Optimizing Prepositioning of Equipment and Personnel for Los Angeles County Fire Department to Fight Wildland Fires

Every day, the Los Angeles County Fire Department uses weather forecasts and automated real-time weather observations, together with field-tested moisture content of soil and vegetation, to decide whether and where to position firefighting equipment and personnel, as well as what equipment to use, for the following day. Anticipating a particularly hazardous “red flag” day, they activate off-duty personnel and reserve equipment and add these to the total augmented, prepositioned force. Analysis of years of detailed daily data can advise these costly decisions. Three models, respectively, predict for each region of the county the probability of a fire start, the area burned by a fire given any particular package of equipment and personnel preassigned to fight it, and which packages to form and send to each position. The conflicting objectives are to minimize the expected number of citizens evacuated and the constrained augmentation cost for personnel and equipment.

Pricing Anomalies and Arbitrage in Container Transport in India

Intermodal transportation requires multiple entities to manage diverse resources under complex regulations and contracts. In this paper, we carry out a multidisciplinary cross-functional analysis of container rail haulage pricing and operations in India. We discover that the total haulage cost of a container train unduly depends on the position of the containers within the train, which is referred to here as position arbitrage. The main objective of this paper is to introduce and analyze this new concept of arbitrage for the first time in the literature. We derive the limits to the arbitrage, present management insights and empirical results, and explain that the arbitrage is undesirable because of its adverse effects on the efficiency of the container supply chain. With a real case, we empirically show that container train operators can save an average of 450 million INR annually by exploiting the arbitrage. On completion of dedicated freight corridors, the annual total value of the arbitrage can increase by one billion Indian rupees. This research is also beneficial for the railways to understand the implications of haulage pricing on operational efficiency and also for the port operators and shippers to understand the implications of the arbitrage for their operations.

Developing Optimal Student Plans of Study

Advisors in a small graduate program needed to be able to help students with a wide variety of needs and preferences in terms of starting term, pace of study, program of study, and mode of course delivery to identify plans of study in a dynamic fashion and enable them to follow those plans. Course sections were limited and needed to serve multiple programs and all types of students in those programs. Last-second schedule changes due to overly large or small registration numbers were problematic. Special arrangements to allow students to graduate on time were frequent and costly and lowered academic quality. Analytical tools were developed to help with the planning and alleviate these issues. The tools and the overall approach should be of interest to educational institutions and programs that need to offer a wide variety of students extensive flexibility and choices within a highly constrained scheduling environment.

Seasonal Inventory Management Model for Raw Materials in Steel Industry

We developed a seasonal inventory management model for raw materials, such as iron ore and coal, for multiple suppliers and multiple mills. The Nippon Steel Corporation imports more than 100 million tons of raw material annually by vessels from Australia, Brazil, Canada, and other countries. Once these raw materials arrive in Japan, they are transported to domestic mills and stored in yards before being treated in a blast furnace. A critical problem currently facing the industry is the limited capacity of the yards, which leads to high demurrage costs while ships wait for space to open up in the yards before they can unload. To reduce the demurrage costs, the inventory levels of the raw materials must be kept as low as possible. However, inventory levels that are too low may lead to inventory shortage resulting from seasonal supply disruptions (e.g., a cyclone in Australia) that delay the supply of raw materials. Because both excess and depleted inventory levels lead to increased costs, optimal inventory levels must be determined. To solve this problem, we developed an inventory management model that considers variations on the supply side, differences that should be observable upon looking at the ship operations. The concept is to model the probability distribution of ship arrival intervals by brand groups and mills. We divided ship operations into two stages: arrival at all mills (in Japan) and arrival at individual mills. We modeled the former as a nonhomogeneous Poisson process and the latter as a nonhomogeneous Gamma process. Our proposed model enables inventory levels to be reduced by 14% in summer and 6% in winter.

