Delivery Schedule Research Articles

Coordinated production and delivery scheduling for service-oriented manufacturing systems with deterioration effect

Service-oriented manufacturing (SOM) is a new industrial model that combines manufacturing with service. To enhance the competitiveness of manufacturers in SOM systems, we investigate a coordinated production and delivery scheduling problem considering a third-party logistics (3PL) provider. The considered problem can be divided into two phases, including production phase and delivery phase. In the production phase, jobs are processed by multiple manufacturers located in different geographical zones. Jobs are with deteriorating processing time and set-up time. Several structural properties of the problem in the production phase are proposed, based on which a heuristic algorithm is designed to make decisions on job batching and batch sequencing. After being processed, the finished jobs are immediately delivered to a customer by manufacturers’ own vehicles or the 3PL provider. A dynamic programming algorithm is developed to solve the problem in the delivery phase. The integrated scheduling problem is proved to be NP-hard. Hence, we develop a general variable neighbourhood search (GVNS) to solve the problem in a reasonable time. The basic variable neighbourhood descent (BVND) is used as a local search method in the GVNS. Finally, the computational experiments are conducted and the results verify the effectiveness and stability of the proposed approach.

Determining optimal fuel delivery strategies under uncertainty

During the preparation before a hurricane makes landfall, affected individuals may be asked to evacuate. Large and small-scale evacuations can cause rapid increases in the demand for gasoline fuel. However, during a hurricane vessels carrying gas may be delayed and/or rerouted, adding to the difficulty of providing the necessary gas in affected areas. In this work, we determine alternate delivery locations and times for vessels carrying fuel that are scheduled to arrive and deliver fuel at ports impacted by an approaching hurricane. Motivated by Hurricane Irma in Florida, we develop a multi-period stochastic scheduling model that incorporates hurricane (weather) advisories, fuel delivery schedules, port storage capacities, and port docking capacities. Our model determines the best schedule based on two objectives: (1) minimize the total unmet demand at each port, and (2) minimize inequities in unmet demands among the ports. We also present a case study and a numerical experiment based on fuel delivery data from ports in Florida. Among our key findings is that port availability is the driving factor in determining feasible schedules for vessel gas deliveries. We also present a scheduling heuristic that dynamically adapts to weather advisories so as to minimize the impact of unmet demand in the affected areas.

Multi-Criteria Coordinated Electric Vehicle-Drone Hybrid Delivery Service Planning

According to recent works, a coordinated delivery strategy in which terrestrial and aerial electric vehicles work together effectively improves delivery throughput and energy efficiency. However, most research on logistics and transportation focuses on delivery performance and does not care about energy efficiency, with three main limitations: 1. Most of these works ignore geographic information along the delivery route, while road slope is one of the most critical energy consumption components. 2. Vehicle and drone power consumption models are simplified as driving mileage, while the delivery time is a significant concern. 3. The battery model is simplified as a linear model even though practical batteries have non-linearity properties. This work proposes a framework to provide energy- and time-efficient delivery schedules with a hybrid delivery service with terrestrial and aerial electric vehicles. We first implement accurate electric van and drone power models and a battery model based on manufacturers' system specifications and experimental data. Then, we propose a heuristic delivery scheduling algorithm to determine the electric van and drone delivery schedule. We also introduce various cost functions to evaluate the delivery scheduling results regarding time, energy, the weighted sum of time and energy, and the economic model. The proposed framework is validated on randomly implemented delivery missions and delivery scenarios in existing cities. Results indicate that the coordinated delivery saves delivery costs up to 27.25% in terms of the economic model compared with the electric van-only delivery schedule.

Accelerated Repetitive Transcranial Magnetic Stimulation to Treat Major Depression: The Past, Present, and Future.

Repetitive transcranial magnetic stimulation (rTMS) is an effective and evidence-based therapy for treatment-resistant major depressive disorder. A conventional course of rTMS applies 20-30 daily sessions over 4-6 weeks. The schedule of rTMS delivery can be accelerated by applying multiple stimulation sessions per day, which reduces the duration of a treatment course with a predefined number of sessions. Accelerated rTMS reduces time demands, improves clinical efficiency, and potentially induces faster onset of antidepressant effects. However, considerable heterogeneity exists across study designs. Stimulation protocols vary in parameters such as the stimulation target, frequency, intensity, number of pulses applied per session or over a course of treatment, and duration of intersession intervals. In this article, clinician-researchers and neuroscientists who have extensive research experience in accelerated rTMS synthesize a consensus based on two decades of investigation and development, from early studies ("Past") to contemporaneous theta burst stimulation, a time-efficient form of rTMS gaining acceptance in clinical settings ("Present"). We propose descriptive nomenclature for accelerated rTMS, recommend avenues to optimize therapeutic and efficiency potential, and suggest using neuroimaging and electrophysiological biomarkers to individualize treatment protocols ("Future"). Overall, empirical studies show that accelerated rTMS protocols are well tolerated and not associated with serious adverse effects. Importantly, the antidepressant efficacy of accelerated rTMS appears comparable to conventional, once daily rTMS protocols. Whether accelerated rTMS induces antidepressant effects more quickly remains uncertain. On present evidence, treatment protocols incorporating high pulse dose and multiple treatments per day show promise and improved efficacy.

