Order Scheduling Research Articles

A network analysis of decision strategies of human experts in steel manufacturing

Steel production scheduling is typically accomplished by human expert planners. Hence, instead of fully automated scheduling systems, steel manufacturers prefer auxiliary recommendation algorithms. Through the suggestion of suitable orders, these algorithms assist human expert planners who are tasked with the selection and scheduling of customer orders. However, it is hard to estimate, what degree of complexity these algorithms should have as steel campaign planning generally lacks precise rule-based procedures; in fact, it requires far-reaching domain knowledge as well as intuition that can only be acquired by years of business experience. Here, contrary to developing new algorithms or improving older ones, we introduce a shuffling-aided network method to assess the complexity of the selection patterns established by a human expert. This technique allows us to formalize and represent the tacit knowledge that enters the campaign planning. As a result of the network analysis, we have discovered that the choice of appropriate customer orders for immediate production is primarily determined by the orders’ carbon content (to be precise: the carbon equivalent). Surprisingly, trace elements like manganese, silicon, and titanium have a lesser impact on the selection decision than assumed by the pertinent literature. Our approach can serve as an input to a range of automated decision-support systems, whenever an expert needs to create groups of orders ('production campaigns') that fulfill certain implicit selection criteria.

Adaptive search strategy based chemical reaction optimization scheme for task scheduling in discrete multiphysical coupling applications

Parallel computing problems of multiphysical coupling applications based on discrete grids can be equivalently transformed into parallel computing problems based on a directed acyclic graph (DAG). Due to the problem of the discrete high-dimensional grids of the multiphysical coupling application, the directed data dependencies usually have parallelism. Moreover, this kind of scheduling problem based on DAG is NP-hard problem. Heuristic algorithms are often used to achieve optimal execution order scheduling of tasks and mapping between tasks and processors. In this paper, we propose an improved chemical reaction optimization algorithm based on adaptive search strategy (ASSCRO), which is used to solve the DAG task scheduling problem of discrete multiphysical coupling applications. ASSCRO is divided into two phases. The first phase is used to search the directed execution order of the tasks, and the second phase aims to use the heuristic strategy to map the tasks to the processors efficiently. In the four basic reactions of CRO, the algorithm can cover a larger search solution space by using an adaptive search strategy, it can obtain a better solution, and achieve less overhead and superior performance than the state-of-the-art. We conducted the experiment that applied our ASSCRO to deal with the multiphysical coupling applications. The experimental results showed that the proposed algorithm outperforms other algorithms in multiple metrics when dealing with DAG scheduling problems.

Prioritizing and Scheduling of Production Orders in Non-homogenous Production Systems

Prioritizing and Scheduling of Production Orders in Non-homogenous Production Systems

Inequitable Access to Transplants: Adults With Impaired Decision-Making Capacity.

Inequitable access to deceased donor organs for transplantation has received considerable scrutiny in recent years. Emerging evidence suggests patients with impaired decision-making capacity (IDC) face inequitable access to transplantation. The “Ethical and Legal Issues” working group of the European Society of Transplantation undertook an expert consensus process. Literature relating to transplantation in patients with IDC was examined and collated to investigate whether IDC is associated with inferior transplant outcomes and the legitimacy of this healthcare inequality was examined. Even though the available evidence of inferior transplant outcomes in these patients is limited, the working group concluded that access to transplantation in patients with IDC may be inequitable. Consequently, we argue that IDC should not in and of itself be considered as a barrier to either registration on the transplant waiting list or allocation of an organ. Strategies for non-discrimination should focus on ensuring eligibility is based upon sound evidence and outcomes without reference to non-medical criteria. Recommendations to support policy makers and healthcare providers to reduce unintended inequity and inadvertent discrimination are set out. We call upon transplant centres and national bodies to include data on decision-making capacity in routine reporting schedules in order to improve the evidence base upon which organ policy decisions are made going forward.

Integrated scheduling of a multi-site mining supply chain with blending, alternative routings and co-production

This paper proposes a multi-site global optimisation model of blending operations, alternative routings and order scheduling with a co-production flow. It is performed in a phosphoric supply chain owning three open-pit mines of different geological structures and chemical compositions, each having a dry blending plant. One main challenge of the phosphate industries is to produce merchantable ores (MO) that verify a quality charter (chemical composition) with chemical heterogeneous source ores (SO). Thus, a process of SO blending followed by a treatment is mostly necessary to obtain the required MO. Six alternative routings exist to produce an MO; one of them involving a calcination plant generates a co-product. The model objective is to determine the least costly production programme of a set of MO orders to be delivered within precise time windows, in given quantities and compliance with a specific quality charter, by assigning for each MO its blending plant, routing, blend of SOs and production schedule; it also defines the feeding of the blending plants. A real case study illustrates the proposed model.

