Exact Method Research Articles

Insights and future research directions from a bibliometric mapping of studies in stope layout optimisation

ABSTRACT Stope layout optimisation is an important sub-problem of the generic underground mining optimisation problem that contributes towards optimal ore extraction. It is a growing research area as some surface mines transition from open-pit to underground mining. Several researchers acknowledge the complexity of optimising underground mine layouts because exact methods are limited in generating three-dimensional optimal solutions, hence the growing use of metaheuristic approaches to solve the problem in three dimensions. This paper presents a PRISMA-based bibliometric mapping of stope layout optimisation approaches using R-Biblioshiny and VOSviewer software, their concomitant limitations, and suggests potential future research directions in stope layout optimisation.

Twisted graphene superlattices: resonating valence bond states and magnetic properties

Resonating valence bond (RVB) states are fundamental for understanding quantum spin liquids in two-dimensional (2D) systems. The RVB state is a collective phenomenon in which spins are uncoupled. 2D lattices such as triangular, honeycomb, and dice lattices were investigated using the Hubbard model and exact diagonalization method. We analyzed the total spin, spin–spin correlation functions, local magnetic moments, and spin and charge gaps as a function of on-site Coulomb repulsion, electron concentration, and electronic hopping parameters. Phase diagrams showed that RVB states can live in half-filled and hole-doped anisotropic triangular lattices. We found two types of RVB states: one in the honeycomb sublattice and the other in the centered hexagons in the triangular lattices. Owing to the novel discovery of exotic magnetic ordering in triangular moiré patterns in twisted bilayer graphene and transition metal dichalcogenide systems, our results provide physical insights into the onset of magnetism and possible spin liquid states in these layered materials.

SAT-based Decision Tree Learning for Large Data Sets

Decision trees of low depth are beneficial for understanding and interpreting the data they represent. Unfortunately, finding a decision tree of lowest complexity (depth or size) that correctly represents given data is NP-hard. Hence known algorithms either (i) utilize heuristics that do not minimize the depth or (ii) are exact but scale only to small or medium-sized instances. We propose a new hybrid approach to decision tree learning, combining heuristic and exact methods in a novel way. More specifically, we employ SAT encodings repeatedly to local parts of a decision tree provided by a standard heuristic, leading to an overall reduction in complexity. This allows us to scale the power of exact SAT-based methods to comparatively very large data sets. We evaluate our new approach experimentally on a range of real-world instances that contain up to several thousand samples. In almost all cases, our method successfully decreases the complexity of the initial decision tree; often, the decrease is significant.

Capacitated Mobile Facility Location Problem with Mobile Demand: Efficient Relief Aid Provision to En Route Refugees

As a humanity crisis, the tragedy of forced displacement entails relief aid distribution efforts among en route refugees to alleviate their migration hardships. This study aims to assist humanitarian organizations in cost-efficiently optimizing the logistics of capacitated mobile facilities utilized to deliver relief aid to transiting refugees in a multi-period setting. The problem is referred to as the Capacitated Mobile Facility Location Problem with Mobile Demands (CMFLP-MD). In CMFLP-MD, refugee groups follow specific paths, and meanwhile, they receive relief aid at least once every fixed number of consecutive periods, maintaining continuity of service. To this end, the overall costs associated with capacitated mobile facilities, including fixed, service provision, and relocation costs, are minimized. We formulate a mixed integer linear programming (MILP) model and propose two solution methods to solve this complex problem: an accelerated Benders decomposition approach as an exact solution method and a matheuristic algorithm that relies on an enhanced fix-and-optimize agenda. We evaluate our methodologies by designing realistic instances based on the Honduras migration crisis that commenced in 2018. Our numerical results reveal that the accelerated Benders decomposition excels MILP with a 46% run time improvement on average while acquiring solutions at least as good as the MILP across all instances. Moreover, our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly. An in-depth exploration of the solution properties underscores the robustness of our relief distribution plans under varying migration circumstances. Across several metrics, our sensitivity analyses also highlight the managerial advantages of implementing CMFLP-MD solutions.