Disaster Medical Responder’s Course for Training of Field Medical Teams in Singapore

Introduction:Casualties need to be triaged, stabilized and treated before they can be evacuated to the hospital. However, when Field Medical Teams (FMTs) arrive at the First Aid Post (FAP), the staff has to perform outside of their usual settings. There are also differences in the conception of medical operations, organization of the FAP, availability of medical equipment and supply, as well as means of communication, command, and control which can affect their performance and eventually the optimal survival of casualties during a mass casualty incident.Method:Guided by Kern’s model for curriculum development, Disaster Medical Responder's Course (DMRC) was developed. The curriculum focused on disaster response operations and processes; roles and responsibilities; command, control and communication; as well as supplies and resources. The content was taught through interactive lectures and skill stations. Course evaluation was based on the Kirkpatrick Model. A feedback form evaluated the reaction of the participants as to whether the course was relevant, if they learnt new knowledge and skills, and if they could apply these to their roles as FMTs. A tabletop exercise evaluated learning with participants working collaboratively.Results:DMRC has been sustainable since 2013 with six to eight courses per year. There had been numerous revisions of the content and delivery to keep up-to-date with the latest concept of operations, best practices from the literature, as well as educational methodologies. The last update was in 2020 in response to the COVID-19 pandemic where course schedule and mode of delivery were adjusted to comply with the safe management measures.Conclusion:FMTs will require training so they can function to their maximum capacity and capability. In Singapore, DMRC is the course for this unique and important training of FMTs. DMRC plays a pivotal role in ensuring the preparedness and operational readiness of FMTs for mass casualty incidents.

Dynamic truck–drone routing problem for scheduled deliveries and on-demand pickups with time-related constraints

The on-demand logistics services have risen continuously with the expansion of e-commerce. Logistics companies face challenges to meet customers’ expectations with high efficiency and reliability at a low cost. Hence, this paper investigates the dynamic truck–drone routing problem with scheduled deliveries and on-demand pickups (D-TDRP-SDOP) for an on-demand logistics system. Trucks and drones are deployed to serve a batch of deterministic deliveries and an uncertain set of pickup requests with deadlines subject to maximum working hour constraints. The drones can serve multiple requests per trip subject to load constraints and endurance capacity restrictions. The service provider aims to maximize the total profits by dynamic reassignment and recourse of the vehicles. We formulate the D-TDRP-SDOP problem as a Markov decision process (MDP) and propose a heuristic solution approach framework, consisting of an offline enhanced construction algorithm (OECA) and a segment-based heuristic, to solve the MDP. The comprehensive numerical experiments demonstrate the effectiveness of the proposed solution approach and the benefits of the model. Our model improves the total profits by 15% by considering on-demand requests, and the drone operations contribute to a 50% improvement in the acceptance rate of dynamic customer requests. Improved drone technology, such as a higher drone speed and a higher battery capacity, can enable the system to serve more on-demand requests and increase the final profits. However, the benefit diminishes when the drone capability reaches a certain threshold.

Pharmacokinetic and Pharmacodynamic Consequences of Cytochrome P450 3A Inhibition on Mitragynine Metabolism in Rats.

