scholarly journals Managing Shutdown Decisions in Merchant Commodity and Energy Production: A Social Commerce Perspective

2020 ◽  
Author(s):  
Alessio Trivella ◽  
Selvaprabu Nadarajah ◽  
Stein-Erik Fleten ◽  
Denis Mazieres ◽  
David Pisinger
Keyword(s):  
Energy Production ◽  
Local Community ◽  
Binary Classification ◽  
Problem Definition ◽  
Stochastic Dynamic ◽  
Anticipated Regret ◽  
Social Commerce ◽  
Asset Value ◽  
Plant Closures ◽  
Operating Policies

Problem definition: Merchant commodity and energy production assets operate in markets with volatile prices and exchange rates. Plant closures adversely affect societal entities beyond the specific plant being shut down, such as the parent company and the local community. Motivated by an aluminum producer, we study if mitigating these hard-to-assess broader impacts of a shutdown is financially viable using the plant’s operating flexibility. Academic/practical relevance: Our social commerce perspective toward managing shutdown decisions deviates from the commonly used asset value maximization objective in merchant operations. Identifying operating policies that delay or decrease the likelihood of a shutdown without incurring a significant asset value loss supports socially responsible plant shutdown decisions. Methodology: We formulate a constrained Markov decision process to manage shutdown decisions and limit the probability of future plant closures. We provide theoretical support for approximating this intractable model using unconstrained stochastic dynamic programs with modified shutdown costs and explore two classes of operating policies. Our first policy leverages anticipated regret theory, and the second policy generalizes, using machine learning, production-margin heuristics used in practice. We compute the former and latter policies using a least squares Monte Carlo method and combining this method with binary classification, respectively. Results: Anticipated-regret policies possess desirable asymptotic properties absent in classification-based policies. On instances created using real data, anticipated-regret and classification-based policies outperform practice-based production-margin strategies. Significant reductions in shutdown probability and delays in plant closures are possible while incurring small asset value losses. Managerial implications: A plant’s operating flexibility provides an effective lever to balance the social objective to reduce closures and the financial goal to maximize asset value. Adhering to both objectives requires combining short-term commitments with external stakeholders to avoid shutdown with longer-term internal efforts to reduce the probability of plant closures.

Optimal Dynamic Appointment Scheduling of Base and Surge Capacity

2021 ◽  
Author(s):  
Benjamin Grant ◽  
Itai Gurvich ◽  
R. Kannan Mutharasan ◽  
Jan A. Van Mieghem
Keyword(s):  
Waiting Times ◽  
Sufficient Conditions ◽  
Capacity Utilization ◽  
Healthcare Setting ◽  
Appointment Scheduling ◽  
Problem Definition ◽  
Stochastic Dynamic ◽  
Surge Capacity ◽  
Trade Off ◽  
Dynamic Stochastic

Problem definition: We study dynamic stochastic appointment scheduling when delaying appointments increases the risk of incurring costly failures, such as readmissions in healthcare or engine failures in preventative maintenance. When near-term base appointment capacity is full, the scheduler faces a trade-off between delaying an appointment at the risk of costly failures versus the additional cost of scheduling the appointment sooner using surge capacity. Academic/practical relevance: Most appointment-scheduling literature in operations focuses on the trade-off between waiting times and utilization. In contrast, we analyze preventative appointment scheduling and its impact on the broader service-supply network when the firm is responsible for service and failure costs. Methodology: We adopt a stochastic dynamic programming (DP) formulation to characterize the optimal scheduling policy and evaluate heuristics. Results: We present sufficient conditions for the optimality of simple policies. When analytical solutions are intractable, we solve the DP numerically and present optimality gaps for several practical policies in a healthcare setting. Managerial implications: Intuitive appointment policies used in practice are robust under moderate capacity utilization, but their optimality gap can quadruple under high load.