Analysis of the relationship between intestinal segmented filamentous bacteria and rotavirus infection in children

Objective: To investigate the association between intestinal colonization of segmented filamentous bacteria (SFB) and the risk of rotavirus infection, and the possible mechanisms by which SFB resist rotavirus infection. Methods: This case-control study enrolled 50 children aged 0 to 5 years who present to the outpatient Department of Children's Hospital, Zhejiang University School of Medicine with diarrhea and positive stool tests for rotavirus. The children were divided into rotavirus enteritis group and control group consisting of 55 children with non-gastrointestinal and non-infectious surgical diseases.The age and sex composition of the two groups was matched. The DNA of the fecal flora was extracted and SFB was detected by real-time fluorescence quantitative PCR analysis. The children in the rotavirus enteritis group and the control group were subgrouped by age and sex to analyze the differences in SFB positivity rates between different groups, and further compare and analyze the differences in SFB positivity rates between these two groups of children in the ≤2 years old subgroup and the >2-5 years old subgroup. Neutralization test was performed with p3340 protein and rotavirus to determine the relationship between rotavirus infection rate and p3340 concentration in Vero cells. χ2 test or Fisher's exact probability method was used for comparison between the two groups. Results: There were 50 children in the rotavirus enteritis group with an age of (1.7±0.9) years, and 55 children in the control group with an age of (1.8±1.1) years. The positive rate of SFB in children with rotavirus enteritis showed a declining trend across ages groups, with the highest rate of 10/14 in the ≤1 year old group, followed by 67% (14/21) in the >1-2 years old group, 9/15 in the >2-5 years old group, and there was no statistically significant difference (P=0.867). The positive rate of SFB in the control group was 12/15 in the ≤1 year old group, 95% (19/20) in the >1-2 years old group, 50% (10/20) in the >2-5 years old group, with statistical significance (P=0.004). The positive rate of SFB in children with rotavirus enteritis was 74% (20/27) in males and 56% (13/23) in females (χ2=1.71, P=0.192). In the control group, it was 79% (22/28) in males and 70% (19/27) in females (χ2=0.49, P=0.485). The positive rate of SFB was 66% (33/50) in the rotavirus enteritis group and 75% (41/55) in the control group, with no statistically significant (χ2=0.56, P=0.454). In the children ≤2 years old, the SFB positivity rate was 69% (24/35) in the rotavirus enteritis group and 89% (31/35) in the control group, with a statistically significant difference (χ2=4.16, P=0.041). However, in the children >2-5 years old, no statistically significant difference was observed, with the positive rate of SFB being 9/15 in the rotavirus enteritis group and 50% (10/20) in the control group (P=0.734). Pearson correlation analysis revealed a negative correlation between rotavirus infection and SFB positivity (r=-0.87,P<0.001). As the concentration of the p3340 specific protein increased, the luminescence intensity of the luciferase in the Vero cells, which were suitable for cultivating rotavirus, exhibited a decreasing trend (F=4.17, P=0.001). Conclusions: SFB colonization in infants less than 2 years old is associated with a reduced risk of rotavirus infection. Cloning of specific SFB functional protein p3340 neutralizes rotavirus infection of Vero cells, and this mechanism of targeting rotavirus infection differs from the common antiviral mechanism.

Volar Plate Draping (VPD) for Severely Comminuted Proximal Phalanx Head Fractures

AbstractWhile proximal phalangeal joint injuries with comminution of the base of the middle phalanx are common injuries, proximal interphalangeal (PIP) joint fracture dislocations with an intact base of middle phalanx and a comminuted head of proximal phalanx are rare. Volar plate arthroplasty and other described techniques prevail for the former injury, while the latter does not have any supportive literature on the exact method of management. We herein present a 20-year-old male with a severely comminuted head of proximal phalanx fracture with dislocation of the PIP joint, which was not reconstructable but was managed successfully with a novel technique of volar plate draping that resurfaced the raw phalangeal head. A 3-year follow-up of the patient, whose finger has full, painless, and complete functional range of movement, gives us the belief that this procedure can provide a fully functional finger where other methods fail to do so.

