Scheduling in parallel machines with two objectives: analysis of factors that influence the Pareto frontier

This paper explores the characteristics of solutions when scheduling jobs in a shop with parallel machines. Three classical objective functions were considered: makespan, total completion time, and total tardiness. These three criteria were combined in pairs, resulting in three bi-objective formulations. These formulations were solved using the ε-constraint method to obtain a Pareto frontier for each pair. The objective of the research is to evaluate the Pareto set of efficient schedules to characterize the solution sets. The characterization of the solutions sets is based on two performance metrics: the span of the objective functions' values for the points in the frontier and their closeness to the ideal point. Results that consider four experimental factors indicate that when the makespan is one of the objective functions, the range of the processing times among jobs has a significant influence on the characteristics of the Pareto frontier. Simultaneously, the slack of due dates is the most relevant factor when total tardiness is considered.

Mathematical modeling for a multiproduct manufacturing system featuring postponement, external suppliers, overtime, and scrap

Manufacturing firms operating in today’s competitive global markets must continuously find the appropriate manufacturing scheme and strategies to effectively meet customer needs for various types of quality of merchandise under the constraints of short order lead-time and limited in-house capacity. Inspired by the offering of a decision-making model to aid smooth manufacturers’ operations, this study builds an analytical model to expose the influence of the outsourcing of common parts, postponement policies, overtime options, and random scrapped items on the optimal replenishment decision and various crucial system performance indices of the multiproduct problem. A two-stage fabrication scheme is presented to handle the products’ commonality and the uptime-reduced strategies to satisfy the short amount of time before the due dates of customers’ orders. A screening process helps identify and remove faulty items to ensure the finished lot’s anticipated quality. Mathematical derivation assists us in finding the manufacturing relevant total cost function. The differential calculus helps optimize the cost function and determine the optimal stock-replenishing rotation cycle policy. Lastly, a simulated numerical illustration helps validate our research result’s applicability and demonstrate the model’s capability to disclose the crucial managerial insights and facilitate manufacturing-relevant decision making.

Collective impact of scrap, random breakdown, overtime and discontinuous issuing on batch production planning in a supply-chain environment

This research explores the collective impact of overtime, random breakdown, discontinuous issuing rule, and scrap on batch production planning in a supply-chain environment. In today’s global business environment, manufacturing firms encounter numerous operational challenges. Externally, they must promptly satisfy the customers’ various requests, while internally, they must cautiously manage several inevitable issues in the fabrication process. These issues might be concerned with scrap, random breakdown, etc. Resolving such issues is crucial for meeting the due dates of customers’ orders, adhering to the expected manufacturing schedules, product quality, and minimizing the total fabrication-transportation-inventory costs. The study develops a model to characterize the system’s features mentioned above and assist the manufacturers with batch fabrication planning. The model proposes a solution process with an algorithm seeking an optimal runtime for the system. Additionally, it gives a numerical illustration depicting the collective and individual impacts of these special features on the operating policy and other performance indices. This model and the research findings can facilitate manufacturers’ decision-making for green batch fabrication and enhance competitive advantage.

Digital Transformation Journey of Field Operations at Abu Dhabi Offshore Field in UAE

Field operations generate large volumes of data from various equipment and associated Meta data such as inspection due dates, maintenance schedule, people on board, etc. The data is often stored in silos with a data guardian for each entity. The objective of this project was to volarize the data by developing engineered KPI's to drive decision making and make data accessible for everyone in the organization to foster cross collaboration. Data analytics and visualization solutions were developed to automate low value-added tasks either using robotic process automation scripts or business intelligence reporting tool. Data was residing either in spreadsheet or native applications. With support of IT, centralized database was established. Scrum agile project management techniques were used to develop digital solutions. A high-level digital road map was created consulting all teams including stake holders. Use cases were identified and captured in lean A3 problem solving format. Each use case clearly identified the benefits to organization, and this was used to prioritize the use cases. A sprint was set-up with agile team and products were developed as per end user's expectation. The constant feedback loop via daily stand-up meetings helped the team deliver value added products. Digital solutions were developed to automate low value-added tasks so employees can focus on improving systems instead of producing reports. By developing engineering KPI's and predictive analytics, technical authority could shift from reactive maintenance to pro-active maintenance. Using linear regression machine learning, early warning digital solution was developed to monitor and notify technical authority to clean strainers. The production team achieved 0.75 full time equivalent (FTE) in time savings by automating reports. By visualizing operations data such as flaring, production profiles; the team minimized flaring leading to 1% OPEX cost saving. Around 10% of chemical budget was saved by monitoring chemical injections at all platforms. Similar cost savings were achieved by visualizing data for other disciplines such as maintenance and HSE teams. By being better informed about wells annuli pressure build-up via email notifications, wells integrity team reduced the associated risk. By forming a multi-disciplinary agile team with business and delivery team, digital team deployed 20+ digital products over a short time frame of 2 years.

