Continuous variable control approach for home care crew scheduling

The home care crew scheduling problem (HCCSP) is defined as a dynamic routing and scheduling problem with caretakers' fixed appointments, and therefore has many similarities with the vehicle routing problem with time windows. Considering frequent demand changes regarding resource priorities, appointment alterations, and time windows in HCCSP, the control theoretic approach with discrete event distributed simulation provide substantial benefits by offering real-time response to demand changes. We develop dynamic models for HCCSP with dynamic patient appointments, and explain dynamics that span from controlling crew work times to home-visit scheduling. Also, the real-time feedback control algorithm is proposed to solve HCCSP, where it is performed based on the time-scaled approach that possibly eliminates the need for directly synchronizing events and thereby eliminates the complexity associated with discrete event distributed simulation approaches

Learning-based logistics planning and scheduling for crowdsourced parcel delivery

•A method is proposed to control orders and schedule routings of parcel pickup and delivery.•An optimal policy for controlling orders to maximize profitability is derived.•A reinforcement learning with an artificial neural network is utilized to solve the problem.•A continuous-variable feedback control approach is used to schedule multiple vehicle routings.•The proposed approach is beneficial when only limited number of vehicles is available. Today many domains have begun dealing with more complex and practical problems thanks to advances in artificial intelligence. In this paper, we study the crowdsourced parcel delivery problem, a new type of transportation, with consideration of complex and practical cases, such as multiple delivery vehicles, just-in-time (JIT) pickup and delivery, minimum fuel consumption, and maximum profitability. For this we suggest a learning-based logistics planning and scheduling (LLPS) algorithm that controls admission of order requests and schedules the routes of multiple vehicles altogether. For the admission control, we utilize reinforcement learning (RL) with a function approximation using an artificial neural network (ANN). Also, we use a continuous-variable feedback control algorithm to schedule routes that minimize both JIT penalty and fuel consumption. Computational experiments show that the LLPS outperforms other similar approaches by 32% on average in terms of average reward earned from each delivery order. In addition, the LLPS is even more advantageous when the rate of order arrivals is high and the number of vehicles that transport parcels is low

Opracowanie modelu oceny kompetencji do pomiaru umiejętności inspekcji wizualnej wykonywanej przez człowieka

Extensive research has already been done on visual inspection as well as on the effect of different factors on human inspection performance. However, a method should be developed to measure their inspection skill based on influencing factors. This study contributes to the literature by proposing a competency assessment model based on the influencing factors that can classify human labour into its respective skill levels. From the literature review, the influencing factors of visual inspection are listed and divided into five observed variables. A team of experts selected the significant factors with respect to the textile and clothing industry. The analytical hierarchy process is used to measure their respective weights so as to calculate the inspection performance in terms of a competency score. A numerical example is presented and the model proposed successfully determined the competency score, and inspectors are classified into their respective skill levels according to the well-defined cut-off values. This study enables organisations to classify available human labour into its skill levels and utilise them according to their capacity.Dotychczas przeprowadzono szeroko zakrojone badania dotyczące inspekcji wizualnej, a także wpływu różnych czynników na wyniki inspekcji wykonywanej przez człowieka. W pracy skupiono się na opracowaniu metody pomiaru umiejętności inspekcji w oparciu o czynniki na nie wpływające. Praca wnosi wkład do literatury, proponując model oceny kompetencji oparty na czynnikach wpływających, które mogą sklasyfikować ludzką pracę według odpowiednich poziomów umiejętności. Na podstawie przeglądu literatury wyszczególniono czynniki wpływające na kontrolę wzrokową i podzielono je na pięć obserwowanych zmiennych. Zespół ekspertów wybrał istotne czynniki w branży tekstylno-odzieżowej. Proces hierarchii analitycznej posłużył do pomiaru ich odpowiednich wag, w celu obliczenia wyników inspekcji w kategoriach oceny kompetencji. Przedstawiono przykład liczbowy, a zaproponowany model z powodzeniem określił punktację kompetencji, tak aby inspektorzy mogli być klasyfikowani według ich odpowiednich poziomów umiejętności zgodnie z dobrze określonymi wartościami granicznymi. Badanie umożliwiło organizacjom klasyfikowanie dostępnej siły roboczej według poziomów umiejętności i wykorzystywanie jej zgodnie z ich możliwościami