Improved discrete imperialist competition algorithm for order scheduling of automated warehouses

An improved discrete imperial competition algorithm (DICA) is proposed for the multi-objective order scheduling problems of automated warehouses. The aim is to minimize the completion time of all orders and the total tardiness of outbound orders. The priority value, influenced by the importance and relative cut-off time of the orders, as well as the number of tasks, is designed to sort the orders. Similar to the genetic algorithm (GA), the assimilation process of the improved DICA is constructed by crossover and mutation. Meanwhile, Hamming distance is introduced to verify the quality of assimilation. Taking a practical case as an example, the improved DICA is used to solve two scheduling strategies: outbound order priority scheduling and compound order scheduling. The main results show that the compound order scheduling can be performed effectively, especially when the number of orders is large, the completion time and total tardiness can be greatly reduced. In addition, compared with GA, the solution quality of the improved DICA is better, but its convergence speed is slower.

Twindemic - An emerging concern in Pakistan.

Dear Madam, As winter approaches, people succumb to a myriad of winter illnesses. Seasonal Influenza (Flu) is predominantly prevalent in the USA and Pakistan. CDC reported 20,000 deaths and 380,000 hospitalizations from flu in 2019 till 2020(1). According to Dr Ebell, we are likely less immune, since we are not exposed to flu for twenty months or even more. There is uncertainty regarding flu strains since it can undergo antigenic shifts and antigenic drift that can cause the evolution of new strains (2). Therefore, CDC estimates that approximately seven hundred thousand people would get hospitalized in the upcoming flu-season (3). Flu season paired with covid-19 classically presents as “Twindemic”. Although influenza (Flu) and Covid share similar clinical symptoms, both can have severe, mild or moderate effects, mode of transmission and preventive strategies, but they still differ in their treatment modalities and vaccines. Therefore, during the covid era it has become challenging to report influenza cases as both diseases present with similar symptoms making clinical diagnosis difficult. However, some studies suggest that preventive measures used for Covid were also effective against the prevention of flu. Hence, the number of flu cases were reported to be lower than expected for the year 2020-2021. People infected with Covid and Influenza at the same time, are more prone to develop severe illness. To keep the pressure off healthcare systems, vaccination programmes are imperative. CDC has issued new guidelines for this potential twindemic this winter, stating that flu vaccines must protect against all 4 influenza virus strains that were noy t the recommendation in prior years. However, in Pakistan Flu shots are not prioritized. Even though CDC recommends a flu shot annually, there is no national influenza vaccination policy in Pakistan. Low levels of vaccination are due to little knowledge regarding the importance of influenza prevention, misinformation about its side effects and lack of public awareness (4), due to which the death toll of influenza was reported to be 42.03 per 100,000 of population as per 2018 data (5). Despite the serious health and economic consequences faced due to influenza, vaccination rates remain low. Pakistan Government must play a pivotal role to include influenza vaccination in its EPI schedule in order to make it available for the citizens at low costs. The risk of co-infection is estimated to be higher as the season changes. ---Continue

Online order scheduling of multi 3D printing tasks based on the additive manufacturing cloud platform

Cloud-based additive manufacturing is one of the important forms of cloud manufacturing service platform, which can match manufacturing resources for multi-task requirements to further improve the utilization of idle resources and achieve cost reductions. This paper proposes a more realistic scenario of online scheduling rather than offline scheduling, in which tasks arrive randomly. According to the scenario, a mixed integer linear programming model of multi 3D printing tasks based on the additive manufacturing cloud platform is constructed, with the goal of minimizing the average cost per volume of material. A heuristic strategy is proposed to solve the problem, and different scale instances are tested to compare the performance. Simulation shows that the proposed algorithm can effectively solve the 3D printing online scheduling problem.

