In order to reduce the negative impact of fuel-powered vehicles on the environment, the use of alternative-fuel vehicles (AFVs), which produce far less pollution than traditional fuel-powered vehicles, is being introd...
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In order to reduce the negative impact of fuel-powered vehicles on the environment, the use of alternative-fuel vehicles (AFVs), which produce far less pollution than traditional fuel-powered vehicles, is being introduced in many countries around the world. However, compared to the fuel-powered vehicles, AFVs such as electric vehicles require frequent recharging of their electrical energy storages (batteries), which results in a short vehicle driving range. Thus, AFV users who want to travel from home to a terminal location and back again must consider whether their AFVs can be recharged on the way. One of the approaches to solve this problem is to install alternative fuel charging stations on suitable locations to provide recharging services. However, when the budget is limited, the selection of locations and the types of alternative fuel charging stations becomes a decision problem, since it will directly affect the number of potential AFV users that can be served. This paper develops a mixed-integer programming model to address this problem and to maximize the number of people who can complete round-trip itineraries. A hybrid heuristic approach is proposed to solve this model. Numerical results show that the proposed heuristic approach only requires a small amount of CPU time to attain confident solutions. (C) 2014 Elsevier Ltd. All rights reserved.
Purpose The objective of this research study is to formulate and develop a novel optimization model that enables planners of modular construction to minimize the total transportation and storage costs of prefabricated...
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Purpose The objective of this research study is to formulate and develop a novel optimization model that enables planners of modular construction to minimize the total transportation and storage costs of prefabricated modules in modular construction projects. Design/methodology/approach The model is developed by identifying relevant decision variables, formulating an objective function capable of minimizing the total transportation and storage costs and modelling relevant constraints. The model is implemented by providing all relevant planner-specified data and performing the model optimization computations using mixed-integer programming to generate the optimal solution. Findings A case study of hybrid modular construction of a healthcare facility is used to evaluate the model performance and demonstrate its capabilities in minimizing the total transportation and onsite storage costs of building prefabricated modules. Research limitations/implications The model can be most effective in optimizing transportation for prefabricated modules with rectangular shapes and might be less effective for modules with irregular shapes. Further research is needed to consider the shape of onsite storage area and its module arrangement. Practical implications The developed model supports construction planners in improving the cost effectiveness of modular construction projects by optimizing the transportation of prefabricated modules from factories to construction sites. Originality/value The original contributions of this research is selecting an optimal module truck assignment from a feasible set of trucks, identifying an optimal delivery day of each module as well as its location and orientation on the assigned truck and complying with relevant constraints including the non-overlap of modules on each truck, shipment weight distribution and aerodynamic drag reduction.
We develop a finite-horizon discrete-time constrained Markov decision process (MDP) to model diagnostic decisions after mammography where we maximize the total expected quality-adjusted life years (QALYs) of a patient...
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We develop a finite-horizon discrete-time constrained Markov decision process (MDP) to model diagnostic decisions after mammography where we maximize the total expected quality-adjusted life years (QALYs) of a patient under resource constraints. We use clinical data to estimate the parameters of the MDP model and solve it as a mixed-integer program. By repeating optimization for a sequence of budget levels, we calculate incremental cost-effectiveness ratios attributable to consecutive levels of funding and compare actual clinical practice with optimal decisions. We prove that the optimal value function is concave in the allocated budget. Comparing to actual clinical practice, using optimal thresholds for decision making may result in approximately 22% cost savings without sacrificing QALYs. Our analysis indicates short-term follow-ups are the immediate target for elimination when budget becomes a concern. Policy change is more drastic in the older age group with the increasing budget, yet the gains in total expected QALYs related to larger budgets are predominantly seen in younger women along with modest gains for older women.
The new energy dispatch problem has aroused more and more attention. In this paper, we investigate the problem of determining the optimal usage of generating power during a scheduling period. A set of MIP formulations...
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The new energy dispatch problem has aroused more and more attention. In this paper, we investigate the problem of determining the optimal usage of generating power during a scheduling period. A set of MIP formulations are adopted for precise modeling of the variety of power systems (different power generation units) and the actual situation in china. Based on these formulations, we construct a new energy dispatch model which includes many MIP sub-problems. An auto-tuning MIP solver CMIP is given to effectively improve the performance of solving the proposed model. The CMIP focuses on optimizations for presolver, the LP solver for corresponding relaxation problem, and the primal heuristics. Actual predict data is used in performance experiments. Computational results conform to the viability of optimization. Our optimizations further reduce 27.6% of the average execution time compared to CPLEX. (C) 2015 Elsevier B.V. All rights reserved.
In this paper, a new approach for solving scheduling problems in low-volume low-variety production systems is proposed. Products assembled in such production systems follow a pre-defined processing order through a ser...
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In this paper, a new approach for solving scheduling problems in low-volume low-variety production systems is proposed. Products assembled in such production systems follow a pre-defined processing order through a series of unique work centers, each budgeted with multiple classifications of resources, responsible to complete a pre-defined statement of work, over the span of an imposed takt-time. Aircraft, heavy aero-structures, and heavy mining and military equipment are examples of products assembled in such production systems. Despite prominent scholarly advancements in sequencing and scheduling optimization of a wide range of production systems, limited research has been reported on mathematical programming approaches for scheduling optimization of activities in low-volume low-variety production systems. This paper fills the gap in the current literature, through the formulation of a set of multi-objective mixed-integer linear mathematical programming models, developed for solving discrete-time work center scheduling problems in low-volume low-variety production systems. Three mathematical models are proposed in this paper, two of which are formulated for scheduling optimization of activities within a work center, differentiated by their objectives and underlying assumptions, reflective of two distinct industrial approaches to scheduling. Additionally, an alternative optimization model is proposed for evaluating a work center's maximum capacity given the complete saturation of resources, recommended for capacity studies and early detection of bottlenecks. The models proposed in this paper are validated and verified for compatibility and reliability through a real-world case study with a global leader in the aerospace industry.