Exact and metaheuristic approaches for unrelated parallel machine scheduling

In this paper, we study an important real-life scheduling problem that can be formulated as an unrelated parallel machine scheduling problem with sequence-dependent setup times, due dates, and machine eligibility constraints. The objective is to minimise total tardiness and makespan. We adapt and extend a mathematical model to find optimal solutions for small instances. Additionally, we propose several variants of simulated annealing to solve very large-scale instances as they appear in practice. We utilise several different search neighbourhoods and additionally investigate the use of innovative heuristic move selection strategies. Further, we provide a set of real-life problem instances as well as a random instance generator that we use to generate a large number of test instances. We perform a thorough evaluation of the proposed techniques and analyse their performance. We also apply our metaheuristics to approach a similar problem from the literature. Experimental results show that our methods are able to improve the results produced with state-of-the-art approaches for a large number of instances.

Two-Dimensional Loading in Vehicle Routing Problem With Release and Due Dates

In this paper, we study a two-dimensional vehicle loading and routing problem, in which customer orders with deadlines become available for dispatch as per their release dates. The objective is to minimize the sum of transportation, inventory, and tardiness costs, while respecting various loading and routing constraints. This scenario allows us to study various tradeoffs that tend to arise due to temporal order aggregation across release dates. We thereby propose an integrated mathematical formulation to simultaneously model both the routing and loading requirements of the problem at hand. Specifically, as the problem is NP-hard, we propose a scatter search based heuristic approach to solve large-size instances. Further, its performance is enhanced using problem-specific procedures and strategic oscillation approaches. Additionally, the numerical experiments illustrate the influence of cost structures on both the optimal loading configurations along with the optimal routes. Importantly, our experiments also suggest that the proposed scatter search method can produce good quality solutions in less time.

“An Overwhelming Cloud of Inertia”: Evaluating the Impact of Course Design Changes Following the COVID-19 Pandemic

In the wake of the COVID-19 pandemic, beginning in March 2020, educators at all levels faced the challenge of responding to student needs and utilizing technology for instruction. While much of the emerging research highlights the experiences of students and instructors as they shifted from face-to-face to remote learning, this study explores the experiences of students in a fully online graduate program as the scope of the pandemic was growing. What is the best way to maintain a community of inquiry (Garrison et al., 2000) when so much is changing? This case study explores the impact of a variety of course design changes that sought to help students meet learning objectives while also seeking to alleviate the unanticipated pressures created by external forces. Ultimately, the findings suggest that increased flexibility with due dates and access to course materials were the most helpful strategy for helping students deal with the disruptive events of the semester. In addition, managing the disruptions and finding some sense of balance were important for both instructors and students.

Epidemiology and trend of rabies post exposure prophylaxis at anti rabies clinic of a tertiary care hospital in India

Background and Objectives Effective prevention of rabies is possible by vaccination following a rabid animal bite. Objectives of this study was to describe demographics, circumstances of bite and the trend of vaccination over last three years (January 2019-November 2021) in an anti-rabies clinic of a tertiary care hospital. Materials and Methods This was an observational study of prospective design. All animal bite victims who attended the anti rabies clinic (ARC) of the study institution during the study period were invited to participate in the study. Data was collected using a structured schedule on first visit and at 28th day to check for on time compliance to vaccination schedule. On time completion was defined as taking all vaccine doses on due dates. Distribution of variables was shown by frequencies and percentages. Indicators were recorded for three consecutive years. Year wise indicators were compared by chi-square test. Results Data was collected for 293 victims. Median age of bite victims was 41.8 years (range 3-78 years) while 58.7 % respondents were below 45 years of age; 71.3 % victims were male. 82.3 % bites were by dogs; 38 % victims had multiple bites. Post-exposure prophylaxis (PEP) with anti-rabies vaccine (ARV) was initiated within 72 hours for 80 % victims and it was completed on time for 66.2 % victims. Three years trend for PEP indicators did not show a statistically significant difference. Conclusion On time PEP schedule completion was fairly high at the studied ARC. Health seeking for PEP following animal bite was not affected by the corona virus pandemic

One-of-a-kind Production in Cyber-Physical Production Systems Considering Machine Failures

Industry 4.0 proposes the incorporation of information technologies at all levels of the production process. By incorporating these technologies, Industry 4.0 provides new tools for production planning processes, allowing to address problems in an innovative and efficient manner. From these technologies and tools, it is that in this work a One-of-a-Kind Production (OKP) process is approached, where the products tend to be highly customized. OKP implies working with a very large variability within production, demanding very efficient planning systems. For this, a planning model based on CONWIP-type strategies was proposed, which seeks to level the production of a shop floor configured in the form of a job shop. Even more, for having a more realistic shop-floor representation, machine failures have been included in the model. In turn, different dispatching rules were proposed to study the performance and analyze the behaviour of the system. From the results obtained, it is observed that, when the production demand is very exigent in relation with the capacity of the system, the dispatching rules that analyze the workload generated by each job tend to perform better. However, when the demand on the capacity of the production system is less intense, the rules associated with due dates are the ones that obtain the best results.