Flexible Scheduling Research Articles

Research on the scheduling of steel mesh production line based on the improved grey wolf algorithm

Abstract To solve the scheduling problem of a flexible production line of reinforced mesh, a mathematical model of flexible production line scheduling of reinforced mesh was established to minimize the maximum completion time. The Improved Gray Wolf Algorithm (IGWO) was designed, and the improvement measures of the Gray Wolf Algorithm (GWO) were proposed from the population initialization method, key parameter improvement, and search mechanism. Through the example verification, the Improved Grey Wolf Algorithm is applied to the scheduling problem of the flexible steel production line. The problem of the flexible production line of steel mesh and the results of the Improved Grey Wolf Algorithm, Grey Wolf Algorithm, Genetic Algorithm (GA), and artificial scheduling are compared. The results verify that the results of the enhanced Grey Wolf Algorithm have better quality and can better guide the production line to carry out efficient and energy-saving production.

Analysis of Workers’ Perception and Demographic Factors - An Insight Into the Slovenian Gig Economy

Abstract This study examines the gig economy in Slovenia, with a particular emphasis on the perceived benefits and challenges for gig workers within this emerging labour model. The research aims to capture the implications of flexible, project-based employment in a smaller European context, as this trend becomes increasingly prevalent globally. A quantitative approach was employed to survey 34 gig workers in Slovenia. The survey analysed their demographic characteristics, reasons for engaging in gig work, and the benefits and challenges they experience. In the findings, 64% of respondents specifically identified schedule flexibility as a predominant benefit of gig work. However, it also uncovers substantial challenges, including increased market competition and a lack of social benefits. Moreover, the research suggests that perceptions of the gig work benefits are widely acknowledged across various age groups and educational levels, with no significant variation. Nevertheless, the findings indicate a gender disparity in gig work perceptions, with males perceiving higher levels of financial freedom and professional development than women. This research suggests the need for policies aimed at ensuring equity and further research into harmonizing gig work with supportive labour market policies.

Recruitment strategies in a lung cancer screening program for Asian women with no smoking history.

e22514 Background: The Female Asian Nonsmoker Screening Study (FANSS) is a pilot study for eligible Asian women with no smoking history to provide low-dose CT (LDCT) Chest scans for lung cancer screening annually for 3 years. To recruit for the study, we took into consideration several factors including potential language barriers, low awareness of lung cancer incidence in non-smoking populations, and participant (pt) availability. Here we describe the use of various recruitment strategies to support enrollment to the FANSS study. Methods: Multimodal recruitment strategies for enrollment into FANSS were used. A dedicated phone line answered by receptionists able to speak English and several Chinese dialects was created. Research staff are bilingual or utilized telephonic interpretation services in all pt interactions. Informational flyers available in both English and Chinese were mailed to primary care physicians serving primarily Asian communities and posted in pharmacies and storefronts in NYC Chinatown. A website was developed for interested individuals to submit their contact information for further information. Working with our institutional Epic EMR, a list of all pts with active MyChart accounts was created with criteria “Asian,” “female,” “OK to contact for Research recruitment,” “age 40-74,” and excluded anyone with a smoking history and “Cancer” in the past 5 years. From this list, pts then were sent an IRB-approved recruitment message through MyChart from 3/2023-6/2023. Interested pts who reached out through the above methods were contacted by research staff to determine eligibility. All eligible pts who agreed to participate were consented either in-person or virtually to allow for schedule flexibility to review the consent. Results: From 3/5/21 to 1/27/24, 1518 women inquired about FANSS through our website, dedicated phone line or MyChart messages and were directly contacted by our team. After determination of eligibility, 760 pts had an informed consent discussion either virtually or in-person. From 3/5/21 to 3/2023, 247 pts underwent the consent process and 229 pts had a baseline LDCT. Following the intervention of directed MyChart messaging in 3/2023, 568 pts responded to the MyChart message indicating interest, however 102 pts were not eligible due to inaccurate info in their Epic medical record (i.e. ethnicity or smoking history). Overall, from 3/2023 to 1/27/24, 513 pts underwent the consent process. Among these, 90 subjects (18%) were recruited via MyChart. Conclusions: Recruitment to a lung cancer screening program in non-smokers is feasible, and specific considerations tailored to engage minority populations can improve enrollment such as bilingual staff and translated materials. The use of directed messaging through the Epic EMR is an important tool to identify potential pts which may be applied to aid in recruitment to other clinical trials or lung cancer screening programs.

