Mixed binary integer programming formulations for the flow shop scheduling problems. A case study: ISD projects scheduling

Ziaee, Mohsen; Sadjadi, Seyed Jafar

doi:10.1016/j.amc.2006.06.092

Cited by 18 publications

(7 citation statements)

References 22 publications

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“…More recently, Pan and Chen (2005) developed a mixed binary integer programming (MBIP) model for reentrant job shop scheduling problem. Ziaee and Sadjadi (2007) developed seven MBIP formulations for the flow shop sequencing problem and considered different constraints such as due dates, ready times, etc., and studied makespan, weighted mean flow time, and weighted mean tardiness as their performance measures. Javadi et al (2008) developed a linear programming model for the no-wait flow shop problem with fuzzy objective functions.…”

Section: Exact Methodsmentioning

confidence: 99%

On the exact solution of the no-wait flow shop problem with due date constraints

Samarghandi

Behroozi

2017

Computers & Operations Research

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This paper deals with the no-wait flow shop scheduling problem with due date constraints. In the nowait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, the jobs should be completed before their respective due dates; due date constraints are dealt with as hard constraints. The considered performance criterion is makespan. The problem is strongly NP-hard. This paper develops a number of distinct mathematical models for the problem based on different decision variables. Namely, a mixed integer programming model, two quadratic mixed integer programming models, and two constraint programming models are developed. Moreover, a novel graph representation is developed for the problem. This new modeling technique facilitates the investigation of some of the important characteristics of the problem; this results in a number of propositions to rule out a large number of infeasible solutions from the set of all possible permutations. Afterward, the new graph representation and the resulting propositions are incorporated into a new exact algorithm to solve the problem to optimality. To investigate the performance of the mathematical models and to compare them with the developed exact algorithm, a number of test problems are solved and the results are reported. Computational results demonstrate that the developed algorithm is significantly faster than the mathematical models.

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Section: Exact Methodsmentioning

confidence: 99%

On the exact solution of the no-wait flow shop problem with due date constraints

Samarghandi

Behroozi

2017

Computers & Operations Research

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“…The optimization method used is the optimization of the binary integer programming (BIP) model. The previous most common and frequently used BIP was in scheduling issues distributing an object (Pan and Chen, 2005;Correa et al, 2015;Wong et al, 2006;Brey et al, 2012;Ziaee and Sadjadi, 2007;Balouchzahi et al, 2015;Gholamnejad and Osanloo, 2007). While scheduling on this research expanded its use into the formation of player line-ups.…”

Section: Introductionmentioning

confidence: 96%

Optimization of The Best Line-up in Football using Binary Integer Programming Model

Mahrudinda¹,

Supian²,

Subiyanto³

et al. 2020

ijgor

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This paper aims to find the formation with the best line-up of the Liverpool FC football team in the English Premier League in the 2020/2021 season. Researchers used binary integer programming (BIP) modeling to determine optimum solutions. The data used for this optimization is the rating value of the players recorded in the performance data from the previous matches. The optimum result of this problem is the selection of variables that are valued at 1, namely {x_1, x_4, x_6, x_8, x_21, x_28, x_34, x_37, and x_39} for formations 4-3-3 with a maximum value of 82.47, and variables {x_1, x_6, x_7, x_8, x_11, x_14, x_16, x_29, x_31, x_32, andx_42} for 4-2-3-1 formations with a maximum value of 80.04. The 4-3-3 formation is more effective because it has a higher maximum rating than the 4-2-3-1 formation. 4-3-3 formation is an attacking formation with a higher intensity of attack and faster than 4-2-3-1 formation that tends to defend moderately.

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“…Scheduling production tasks on a production line can be formulated as a machine scheduling problem which is known to be NP-hard [1] . Exact optimization algorithms (e.g., [2] , [3] , [4] , [5] ) often have very large computational times that are infeasible on even moderate-size problem instances. As for moderate- and large-size instances optimal solutions are rarely needed in practice, heuristic approximation algorithms, including constructive heuristics (e.g., [6] , [7] , [8] ) and metaheuristic evolutionary algorithms (e.g., [9] , [10] , [11] , [12] , [13] , [14] , [15] ), are more feasible to achieve a trade-off between optimality and computational costs.…”

Section: Introductionmentioning

confidence: 99%

Real-time neural network scheduling of emergency medical mask production during COVID-19

Liao

Karataş

et al. 2020

Applied Soft Computing

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During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that must be processed within a short response time. It is of critical importance for the manufacturer to schedule and reschedule mask production tasks as efficiently as possible. However, when the number of tasks is large, most existing scheduling algorithms require very long computational time and, therefore, cannot meet the needs of emergency response. In this paper, we propose an end-to-end neural network, which takes a sequence of production tasks as inputs and produces a schedule of tasks in a real-time manner. The network is trained by reinforcement learning using the negative total tardiness as the reward signal. We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China. Computational results show that the neural network scheduler can solve problem instances with hundreds of tasks within seconds. The objective function value obtained by the neural network scheduler is significantly better than those of existing constructive heuristics, and is close to those of the state-of-the-art metaheuristics whose computational time is unaffordable in practice.

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Mixed binary integer programming formulations for the flow shop scheduling problems. A case study: ISD projects scheduling

Cited by 18 publications

References 22 publications

On the exact solution of the no-wait flow shop problem with due date constraints

On the exact solution of the no-wait flow shop problem with due date constraints

Optimization of The Best Line-up in Football using Binary Integer Programming Model

Real-time neural network scheduling of emergency medical mask production during COVID-19

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