scholarly journals Livestock plants and COVID-19 transmission

2020 ◽  
Vol 117 (50) ◽  
pp. 31706-31715 ◽  
Author(s):  
Charles A. Taylor ◽  
Christopher Boulos ◽  
Douglas Almond
Keyword(s):  
Supply Chains ◽  
Local Community ◽  
Meat Production ◽  
Department Of Agriculture ◽  
Risk Characteristics ◽  
Strong Positive Relationship ◽  
The Us ◽  
Processing Plants ◽  
Community Transmission ◽  
Plant Closures

Policy responses to the COVID-19 outbreak must strike a balance between maintaining essential supply chains and limiting the spread of the virus. Our results indicate a strong positive relationship between livestock-processing plants and local community transmission of COVID-19, suggesting that these plants may act as transmission vectors into the surrounding population and accelerate the spread of the virus beyond what would be predicted solely by population risk characteristics. We estimate the total excess COVID-19 cases and deaths associated with proximity to livestock plants to be 236,000 to 310,000 (6 to 8% of all US cases) and 4,300 to 5,200 (3 to 4% of all US deaths), respectively, as of July 21, 2020, with the vast majority likely related to community spread outside these plants. The association is found primarily among large processing facilities and large meatpacking companies. In addition, we find evidence that plant closures attenuated county-wide cases and that plants that received permission from the US Department of Agriculture to increase their production-line speeds saw more county-wide cases. Ensuring both public health and robust essential supply chains may require an increase in meatpacking oversight and potentially a shift toward more decentralized, smaller-scale meat production.

scholarly journals CLIENT-BASED CORNERSTONE DESIGN PROJECTS

2019 ◽  
Author(s):  
Justine Boudreau ◽  
Hanan Anis
Keyword(s):  
Engineering Design ◽  
Engineering Students ◽  
Local Community ◽  
Unmet Need ◽  
Lessons Learned ◽  
Problem Definition ◽  
Student Groups ◽  
Engineering Teams ◽  
Design Projects ◽  
Second Year

Engineering students at the University of Ottawa are exposed to engineering design in first- and second-year courses. Both courses are open to all engineering students and are multidisciplinary in nature. Students work in teams to deliver a physical prototype by the end of the term. The design projects are all community-based and involve a client from the local community with a specific unmet need. Examples of such clients include local hospitals, accessibility organizations, Ottawa police, Indigenous elders and many more. The client meets with the students a minimum of three times throughout the semester to provide the problem definition and give feedback to the student groups at different stages of the design process. The goal of this paper is to share best practices in selecting and delivering client-based projects targeting first- and second-year students in multidisciplinary engineering teams. The paper discusses the choice of project themes and specific projects. In addition, it presents lessons learned based on student-client interactions, lab manager-client interactions and client satisfaction. Examples are presented from the past three years of delivering such engineering design courses, with testimonials from clients and students.

scholarly journals Community action and the regulation of alcohol availability: Modelling the connections

2003 ◽  
Vol 20 (1_suppl) ◽  
pp. 56-68
Author(s):  
Peter D'abbs
Keyword(s):  
Public Health ◽  
Local Community ◽  
Local Level ◽  
Community Action ◽  
Alcohol Problems ◽  
Problem Definition ◽  
Local Action ◽  
Alcohol Availability ◽  
Regulatory Systems ◽  
Laws And Regulations

In Australia, as in other countries, recent initiatives aimed at reducing alcohol-related harm have focused on the local community as the site of interventions, and in many cases have included local controls on alcohol availability as a key component. In this process, liquor licensing authorities – as the statutory agency primarily responsible for regulating alcohol availability – have been called upon to act as instruments of public health. Historically, however, their primary function has not been to promote public health, but rather to maintain orderly markets. Moreover, their power to intervene in market processes has in many instances been curtailed under deregulatory policies accompanying globalisation. Taken together, these trends generate a need for a theoretically-informed understanding of the role of liquor licensing bodies and other regulatory agencies in a context of locally-based initiatives aimed at reducing alcohol-related problems. This paper proposes a conceptual framework for meeting this need. Liquor regulatory systems are seen as agencies of social control mandated by the state. Three key components of these systems are identified: 1) laws and regulations governing the activities of liquor licensing authorities; 2) the structure and resourcing of agencies established to uphold the laws and regulations, and 3) practices through which decisions are reached by the licensing authorities. Each of these has influence independently of, but also in interaction with, each other. The initiation of local action focusing on alcohol problems generates a complex social field within which economic and political agencies, some operating at a purely local level, others at a national or even global level, compete to promote and defend their interests, and in which culturally ascribed beliefs and practices associated with drinking alcohol at the micro-social level are endorsed, challenged and/or defended. Within this field, liquor licensing authorities become agencies upon which competing claims are made. The processes involved can be analysed in terms of four phases: 1) agenda setting and problem definition; 2) specification of alternatives; 3) decision-making; 4) implementation. The components and processes outlined in the paper are illustrated with reference to instances of local action in northern Australia. The model proposed will serve, it is argued here, as a framework for more systematic comparative analysis of such local actions.