Joint charging scheduling of electric vehicles with battery to grid technology in battery swapping station

With the proposal of carbon peak and carbon neutrality policy, electric vehicle as a clean new energy transportation tool has attracted more and more attention. Battery swapping station (BSS) is an alternative method to charge electric vehicles (EVs). This paper summarizes the development and present situation of battery swapping station and analyzes the distribution probability model of EVs’ arrival based on the historical data of electrical swapping station, which approximately obeys Poisson distribution. The convergence accuracy of traditional particle swarm optimization algorithm is improved by combining the particle swarm optimization algorithm with the immune algorithm, which can find the global optimal solution more easily. Then an economic scheduling method for battery swapping station based on monte carlo simulation was proposed, and the function of BSS as an energy storage device to power grid (B2G) is analyzed. Next a mathematical model of multi-objective joint optimization of battery swapping station based on B2G technology is established, considering the revenue of operator, RMS of grid load and peak–valley difference as functions. Finally, the simulation analysis of this model is carried out. The optimization results of ordered and disordered charging scheduling with B2G and without B2G are compared to verify the effectiveness and superiority of this model. It can be seen that B2G root mean square and peak–valley difference decreased by 15.72% and 14.63% respectively, and the revenue of battery swapping station increased by 1.4%. Therefore, the application of B2G technology in orderly charging scheduling is better than that without B2G technology.

Order Releasing and Scheduling for a Multi-Item MTO Industry: An Efficient Heuristic Based on Drum Buffer Rope

Increasing productivity and efficiency in factories with make-to-order (MTO) production systems have attracted growing interest of academia and industry. In MTO companies, efficient order release and schedule are essential for succeeding in today’s marketplace. However, dynamic demand of customers and constrained resources make it difficult to achieve, as well as limiting the profits. Thus, to overcome the problem of order releasing and multi-item scheduling considering the capacity constrained resources investigated, a heuristic approach is proposed based on the drum-buffer-rope (DBR) method. The proposed heuristic is tested on different types of problems based on due date tightness and demand of products. The performance of the proposed heuristic is compared with other famous heuristic methods in literature. End results indicate that the proposed heuristic based on the DBR method outperforms against the other competitors, and it gives more significant results when optimal buffer size is adopted.

Numerical Analysis of Digital Twin System Modeling Methods Aided by Graph-Theoretic Combinatorial Optimization

This paper combines the digital twin system modeling method to conduct an in-depth study and analysis of graph-theoretic combinatorial optimization. This paper provides new ideas and approaches for optimal numerical analysis work by studying the digital twin modeling method that integrates digital modeling and graph theory combination, provides theoretical support for safe, stable, and economic operation of the system, proposes a solution for digital twin model based on big data platform, focuses on the nearest neighbor propagation (AP) and graph theory combination, solves the digital twin real-time monitoring data asynchronous, incomplete problem, and applies the algorithm to the digital twin model based on the big data platform for data preprocessing to achieve better results. This paper also presents a web-based digital twin system based on intelligent practical needs, analysis, and comparison of existing models, combined with digital twin technology, detailing the differences and connections between the various levels of numerical analysis and the implementation of this data in various fields, such as user management, equipment health management, product quality management, and workshop 3D navigation and detailed modeling of the digital twin system based on this numerical analysis to realize remote online monitoring, analysis, and management. In this paper, for the numerical analysis process, firstly, the key technologies of modeling and simulation operation control of production line based on digital twin are studied, and the rapid response manufacturing system based on a digital twin is designed and validated. Secondly, a scheduling technology framework for capacity simulation evaluation and optimization is established, and batching optimization, outsourcing decision, and rolling scheduling techniques are thus proposed to form a batching optimization algorithm based on priority rules, which realizes batching processing, outsourcing decision, and rolling scheduling of production orders to optimize equipment utilization and capacity. Finally, digital twin-based modeling is designed, and the validation results demonstrate the system’s superior performance in achieving information interaction between physical and virtual production lines, optimization of numerical analysis, and display of results.

Efficiency of Blood Utilization in Elective Oncosurgeries in a Tertiary Care Cancer Centre: A Case for Data Disaggregation.

Oncosurgeries based on radical resection significantly increase the chances of intraoperative blood transfusion which leads to blood requisition based on subjective anticipation. Periodic audit and revisiting practice based on disaggregating data on the basis of surgical region could preserve blood, which is a scarce resource globally. This report proposes to use the results for reviewing the existing blood ordering schedule, in addition to proposing wider adoption in other cancer institutes using this report as a template. A retrospective analysis was performed for the entire patient cohort who underwent elective oncosurgeries between January 1, 2020, and January 31, 2021, and for whom blood reservation was done. Number of blood units cross matched and transfused for each patient for each surgery was noted. Efficiency of blood utilization was calculated using cross match transfusion ratio (CTR), transfusion probability (TP), and transfusion index (TI) indices and, finally, maximum surgical blood order schedule (MSBOS) was computed. Out of 718 elective oncosurgeries performed, blood requisition of 710 units was made for 345 procedures. Of the total units cross matched, only 76 units (10.70%) were transfused, leaving 89.29% units unutilized. Overall, CTR, TP, and TI were 9.34, 11.59%, and 0.22, respectively. Head and neck surgeries had the highest number of patients cross matched as well as units reserved, but not transfused. Conversely, abdominal surgeries had the highest transfusion probability. There is inefficient blood utilization in elective oncosurgeries and we recommend implementation of MSBOS based on anatomic region to improve efficiency of blood utilization.