Exact and Approximation Algorithms for Sparse Principal Component Analysis

Sparse principal component analysis (SPCA) is designed to enhance the interpretability of traditional principal component analysis by optimally selecting a subset of features that comprise the first principal component. Given the NP-hard nature of SPCA, most current approaches resort to approximate solutions, typically achieved through tractable semidefinite programs or heuristic methods. To solve SPCA to optimality, we propose two exact mixed-integer semidefinite programs (MISDPs) and an arbitrarily equivalent mixed-integer linear program. The MISDPs allow us to design an effective branch-and-cut algorithm with closed-form cuts that do not need to solve dual problems. For the proposed mixed-integer formulations, we further derive the theoretical optimality gaps of their continuous relaxations. Besides, we apply the greedy and local search algorithms to solving SPCA and derive their first-known approximation ratios. Our numerical experiments reveal that the exact methods we developed can efficiently find optimal solutions for data sets containing hundreds of features. Furthermore, our approximation algorithms demonstrate both scalability and near-optimal performance when benchmarked on larger data sets, specifically those with thousands of features. History: Accepted by Andrea Lodi, Area Editor for Design &amp; Analysis of Algorithms—Discrete. Funding: This research was supported in part by the Division of Civil, Mechanical and Manufacturing Innovation [Grant 224614], the Division of Computing and Communication Foundations [Grant 2246417], and the Office of Naval Research [Grant N00014-24-1-2066]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0372 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0372 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for Exact and Approximation Algorithms for Sparse Principal Component Analysis

Sparse principal component analysis (SPCA) is designed to enhance the interpretability of traditional principal component analysis by optimally selecting a subset of features that comprise the first principal component. Given the NP-hard nature of SPCA, most current approaches resort to approximate solutions, typically achieved through tractable semidefinite programs or heuristic methods. To solve SPCA to optimality, we propose two exact mixed-integer semidefinite programs (MISDPs) and an arbitrarily equivalent mixed-integer linear program. The MISDPs allow us to design an effective branch-and-cut algorithm with closed-form cuts that do not need to solve dual problems. For the proposed mixed-integer formulations, we further derive the theoretical optimality gaps of their continuous relaxations. Besides, we apply the greedy and local search algorithms to solving SPCA and derive their first-known approximation ratios. Our numerical experiments reveal that the exact methods we developed can efficiently find optimal solutions for data sets containing hundreds of features. Furthermore, our approximation algorithms demonstrate both scalability and near-optimal performance when benchmarked on larger data sets, specifically those with thousands of features.

Parallel implementation of an exact two-phase method for the biobjective knapsack problem

Parallel implementation of an exact two-phase method for the biobjective knapsack problem

Analysis of the efficacy of left subclavian artery laser in situ fenestration combined with hybrid arch debranching surgery for aortic arch reconstruction in patients with Stanford type A aortic dissection

Objective: To observe the short-and mid-term efficacy of left subclavian artery(LSA) laser in situ fenestration combined with arch debranching surgery for aortic arch reconstruction in patients with Stanford type A aortic dissection aged 60 years and above. Methods: This is a retrospective cohort study. A total of 41 Stanford type A aortic dissection patients aged 60 years and above who received combined surgery in Department of Endovascular Surgery, the First Affiliated Hospital of Zhengzhou University from January 2018 to December 2020 were retrospectively analyzed. There were 25 males and 16 females, aged (67.3±5.9)years(range: 60 to 75 years). Among them, 19 patients underwent LSA laser in situ fenestration combined with arch debranching surgery(combined surgery group) and 22 patients underwent hybrid aortic arch debranching surgery(non-combined surgery group). Independent sample t test, χ2 test and Fisher exact probability method were used to compare the clinical characteristics of the two groups. Kaplan-Meier method was used for survival analysis, and the 5-year survival rate of the two groups was compared by Log-rank test. Results: Body mass index in the combined operation group was significantly higher than that in the non-combined operation group ((27.1±1.6)kg/m2 vs.(26.9±1.9)kg/m2; t=2.766,P=0.006), and the difference was statistically significant. There was no statistical significance in the comparison of other general data (all P>0.05). The operation time ((321.3±11.4) minutes vs. (329.6±7.3)minutes; t=-2.733, P=0.010) and LSA reconstruction time ((32.4±3.0)minutes vs. (42.4±6.0)minutes; t=-6.842, P<0.01) in the combined operation group were significantly shortened, and the difference was statistically significant. The rate of LSA reconstruction in the combined operation group (100% vs. 72.7%; P=0.023) was significantly higher than that in the non-combined operation group, and the difference was statistically significant. There were no significant differences in the incidence of pulmonary infection, unplanned second operation, continuous renal replacement therapy, neurological complications and the in-hospital mortality between the two groups. Compared with the non-combined surgery group, the total complication rate related to LSA reconstruction was significantly lower in the combined surgery group (0 vs. 27.3%; P=0.023). Kaplan-Meier survival analysis showed that there was no difference in 5-year survival rate between the combined operation group and the non-combined operation group (84.2% vs. 77.3%; χ2=0.310, P=0.578). Conclusion: Laser in situ fenestration of the LSA combined with arch debranching surgery to reconstruct the aortic arch can significantly shorten the operation and LSA reconstruction time in patients aged 60 years and above with Stanford type A aortic dissection, improve the success rate of LSA reconstruction, and reduce the occurrence rate of LSA reconstruction complications.

