Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA

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.

Mathematical predication models to optimize post-treatment surveillance in HPV-associated oropharyngeal cancer.

6027 Background: In this study we develop post-treatment imaging surveillance schedules for locally advanced oropharyngeal carcinoma (OPC) specific to the unique recurrence patterns of tumor stage and HPV status, using mathematical models. Current post-treatment imaging surveillance recommendations for OPC are not evidence based. The exception is the use of a positron emission tomography (PET) scan at 3 months post-treatment, after which practice across institutions diverge. An optimized and personalized surveillance schedule for OPC patients can minimize costs and diagnostic delays. Methods: A Markov multi-state model defining local and distant recurrences was trained using 2159 patients from the National Cancer Database. Patients from 2010-2015 treated at an academic or major cancer center with curative radiotherapy were included. Tumors must have been stage III to IVB (AJCC 7th edition) with known p16/HPV status. Model performance was then successfully externally validated using the 2016 International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S) study. Optimized radiographic surveillance schedules were created using this model, assuming a PET at month 3 and including 0 to 6 additional computed tomography (CT) scans of the neck and chest. Optimization was done for minimization of latency, defined as time between disease recurrence and radiographic discovery. Results: Model-selected schedules varied significantly from commonly utilized-surveillance schedules (such as imaging every 3 months within the first year from treatment) and showed lower mean diagnostic latency for every stage and HPV status (shown in Table). In the lowest risk cohort (Stage III HPV+), the optimized schedule had a sensitivity of 65% and latency of 3.1 months. In the highest risk group (Stage IVB HPV-), the optimized schedule had a sensitivity of 76% and latency of 1.9 months. Conclusions: Mathematical model optimization for HPV status and stage is feasible and produces non-intuitive results. These results could be used to inform surveillance if payors reimburse for fewer total scans. Across all cohorts, each added CT scan increases surveillance sensitivity and decreases latency. Incorporation of physical exam and direct visualization results into the model are still needed. Future steps include cost effectiveness research and prospective clinical trials.[Table: see text]

Optimizing Production Schedule of Coalbed Methane Wells Using a Stochastic Evolution Algorithm

Production optimization of coalbed methane (CBM) is a complex constrained nonlinear programming problem. Finding an optimal decision is challenging since the coal seams are generally heterogeneous with widespread cleats, fractures, and matrix pores, and the stress sensitivities are extremely strong; the production of CBM wells needs to be adjusted dynamically within a reasonable range to fit the complex physical dynamics of CBM reservoirs to maximize profits on a long-term horizon. To address these challenges, this paper focuses on the step-down production strategy, which reduces the bottom hole pressure (BHP) step by step to expand the pressure drop radius, mitigate the formation damage, and improve CBM recovery. The mathematical model of CBM well production schedule optimization problem is formulated. The objective of the optimization model is to maximize the cumulative gas production and the variables are chosen as BHP declines of every step. BHP and its decline rate constraints are also considered in the model. Since the optimization problem is high dimensional, nonlinear with many local minima and maxima, covariance matrix adaptation evolution strategy (CMA-ES), a stochastic, derivative-free intelligent algorithm, is selected. By integrating a reservoir simulator with CMA-ES, the optimization problem can be solved successfully. Experiments including both normal wells and real featured wells are studied. Results show that CMA-ES can converge to the optimal solution efficiently. With the increase of the number of variables, the converge rate decreases rapidly. CMA-ES needs 3 or even more times number of function evaluations to converge to 100% of the optimum value comparing to 99%. The optimized schedule can better fit the heterogeneity and complex dynamic changes of CBM reservoir, resulting a higher production rate peak and a higher stable period production rate. The cumulative production under the optimized schedule can increase by 20% or even more. Moreover, the effect of the control frequency on the production schedule optimization problem is investigated. With the increases of control frequency, the converge rate decreases rapidly and the production performance increases slightly, and the optimization algorithm has a higher risk of falling into local optima. The findings of this study can help to better understanding the relationship between control strategy and CBM well production performance and provide an effective tool to determine the optimal production schedule for CBM wells.

Designing an optimized schedule of transit electric bus charging A municipal level case study

This paper deals with the optimal scheduling of public transport e-buses by considering charging rate, discharging rate of batteries, rest time between consecutive trips, and distance between charging stations & estimation of the minimum required charging time between consecutive trips considering given constraints of the journey, also explains how does it help in the optimized scheduling of electric buses.

Designing an optimized schedule of transit electric bus charging A municipal level case study

This paper deals with the optimal scheduling of public transport e-buses by considering charging rate, discharging rate of batteries, rest time between consecutive trips, and distance between charging stations & estimation of the minimum required charging time between consecutive trips considering given constraints of the journey, also explains how does it help in the optimized scheduling of electric buses.