Dynamic flexible scheduling with transportation constraints by multi-agent reinforcement learning

Reinforcement learning-based methods have addressed production scheduling problems with flexible processing constraints. However, delayed rewards arise due to the dynamic arrival of jobs and transportation constraints between two successive operations. The flow time of operations can only be determined after processing due to the possibility that the solution for job sequencing may change if new operations are inserted in dynamic environments. Job sequencing is often overlooked in single-agent-based scheduling methods. The lack of information sharing between multiple agents necessitates that researchers manually design reward functions to fit the relationship between optimization objectives and rewards, thereby reducing the accuracy of the learned policies. Thus, this paper proposes a multi-agent-based scheduling optimization framework that facilitates collaboration between the agents of both machines and jobs to address dynamic flexible job-shop scheduling problems (DFJSP) with transportation time constraints. Then, this paper formulates the Partial Observation Markov Decision Process and constructs a reward-sharing mechanism to tackle the delayed reward issue and facilitate policy learning. Finally, we develop an improved multi-agent dueling double deep Q network algorithm to optimize scheduling policy during long-term training. The results show that, compared with the state-of-the-art methods, the proposed method efficiently shortens the weighted flow time under the trained and unseen scenarios. Additionally, the case study results demonstrate its efficiency and responsiveness. It indicates that the proposed method efficiently addresses production scheduling problems with complex constraints, including the insertion of jobs, transportation time constraints, and flexible processing routes.

Revolutionizing Primary Education Assessment: Opportunities and Challenges in Bandar Lampung's Technology-Driven Landscape

This study aims to analyze the opportunities and challenges associated with implementing a technology and information-based educational assessment system in elementary schools in Bandar Lampung. The study recruited ten respondents who were IT staff members, teachers, and principals from upper-grade primary levels (grades IV, V, and VI). It used a qualitative descriptive research design. The research focused on identifying the opportunities and challenges related to IT-based assessment systems. Data collection methods included observations, interviews, and documentation, while data analysis was conducted using the Miles and Huberman model, encompassing data reduction, data display, and data verification. The findings reveal significant opportunities in adopting a technology-based assessment system, such as using Google Forms for flexible assessment scheduling and enhanced data analysis capabilities afforded to teachers through IT tools. However, notable challenges were also identified, including insufficient IT proficiency among teachers and staff, concerns over data privacy and protection, and inadequate internet connectivity within school premises, which hampers the effective use of IT-based applications. The study recommends that schools provide intensive training for teachers and staff to enhance their IT skills. The conclusion is that while technology-based assessment systems offer considerable advantages in facilitating teachers' tasks, the identified challenges must be addressed to optimize the benefits and overcome the obstacles encountered.

Assessing Weekly Wellness Sessions: Cordillera Administrative Region's Social Welfare Impact

This literature review examines the efficacy of workplace wellness initiatives, focusing on the Weekly Wellness Sessions (WWS) conducted by the Department of Social Welfare and Development (DSWD) Field Office Cordillera Administrative Region. The study evaluates staff participation rates, the effectiveness of wellness programs, and challenges encountered in availing the WWS. Primary and secondary sources materials were used in the analysis of this literature review. Casual observations were also integrated. Findings reveal coffee sessions as the most popular activity, while limited facilities and costs hindered participation in other activities. The WWS were perceived as highly effective across multiple dimensions of well-being, with minimal variation based on gender or age. However, differences in effectiveness were noted among different positions and offices/divisions. Common challenges included time constraints, financial limitations, and personal barriers. Recommendations include enhancing financial accessibility, tailoring interventions, offering flexible scheduling, promoting awareness, and cultivating a culture of health and wellness. Addressing these recommendations can optimize the impact of the WWS and contribute to a healthier, more productive workforce within DSWD FO CAR.

