The fixed-route vehicle-refueling problem (FRVRP) is a difficult combinatorial problem that is used extensively in the US truckload industry to manage fuel costs. This paper proposes a preprocessing technique for the ...
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The fixed-route vehicle-refueling problem (FRVRP) is a difficult combinatorial problem that is used extensively in the US truckload industry to manage fuel costs. This paper proposes a preprocessing technique for the FRVRP that cuts the problem size noticeably without eliminating the optimal solution(s), which allows users to enlarge the size of solvable instances or save the CPU time of solving the problem dramatically. Empirical testing with real-world instances shows that our method: (i) reduces the problem size by 54.8% and (ii) solves the FRVRPs to optimality in roughly 1/4 of the time it is currently taking. (C) 2013 Elsevier Ltd. All rights reserved.
We consider semi-continuous network flow problems, that is, a class of network flow problems where some of the variables are restricted to be semi-continuous. We introduce the semi-continuous inflow set with variable ...
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We consider semi-continuous network flow problems, that is, a class of network flow problems where some of the variables are restricted to be semi-continuous. We introduce the semi-continuous inflow set with variable upper bounds as a relaxation of general semi-continuous network flow problems. Two particular cases of this set are considered, for which we present complete descriptions of the convex hull in terms of linear inequalities and extended formulations. We consider a class of semi-continuous transportation problems where inflow systems arise as substructures, for which we investigate complexity questions. Finally, we study the computational efficacy of the developed polyhedral results in solving randomly generated instances of semi-continuous transportation problems.
This paper concerns roundwood transport on gravel roads. The forest sector in Sweden has identified the road quality as a major problem, in particular in the spring, when the ground is thawing. The model simultaneousl...
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This paper concerns roundwood transport on gravel roads. The forest sector in Sweden has identified the road quality as a major problem, in particular in the spring, when the ground is thawing. The model simultaneously optimizes roundwood transport and road investments on gravel roads. The model is a mixed-integer programming model, since the road accessibility variables need to be binary. Investments in surface roads, maintenance and construction of new roads are not included in the model. We test the model with a sample problem using a small area. With a simple heuristic method 'near to optimal' solutions are obtained in reasonable time. In the last section we make some concluding remarks and discuss future use of the model. Heuristics and aggregation are two different ways to solve the problem in reasonable time. (c) 2004 Elsevier B.V. All rights reserved.
Pumped storage hydro units (PSHU) can provide flexibility to power systems. This becomes particularly valuable in recent years with the increasing shares of intermittent renewable resources. However, due to emphasis o...
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Pumped storage hydro units (PSHU) can provide flexibility to power systems. This becomes particularly valuable in recent years with the increasing shares of intermittent renewable resources. However, due to emphasis on thermal generation in the current market practices, the flexibility from PSHUs has not been fully explored and utilized. This paper proposes a configuration based pumped storage hydro (PSH) model for the day-ahead market, in order to enhance the use of PSH resources in the system. A strategic design of incorporating and fully optimizing PSHUs in the day-ahead market is presented. We show the compactness of the proposed model. Numerical studies are presented in an illustrative test system and the Midcontinent Independent System Operator (MISO) system.
Due to climate change and the urgent need to reduce the greenhouse gas emissions of industrial processes, decarbonizing energy-intensive industries is highly important. In Europe, emissions trading is the main regulat...
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Due to climate change and the urgent need to reduce the greenhouse gas emissions of industrial processes, decarbonizing energy-intensive industries is highly important. In Europe, emissions trading is the main regulatory instrument providing incentives to replace conventional production facilities with low-carbon alternatives. Using the example of primary steelmaking, this study aims to evaluate the impacts of product benchmark revisions on the transformation of integrated steel mills. To this end, an optimization model for the strategic planning of economically favorable transformation pathways is developed. The model is combined with a prospective assessment of economic and climate impacts from steelmaking processes. By applying the model to a case study from Germany, legislative conditions in which steel manufacturers are encouraged to transform their production infrastructure are identified. Based on current regulations on product benchmarks for steelmaking, investments into low-carbon technologies are slowed down until allocations of emissions allowances expire. However, benchmark revisions have the potential to speed up transformation plans significantly. In the analyzed scenarios, the greenhouse gas emissions of primary steelmaking between 2024 and 2034 decreased by up to 15%. These results are further strengthened by sensitivity analyses on the main market influences. Hence, this study provides policy recommendations on favorable revisions of product benchmarks for primary steelmaking.
In this paper, we study an e-grocer's tactical problem of differentiated time slot pricing in attended home delivery. The purpose of differentiating delivery prices is to influence customers' choice behavior c...
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In this paper, we study an e-grocer's tactical problem of differentiated time slot pricing in attended home delivery. The purpose of differentiating delivery prices is to influence customers' choice behavior concerning the offered time slots, such that cost-effective delivery schedules on an operational level can be expected and overall profit is maximized. We present a mixed-integer linear programming formulation of the problem, in which delivery costs are anticipated by explicitly incorporating routing constraints, and we model customer behavior by a general nonparametric rank-based choice model. Concerning cost anticipation, we also propose a model-based approximation that enables application to real-world problem sizes. In a setup inspired by an industry partner operating in urban areas, we then perform a comprehensive computational study that reveals the value of the model-based approximation as a supporting instrument for an e-grocer's pricing decisions in practice. In particular, we demonstrate the superiority of the model-based approximation for real-world problem sizes to several benchmark approaches applied in the scientific literature and in practice (e.g., a unit price approach and other standard pricing heuristics).