Mitragynine, an opioidergic alkaloid present in Mitragyna speciosa (kratom), is metabolized by cytochrome P450 3A (CYP3A) to 7-hydroxymitragynine, a more potent opioid receptor agonist. The extent to which conversion to 7-hydroxymitragynine mediates the in vivo effects of mitragynine is unclear. The current study examined how CYP3A inhibition (ketoconazole) modifies the pharmacokinetics of mitragynine in rat liver microsomes in vitro. The study further examined how ketoconazole modifies the discriminative stimulus and antinociceptive effects of mitragynine in rats. Ketoconazole [30 mg/kg, oral gavage (o.g.)] increased systemic exposure to mitragynine (13.3 mg/kg, o.g.) by 120% and 7-hydroxymitragynine exposure by 130%. The unexpected increase in exposure to 7-hydroxymitragynine suggested that ketoconazole inhibits metabolism of both mitragynine and 7-hydroxymitragynine, a finding confirmed in rat liver microsomes. In rats discriminating 3.2 mg/kg morphine from vehicle under a fixed-ratio schedule of food delivery, ketoconazole pretreatment increased the potency of both mitragynine (4.7-fold) and 7-hydroxymitragynine (9.7-fold). Ketoconazole did not affect morphine's potency. Ketoconazole increased the antinociceptive potency of 7-hydroxymitragynine by 4.1-fold. Mitragynine (up to 56 mg/kg, i.p.) lacked antinociceptive effects both in the presence and absence of ketoconazole. These results suggest that both mitragynine and 7-hydroxymitragynine are cleared via CYP3A and that 7-hydroxymitragynine is formed as a metabolite of mitragynine by other routes. These results have implications for kratom use in combination with numerous medications and citrus juices that inhibit CYP3A. SIGNIFICANCE STATEMENT: Mitragynine is an abundant kratom alkaloid that exhibits low efficacy at the μ-opioid receptor (MOR). Its metabolite, 7-hydroxymitragynine, is also an MOR agonist but with higher affinity and efficacy than mitragynine. Our results in rats demonstrate that cytochrome P450 3A (CYP3A) inhibition can increase the systematic exposure of both mitragynine and 7-hydroxymitragynine and their potency to produce MOR-mediated behavioral effects. These data highlight potential interactions between kratom and CYP3A inhibitors, which include numerous medications and citrus juices.

British Society for Haematology guideline for anticoagulantmanagement of pregnant individuals with mechanical heart valves.

British Society for Haematology guideline for anticoagulantmanagement of pregnant individuals with mechanical heart valves.

The Effectiveness of Service Delivery in Fast Moving Consumer Goods Supply Value Chain: A Case Study of Brands Africa Zambia Limited Company

The study focused on analysing the effectiveness of service delivery in fast moving goods supply chains taking Brands Africa as a case study. Some observed supply chain related challenges of Brands Africa that included lower sales volume, high volume of product returns, sale of some expired products out of stock of some product lines and high prices of some merchandise made the study to be mandatory. A pragmatic research paradigm was used in collecting data from a cross-sectional survey sample of 87 respondents and a case study sample of 5 research participants using a survey questionnaire and an in-depth interview guide as research instruments, respectively. The study concluded that supplier contact was strongest on communication with suppliers, importing goods to Zambia and good relationships with suppliers. The study showed Brands Africa had good advertising and merchandising services, and effective marketing research activities in its supply chains The study established that quality of merchandise high, and market image and reputation of Brands Africa was high, and that inventory management, price of merchandise and efficiency of supply chains needed improvement. The study also concluded that there is a significant relationship between supplier contact and marketing contact activities of Brands Africa with the overall satisfaction of its stakeholders. The research recommends Brands Africa to upgrade its product, financial, information and social with its suppliers, transporters, wholesalers and retailers. The study will also guide managers on cost cutting ways, quality management, ensuring efficient operations and maintaining positive image of Brands Africa. The study recommended Brands Africa’s on policy issues like quality of merchandise, quantity of merchandise, price range of merchandise, delivery schedules and communication systems will be improved. The researcher recommends another research to be carried out at national level covering supply chain of other fast-moving consumer goods companies and also considering more independent variables.

The “30-minute rule” for expedited delivery: fact or fiction?

Initially developed from hospital feasibility data from the 1980s, the "30-minute rule" has perpetuated the belief that the decision-to-incision time in an emergency cesarean delivery should be <30 minutes to preserve favorable neonatal outcomes. Through a review of the history, available data on delivery timing and associated outcomes, and consideration of feasibility across several hospital systems, the use and applicability of this "rule" are explored, and its reconsideration is called for. Moreover, we have advocated for balanced consideration of maternal safety with rapidity of delivery, encouraged process-based approaches, and proposed standardization of terminology regarding delivery urgency. Furthermore, a standardized 4-tier classification system for delivery urgency, from class I, for a perceived threat to maternal or fetal life, to class IV, a scheduled delivery, and a call for further research with a standardized structure to facilitate comparison have been proposed.

LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation

PurposeThe artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.Design/methodology/approachMeeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.FindingsThe proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.Originality/valueLong short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.