Power demand risk models on milling machines

The measurement of power demand risks in manufacturing power systems will benefit manufacturers and the wider society. If the risks can be characterized using manufacturing parameters, manufacturers can better control risks originating in those parameters, select less power-risky production plans, and reduce utility costs and resource consumption. The measurement of risk can also help manufacturers and power suppliers to protect their power systems from unexpected disturbances. Existing measures of risk, however, do not consider time duration, and thus cannot accurately quantify the risks in manufacturing power systems; the risks of a period of high power demand must be evaluated with the duration of the surge. Therefore, new methods of measuring power demand risks are proposed, adapting measures drawn from the field of finance. With a focus on milling operations, processing power is shown to be a function of processing amount (A) and processing time (T), and a power demand distribution is directly derived as a joint distribution of A and T. A bivariate random variable model with copulas is applied to examine the correlation in the joint distribution. Then, based on evaluation of a probability distribution of power demand from A and T, new risk measures are introduced. Illustrative examples are provided to show how the proposed measures can quantify the power demand risks from milling machines, based on manufacturing parameters. Certain manufacturing parameters are found to affect overall power demand risks, including i) raw material type, ii) variability in processing time, and iii) correlation between A and T. In the examples, these three factors increase power demand risks by up to 108%, 67%, and 1% respectively

Smart logistics: distributed control of green crowdsourced parcel services

This paper presents the development of an integrated decision-making framework for on-demand parcel delivery services that considers Just-In-Time delivery, fuel consumption and carbon emissions. Optimal policies based on the Markov decision process are established to allow for inclusion of parcel delivery requests. The framework's integrated dynamic algorithm, based on a continuous variable feedback control, allows for unified processing of delivery requests and route scheduling. Computational experiments show that the integrated approach could increase revenue by 6.4% by reducing fuel and emission costs by 2.5%; however, the approach may incur more cost in terms of timeliness compared to a myopic approach

Predictive analytics for delivering prevention services

•Logical Analysis of Data (LAD) is applied to prevention research.•Real data sets from Baltimore City Public Schools are used for LAD analysis.•Data patterns explaining children’s conduct disorder are extracted by LAD.•We showed how LAD can reduce the cost of delivering prevention services. Early diagnosis and prevention of problematic behaviors in youth play an important role in reducing treatment costs and decreasing the toll of antisocial behavior. Over the last several years, the science of preventing antisocial behavior in youth has made significant strides, with the development of evidence-based prevention programs (EBP) using randomized clinical trials. In this paper, we use a real case implemented by schools in an urban school district of 80,000 students in a mid-Atlantic state to show how predictive analytics can help to improve the quality of prevention programs and reduce the cost of delivering associated services. Data patterns are extracted from conduct disorder assessments using the Teacher Observation of Classroom Adaptation (TOCA) screening instrument and evaluated using the results of the Diagnostic Interview Schedule for Children (DISC). A mathematical method called Logical Analysis of Data (LAD) is used to analyze data patterns. Experimental results show that up to 91.58% of the cost of administering DISC would be saved by correctly identifying participants without conduct disorder and excluding them from the DISC test

Determination of Bicycle Handle Diameters considering Hand Anthropometric Data and User Satisfaction

Ergonomic product design considering both anthropometric variability and user preference is required for harmonizing the target users and products. In this study, bicycle handle diameters for three size categories were determined by considering anthropometric variability and preference. To design the bicycle handles, a four-step process was applied: (1) define anthropometric data, (2) develop size chart, (3) define a design equation, and (4) determine design values. In the first step, the 1988 US Army data was chosen as anthropometric data for the design target population. In the second step, to develop a size chart of bicycle handle, hand length and circumference were selected as key dimensions by principal component analysis on six representative hand dimensions. Next, a size chart of three categories (small: 175.5 mm, medium: 186.7 mm, and large: 196.2 mm) were derived by K-means clustering analysis for hand length and circumference. In the third step, the design equation accounting geometrical relationship between the sizes of two key dimensions and diameters of bicycle handle was adopted from a relevant existing research. In the last step, design values (40.9 mm, 43.5mm, and 45.7 mm) for each size category were calculated by inputting the sizes of the key dimensions to the design equation. To evaluate user satisfaction level of the bicycle handles, a user testing of three handle prototypes was conducted for 17 participants with various hand sizes. The test results showed that satisfaction scores for each hand group were significantly higher at the corresponding size category