Managing Uncertain Capacities for Network Revenue Optimization

2021 ◽  
Author(s):  
Fabricio Previgliano ◽  
Gustavo Vulcano
Keyword(s):  
Programming Model ◽  
Numerical Study ◽  
Problem Definition ◽  
Gradient Algorithm ◽  
Stationary Points ◽  
Stochastic Dynamic ◽  
Revenue Optimization ◽  
Penalty Cost ◽  
Net Revenue ◽  
The Impact

Problem definition: We study the problem of managing uncertain capacities for revenue optimization over a network of resources. The uncertainty could be due to (i) the need to reallocate initial capacities among resources or (ii) the random availability of physical capacities by the time of service execution. Academic/practical relevance: The analyzed control policy is aligned with the current industry practice, with a virtual capacity and a bid price associated with each network resource. The seller collects revenues from an arriving stream of customers. Admitted requests that cannot be accommodated within the final, effective capacities incur a penalty cost. The objective is to maximize the total cumulative net revenue (sales revenue minus penalty cost). The problem arises in practice, for instance, when airlines are subject to last-minute change of aircrafts and in cargo revenue management where the capacity left by the passengers’ load is used for freight. Methodology: We present a stochastic dynamic programming formulation for this problem and propose a stochastic gradient algorithm to approximately solve it. All limit points of our algorithm are stationary points of the approximate expected net revenue function. Results: Through an exhaustive numerical study, we show that our controls are computed efficiently and deliver revenues that are almost consistently higher than the ones obtained from benchmarks based on the widely adopted deterministic linear programming model. Managerial implications: We obtain managerial insights about the impact of the timing of the capacity uncertainty clearance, the capacity heterogeneity, the network congestion, and the penalty for not being able to accommodate the previously accepted demand. Our approach tends to offer the best performance across different parameterizations of the problem.

scholarly journals Reservoir optimization model incorporating inflow forecasts with various lead times as hydrologic state variables

2009 ◽  
Vol 12 (3) ◽  
pp. 292-302 ◽  
Author(s):  
Tang Guolei ◽  
Zhou Huicheng ◽  
Li Ningning
Keyword(s):  
Performance Indicators ◽  
Stochastic Dynamic Programming ◽  
Programming Models ◽  
Lead Times ◽  
Stochastic Dynamic ◽  
State Variables ◽  
Hydropower Generation ◽  
Operating Policy ◽  
Hydropower Production ◽  
Operating Policies

This paper presents two Stochastic Dynamic Programming models (SDP) to investigate the potential value of inflow forecasts with various lead times in hydropower generation. The proposed SDP frameworks generate hydropower operating policies for the Ertan hydropower station, China. The objective function maximizes the total hydropower generation with the firm capacity committed for the system. The two proposed SDP-derived operating policies are simulated using historical inflows, as well as inflow forecasts with various lead times. Four performance indicators are chosen to assist in selecting the best reservoir operating policy: mean annual hydropower production, Nash–Sutcliffe sufficiency score, reliability and vulnerability. Performances of the proposed SDP-derived policies are compared with those of other existing policies. The simulation results demonstrate that including inflow forecasts with various lead times is beneficial to the Ertan hydropower generation, and the chosen operating policy cannot only yield higher hydropower production, but also produces reasonable storage hydrographs effectively.