Autonomous vehicles have the potential to transform the way people are transported. While driverless technology may mean fewer vehicles are required to transport people to and from their daily activities, such changes...
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Autonomous vehicles have the potential to transform the way people are transported. While driverless technology may mean fewer vehicles are required to transport people to and from their daily activities, such changes may result in increased congestion or total miles traveled. In this study, we solve the single-household shared autonomous vehicle problem to identify cost-optimal routings of vehicles throughout the day. Such a tool will be useful for consumers seeking to minimize cost and for regulators seeking to understand and predict how people may behave in different scenarios. We provide a thorough literature review and construct a mixed-integer linear program to minimize the daily travel cost of a household attending a given set of activities. Since solution time is a determinant for applicability of such a model, we present the model in a component-wise fashion. This approach allows us to understand which features most affect the problem complexity and solution time. We note that modeling carpooling is the feature that most increases time to find an optimal solution, and we therefore propose a novel modeling technique for carpooling two people. We illustrate the performance of our model by comparing it with other models from the literature and note that our model can solve significantly larger problem instances and in a time that is short enough to facilitate real-time scheduling. We also highlight the utility of our model for regulators, who can use it to analyze quickly produced optimal routes under different cost/tax scenarios. (C) 2020 Elsevier Ltd. All rights reserved.
A novel efficient agent-based method for scheduling network batch processes in the process industry is proposed. The agent-based model is based on the resource-task network. To overcome the drawback of localized solut...
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A novel efficient agent-based method for scheduling network batch processes in the process industry is proposed. The agent-based model is based on the resource-task network. To overcome the drawback of localized solutions found in conventional agent-based methods, a new scheduling algorithm is proposed. The algorithm predicts the objective function value by simulating another cloned agent-based model. Global information is obtained, and the solution quality is improved. The solution quality of this approach is validated by detailed comparisons with the mixed-integer programming (MIP) methods. A solution close to the optimal one can be found by the agent-based method with a much shorter computational time than the MIP methods. As a scheduling problem becomes increasingly complicated with increased scale, more specifications, and uncertainties, the advantages of the agent-based method become more evident. The proposed method is applied to simulated industrial problems where the MIP methods require excessive computational resources. (c) 2013 American Institute of Chemical Engineers AIChE J, 59: 2884-2906, 2013
We present a framework for the formulation of MIP scheduling models based on multiple and nonuniform discrete time grids. In a previous work we showed that it is possible to use different (possibly nonuniform) time gr...
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We present a framework for the formulation of MIP scheduling models based on multiple and nonuniform discrete time grids. In a previous work we showed that it is possible to use different (possibly nonuniform) time grids for each task, unit, and material. Here, we generalize these ideas to account for general resources, and a range of processing characteristics such as limited intermediate storage and changeovers. Each resource has its own grid based on resource consumption and availability allowing resource constraints to be modeled more accurately without increasing the number of binary variables. We develop algorithms to define the unit-, task-, material-, and resource-specific grids directly from problem data. Importantly, we prove that the multi-grid formulation is able to find a schedule with the same optimal objective as the discrete-time single-grid model with an arbitrarily fine grid. The proposed framework leads to the formulation of models with reduced number of binary variables and constraints, which are able to find good solutions faster than existing models. (C) 2014 Elsevier Ltd. All rights reserved.
The railroad blocking problem is an important issue at the tactical level of railroad freight transportation. This problem consists of determining paths between the origins and destinations of each shipment to minimiz...
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The railroad blocking problem is an important issue at the tactical level of railroad freight transportation. This problem consists of determining paths between the origins and destinations of each shipment to minimize the operating and user costs while satisfying the railroad supply and demand restrictions. A mixed-integer program (MIP) is developed to find the optimal paths, and a new heuristic is developed to solve the proposed model. This heuristic decomposes the model into two sub-problems of manageable size and then provides feasible solutions. We discuss the performance of the proposed heuristic for a set of instances with up to 90 stations. A comparison with the CPLEX MIP solver shows that the heuristic gives the exact solution for 10 out of 15 instances. For the remaining instances, the heuristic obtained solutions within a tolerance of 0.03-0.84%. Furthermore, compared with the CPLEX MIP solver, the heuristic reduced the run time by an average of 85% for all 15 instances. Finally, we present the computational results of the heuristic applied to Iranian railroads. (C) 2017 Elsevier Inc. All rights reserved.
We address an inventory routing problem (IRP) in which routing and inventory decisions are dictated by supply rather than demand. Moreover, inventory is held in containers that act as both a storage container and a mo...
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We address an inventory routing problem (IRP) in which routing and inventory decisions are dictated by supply rather than demand. Moreover, inventory is held in containers that act as both a storage container and a movable transport unit. This problem emanates from logistics related to biogas transportation in which biogas is transported in containers from many suppliers to a single facility. We present a novel and compact formulation for the supply-driven IRP which addresses the routing decisions in continuous-time in which inventory levels within the containers are continuous. Valid inequalities are included and realistic instances are solved to optimality. For all experiments, we found that the total transportation time is minimized when the storage capacity at each supplier is larger than or equal to the vehicle capacity. These routes are characterized by tours in which mostly single suppliers are visited. In 95% of the instances, the average content level of the exchanged containers exceeded 99.6%. (C) 2019 Elsevier Ltd. All rights reserved.
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