Enhancing Instructor and Student Experiences: A Guide to Successful Capstone Research Courses

Introduction Providing a quality undergraduate research capstone is a meaningful high-impact practice to facilitate American Psychological Association learning goals, and help students learn skills critical for both graduate school and future careers. Statement of the Problem The demands of such courses are often time-consuming and stressful, especially for instructors without teaching assistants. The goal of the current manuscript is to provide guidance and resources for faculty teaching undergraduate research experience (UREs) courses. Literature Review Previous research suggests that faculty workload and burnout are recurring sources of stress, and faculty often struggle to implement UREs effectively. However, these experiences provide experiential learning, and increase student engagement and motivation. Teaching Implications In a three-credit course with strict deadlines and flexible scheduling, guiding student-led projects becomes less daunting. Required attendance, deduction-only participation points, and other policies ease grading loads while reinforcing course objectives. Conclusion Student feedback, performance, and positive instructor experiences suggest that this structure helps overcome logistical barriers and create positive outcomes. Course resources are provided in open materials.

Chinese Traditional Health Exercises in Blended Learning Modality: Its Effectiveness and Students’ Satisfaction

The purpose of this research is to assess the effectiveness and satisfaction levels of Chinese Traditional Health Exercises (CTHE) instruction within a blended learning modality and develop a reinforcement program which focused on improving the teaching and learning. Utilizing a descriptive-comparative research design, 154 students and 5 teachers from Changsha Medical University were randomly selected for participation. Key findings indicate that supplementary materials, clear explanations, effective time management, appropriate balance between online and offline components, flexibility in course scheduling, and effective delivery of instruction contribute to enhanced learning experiences for students. Despite encountering technical, engagement, adaptation, communication, time management, assessment, and resource-related challenges, the blended model was perceived more positively than traditional learning. Successful implementation of learning activities and media positively influenced learner satisfaction and perception. Addressing these challenges through strategic solutions, teacher training, and resource accessibility is crucial for ensuring a more engaging and satisfactory learning experience for students.

Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load

With the significant increase in the proportion of volatile new energy in the power system in recent years, the difficulty of system scheduling has increased. Accurate load forecasting is an important prerequisite for flexible scheduling. The load itself is a highly regular object that is relatively easy to predict. However, steep changes in load can cause significant deviations in load forecasting. In response to this issue, this article first selects input variables that can help the model identify steep changes in load based on Pearson correlation coefficient and the proposed “Steep change impact rate”. Then, Conv2D-Gate Recurrent Unit (Conv2D-GRU) model is built to fully extract steep changes information from inputs and achieve day-ahead load forecasting. Naive persistence, Auto regressive (AR), Gradient boosting decision trees (GBDT), Convolutional neural network (CNN), Long short-term memory (LSTM) and Gate recurrent unit (GRU) are used for comparison. Compared to Naive persistence, the Conv2D-GRU-SC resulted in a decrease of 54.08 % in Mean absolute error (MAE), a decrease of 57.58 % in Root mean square error (RMSE) and an increase of 51.31 % in the R-Square (R2).

Exploring the Work Motivational Factors of Generation Z Workforce in Selected BPO Companies: Basis For Job Satisfaction

This qualitative study explored the work motivational factors of the Generation Z workforce. Herzberg’s Approach guided the study in relating situations where Generation Z participants perceived themselves as motivated (job satisfaction) with their work and, equally, in circumstances where they felt demotivated (job dissatisfaction). Hygiene factors (extrinsic) identified for motivating and satisfying this generation at work included a conducive work environment, favorable policies, supportive leaders and teammates, and a competitive salary. For the intrinsic motivating factors, Generation Z participants expressed a desire for continuous personal and career growth, as well as aspirations for job promotions. Regarding demotivators, Generation Z participants perceived issues related to salary disputes, changes in work conditions like remote work (WFH), undesirable interactions with teammates, and unsupportive leaders. They also mentioned lack of rewards and recognition in terms of employee engagement from their company. Additionally, they were demotivated by the absence of training and development opportunities. This study recommended programs for BPO companies aimed at motivating and satisfying Generation Z in the workplace. These included work schedule options, recognition and rewards, and training and development. Work schedule options encompassed flexible scheduling and compressed work weeks, while recognition and rewards consisted of flexible reward options and employee-driven recognition programs. Finally, training and development programs focused on technology utilization, leadership, and team building. Implementing these programs would assist BPO companies in adapting to Generation Z behavior, thereby retaining them in the workforce.