PENGARUH INSENTIF TERHADAP PRESTASI KERJA TEAM MARKETING PT IKAFOOD PUTRAMAS

PT Ikafood Putramas hopes to carry out its activities on a set schedule in order to optimally meet its productivity. In this context, how employees can perform at their best and provide incentives to motivate employees to get the job done. In this context, the aim of this study is to show how important the influence of material and immaterial incentives is on employee performance. This type of survey uses questionnaires as a data collection tool that are distributed among respondents with a sample of 54 people. The data analysis used a descriptive analysis that included the distribution of the elements in each variable and then a simple linear regression analysis. The results of the linear regression test and partial regression analysis showed that the motivation of the simultaneous variable (X) on employee performance (Y) with the value R-squared was 0.129. This figure shows that the simultaneous incentive variable (X) used in this regression equation contributes 12.9% to employee performance (Y), while the remaining 87.1% is examined under the influence of other variables outside the two independent variables. Proposals for PT Ikafood Putramas are consistent with research and discussions about employee incentives, and it is recommended that companies pay more attention to incentives that influence business performance.

Expansion planning of electric vehicle charging stations considering the benefits of peak‐regulation frequency modulation

The China Energy Administration has issued policies to encourage energy storage to participate in the electric auxiliary service market, which will provide ideas for electric vehicle charging stations (EVCSs) to collectively participate in peak-regulation and frequency modulation (FM). Taking into account the benefits of EVs participating in peak-regulation and FM, this paper studies expansion planning for EVCSs. First, the orderly scheduling strategy of EVs under vehicle-to-grid (V2G) technology is established in this paper. And a peak regulation method is proposed to maximize the economic benefits of charging and discharging during peak and valley periods. Second, based on the temporal and spatial prediction model of EV charging demand, the expansion planning model of EVCSs is constructed considering the benefits of peak-regulation and FM. Aiming at minimizing the comprehensive annual social cost of charging stations, the expansion site selection and capacity scheme of EVCSs are obtained. Finally, taking the 24 nodes in Xiangzhou District of Zhuhai City as an example, the location and capacity of the final annual expansion plan are obtained. And the influence of main control parameters on the results of the whole model and method is analysed in detail, the feasibility and effectiveness of the whole model and method are verified.

Order processing task allocation and scheduling for E-order fulfilment

This paper mainly studies a task allocation and scheduling problem in the multi-thread fulfilment process of electronic order, which seeks to minimise the makespan under thread constraints and order precedence constraints. The problem is formulated as a Mixed Integer Programming (MIP) model and a novel depth-first heuristic is proposed to solve it. The depth-first heuristic shows high effectiveness and efficiency, compared with the current policy and the genetic algorithm in both small/medium-scale and large-scale cases from the real transaction data. In addition, two extensions on precedence constraint reduction and resource allocation are discussed to further improve and manage the e-order fulfilment process.

ЭКСПЕРТНОЕ ОЦЕНИВАНИЕ РАСПИСАНИЯ ЗАНЯТИЙ ПРИ ЕГО СОСТАВЛЕНИИ И КОРРЕКТИРОВКЕ

An approach is presented to the formation of evaluative (target) functions of the quality of the schedule in order to further automate the processes of monitoring, evaluating and analyzing changes directly during the work on compiling and adjusting the schedule of classes. The approach provides the possibility of operational work with the schedule, taking into account the values of the evaluation functions, the selection of the best possible options for changing the schedule in order to improve the values of the evaluation functions.

Minimizing total tardiness for the order scheduling problem with sequence-dependent setup times using hybrid matheuristics

This paper aims at presenting a customer order scheduling environment in which the setup times are explicit and depend on the production sequence. The considered objective function is the total tardiness minimization. Since the variant under study is NP-hard, we propose a mixed-integer linear programming (MILP) model, an adaptation of the Order-Scheduling Modified Due-Date heuristic (OMDD) (referred to as Order-Scheduling Modified Due-Date Setup (OMMD-S)), an adaptation of the Framinan and Perez-Gonzalez heuristic (FP) (hereinafter referred to as Framinan and Perez-Gonzalez Setup (FP-S)), a matheuristic with Same Permutation in All Machines (SPAM), and the hybrid matheuristic SPAM-SJPO based on Job-Position Oscillation (JPO). The algorithms under comparison have been compared on an extensive benchmark of randomly generated test instances, considering two performance measures: Relative Deviation Index (RDI) and Success Rate (SR). For the small-size evaluated instances, the SPAM is the most efficient algorithm, presenting the better values of RDI and SR. For the large-size evaluated instances, the hybrid matheuristic SPAM-JPO and MILP model are the most efficient methods.