It Takes a Village: Leveraging a Multidisciplinary Team and Technology for Urine Culturing Stewardship

Background: Patients without urinary tract infection (UTI) symptoms but with a positive urine culture are considered to have asymptomatic bacteriuria (ASB). This often represents colonization and treatment is not recommended or clinically beneficial. Treatment of ASB can promote antimicrobial resistance and increased rates of Clostriodies difficile infections. Many cases of ASB are incorrectly assigned as CAUTIs due to over-culturing practices. We hypothesized that a urine culture algorithm, embedded within a best practice alert (BPA) in the electronic medical record (EMR), would reduce urine culturing practices for ASB. Methods: From Feb 2022 through May 2023, a multidisciplinary team implemented an Inpatient Urine Culturing Stewardship Guideline. A BPA fired when a provider placed a urinalysis with reflex to culture (UACC) or urine culture (UC) order for patients who met criteria (Image 1). The BPA directed providers to remove the order, select the appropriate pathway from the guideline, or provide a rationale for placing the order. The intervention was piloted on three intensive care units and two progressive care units, containing both medical and surgical patients. Monthly ordering practices, CAUTI rates, and gram-negative rod (GNR) bacteremia rates from a 13-month pre-intervention baseline period were compared to a 16-month intervention period. Over the same time periods, we also assessed changes in ordering practices for comparison units which did not implement the intervention. Pre-and-post intervention cohorts were analyzed using median two sample tests and Exact Poison Method, as appropriate. Results: On intervention units there was a 41.0% reduction in the median number of UACC and UC orders per 1000 patient days from 16.31 during the baseline period to 9.62 in the intervention period (p=0.0036). Pan cultures per 1000 patient days in which one of the orders was a UACC or UC fell by 42.2% from a median of 10.20 per 1000 patient days to 5.90 (p=0.0008). The comparison units saw no significant reductions in UACC and UC orders (p=0.21) or pan cultures (p=1.0). On the intervention units, the CAUTI rate for the baseline period was 1.31 per 1000 catheter days versus 0.79 in the intervention period (IRR = 1.65; p=0.44). GNR bacteremias remained stable on the intervention units between the baseline and intervention periods (p=0.82). Conclusion: This multidisciplinary intervention, leveraging EMR clinical decision support, reduced urine and pan culturing practices while demonstrating a trend towards a reduced CAUTI rate. The prevalence of GNR bacteremias remained consistent with baseline levels, suggesting the intervention did not cause harm.

Almost sure exponential stability and stochastic stabilization of discrete-time stochastic systems with impulses

This paper considers the almost sure exponential stability and stochastic stabilization problems of discrete-time stochastic systems (DTSSs) with impulsive effects, where the average impulsive interval is taken into account. By using the average impulsive interval approach and the strong law of large numbers, we not only establish the criteria for almost sure exponential stability of general nonlinear discrete-time impulsive stochastic systems (DTISSs) but also design an exact method of a stochastic perturbation to stabilize a given unstable impulsive discrete-time systems. Adopting the average impulsive interval approach and the strong law of large numbers, we established a criterion for almost sure exponential stability of general nonlinear DTISSs. Furthermore, a method of stochastic perturbation has been developed to stabilize an unstable impulsive discrete-time system. Finally, two simulation examples demonstrate the effectiveness of the derived results.

