In this study, we consider rescheduling freight trains to reduce the effects of major interruptions. We assume that the interruption is an unexpected marshalling-yard closure, and we develop a macroscopic Mixed-Intege...
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Egon Balas's additive algorithm, also known as implicit enumeration, is a technique that uses a branch-and-bound (B&B) approach to finding optimal solutions to 0-1 integer programming problems. Three common se...
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Egon Balas's additive algorithm, also known as implicit enumeration, is a technique that uses a branch-and-bound (B&B) approach to finding optimal solutions to 0-1 integer programming problems. Three common search strategies in B&B are depth-first search, breadth-first search and best-first search. The B&B approach generates a list of pending nodes to be evaluated and storage of these nodes becomes a memory issue for larger problems. In this paper, we propose a simple bookkeeping method that tracks the state of the problem using a single array when performing a depth-first search, dramatically reducing memory requirements. The method also provides the ability to calculate, at any point of the search, the theoretical maximum number of remaining nodes to be evaluated. We note in this paper that when using the best-first search strategy, the first candidate solution found is the optimal solution.
The Covid-19 pandemic challenges healthcare systems worldwide while severely impacting mental health. As a result, the rising demand for psychological assistance during crisis times requires early and effective interv...
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The Covid-19 pandemic challenges healthcare systems worldwide while severely impacting mental health. As a result, the rising demand for psychological assistance during crisis times requires early and effective intervention. This contributes to the well-being of the public and front-line workers and prevents mental health disorders. Many countries are offering diverse and accessible services of tele-psychological intervention;Ecuador is not the exception. The present study combines statistical analyses and discrete optimization techniques to solve the problem of assigning patients to therapists for crisis intervention with a single tele-psychotherapy session. The statistical analyses showed that professionals and healthcare workers in contact with Covid-19 patients or with a confirmed diagnosis had a significant relationship with suicide risk, sadness, experiential avoidance, and perception of severity. Moreover, some Covid-19-related variables were found to be predictors of sadness and suicide risk as unveiled via path analysis. This allowed categorizing patients according to their screening and grouping therapists according to their qualifications. With this stratification, a multi-periodic optimization model and a heuristic are proposed to find an adequate assignment of patients to therapists over time. The integer programming model was validated with real-world data, and its results were applied in a volunteer program in Ecuador.
Automatic heavy-haul train (HHT) operation technology has recently received considerable attention in the field of rail transportation. In this paper, a discrete-time-based mathematical formulation is proposed to addr...
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Automatic heavy-haul train (HHT) operation technology has recently received considerable attention in the field of rail transportation. In this paper, a discrete-time-based mathematical formulation is proposed to address the speed profile optimization problem in order to ensure the safe, efficient, and economical operation of heavy-haul trains (HHTs). Due to the presence of long and steep downgrades (LSDs) on some heavy-haul lines, the brake forces of the HHT are typically jointly determined by air braking and electric braking. The time characteristics of the air braking, such as the command delay and the change process caused by the air pressure, are taken into account, and then formulas are presented to calculate the air brake force. In addition, the influence of the neutral section on the control of the electric braking is considered via space-based constraints. The resulting problem is a nonlinear optimal control problem. To achieve linearization, auxiliary 0-1 binary variables and the big-M approach are introduced to transform the nonlinear constraints regarding slope, curve, neutral section, air brake force, and air-filled time into linear constraints. Moreover, piecewise affine (PWA) functions are used to approximate the basic resistance of the HHT. Finally, a mixed integer linear programming (MILP) model is developed, which can be solved by CPLEX. The experiments are carried out using data from a heavy-haul railway line in China, and the results show that the proposed approach is effective and flexible.
The legal driver rules in the European Union defines a general framework with restrictions in driving time or working time between breaks and rest periods. These constraints must be addressed for an evaluation of any ...
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ISBN:
(纸本)9781665418737
The legal driver rules in the European Union defines a general framework with restrictions in driving time or working time between breaks and rest periods. These constraints must be addressed for an evaluation of any vehicle trip that met the regulation rules. For many real-world applications, the final trips have to satisfy these rules. The set of EU rules is the most complex design of trips and extension to other rules should be easy following the model we propose here. Compared to previous contributions we provide a new mixed integer linear program, which includes all the weekly rules to schedule when the sequence of visits is fixed. We also provide a new benchmark with detailed optimal solutions. Based on a set of numerical experiments, we discuss the relevance of different simplifications in the model used in the literature.
