A two-phase variable neighbourhood search algorithm for assembly line worker assignment and balancing problem type-II: an industrial case study

The assembly line worker assignment and balancing problem type-II (ALWABP-2) occurs when workers and tasks (where task times depend on workers’ skills) are to be simultaneously assigned to a fixed number of workstations with the goal of minimising the cycle time. In this study, a two-phase variable neighbourhood search (VNS) algorithm is proposed to solve the ALWABP-2 due to the NP-hard nature of this problem. In the first phase of the algorithm, a VNS approach is applied to assign tasks to workstations with the aim of minimising the cycle time while in the second phase, a variable neighbourhood descent method is applied to assign workers to workstations. The performance of the proposed algorithm is tested on well-known benchmark instances. In addition, the proposed algorithm has been used to solve a real case study from a consumer electronics company that manufactures LCD TVs. The results show that the algorithm is superior to the methods reported in the literature in terms of its higher efficiency and robustness. Furthermore, the algorithm is easy to implement and significantly improves the performance of the final assembly line for the investigated LCD TV real case study.     

Development and gamma-scintigraphy study of Hibiscus rosasinensis polysaccharide-based microspheres for nasal drug delivery

Objective: This work describes the application of natural plant polysaccharide as pharmaceutical mucoadhesive excipients in delivery systems to reduce the clearance rate through nasal cavity.

Methods: Novel natural polysaccharide (Hibiscus rosasinensis)-based mucoadhesive microspheres were prepared by using emulsion crosslinking method for the delivery of rizatriptan benzoate (RB) through nasal route. Mucoadhesive microspheres were characterized for different parameters and nasal clearance of technetium-99m (^99mTc)-radiolabeled microspheres was determined by using gamma-scintigraphy.

Results: Their Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) studies showed that the drug was stable during preparation of microspheres. Aerodynamic diameter of microspheres was in the range 13.23?±?1.83–33.57?±?3.69?µm. Change in drug and polysaccharide ratio influenced the mucoadhesion, encapsulation efficiency and in-vitro release property. Scintigraphs taken at regular interval indicate that control solution was cleared rapidly from nasal cavity, whereas microspheres showed slower clearance (p?Conclusion: Natural polysaccharide-based microspheres achieved extended residence by minimizing effect of mucociliary clearance with opportunity of sustained delivery for longer duration.     

Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product. Due to resource constraints, when software is subjected to modifications, the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy. One such strategy is test case prioritization (TCP). Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest. Nonetheless, singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches. Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile, this study has introduced a memetics-inspired methodology for TCP. The proposed structure is first embedded with diverse parameters, and then traditional steps of the shuffled-frog-leaping approach (SFLA) are followed to prioritize the test cases at unit and integration levels. On 5 standard test functions, a comparative analysis is conducted between the established algorithms and the proposed approach, where the latter enhances the coverage rate and fault detection of re-ordered test sets. Investigation results related to the mean average percentage of fault detection (APFD) confirmed that the proposed approach exceeds the memetic, basic multi-walk, PSO, and optimized multi-walk by 21.7%, 13.99%, 12.24%, and 11.51%, respectively.     

PVC-Based 2,2,2-Cryptand Sensor for Zinc Ions

A PVC-based membrane of 2,2,2-cryptand exhibits a very good response for Zn(2+) in a wide concentration range (from 2.06 ppm to 6.54 × 10(3) ppm) with a slope of 22.0 mV/decade of Zn(2+) concentration. The response time of the sensor is <10 s, and the membrane can be used for more than 3 months without any observed divergence in potentials. The proposed sensor exhibits very good selectivity for Zn(2+) over other cations and can be used in a wide pH range (2.8-7.0). It has also been possible to use this assembly as an indicator electrode in potentiometric titrations involving zinc ions.     

Alternating time versus deterministic time: A separation

Paulet al. [12] proved that deterministic Turing machines can be speeded up by a factor of log*t (n) using four alternations; that is, DTIME(t(n) log*t(n)) σ₄(t(n)). Balcázaret al. [1] noted that two alternations are sufficient to achieve this speed-up of deterministic Turing machines; that is, DTIME(t(n) log*t(n)) Σ₂(t (n)). We supply a proof of this speed-up and using that show that for each time-constructible functiont(n), DTIME(t(n)) ⊂ Σ₂(t(n)); that is, two alternations are strictly more powerful than deterministic time. An important corollary is that at least one (or both) of the immediate generalizations of the result DTIME(n) ⊂ NTIME(n) [12] must be true: NTIME(n) ≠ co-NTIME(n) or, for each time-constructible functiont(n), DTIME(t (n)) ⊂ NTIME(t (n)). This work was supported in part by NSF Grant CCR-8909071. The preliminary version of the work was done when the author was a graduate student at The Ohio State University.     

A hybrid approach to asset allocation with simultaneous consideration of suitability and optimality

In this paper, we develop mathematical models for simultaneous consideration of suitability and optimality in asset allocation. We use a hybrid approach that combines behavior survey, cluster analysis, analytical hierarchy process and fuzzy mathematical programming.     

Leveraging temporal and spatial separations with the 24-hour knowledge factory paradigm

The 24-H Knowledge Factory facilitates collaboration between geographically and temporally distributed teams. The teams themselves form a strategic partnership whose joint efforts contribute to the completion of a project. Project-related tasks are likewise distributed, allowing tasks to be completed on a continuous basis, regardless of the constraints of any one team’s working hours. However, distributing a single task between multiple teams necessitates a handoff process, where one team’s development efforts and task planning are communicated from one team ending their shift to the next that will continue the effort. Data management is, therefore, critical to the success of this business model. Efficiency in data management is achieved through a strategic leveraging of key tools, models, and concepts.     

Design of interval networks based on neural network and Choquet integral

Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.