Comparative Study of Reduced Parameter Versions of the Bees Algorithm for Traveling Salesman Problem

Metaheuristics have shown dominance over exact methods with their capability to find near-optimal solutions to complex problems in a shorter time. Among these metaheuristics, the Bees Algorithm (BA) has proven its performance in various applications. However, fine-tuning the parameters of the BA is challenging due to its numerous parameters. There have been few studies aiming to reduce the number of parameters while maintaining or improving performance, such as the ternary BA, two-parameter BA, and Fibonacci BA. This paper reviews these variants for combinatorial problems using 13 datasets from the Travelling Salesman Problem TSPLIB. The results were compared using an independent t-test in conjunction with descriptive statistics. The findings show that the Fibonacci BA outperforms other variants, and potential suggestions for improvements in the future were proposed.

Neural network assisted branch and bound algorithm for dynamic berth allocation problems

One of the key challenges in maritime operations at container terminals is the need to improve or optimize berth operation schedules, thus allowing terminal operators to maximize the efficiency of quay usage. Given a set of vessels and a set of berths, the goal of the dynamic berth allocation problem is to determine the allocation of each vessel to a berth and the berthing time that minimizes the total service time. This problem can be solved using exact solution methods such as branch and bound (BB) algorithms or heuristic methods, however, exact methods do not scale to large-scale terminal operations. To this end, this paper proposes a BB algorithm in which branching decisions are made with a deep neural network. The proposed exact algorithm utilizes the search order of nodes based on the output of the neural network, with the goal of speeding up the search. Three types of solution representations are compared, along with machine learning models are created for each of them. Computational results confirm the effectiveness of the proposed method, which leads to computation times that are on average around half of those without the neural network.

Conway-Bromage-Lyndon (CBL): an exact, dynamic representation of k-mer sets.

In this article, we introduce the Conway-Bromage-Lyndon (CBL) structure, a compressed, dynamic and exact method for representing k-mer sets. Originating from Conway and Bromage's concept, CBL innovatively employs the smallest cyclic rotations of k-mers, akin to Lyndon words, to leverage lexicographic redundancies. In order to support dynamic operations and set operations, we propose a dynamic bit vector structure that draws a parallel with Elias-Fano's scheme. This structure is encapsulated in a Rust library, demonstrating a balanced blend of construction efficiency, cache locality, and compression. Our findings suggest that CBL outperforms existing dynamic k-mer set methods. Unique to this work, CBL stands out as the only known exact k-mer structure offering in-place set operations. Its different combined abilities position it as a flexible Swiss knife structure for k-mer set management. https://github.com/imartayan/CBL.

On some methods of determining proper frequencies torsional oscillations in metal-working machines

Forced torsional vibrations that occur in machine tool drives are one of the pressing problems of modern machine tool industry. The reasons for these fluctuations may be imbalances of rotating masses, structural imperfections of bearing units, operating or technological process features (for metal-cutting machines – eccentric clamping of the workpiece, processing of non-round workpieces, etc.). In this work, using the example of a real metal-cutting machine, the task of dynamic analysis of the drive of the main movement of the machine with determination of the natural frequencies of torsional vibrations is realized. The real object is replaced by a dynamic model (DM). When constructing the DM, the inertial and rigidity characteristics are determined from the conditions: – the kinetic energy of the object is equivalent to the kinetic energy of the DM (from this condition the axial moments of inertia of the concentrated masses of the row model are obtained); – the potential energy of elastic deformations of the object is equivalent to the potential energy of deformation of the DM (from this condition the rigidity coefficients of the DM are obtained). The natural frequencies of the row DM were determined in three ways: 1) by solving the frequency equation (exact method) in combination with the method of reducing the number of masses of A.P. Cherevkov; 2) partial systems method (fundamental frequency only); 3) method of residuals (Tolle). The following conclusions were made: 1) the main (smaller) frequency can be found by any of the methods considered (since the results of the three methods coincided); 2) when simplifying the DM using the method of partial systems, the criterion for the possibility of further simplifications over the specified frequency range should be taken into account; the transition from a three-mass DM to a two-mass one if the criterion in this calculation was not met led to an error in determining the value of the fundamental frequency of about 10%; 3) dynamic calculations of machines using DM and methods for determining natural frequencies make it possible, based on known frequencies of disturbing forces, to identify possible near-resonant operating modes and avoid them in advance by making changes to the design of the machine at the design stage