A property-based genetic algorithm for order acceptance and scheduling with common due window

In a competitive make-to-order production environment, limited production capacity and strict delivery requirement prompt the manufacturer to make decisions of order acceptance and scheduling simultaneously with maximization of the total net profit (TNP). In this context, this paper studies an order acceptance and scheduling (OAS) problem in a single machine environment, and the earliness/tardiness penalties are exsiting when an order is not delivered on time under the common due window. To solve this problem, the membership function of earliness/tardiness penalties is designed and a mathematical model for maximizing the TNP is established. According to the characteristics of the problem, three properties that are related to rejecting orders, sequencing orders and determining the starting time of orders are derived separately. Based on these, a property-based genetic algorithm (P-GA) is designed to search for the optimal solution. In numerical experiments, the effectiveness and superiority of the proposed P-GA are demonstrated by comparing with the solutions that are obtained by other two heuristic algorithms.

Determination of Infiltration Rate under Different Levels of Compaction by the Use of Double Ring Infiltrometer, A Case of Kagera Sugar Limited (KSL)

Irrigation is a major factor in sugarcane growing. Unquestionably, sugarcane is one of the most Water - demanding crops after rice. The amount of water recorded from rainfall or irrigation systems in a given field are always considered as uniform despite of heterogeneous condition within the field. The application of water in the soil is one but the uptake by plant is the most side of consign. Water uptake by plant which is among of the root function, is mainly influenced by infiltration rate. Other side of these is right to say, any hindrance factor to infiltration rate and its capacity is primary killer for plant growth. Soil compaction is one of greatest factor which hinder water percolation into the ground as it deform soil structure. In infiltration test conducted at 6P4D shows the statistical difference between infiltration rate in compacted and non-compacted portions. The highest and lowest infiltration rate recorded was 383mm/hour to 116mm/hour and 220mm/hour to 35mm/hour in non-compacted and compacted portions respectively. Once the data were subjected to ANOVA using GenStat software was found to be statistically difference at 5% level of confidence. Being in a side of quantifying its consequences will help to make friendly decision in our farming system and irrigation scheduling in order to avoid water loss through runoff and flooding as a result of poor infiltration. The main purpose of this writings is to examine quantitatively on how compaction influence infiltration rate and come-up with recommendation such as Controlled Traffic system.

Profile of Brazilian inpatients with COVID-19 vaccine breakthrough infection and risk factors for unfavorable outcome.

ABSTRACTObjective.To characterize the epidemiological and clinical profile of individuals more likely to become infected with SARS-CoV-2 after the fully vaccination schedule in order to profile priority groups to receive a booster dose in situations of vaccine doses shortage as well as for maintenance of personal protective care.Methods.This cross-sectional study used data from hospitalized COVID-19 patients aged ≥18 years, who had been fully vaccinated and had a SARS-CoV-2 infection positive diagnosis collected from the SIVEP-Gripe database (Influenza Epidemiological Surveillance Information System) from January 18, 2021 to September 15, 2021. Demographic data, clinical symptoms and preexisting medical conditions (comorbidities) were analyzed. The primary outcome was in-hospital death.Results.The majority of hospitalized patients with vaccine breakthrough infection were ≥60 years old, male, with critical or severe COVID-19. The fatality rate was extremely high (50.27%) and more pronounced in elderly groups. The most prevalent symptoms were cough, dyspnea, respiratory distress, and low blood oxygen saturation. The most frequent comorbidities were heart disease and diabetes. High fatality rates were observed among patients admitted to the intensive care units (72.88%) and those who required invasive mechanical ventilation (87.82%). The main risk factors for an unfavorable outcome were older age, respiratory compromise, inactivated virus vaccine immunization, and preexisting medical conditions.Conclusions.We characterized the profile of hospitalized Brazilian patients with COVID-19 vaccine breakthrough infection and the risk factors for an unfavorable outcome. These data allow to identify priority groups to receive a booster dose and to continue using personal protection.