Technology Adoption in Food Supply Chain Management in Developing Countries: A Review

Food loss and waste is one of the major reasons for global food insecurity. Food losses occur at all stages of the food supply chain, including agricultural produce, harvesting, transport, storage, and processing. Food loss and waste are exacerbated by a lack of adequate and effective food supply chain management (FSCM). An effective food supply chain (FSC) includes designing processing and distribution centres, managing the cold chain, and designing reverse logistics. It also includes functions like allocating warehouse storage capacity, vehicle routing and material flow issues, delivery scheduling, and inventory management issues. Adoption of new information technology such as big data and Internet of Things can improve FSC performance and have a significant impact on reducing food waste and loss. An effective system for sharing information can enhance FCC performance, and intelligence in automated retail ordering can prevent food spoilage, while machine learning, and blockchain can improve FSC traceability. However, there are several obstacles to the use of technology in the FSC, particularly in developing countries. These are classified as technical, financial, social, operational, educational, and governmental. This paper provides an in-depth review of the existing literature on the FSC, technology applications in improving FSCM, key challenges associated with technology adoption, and interventions that may help overcome these challenges. It adds to the existing literature on FSCM, especially on issues related to technology adoption in the FSC by developing countries, and is a useful resource for students, researchers and food supply chain professionals.

Fetomaternal Outcomes of Postdated Pregnancy: A Prospective Observational Study

Background: With increasing gestational age, the risk to the mother and the foetus increases. Our research aims to examine maternal and foetal outcomes in pregnancies that go over the scheduled delivery date in order to establish safe induction times and the most suitable delivery method. Method: This was a 6-month prospective observational study conducted in the Department of obstetrics and gynaecology, LD Hospital GMC Srinagar from July 2017 to December 2017. The patient provided written, fully informed consent. The non-random sampling strategy resulted in the inclusion of 100 patients in total. Results: We observed that with an average gestational age of (40.8±2.76) weeks, majority of our patients (77.8%) had gestational age of 40-41 weeks. Majority of our patients (65.6%) had NVD mode of delivery. Meconium stain liquor with fetal distress was indication for cesarean section for 29% of patients, followed by failure of induction as the cesarean indication for 25.8% patients. Respiratory distress syndrome was evident in 8.9%, meconium aspiration syndrome was found in 6.7% patients, 3.3% and there were two neonatal deaths. Conclusion: Fetal discomfort, meconium aspiration syndrome, and foetal hypoxia were all connected with postdated pregnancy among other perinatal issues. Postpartum haemorrhage (PPH), perineal tears, cervical tears, and shoulder dystocia were among the consequences of childbirth that were more likely to occur. Obstetricians find managing postdated pregnancies challenging, but with good advice and monitoring, they can reduce maternal anxiety and unfavourable outcomes. Keywords: Last menstrual period, Maternal complication, Postdated, Pregnancy outcome

Determination of Requirements for the Improvement of Occupational Safety in the Cleaning of Vertical Tanks of Petroleum Products

Since the beginning of the second industrial revolution, the use of tanks for the storage of petroleum products ensured the permanent supply of equipment that depended on fossil fuel derived from petroleum, either for direct consumption or as an element for power generation. For correct operation, periodic cleaning of these confined spaces was required, being a common practice for the direct exposure of operators to explosive atmospheres. Currently, there are many industries that keep this kind of deposit, and cleaning works are considered of high occupational risk. In this context, the question arises as to whether human–machine collaboration thanks to the technologies that compose Industry 5.0 can mitigate these risks while generating a sustainable balance by optimizing costs and protecting the environment. In the present work, the analytic hierarchy process (AHP) method is used to prioritize the requirements that should be compiled to establish safe protocols in tank cleaning works, solving the multi-criteria problem. Results prove that a couple of alternatives improve the working conditions of the people involved in this process: the chemical cleaning and the robotic cleaning, which approximately accounts for two thirds of the decision. These requirements are aligned with the Industry 5.0 paradigm, encouraging the use of robots for high-risk processes, and influencing human behavior. In addition, cost reduction is achieved without compromising on quality of service or delivery schedule, thus enabling a circular economy that promotes occupational safety in company policies.

Main operating room deliveries for patients with high-risk cardiovascular disease

BackgroundHigh-risk cardiovascular disease (CVD) prevalence in pregnant patients is increasing. Management of this complex population is not well studied, and little guidance is available regarding labour and delivery planning for...

The Design Of Donut Ordering Information System To Expand Website-Based Market Share (WEB) (Case Study On UD Brandan Donuts)

Brandan Donuts is a business that is engaged in the culinary field, especially delicious and healthy snacks, namely donuts. UD Brandan Donuts was founded by Jumardani, on September 1, 2015, at Jalan Sisingamangaraja, Simpang Limun, Medan City. The problem with UD Brandan Donuts is that the ordering and marketing system still relies on media for direct interaction with consumers (buyers) which makes it difficult for the management to expand market share. The purpose of this research is to build an order information system using the DFD method and apply the design of an online ordering information system at UD Brandan Donuts (WEB). The research uses the data flow diagram (DFD) method to design the system flow and uses the implementation of a software program, Xampp in designing a website (WEB). The results on the website design there are 2 levels that will run in ordering donuts online, namely Level 1 consisting of the buyer login process, ordering, delivery, admin login, financial reports. And level 2 consists of the process of registering a buyer's account, logging in, selecting a donut product, filling in order data, selecting the type of transaction, payment, confirmation of order completion, delivery schedule, order confirmation until, input of donut category, input of donut stock, confirmation of order data, confirmation payment, promotion, daily report processing, monthly report processing, and annual report processing. The conclusion in this research is that the context diagram design contains 6 entities and for the data flow diagram (DFD) design it has a level of 2.17 and as many as 22 flowcharts. And produce a website (WEB) that displays an admin page, buyer page and courier page to make it easier for consumers (buyers) to order online.

Analyzing of Project Delivery Risks in Highway Construction During the Covid-19 Pandemic

The Covid-19 pandemic has hit all sectors of the business world, and the construction sector is no exception, resulting in delays, loss of efficiency, and cost impacts due to Covid-19 and the response to related government regulations, there is little or no precedent to help contractor companies understand what the potential impacts are. Covid-19 in the future and construction program when the lockdown begins. However it is very likely that work will need to be stopped due to health and safety concerns. Even if the project does not stop altogether, it is likely to be more expensive and take more time. This study attempts to conduct a risk assessment of project delivery with the most popular system, to see risk factors and then analyze risk factor ratings at all stages of project implementation until construction handover. The Covid-19 pandemic can be said to be a new phenomenon that has not much literature, so the identification of risk factors using a literature review was validated by expert discussion method, assessment by probability and impact, while the analytical method used the Analytical Hierarchy Process (AHP) approach. The results of dominant and major priority of risks including construction work health and safety, delay in delivery schedule, delays in procuring materials, labor, and equipment and delay in certification of bills. Keywords: Pandemic Covid-19 · Risk · Project · Highway Construction

Distributionally robust energy-transportation coordination in coal mine integrated energy systems

In this paper, a coordinated operation approach is proposed for scheduling the energy-transportation coupled coal mine integrated energy system (CMIES) under diverse uncertainties. As the coupling equipment in the coal transportation network (CTN) and the CMIES, the belt conveyors are able to coordinate the coal delivery scheduling and energy management. However, lacking the CTN modelling remains an unsolved challenge. Firstly, this paper proposed a novel energy-transportation coordinated model, consisting of the radial CTN and second-order cone programming (SOCP) relaxed CMIES. To address uncertainties from renewable energy generation output and raw coal production, the distributionally robust optimization (DRO) method is applied under the two-time scale operation framework to overcome the drawbacks of robust optimization and stochastic programming. The first timescale, i.e., the day-ahead scheduling, is focused on energy dispatching at long time intervals while the second scale i.e., the intra-day.scheduling, deals with uncertainties at short time intervals. Specifically, an event-wise Wasserstein ambiguity set is devised to handle the issue of probability distribution function information requirement, which is hard to obtain in practice. Finally, a real case of CMIES is simulated to validate the effectiveness of our proposed model and method. The results reveal that our method can effectively enhance the operational economy, realize decarbonization, and fulfill the coal transportation continuity compared to conventional methods.

The cyclic production routing problem

This paper introduces the Cyclic Production Routing Problem (CPRP). The CPRP is an extension of the well-known NP-hard Production Routing Problem (PRP), which is a hard-to-solve combinatorial optimisation problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. Under the PRP setting, a manufacturer is responsible for determining production decisions, as well as the timing and quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimising the production, inventory, distribution and routing decisions. In this paper, the basic PRP model is modified to generate repeatable cyclic production and delivery schedules. A two-commodity flow formulation is proposed along with valid inequalities. Extensive comparisons between the basic PRP and the proposed cyclic variant on well-known benchmark instances are provided. The new variant is significantly harder to solve, especially when the vehicle fleet is limited. From a managerial perspective, the generation of cyclic production-routing schedules significantly increases all costs, whereas the number of vehicle routes required to implement a cyclic schedule is higher.

Delivery scheduling in meat industry using reinforcement learning

Delivery scheduling in meat industry using reinforcement learning