A novel heuristic algorithm for disruption mitigation in a global food supply chain

The Global Food Supply Chain (GFSC) often encounters challenges in maintaining the continuous flow of essential food products such as rice and wheat. In this paper, we consider possible disruptions in supply and transportation in GFSC. Disruptions may cause severe food shortages in some parts of the world. Moreover, a disruption is not necessarily a single independent event, as multiple disruptions may occur sequentially or simultaneously. After any interruption, it is essential to reoptimize the remaining flow activities in a short period under the changed conditions. Although both the initial flow plan and post-disruption plan can be generated by formulating them as Mixed Integer Linear Programming (MILP) models, such a mitigation plan is challenging because of time constraints and when multiple disruptions are considered. To address this issue, we proposed a novel heuristic algorithm that revises the ideal plan in the event of a disruption. The developed heuristic can deal with different types of disruptions, as well as a series of disruptions. The performance of the heuristic was judged by solving 300 problem instances and comparing the results with those obtained from the exact method. To demonstrate the applicability of the proposed algorithm in practice, we have solved three real-world disruption Scenarios. The analysis of results uncovered crucial managerial insights, recommending strategies for disruption mitigation, and proved to be an effective method for post-disruption planning.

Comparative Analysis of Exact Methods for Testing Equivalence of Prevalences in Bilateral and Unilateral Combined Data with and without Assumptions of Correlation

In clinical studies focusing on paired body parts, diseases can manifest on either both sides (bilateral) or just one side (unilateral) of the organs. Consequently, the data in these studies may consist of records from both bilateral and unilateral cases. There are two different methods of analyzing the data. One of the methods is assuming that the pair of measurements from the same subject are independent, while the other considers the correlation between paired organs. In terms of the homogeneity test of proportions, asymptotic methods have been proposed given the moderate size of data. This article extends the existing work by proposing exact methods to deal with the scenarios when the sample size is small and asymptotic methods perform poorly. The impact of the correlation assumption is also explored. Among the proposed methods, calculating p-values by replacing unknown parameters with estimated values while accounting for the correlation is recommended based on its satisfactory type I error controls and statistical powers. The proposed methods are applied to three real examples for illustration.

Efficacy and tolerability of Brivaracetam in people with intellectual disability compared to those without intellectual disability

IntroductionIn England, nearly a quarter of people with intellectual disability (PwID) have epilepsy. Though 70 % of PwID have pharmaco-resistant seizures only 10 % are prescribed anti-seizure medication (ASMs) licenced for pharmaco-resistance. Brivaracetam (BRV) licenced in 2016 has had nine post-marketing studies involving PwID. These studies are limited either by lack of controls or not looking at outcomes based on differing levels of ID severity. This study looks at evidence comparing effectiveness and side-effects in PwID to those without ID prescribed Brivaracetam (BRV). MethodsPooled case note data for patients prescribed BRV (2016–2022) at 12 UK NHS Trusts were analysed. Demographics, starting and maximum dose, side-effects, dropouts and seizure frequency between ID (mild vs. moderate-profound (M/P)) and general population for a 12-month period were compared. Descriptive analysis, Mann-Whitney, Fisher’s exact and logistic regression methods were employed. Results37 PwID (mild 17 M/P 20) were compared to 102 without ID. Mean start and maximum dose was lower for PwID than non-ID. Mean maximum dose reduced slightly with ID severity. No difference was found between ID and non-ID or between ID groups (Mild vs M/P) in BRV’s efficacy i.e. >50 % seizure reduction or tolerability. Mental and behavioural side-effects were more prevalent for PwID (27.0 % ID, 17.6 % no ID) but not significantly higher (P = 0.441) or associated with ID severity (p = 0.255). ConclusionThis is the first study on BRV, which compares ID cohorts with differing severity and non-ID. Efficacy, tolerability and side-effects reported are similar across differing ID severity to those with no ID.

Scheduling of Automated Wet-Etch Stations with One Robot in Semiconductor Manufacturing via Constraint Answer Set Programming

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constraint solvers is comparatively better than a decade ago, and some complex combinatorial problems can be better solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics.