Two components of a supply chain with a significant impact on its performances are inventories and transportation costs. Mutual dependences between the activities of inventory allocation and vehicle routing have recen...
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Two components of a supply chain with a significant impact on its performances are inventories and transportation costs. Mutual dependences between the activities of inventory allocation and vehicle routing have recently motivated some authors to model them simultaneously. This practical and challenging logistical problem is known as the integrated Inventory Routing Problem (IRP). The IRP assumes application of vendor management inventory (VMI) concept where suppliers determine an order quantity and the time of its delivery. The IRP is usually formulated and solved under the assumptions of deterministic consumption and delivery times which, in turn, provide an opportunity of obtaining a solution of the problem regardless of its NP hardness, even in this simplified form. However, since fuel consumption is stochastic in its nature, delivery plans based on a deterministic assumption, particularly covering longer planning periods, may introduce certain problems in real life applications, imposing shortages, higher inventories levels, or even the necessity of changing delivery routes. This paper presents a simulation approach to the analysis of the applicability of deterministic IRP solution to real life stochastic fuel consumption and delivery process. The applicability of deterministic IRP solutions to stochastic problems with planning periods of different lengths is analyzed through observing different process performances. From obtained results it can be concluded that solutions based on deterministic consumption can be applied in stochastic IRP by applying and balancing two additional measures: emergency deliveries and safety stocks.
Improving energy efficiency in the residential sector is a pressing issue in Japan. This study examines the economic and environmental impacts of introducing the following distributed energy resources: photovoltaics (...
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Improving energy efficiency in the residential sector is a pressing issue in Japan. This study examines the economic and environmental impacts of introducing the following distributed energy resources: photovoltaics (PV), a fuel cell, and a battery. We estimate electricity and hot water demand profiles of a household by using simulated living activities. Electric power from a residential PV system is also calculated from the observed solar radiation. By using mixed integer programming, we perform a cost minimization operating simulation of a residential PV, fuel cell, and battery. The result suggests that we can create a net-zero energy house by installing both a PV system and a fuel cell into one house. On the other hand, using a battery with a fuel cell increases the household energy cost, and has few effects on CO 2 emission reduction.
ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature req...
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ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** recent years, plant factories have been drawing attention for their capability to solve global food crisis. However, the energy cost of plant factories is high due to the power consumption of air conditioning and cultivation systems necessary for realizing semi-automatic production. As this high cost hinders their propagation,, plant factories must minimize the energy cost of growing plants. We propose an operation planning method of cultivation systems for minimizing energy cost minimization while producing plants with the same amount and quality. The effect of electricity charge and weather factors on electric power consumption under various real-world constraints are used to decide the appropriate operation plan of cultivation systems. Simulation results show that the operation plan of cultivation systems properly reflects the effect of electricity charge and weather factors on electric power consumption to reduce energy cost.
In seeking to maximise NPV (Net Present Value), open pit mining schedules depend on optimal sequencing of pit progress along with ore-waste segregation. Although extensively researched using mathematical models and li...
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In seeking to maximise NPV (Net Present Value), open pit mining schedules depend on optimal sequencing of pit progress along with ore-waste segregation. Although extensively researched using mathematical models and linear programs dating from the late 1960s, this subject continues to evolve with the emergence of innovative approaches and new computational techniques. Whereas past models have focused almost exclusively on ore extraction as the principal cost-intensity factor, waste movement and handling are less well understood. However, as it comprises some of the largest operational and reclamation expenses, waste management has been progressively incorporated into the mine scheduling process, with dedicated studies commencing from 2007. This paper focuses on the impact of waste movement and placement on pit schedule optimisation, with strategic plans that honour both cost and environmental constraints.
This research aims at studying and improving the attended home services delivery in the city of Bogota (Colombia) by considering varying traffic patterns along the day. The goal is to improve routing decisions by adop...
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This research aims at studying and improving the attended home services delivery in the city of Bogota (Colombia) by considering varying traffic patterns along the day. The goal is to improve routing decisions by adopting appropriate travel time functions between pairs of locations that are built upon the basis of freely available information gathered from collaborative consumption platforms. In first place, data collected from Uber Movement ® platform is used to identify the 1-hour consolidated traffic patterns based on coordinates and time of the day for all pairs of neighborhoods within the city. However, not all the information is available, as there might exist some journeys scarcely required by Uber users. To overcome this problem, a K-nearest neighbor regression is used to predict missing travel times when required and considering as an input the coordinates and the time of the day. Then, a piecewise linear function representing the travel time between pairs of locations is constructed by assuming that a breakpoint, or change in the travel time behavior, takes place at the middle point of each one-hour time frame. A set of piecewise linear functions is then obtained after solving a system of linear equations. Following, two different approaches are used to generate solutions for a technician routing and scheduling problem with hard time windows. The first consists in an approximation integerprogramming model that represents the problems using an acyclic directed graph. The second consist in a Memetic Algorithm that minimizes the number of vehicles and vehicles field time. Preliminary results show that both approaches present similar results and can be used in practical applications.
