Algorithm 1 [20]. In Algorithm 1, f(·) is the objective function and N H (·) is a function that returns a random neighbor solution to neighborhood structure H.…”

Section: Brief Of the Simulated Annealing Algorithmmentioning

confidence: 99%

“…Choose the K largest elements from x ij as I temp ; (16) Calculate δ � f(I temp ) − f(I ij ); (17) if δ < 0 then (18) I temp � I temp (19) else (20) Calculate β � min(1, exp(−(abs(δ))/T 0 )) (21)…”

Section: Theorem 1 Suppose the Support Set I Is The Unique Solution mentioning

confidence: 99%

The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing

Zhang

Sun

et al. 2019

Journal of Electrical and Computer Engineering

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is paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. is algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. en, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. e proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms.

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“…Santos et al. () applied four different stochastic local search based heuristics, including an ILS, to tackle an unrelated parallel machines problem with a deterioration factor, in which the performance of the machines gets worse as the jobs are executed. The work details the algorithms tuning process and indicates that they outperform two of the best current algorithms for that problem.…”

Section: Solution Proceduresmentioning

confidence: 99%

An ILS heuristic for the ship scheduling problem: application in the oil industry

CUNHA

Santos

Pessôa

et al. 2018

Int Trans Operational Res

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This paper addresses a real‐life rescheduling problem of a pipe‐laying support vessel (PLSV) fleet in charge of subsea oil well connections. The short‐term schedule of these vessels is subject to uncertainties inherent to its operations, resulting in ships idleness or delays in oil production. The objective of this study is to develop methods to support a Brazilian oil and gas company in overcoming impacts caused by operational disruptions, while reaching its planned production level. The PLSV rescheduling problem was treated as an identical parallel machine scheduling problem, where the machines represent the vessels and the jobs are the activities for the subsea well connections. We propose a mathematical programming model and a method based on the iterated local search (ILS) metaheuristic to solve the problem. This paper contributes to this by considering simultaneously setup times, machine eligibility, release dates, due dates, and machine availability. Both methods were applied on 10 instances based on real PLSV data. Taking into account an objective function that measures the operational impact on schedules, the ILS provided an average improvement above 91% in schedules when compared to the initial solution provided by the studied company. The ILS outperformed a mathematical programming model for the problem, in eight instances, within a 30‐minute execution time limit, fitting to the company process.

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Analysis of stochastic local search methods for the unrelated parallel machine scheduling problem

Cited by 40 publications

References 24 publications

An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls

An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls

The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing

An ILS heuristic for the ship scheduling problem: application in the oil industry

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