This article presents a new model for constructing annual block schedules for family medicine residents based on the rules and procedures followed by the Family Medicine Department at the University of Texas Health Sc...
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This article presents a new model for constructing annual block schedules for family medicine residents based on the rules and procedures followed by the Family Medicine Department at the University of Texas Health Science Center in San Antonio (UTHSC-SA). Such residency programs provide 3years of specialty training for recent medical school graduates. At the beginning of each academic year, each trainee is given an annual block schedule that indicates his or her monthly assignments. These assignments are called rotations and include a variety of experiences, such as pediatric ambulatory care, the emergency room, and inpatient surgery. An important requirement associated with a subset of the rotations is that the residents spend multiple half-day sessions a week in a primary care clinic treating patients from the community. This is a key consideration when constructing the annual block schedules. In particular, one of the primary goals of most residencies is to ensure that the number of residents in clinic each day is approximately the same, so that the number of patients that can be seen each day is also the same. Uniformity provides for a more efficient use of supervisory and staff *** difficulty in achieving this goal is that not all rotations allow for clinic duty and that the number of patients that can be seen by a resident each session depends on his or her year of training. When constructing annual block schedules, two high-level sets of variables are available to the program coordinator. The first is the assignment of residents to rotations for each of the 12 blocks, and the second is the (partial) ability to adjust the days on which a resident has clinic duty during each rotation. In approaching the problem, our aim was to redesign the current rotations while giving all residents a 12-month schedule that concurrently (i) balances the number of patients that can be seen in the clinic during each half-day session and (ii) minimizes the number of ad
Increasing individual vehicular traffic is a major concern all around the world. This leads to more and more oversaturated intersections. Traffic signal control under oversaturated condition is a long-lasting challeng...
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Increasing individual vehicular traffic is a major concern all around the world. This leads to more and more oversaturated intersections. Traffic signal control under oversaturated condition is a long-lasting challenge. To address this challenge thoroughly, this paper introduces grade separation at signalized intersections. A lane-based optimization model is developed for the integrated design of grade-separated lanes (e.g. tunnels), lane markings (e.g. left turns, through traffic, right turns, etc.) and signal timing settings. We take into account two types of lane configurations. One is conventional surface lanes controlled by signals, and the other is grade-separated lanes. This problem is formulated as a mixedinteger Linear Program (MILP), and this can be solved using the regular branch-and-bound methods. The integer decision variables help in finding if the movement is on grade-separated or surface lanes, and also the successor functions to govern the order of signal display. The continuous variables include the assigned lane flow, common flow multiplier, cycle length, and start and duration of green for traffic movements and lanes. The optimized signal time settings and lane configurations are then represented in Vissim simulation. Numerical examples, along with a benefit-cost analysis show the good savings of the proposed optimization model for oversaturated traffic conditions. The benefit-cost ratio for installing 4 grade-separated lanes (as a tunnel) at a heavily oversaturated intersection (intersection capacity utilization rate equal to 1.57) exceeds 5.4. (C) 2016 Elsevier Ltd. All rights reserved.
mixed-integer optimal control problems governed by partial differential equations (MIPDECOs) are powerful modeling tools but also challenging in terms of theory and computation. We propose a highly efficient state eli...
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mixed-integer optimal control problems governed by partial differential equations (MIPDECOs) are powerful modeling tools but also challenging in terms of theory and computation. We propose a highly efficient state elimination approach for MIPDECOs that are governed by partial differential equations that have the structure of an abstract ordinary differential equation in function space. This allows us to avoid repeated calculations of the states for all time steps, and our approach is applied only once before starting the optimization. The presentation of theoretical results is complemented by numerical experiments.
T he current airline practice in conducting fleet assignments is to begin assigning aircraft capacity to scheduled flights well in advance of departures. However, the accuracy of the passenger demand forecast improves...
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T he current airline practice in conducting fleet assignments is to begin assigning aircraft capacity to scheduled flights well in advance of departures. However, the accuracy of the passenger demand forecast improves markedly over time, and revisions to the initial fleet assignment become naturally pertinent when the observed demand differs considerably from the assigned aircraft capacities. The demand-driven refleeting (DDR) approach proposed in this paper offers a dynamic reassignment of aircraft capacities to the flight network, when improved demand forecasts become available, so as to maximize the total revenue. Because of the need to preserve the initial crew schedule, this reassignment approach is limited within a single family of aircraft types and to the flights assigned to this particular family. This restriction makes it computationally tractable to include more relevant path-level demand information into the DDR model. Accordingly, we construct a mixed-integer programming model for this enhanced problem context and study its polyhedral structure to explore ways for tightening its representation and for deriving certain classes of valid inequalities. Various schemes for implementing such reformulation techniques are investigated and tested using a set of simulated and real instances obtained from United Airlines.
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