Book Reviews

In Book Reviews, we review an extensive and diverse range of books. They cover theory and applications in operations research, statistics, management science, econometrics, mathematics, computers, and information systems. In addition, we include books in other fields that emphasize technical applications. The editor will be pleased to receive an email from those willing to review a book, with an indication of specific areas of interest. If you are aware of a specific book that you would like to review, or that you think should be reviewed, please contact the editor. The following books are reviewed in this issue of INFORMS Journal on Applied Analytics, 51(4), July–August: Applications of Operations Research and Management Science for Military Decision Making, William P. Fox, Robert Burks; Behavioral Operational Research: A Capabilities Approach, Leroy White, Martin Kunc, Katharina Burger, Jonathan Malpass.

Portfolio-Wide Optimization of Pharmaceutical R&D Activities Using Mathematical Programming

The research and development (R&D) management in any major research pharmaceutical company is constantly faced with the need to make complicated activity scheduling and resource allocation decisions, as they carry out scientific work to develop new therapeutic products. This paper describes how we develop a decision support tool that allows practitioners to determine portfolio-wide optimal schedules in a systematic, quantitative, and largely automated fashion. Our tool is based on a novel mixed-integer linear optimization model that extends archetypal multimode resource-constrained project scheduling models in order to accommodate multiple rich features that are pertinent to the Chemistry, Manufacturing, and Controls (CMC) activities carried out within the pharmaceutical R&D setting. The tool addresses this problem at the operational level, determining schedules that are optimal in light of chosen business objectives under activity sequencing, resource availability, and deadline constraints. Applying the tool on current workload data demonstrates its tractability for practical adoption. We further illustrate how, by utilizing the tool under different input instances, one may conduct various tactical analyses to assess the system’s ability to cope with sudden changes or react to shifting management priorities.

Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization

Thelimousine service in luxury hotels is an integral component of the whole customer journey in the hospitality industry. One of the largest hotels in Singapore manages a fleet of both in-house and outsourced vehicles around the clock, serving 9,000 trips per month on average. The need for vehicles may scale up rapidly, especially during special events and festive periods in the country. The excess demand is met by having additional outsourced vehicles on standby, incurring millions of dollars of additional expenses per year for the hotel. Determining the required number of limousines by hour of the day is a challenging service capacity planning problem. In this paper, a recent transformational journey to manage this problem for the hotel is introduced, having driven up to S$3.2 million of savings per year while improving the service level. The approach builds on widely available open-source statistical and spreadsheet optimization tools, along with robotic process automation, to optimize the schedule of the hotel’s fleet of limousines and drivers and to support decision making for planners and controllers to cultivate sustained business value.

Contextual Complications in Analytical Modeling: When the Problem is Not the Problem

In this essay, I describe 10 critical complicating factors that directly affect the six basic modeling components of problem definition, assumptions, decision variables, objective functions, constraints, and solution approach. The proposed 10 contextual complicating factors are (1) organization, (2) decision-making processes, (3) measures and key performance indicators, (4) rational and irrational biases, (5) decision horizon and interval, (6) data availability, accuracy, fidelity, and latency, (7) legacy and other computer systems, (8) organizational and individual risk tolerance, (9) clarity of model and method, and (10) implementability and sustainability of the approach. I hypothesize that the core analytical problem cannot be adequately described or usefully solved without careful consideration of these factors. I describe detailed examples of these contextual factors’ effects on modeling from six published applied prescriptive analytics projects and provide other examples from the literature. The complicating factors are pervasive in these projects, directly and dramatically affecting basic modeling components over half the time. Further, in the presence of these factors, 23 statistically significant correlations tend to form in three clusters, which I characterize as culture, decision, and project clusters. Unrecognized, these factors would have hampered the implementation and ongoing use of these analytical models; in a sense, the models themselves were wrong, absent consideration of these contextual considerations. With these insights, I hope to help practitioners identify the effects of these common complications and avoid project failure by incorporating these contextual factors into their modeling considerations. Future research could seek to better understand these factors and their effects on modeling.