Performing maintenance operations at offshore wind farms involves one major challenge compared with the onshore counterpart: All maintenance personnel and spare parts need to be transported from an onshore port or off...
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Performing maintenance operations at offshore wind farms involves one major challenge compared with the onshore counterpart: All maintenance personnel and spare parts need to be transported from an onshore port or offshore station to the individual wind turbines by vessels or helicopters. The vessels and helicopters required for these tasks will constitute a major part of the maintenance costs for the offshore wind farms, and to reduce the cost of energy it is essential to keep an optimal or near-optimal vessel fleet for this purpose. We study the vessel fleet optimization problem that arises for offshore wind farms and propose an appropriate optimization model. Computational experiments show that our model can be solved to provide decision makers with an optimal vessel fleet within acceptable time limits.
One of the main issues in the event of a major industrial disaster (fire, explosion or toxic gas dispersion) is to efficacy manage emergencies by considering both medical and logistics issues. From a logistics point o...
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One of the main issues in the event of a major industrial disaster (fire, explosion or toxic gas dispersion) is to efficacy manage emergencies by considering both medical and logistics issues. From a logistics point of view the purpose of this work is to correctly address critical patients from the emergency site to the most suitable hospitals. A mixed integer programming (MIP) Model is proposed, able to determine the optimal number and allocation of emergency vehicles involved in relief operations, in order to maximize the number of successfully treated injured patients. Moreover, a vehicles reallocation strategy has been developed which takes into account the evolution of the patients health conditions. Alternative scenarios have been tested considering a dynamic version of the Emergency Vehicles Allocation Problem, in which patient health conditions evolves during the rescue process. A company located in Italy has been considered as case-study in order to evaluate the performance of the proposed methodology.
This paper proposes an efficient solution approach for the line balancing problem in state-of-the-art baggage handling systems that are based on destination coded vehicles that transport the bags from their origins to...
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This paper proposes an efficient solution approach for the line balancing problem in state-of-the-art baggage handling systems that are based on destination coded vehicles that transport the bags from their origins to their destinations. First, a simple event-driven model of the process is presented. Next, this model is applied within the context of model predictive control to determine the optimal number of destination coded vehicles to be dispatched from the central depot to each loading station. The performance criterion is minimizing the overall baggage waiting time as well as the energy consumption.
In many fields, 0-1 mixed integer programming (MIP) problems are prevailingly used. These problems are often solved by the branch and bound method. However, it requires much calculation time. Some of the 0-1 MIP probl...
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In many fields, 0-1 mixed integer programming (MIP) problems are prevailingly used. These problems are often solved by the branch and bound method. However, it requires much calculation time. Some of the 0-1 MIP problems have multiple optimal solutions on the first relaxed linear programming problems. In this paper, a method which is an application of the interior point method is proposed to solve these problems. It will be shown that the proposed method is more effective than the conventional method and by the numerical experiments it will be shown that the proposed method reduces the number of created subproblems and the calculation time on the various size problems.
The investment portfolio with stochastic returns can be represented as a maximum flow generalized network with sto chastic multipliers. Modern portfolio theory (MPT) [1] provides a myopic short horizon solution to thi...
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The investment portfolio with stochastic returns can be represented as a maximum flow generalized network with sto chastic multipliers. Modern portfolio theory (MPT) [1] provides a myopic short horizon solution to this network by adding a parametric variance constraint to the maximize flow objective function. MPT does not allow the number of securities in solution portfolios to be specified. integer constraints to control portfolio size in MPT results in a nonlinear mixedinteger problem and is not practical for large universes. Digital portfolio theory (DPT) [2] finds a non-myopic long-term solution to the nonparametric variance constrained portfolio network. This paper discusses the long horizon nature of DPT and adds zero-one (0-1) variables to control portfolio size. We find optimal size constrained allocations from a universe of US sector indexes. The feasible size of optimal portfolios depends on risk. Large optimal portfolios are infeasible for low risk investors. High risk investors can increase portfolio size and diversification with little effect on return.
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