A STUDY ON HEALTH, SAFETY AND WELFARE MEASURES AND ITS IMPACT ON EMPLOYEE PRODUCTIVITY

This study aims to examine the influence of health, safety, and welfare measures on employee productivity, with a specific emphasis on BBK Shoes. BBK Shoes stands out for its proactive approach to safety, encompassing rigorous training and regular maintenance to ensure a secure work environment. Additionally, the organization prioritizes the well-being of its workers by providing flexible scheduling alternatives and wellness programs. By recognizing the intrinsic link between well-being and productivity, BBK Shoes fosters a culture of loyalty and engagement among its workforce. Research consistently indicates that organizations prioritizing employee welfare experience higher levels of performance and job satisfaction. The symbiotic relationship between employee well-being and productivity underscores the significance of initiatives undertaken by BBK Shoes. This study aims to provide valuable insights for businesses seeking to enhance productivity. The findings are expected to inform strategic decision-making processes, guiding organizations towards the adoption of practices that prioritize employee well- being. The data was analyzed using SPSS software and nearly 205 responses were collected for the research. Keywords: Employee Productivity, Health, Safety, Welfare.

One-Stop Flair Slot Booking Platform for All Needs

Pick Your Slot (PYS) is a versatile booking and scheduling web-based application that caters to a single platform for all your needs. PYS offers services ranging from sports such as football, cricket and badminton, personal grooming services like salons, fitness facilities such as gyms, entertainment options like a dance studio, rage room, and car wash services. The platform, built using React.js and React Native for web and mobile respectively, utilizes a SQL database backend supported by Java. It is an open-source application accessible to both customers and vendors. The app facilitates slot booking according to customers’ flexible time, PYS not only accommodates flexible scheduling but also utilizes geolocation API to detect customer locations. This feature enables the app to suggest nearby services tailored to the customer's requirements, leveraging real-time availability updates and offering on-demand services. Real-time availability in our app ensures instant slot booking, adapting to users' dynamic schedules for hassle-free planning. Users can quickly secure appointments, saving time and reducing frustration. Security is paramount, with authentication mechanisms for both vendors and customers. With its blend of intuitive design and advanced functionality, PYS offers a comprehensive solution for booking needs, making it a reliable choice for users seeking convenience and peace of mind. In a world where time is precious, our app offers swift slot bookings, recognizing the impatience of modern life. With just a few taps, users can secure their slots, bypassing unnecessary delays in the nearest location. Designed for speed and convenience, we ensure no one waits longer than necessary.

Multi-objective fuzzy green scheduling optimization method of special vehicle body-in-white prototype shop considering equipment preventive maintenance

Based on the advantages of non-contact, flexibility, and small heat-affected zone, laser processing technology is widely used in material removal, addition, modification and other manufacturing fields. However, the emission of large amounts of smoke during laser processing of metal materials will result in environmental pollution in the job shop. Additionally, the processing time of laser equipment becomes uncertain as the power decays with prolonged use. Therefore, this paper is mainly focus on a multi-objective flexible job shop fuzzy green scheduling problem to minimize the fuzzy makespan, fuzzy total energy consumption and fuzzy total smoke emission. To solve it, an improved multi-objective particle swarm optimization algorithm has been designed. Firstly, a preventive maintenance strategy is proposed to reduce the makespan and equipment failure frequency by considering periodic power attenuation of laser equipment. Next, different particle updating strategies are employed to enhance particles’ exploration capabilities. Subsequently, five neighborhood structures are introduced to improve the performance of local search. Finally, comparison experiments are conducted with an expanded common benchmark, and a production case study in a special vehicle body-in-white prototype job shop. The results effectively demonstrate the feasibility and effectiveness of the developed model and improved multi-objective particle swarm optimization algorithm.

Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G

With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated.

Advantages And Disadvantages of WFH

In this paper, we explore the cost-saving benefits associated with WFH, such as reduced overhead expenses for office space and utilities, as well as potential productivity gains stemming from flexible work schedules. However, we also delve into the challenges that WFH poses for financial professionals, including the loss of in-person collaboration opportunities, heightened cybersecurity risks, and implications for organizational culture and employee engagement. Through a comprehensive analysis of existing literature, case studies, and empirical data, this paper seeks to offer insights into how financial firms can effectively navigate the transition to WFH, capitalize on its advantages, and mitigate its drawbacks. Ultimately, it contributes to a deeper understanding of the evolving dynamics of remote work within the finance sector and provides valuable recommendations for practitioners and policymakers alike.This paper amalgamates extant literature, empirical inquiries, and real-world illustrations to furnish a holistic comprehension of the intricacies surrounding WFH. By scrutinizing the positive and negative facets, organizations can craft judicious WFH policies to optimize benefits while navigating challenges in the ever-evolving terrain of remote work. Key Words: Work-From-Home (WFH), finance specialization, remote work, advantages, disadvantages, productivity, flexibility, collaboration, communication, cost-efficiency

Side-channel attack–resistant PRESENT cipher by versatile key scheduling and compact S-box utilizing optimized three-phase logic design

The most widely used technology at the moment is the Internet of Things (IoT), which is expanding at a rapid pace because it allows us to remotely control and manage a variety of smart devices and relieves us from our demanding daily routines. Technology is also vulnerable to security breaches, as evidenced by the rise in cybercrimes and identity theft. More so than any other known IoT security breach, side-channel analysis is extremely insecure. A current issue for lightweight IoT applications is differential power analysis, a kind of side-channel analysis that is very accurate in obtaining the secret key. As a result, developing a safe style takes more time and effort. PRESENT is an easy-to-use, low-power encryption algorithm that is based on Advanced Encryption Standard (AES) and is utilized in IoT devices like RFID tags and access cards. Even with the plethora of countermeasures proposed in the last ten years, none are able to keep up with the IoT’s rapid expansion. A clever combination of masking and hiding techniques could stop several security breaches. Masked three-phase dual rail pre-charge logic is a new hybrid countermeasure that thwarts combined side-channel attacks and is suggested as a means of attaining the necessary degree of security. The hybrid design utilized in PRESENT resulted in an increase in complexity and required more work in terms of area and power. Consequently, more PRESENT optimization techniques are applied. Additionally, for PRESENT hardware implementation, a flexible key scheduling structure with parallel input and output is proposed. Using the Cadence Virtuoso simulation tool with 180-nm technology, a secured structure combining optimized algorithm and secured logic is designed, and the outcomes are analyzed. Normalized energy deviation (NED)/normalized standard deviation (NSD) analysis and Pearson correlation are used to analyze safety. The simulation results for the gates show that our proposed logic has the lowest values of NED and NSD at the assigned frequencies. When compared with side-channel analysis, the outcomes have shown to be both safe and effective. In comparison to earlier designs, the suggested system’s area and power consumption are cut by 67% and 64%, respectively.

Work-life balance strategies and employees’ engagement in the food and beverage industry in rivers state

