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2022 ◽  
Vol 7 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Muberra Allahverdi

Since scheduling literature has a wide range of uncertainties, it is crucial to take these into account when solving performance measure problems. Otherwise, the performance may severely be affected in a negative way. In this paper, an algorithm is proposed to minimize the total completion time (TCT) of a two-machine no-wait flowshop with uncertain setup times within lower and upper bounds. The results are compared to the best existing algorithm in scheduling literature: the programming language Python is used to generate random samples with respect to various distributions, and the TCT of the proposed algorithm is compared to that of the best existing one. Results reveal that the proposed one significantly outperforms the best one given in literature for all considered distributions. Specifically, the average percentage improvement of the proposed algorithm over the best existing one is over 90%. A test of hypothesis is conducted to further confirm the results.


Author(s):  
Ali Allahverdi ◽  
Harun Aydilek ◽  
Asiye Aydilek

We consider a no-wait m-machine flowshop scheduling problem which is common in different manufacturing industries such as steel, pharmaceutical, and chemical. The objective is to minimize total tardiness since it minimizes penalty costs and loss of customer goodwill. We also consider the performance measure of total completion time which is significant in environments where reducing holding cost is important. We consider both performance measures with the objective of minimizing total tardiness subject to the constraint that total completion time is bounded. Given that the problem is NP-hard, we propose an algorithm. We conduct extensive computational experiments to compare the performance of the proposed algorithm with those of three well performing benchmark algorithms in the literature. Computational results indicate that the proposed algorithm reduces the error of the best existing benchmark algorithm by 88% under the same CPU times. The results are confirmed by extensive statistical analysis. Specifically, ANOVA analysis is conducted to justify the difference between the performances of the algorithms, and a test of hypothesis is performed to justify that the proposed algorithm is significantly better than the best existing benchmark algorithm with a significance level of 0.01.


2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Rongshen Lai ◽  
Bo Gao ◽  
Wenguang Lin

Aiming at the no-wait flow shop scheduling problem with the goal of minimizing the maximum makespan, a discrete wolf pack algorithm has been proposed. First, the methods for solving the no-wait flow shop scheduling problem and the application research of the wolf pack algorithm were summarized, and it was pointed out that there was lack of research on the application of the wolf pack algorithm to solve the no-wait flow shop scheduling problem. According to the analysis of characteristics of the no-wait flow shop scheduling problem, the individual wolf was coded by a decimal integer; wolf searching behavior was realized through the exchange of different code bits in the individual wolf, and the continuous code segment of the head wolf was randomly selected to replace the corresponding code of the fierce wolf, by which the behaviors of wolves raiding and sieging were realized, and the population was updated according to the rule of “survival of the strong.” In particular, to fully explore the potential optimal solution in the solution space, loop operations were added to the wandering, summoning, and siege processes. Finally, based on a comparison with the leapfrog algorithm and the genetic algorithm, the effectiveness of the algorithm was verified.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 780-780
Author(s):  
Abby Schwartz ◽  
Alice Richman ◽  
Mallary Scott ◽  
Haiyong Liu ◽  
Weyling White ◽  
...  

Abstract Eastern North Carolina (eNC) is a rural, poor, and underserved region of the state with 1 in 5 adults living below the poverty level. Residents experience health disparities driven by limited access to healthcare and inequitable distribution of social determinants of health. Project TRIP (Transporting Residents with Innovative Practices) is a potential solution to barriers in accessing care in eNC. Results presented include the first phase of a multi-phase study evaluating and replicating TRIP’s effectiveness. Data from qualitative interviews with TRIP riders, drivers, and staff (e.g., case managers) will be presented (n= 20). As a result of the COVID-19 pandemic, interviews were conducted by telephone with the goal of understanding both strengths and weaknesses of the transportation program from riders, drivers, and staff to gain a holistic understanding of TRIP. Of the riders interviewed, the majority (91%) were age 50 and over and African American. Themes that emerged from the data that highlighted strengths of the program included: improved health outcomes, no wait times for pick up or drop offs, cost free, and accommodating service. Themes related to areas of weaknesses or improvement included: needing more transportation vendors and a dedicated TRIP case manager and scheduling concerns. The presentation will conclude with considerations in translating the findings into a pilot and expansion of TRIP in another eNC county (study phases 2 & 3), and how the data can inform the development of transportation interventions in other states, with the goal of increasing access to healthcare for vulnerable rural populations.


Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2044
Author(s):  
Majharulislam Babor ◽  
Julia Senge ◽  
Cristina M. Rosell ◽  
Dolores Rodrigo ◽  
Bernd Hitzmann

In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.


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