Understanding Rural Village Energy Needs and Design Constraints

2011 ◽  
Author(s):  
Kenneth M. Bryden ◽  
Nathan G. Johnson
Keyword(s):  
Local Community ◽  
Developing World ◽  
Problem Definition ◽  
Rural Poor ◽  
Community Members ◽  
Design Constraints ◽  
Sustainable Technologies ◽  
Energy Needs ◽  
Field Methodology ◽  
Sub Saharan

Today the primary challenge confronting engineers is to develop clean, sustainable technologies that can meet the needs of all of the world’s people. Traditionally this effort has focused on meeting the needs of the developed world. It is generally assumed that products needed for the developing world already exist or are relatively simple and hence do not require significant engineering design effort. As a consequence, many of the products intended to meet the needs of the poor miss the mark and do not meet their needs. This is particularly true in the design of products and processes intended to address the energy needs of the rural poor. Too often a set of standard assumptions is used, resulting in poor problem definition. And, because the design problem is not well defined, the resulting products and processes fail. Throughout the developing world it is common to find village water and energy projects that have failed. To design products and processes that meet the energy needs of the rural poor, the critical first step in the design process is a detailed in-village study of energy production and consumption dynamics. Quantifying village energy dynamics provides insight into the unfulfilled or unsatisfied needs of the consumer, establishes the design constraints, aids the engineer and the community members in prioritizing needs, and builds trust with the local community. This paper presents a field methodology developed to understand the energy needs of a rural sub-Saharan village of 700 people and discusses how this field methodology was used to establish the design constraints needed for a comprehensive energy solution.

scholarly journals Warranty Matching in a Consumer Electronics Closed-Loop Supply Chain

2020 ◽  
Author(s):  
Andre P. Calmon ◽  
Stephen C. Graves ◽  
Stef Lemmens
Keyword(s):  
Supply Chain ◽  
Assignment Problem ◽  
Closed Loop ◽  
Problem Definition ◽  
Consumer Electronics ◽  
Stochastic Dynamic ◽  
Matching Problem ◽  
Closed Loop Supply Chain ◽  
Dynamic Assignment ◽  
Better Than

Problem definition: We examine a dynamic assignment problem faced by a large wireless service provider (WSP) that is a Fortune 100 company. This company manages two warranties: (i) a customer warranty that the WSP offers its customers and (ii) an original equipment manufacturer (OEM) warranty that OEMs offer the WSP. The WSP uses devices refurbished by the OEM as replacement devices, and hence their warranty operation is a closed-loop supply chain. Depending on the assignment the WSP uses, the customer and OEM warranties might become misaligned for customer-device pairs, potentially incurring a cost for the WSP. Academic/practical relevance: We identify, model, and analyze a new dynamic assignment problem that emerges in this setting called the warranty matching problem. We introduce a new class of policies, called farsighted policies, which can perform better than myopic policies. We also propose a new heuristic assignment policy, the sampling policy, which leads to a near-optimal assignment. Our model and results are motivated by a real-world problem, and our theory-guided assignment policies can be used in practice; we validate our results using data from our industrial partner. Methodology: We formulate the problem of dynamically assigning devices to customers as a discrete-time stochastic dynamic programming problem. Because this problem suffers from the curse of dimensionality, we propose and analyze a set of reasonable classes of assignment policies. Results: The performance metric that we use for a given assignment policy is the average time that a replacement device under a customer warranty is uncovered by an OEM warranty. We show that our assignment policies reduce the average uncovered time and the expected number of out-of-OEM-warranty returns by more than 75% in comparison with our industrial partner’s current assignment policy. We also provide distribution-free bounds for the performance of a myopic assignment policy and of random assignment, which is a proxy for the WSP’s current policy. Managerial implications: Our results indicate that, in closed-loop supply chains, being completely farsighted might be better than being completely myopic. Also, policies that are effective in balancing short-term and long-term costs can be simple and effective, as illustrated by our sampling policy. We describe how the performance of myopic and farsighted policies depend on the size and length of inventory buildup.

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