Avoiding “conflicts of interest”: a computational approach to scheduling parallel conference tracks and its human evaluation

Conferences with contributed talks grouped into multiple concurrent sessions pose an interesting scheduling problem. From an attendee’s perspective, choosing which talks to visit when there are many concurrent sessions is challenging since an individual may be interested in topics that are discussed in different sessions simultaneously. The frequency of topically similar talks in different concurrent sessions is, in fact, a common cause for complaint in post-conference surveys. Here, we introduce a practical solution to the conference scheduling problem by heuristic optimization of an objective function that weighs the occurrence of both topically similar talks in one session and topically different talks in concurrent sessions. Rather than clustering talks based on a limited number of preconceived topics, we employ a topic model to allow the topics to naturally emerge from the corpus of contributed talk titles and abstracts. We then measure the topical distance between all pairs of talks. Heuristic optimization of preliminary schedules seeks to balance the topical similarity of talks within a session and the dissimilarity between concurrent sessions. Using an ecology conference as a test case, we find that stochastic optimization dramatically improves the objective function relative to the schedule manually produced by the program committee. Approximate Integer Linear Programming can be used to provide a partially-optimized starting schedule, but the final value of the discrimination ratio (an objective function used to estimate coherence within a session and disparity between concurrent sessions) is surprisingly insensitive to the starting schedule. Furthermore, we show that, in contrast to the manual process, arbitrary scheduling constraints are straightforward to include. We applied our method to a second biology conference with over 1,000 contributed talks plus scheduling constraints. In a randomized experiment, biologists responded similarly to a machine-optimized schedule and a highly modified schedule produced by domain experts on the conference program committee.

Avoiding "conflicts of interest'': A computational approach to scheduling parallel conference tracks and its human evaluation

Conferences with contributed talks grouped into multiple concurrent sessions pose an interesting scheduling problem. From an attendee's perspective, choosing which talks to visit when there are many concurrent sessions is challenging since an individual may be interested in topics that are discussed in different sessions simultaneously. The frequency of topically similar talks in different concurrent sessions is, in fact, a common cause for complaint in post-conference surveys. Here, we introduce a practical solution to the conference scheduling problem by heuristic optimization of an objective function that weighs the occurrence of both topically similar talks in one session and topically different talks in concurrent sessions. Rather than clustering talks based on a limited number of preconceived topics, we employ a topic model to allow the topics to naturally emerge from the corpus of contributed talk titles and abstracts. We then measure the topical distance between all pairs of talks. Heuristic optimization of preliminary schedules seeks to balance the topical similarity of talks within a session and the dissimilarity between concurrent sessions. Using an ecology conference as a test case, we find that simulated annealing improves the objective function over an order of magnitude relative to the schedule manually produced by the program committee. Approximate Integer Linear Programming can be used to provide a partially-optimized starting schedule, but the final value of the discrimination ratio (an objective function used to estimate coherence within a session and disparity between concurrent sessions) is surprisingly insensitive to the starting schedule. Furthermore, we show that, in contrast to the manual process, arbitrary scheduling constraints are straightforward to include. We applied our method to a second biology conference with over 1000 contributed talks plus scheduling constraints. In a randomized experiment, biologists responded similarly to a machine-optimized schedule and a highly modified schedule produced by domain experts on the conference program committee.

Avoiding "conflicts of interest'': A computational approach to scheduling parallel conference tracks and its human evaluation

Conferences with contributed talks grouped into multiple concurrent sessions pose an interesting scheduling problem. From an attendee's perspective, choosing which talks to visit when there are many concurrent sessions is challenging since an individual may be interested in topics that are discussed in different sessions simultaneously. The frequency of topically similar talks in different concurrent sessions is, in fact, a common cause for complaint in post-conference surveys. Here, we introduce a practical solution to the conference scheduling problem by heuristic optimization of an objective function that weighs the occurrence of both topically similar talks in one session and topically different talks in concurrent sessions. Rather than clustering talks based on a limited number of preconceived topics, we employ a topic model to allow the topics to naturally emerge from the corpus of contributed talk titles and abstracts. We then measure the topical distance between all pairs of talks. Heuristic optimization of preliminary schedules seeks to balance the topical similarity of talks within a session and the dissimilarity between concurrent sessions. Using an ecology conference as a test case, we find that simulated annealing improves the objective function over an order of magnitude relative to the schedule manually produced by the program committee. Approximate Integer Linear Programming can be used to provide a partially-optimized starting schedule, but the final value of the discrimination ratio (an objective function used to estimate coherence within a session and disparity between concurrent sessions) is surprisingly insensitive to the starting schedule. Furthermore, we show that, in contrast to the manual process, arbitrary scheduling constraints are straightforward to include. We applied our method to a second biology conference with over 1000 contributed talks plus scheduling constraints. In a randomized experiment, biologists responded similarly to a machine-optimized schedule and a highly modified schedule produced by domain experts on the conference program committee.