We address the problem of minimizing the energy cost generated by pumping operations for supplying an elevated water tanks from a low source reservoir of water distribution systems (WDS). Pumping operations can be act...
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ISBN:
(纸本)9781665404358
We address the problem of minimizing the energy cost generated by pumping operations for supplying an elevated water tanks from a low source reservoir of water distribution systems (WDS). Pumping operations can be activated by using simple ON-OFF trigger levels without advanced control system. A static optimal solution of simple trigger levels may not be robust when being applied to real world situation. In this paper, we propose an integer programming model using the idea of Additional Time Slots of [Quintiliani et. al., Water Resour Manage, 2019] to make the optimal solution more robust. In addition, computational results on real-world data with single and multiple pumps will be presented.
We propose a deep reinforcement learning (RL) method to learn large neighborhood search (LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to select a subset of variables at ea...
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ISBN:
(纸本)9781713845393
We propose a deep reinforcement learning (RL) method to learn large neighborhood search (LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to select a subset of variables at each step, which is reoptimized by an IP solver as the repair operator. However, the combinatorial number of variable subsets prevents direct application of typical RL algorithms. To tackle this challenge, we represent all subsets by factorizing them into binary decisions on each variable. We then design a neural network to learn policies for each variable in parallel, trained by a customized actor-critic algorithm. We evaluate the proposed method on four representative IP problems. Results show that it can find better solutions than SCIP in much less time, and significantly outperform other LNS baselines with the same runtime. Moreover, these advantages notably persist when the policies generalize to larger problems. Further experiments with Gurobi also reveal that our method can outperform this state-of-the-art commercial solver within the same time limit.
With the connected automated vehicle (CAV) growing, their interactions with the human-driven vehicles will significantly change the traffic flow characteristics. While traditional traffic flow studies mostly assume mo...
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ISBN:
(纸本)9781728191423
With the connected automated vehicle (CAV) growing, their interactions with the human-driven vehicles will significantly change the traffic flow characteristics. While traditional traffic flow studies mostly assume molecular driving behaviors, it increasingly becomes arguable for heterogeneous traffic flow because of the different behaviors between CAVs and human-driven vehicles. To address this issue, we present an integer programming formulation for vehicle-oriented traffic dynamics model in space-time networks. In this model, individual vehicles and their driving behaviors can be explicitly distinguished. As such the diverse attributes of heterogeneous traffic flow can be well convoyed. Through analyzing queuing propagations at various bottlenecks, we demonstrate how the proposed model can be used for traffic dynamics analysis. The propagation of backward shockwave for scheduled CAVs can be precisely estimated based on each queuing vehicle's delay, freeflow travel time and travel speed in the queue. Overall, the proposed model can possibly serve as a building block for controlling both queue evolution and traffic assignment for a large-scale heterogeneous traffic at a network level.
The staff scheduling problem (SSP) in call centers is defined as determining working/non-working days for a specific number of employees and their shift types in working days, considering service level requirements, s...
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The staff scheduling problem (SSP) in call centers is defined as determining working/non-working days for a specific number of employees and their shift types in working days, considering service level requirements, some labor regulations and preferences. The SSP is often difficult to solve to optimality in practice for its complex structure. In this paper, a two-stage approach is proposed to tackle the problem, where the former generates preliminary solutions satisfying all the hard constraints, and the latter refines the solutions by fulfilling soft constraints as much as possible. The proposed approach employs artificial bee colony (ABC) algorithm as the main search framework in both stages, and appropriate neighborhood structures are designed respectively. Specifically, in the latter stage, integer programming (IP) employed under several ruin-and-recreate principles is embedded in the ABC algorithm, which helps intensify and diversify solutions. The experimental results show that the proposed approach is effective and efficient in achieving good solutions for large-scale problem in-stances. In addition, we give some guidance on how to weigh various employees' working preferences and how to balance labor costs and staff satisfaction. Finally, a case study is given to explain how the proposed model and algorithm work for a call center. This study will help call centers make appropriate decisions when planning their employees.
Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted...
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Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (***-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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