This study explores the nexus between work-life balance (WLB) strategies and employees’ engagement (EE) within the food and beverage (F&B) industry. Employing a quasi-experimental design, data was gathered from 165 employees across selected F&B organizations using a closed-ended Likert-type scale questionnaire. Data analysis via Pearson’s Product Moment correlation (PPMC) statistical technique revealed a positive and significant relationship between the dimensions of work-life balance (WLB) strategies and measures of employees’ engagement (EE) within the food and beverage (F&B) industry. Specifically, the results indicate a robust positive and significant correlation between Work interference with personal life (WIPL) and the measures of employees’ engagement: employees’ emotional engagement (EEE) and employees’ behavioral engagement (EBE), with values ranging from 0.894 to 0.881 respectively. Additionally, personal life interference with work (PLIW) demonstrates a notable positive correlation with the measures of employees’ engagement, attributing to 72.5% to 83.5% of emotional and behavioral engagement, respectively. Furthermore, work enhancement of personal life (WEPL) initiatives and personal life enhancement of work (PLEW) initiatives exhibited very strong positive associations with the measures of employees’ engagement, explaining up to 74% to 85% of emotional and behavioral engagement, respectively. These findings corroborate prior research, highlighting the significance of addressing Work-Life Balance issues to bolster employees’ engagement levels. Practically, the study underscores the importance for managers in the Food and Beverage to prioritize strategies that mitigate work-life interference, such as flexible scheduling and family-friendly policies, to foster higher EE levels. Tailored initiatives and training programs should be implemented to address specific WLB aspects identified within organizations, while fostering open communication channels to identify areas for improvement. By prioritizing WLB and supporting employee well-being, managers can cultivate a more engaged and productive workforce, ultimately contributing to organizational success. Other theoretical and practical implications for promoting WLB and fostering employees’ engagement in the Nigerian workplace are also discussed.

The pandemic and working mothers: perspectives from American and South African working mothers

The purpose of the current study was to examine the effects of the COVID-19 pandemic on working mothers from the United States and South Africa. This study sought to highlight both cross-cultural similarities and differences. American and South African working mothers took part in online interviews. Eighteen participants were included in the sample, ten participants lived in the United States, while eight participants currently resided in South Africa. The age range of the participants’ children was from 0 to 21 years old. Each participant met individually with the researcher and answered a series of open-ended questions regarding their experiences of being a working mother during the COVID-19 pandemic. Researchers analyzed interviews using thematic analysis. Common themes emerged in the interviews with both American and South African working mothers. Mothers in the United States and South Africa both noted the need for flexible work schedules as well as expressed difficulty balancing roles as mothers and professionals during the pandemic. Increased motivation and resiliency as well as lack of representation of women leaders were themes specific to South African mothers in this study. Conversely, feelings of decreased interest in pursuing leadership opportunities was a theme specific to American mothers in this study. The current study adds to the growing body of literature documenting the effect of the pandemic on working mothers, and builds on this research by including a qualitative component to gain knowledge from working mothers themselves. The narratives collected in the current study highlight commonalities as well as differences in the experiences of working mothers in the United States and South Africa, which holds important implications for how workplaces and the broader community can best attend to the unique needs of working mothers.

Dynamic Scheduling and Power Allocation with Random Arrival Rates in Dense User-Centric Scalable Cell-Free MIMO Networks

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency.

Peer-to-peer multi-period energy market with flexible scheduling on a scalable and cost-effective blockchain

Peer-to-Peer (P2P) electricity trading is an emerging means for allowing communities to share local energy production and demand response through direct trading. It is projected to play an important role in the future energy market by increasing the incentive to own and operate distributed energy resources (DERs) and participate in demand response (DR). A common approach to implementing P2P trading is with blockchain, however, existing solutions feature limitations. Typically, concerns involve either scalability and high transaction costs or projects implementing a permissioned blockchain that does not provide the trust and decentralization desired. In addition, the majority of market designs are simplified and do not capture the distinct complexity of electricity markets. They do not consider the inter-temporal constraints of appliances and DERs that need to run for multiple periods. Complex market designs that address these issues have not been implemented in blockchain based systems due to the computational limitations of such systems. To improve these two aspects, this paper proposes a market formulation that supports products running multiple periods with flexible scheduling, modeled as a mixed integer linear programming problem that optimizes social welfare. The trading platform is then implemented as a side chain of Polkadot, a network of blockchains designed to be secure, scalable, and economically feasible to foster an ecosystem of application-specific sidechains. This design is demonstrated to improve social welfare and achieve scalability by benchmarking the number of transactions that can be included per second, based upon typical assumptions of the market size. The result is a decentralized and performant system that allows for complex market designs to be implemented in a blockchain environment. This approach simplifies the process for participants, allowing them to bid DER or DR devices, directly and easily, even when this requires constraints across periods.