Exact algorithms for a scheduling problem with unrelated parallel machines and sequence and machine-dependent setup times

Rocha, Pedro Leite; Ravetti, Martı́n Gómez; Mateus, Geraldo Robson; Pardalos, Pãnos M.

doi:10.1016/j.cor.2006.07.015

Cited by 113 publications

(48 citation statements)

References 15 publications

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“…These problems are notoriously hard to optimize using exact MIP solution approaches, such as Branch-andBound or Branch-and-Cut, and the optimality gap of the relaxed problems is traditionally large for instances involving more than 30 to 50 jobs (Rocha et al, 2008). For this reason, a significant body of the literature favors the development of metaheuristic algorithms (Wilson et al, 2013;Repoussis et al, 2009;Repoussis & Tarantilis, 2010;Tarantilis et al, 2013).…”

Section: Solution Methodologymentioning

confidence: 99%

“…This is not necessary for capturing the operational realties of the problem. However, as described by Rocha et al (2008), although the number of variables increases, this type of discretization is expected to produce tighter lower bounds during resolution, and to be faster on large instances, compared to other mathematical formulations.…”

Section: Model Formulationmentioning

confidence: 99%

“…Considering that the patients can be seen as a set of jobs (see Wilson et al (2013) for a similar approach), the proposed model formulation follows the representation of a Flexible Job Shop Scheduling Problem (FJSP) (Rocha et al, 2008) with unrelated parallel machines and sequence-and machinedependent setup times. The FJSP consists of programming several jobs to be processed by several parallel identical and/or unrelated machines.…”

Section: Model Formulationmentioning

confidence: 99%

“…Following the formulations that appear in the earlier work of Rocha et al (2008) for the FJSP, we define two sets of binary variables with discrete positions in the processing sequence for each ordered list of tasks as follows…”

Section: Model Formulationmentioning

confidence: 99%

See 3 more Smart Citations

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

Repoussis

Paraskevopoulos

Vazacopoulos

et al. 2016

European Journal of Operational Research

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThis paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. The goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIPEmail addresses: prepouss@stevens.edu (Panagiotis P. Repoussis), d.paraskevopoulos@bath.ac.uk (Dimitris C. Paraskevopoulos), alkis@optimizationdirect.com (Alkiviadis Vazacopoulos), nah2005@med.cornell.edu (Nathaniel Hupert) Preprint submitted to European Journal of Operational ResearchMay 25, 2016 based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times.

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Section: Solution Methodologymentioning

confidence: 99%

Section: Model Formulationmentioning

confidence: 99%

Section: Model Formulationmentioning

confidence: 99%

Section: Model Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

Repoussis

Paraskevopoulos

Vazacopoulos

et al. 2016

European Journal of Operational Research

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show abstract

“…This paper also presented a threshold accepting algorithm with tabu list to solve the UPMSP to minimize the total tardiness. The branch and bound (B&B) methods are commonly used to optimally solve the UPMSP in the literature [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Criteria Decision Making for the Unrelated Parallel Machines Scheduling Problem

Chang

Chyu

2009

JSEA

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Advanced discrete firefly algorithm with adaptive mutation‐based neighborhood search for scheduling unrelated parallel machines with sequence‐dependent setup times

Ezugwu

2021

Int J of Intelligent Sys

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The unrelated parallel machine scheduling problem with sequence-dependent setup times is addressed in this paper with the objective of minimizing the elapsed time between the start and finish of a sequence of operations in a set of unrelated machines. The machines are considered unrelated because the processing speed is dependent on the job being executed and not on the individual machines. Generally, the problem is considered NP-hard, as it presents additional complexity to find an optimal solution in terms of minimum makespan. An advanced firefly metaheuristic optimization algorithm is introduced to solve this problem. The proposed method, called the FAII algorithm, aims to improve the standard firefly algorithm's performance by incorporating an enhanced global best solution update mechanism and adaptive mutationbased local and global neighborhood search scheme to improve the quality of the proposed algorithm's generated solution. Several experiments were conducted to compare and validate the proposed algorithms' performance on small and large-scale benchmarked problem instances with up to 12 machines and 120 job combinations. Moreover, the performance of the FAII was also compared with eight other metaheuristic

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Exact algorithms for a scheduling problem with unrelated parallel machines and sequence and machine-dependent setup times

Cited by 113 publications

References 15 publications

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

A Multi-Criteria Decision Making for the Unrelated Parallel Machines Scheduling Problem

Advanced discrete firefly algorithm with adaptive mutation‐based neighborhood search for scheduling unrelated parallel machines with sequence‐dependent setup times

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