This article addresses a street sweeping problem that extends the class of multi-depot arc routing problem by integrating a flexible assignment of end depot for each working shift. A unique team of specialized vehicle...
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This article addresses a street sweeping problem that extends the class of multi-depot arc routing problem by integrating a flexible assignment of end depot for each working shift. A unique team of specialized vehicles starts each shift from a depot, visits the required arcs and returns at the end of each shift to a depot. The service in the next shift should start from the end depot of the previous one. An additional new constraint imposes that the highway arcs must be serviced during a night shift while all others arcs (boulevard, street, etc.) can be swept during both day and night. The aim of this arc routing problem is to find optimal shifts that satisfy these two practical aspects, along with other constraints such as maximum shift duration. The problem is motivated by a real-world application that has not been previously studied in the literature. A mixed integer linear programming model is formulated with the objective of minimizing the total travel time and tested on newly generated instances based on Cordeau's multi-depot vehicle routing problem instances. The results show a generation of total travel time savings up to 12 % compared to the single depot arc routing problem.
This paper describes a method for solving task planning and motion planning problems simultaneously. We target a fetch-and-carry of a small item by a single-arm mobile manipulator and introduce a method that can gener...
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ISBN:
(数字)9798331531614
ISBN:
(纸本)9798331531621
This paper describes a method for solving task planning and motion planning problems simultaneously. We target a fetch-and-carry of a small item by a single-arm mobile manipulator and introduce a method that can generate a sequence of actions and motions required for each action, even in environments with narrow open spaces such as corridors. In addition, to deal with a case where another object is already placed at the target location, we introduce a push-aside action and extend the previous method to include this action. As our method is formulated using mixed integer linear programming (MILP), the calculation time is relatively short, irrespective of the complexity of the target problem. To verify the efficiency of the proposed method, we performed a quantitative evaluation through simulation and conducted experiments on an actual mobile manipulator to verify feasibility of the methods.
This paper addresses the planning and scheduling problem of a multiproduct multistage continuous plant by three novel MILP-based (mixed integer linear programming) models. These models combine a TSP (traveling salesma...
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This paper addresses the planning and scheduling problem of a multiproduct multistage continuous plant by three novel MILP-based (mixed integer linear programming) models. These models combine a TSP (traveling salesman problem) formulation with the main ideas of general precedence and unit-specific general precedence concepts to provide hybrid discrete/continuous time representations of the system. Also, an efficient solution approach involving rolling horizon and iterative-improvement algorithm is derived for solving medium-size instances of the problem. Results analyses for different model's parameters demonstrate the benefits of the new formulations and the effectiveness of the solution approach presented in this work.
We introduce two new formulations for probabilistic constraints based on extended disjunctive formulations. Their strength results from considering multiple rows of the probabilistic constraints together. The properti...
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We introduce two new formulations for probabilistic constraints based on extended disjunctive formulations. Their strength results from considering multiple rows of the probabilistic constraints together. The properties of the first can be used to construct hierarchies of relaxations for probabilistic constraints, while the second provides computational advantages over other formulations. (C) 2012 Elsevier B.V. All rights reserved.
A wide range of problems can be modeled as mixed integer linear programming (MIP) problems using standard formulation techniques. However, in some cases the resulting MIP can be either too weak or too large to be effe...
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A wide range of problems can be modeled as mixed integer linear programming (MIP) problems using standard formulation techniques. However, in some cases the resulting MIP can be either too weak or too large to be effectively solved by state of the art solvers. In this survey we review advanced MIP formulation techniques that result in stronger and/or smaller formulations for a wide class of problems.
In this paper, we propose the modeling of a real-case problem where a farmer has to optimize the use of his/her land by selecting the best mix of crops to cultivate. Complexity of the problem is due to the several fac...
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In this paper, we propose the modeling of a real-case problem where a farmer has to optimize the use of his/her land by selecting the best mix of crops to cultivate. Complexity of the problem is due to the several factors that have to be considered simultaneously. These include the market prices variability of harvested products, the specific resource requests for each crop, the restrictions caused by limited machines availability, and the timing of operations required to complete each crop cultivation. We provide two different mathematical formulations for the analyzed problem. The first one represents a natural integerprogramming formulation looking for the crop-mix that maximizes the farmer's expected profit measured as the difference between revenues obtained by selling the harvested products and the production costs. Since the revenue of each crop depends on the price as quoted at the exchange market and the yield per hectare of harvested product, we define it as a random variable. Then, the second model uses the maximization of the Conditional Value-at-Risk (CVaR) as objective function and looks for the crop-mix that allows to maximize the average expected profit under a predefined quantile of worst realizations. To test and compare the proposed models with the cultivation choice made by the farmer, we use Italian historical data represented by monthly returns of different crops over a time period of 16 years. Computational results emphasize the advantage of using the CVaR model for a risk-averse farmer and provide interesting insights for farmers involved in similar problems. (C) 2016 Elsevier Ltd. All rights reserved.
The traditional strategy for ground-level ozone control is to apply emission reductions across the board throughout certain time periods and locations. In this paper, we study various mixed integer linear programming ...
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The traditional strategy for ground-level ozone control is to apply emission reductions across the board throughout certain time periods and locations. In this paper, we study various mixed integer linear programming (MILP) models that seek to select targeted control strategies for the Dallas Fort-Worth (DFW) region to reduce emissions, in order to achieve the State Implementation Plan (SIP) requirements with minimum cost. Statistics and optimization methods are used to determine a potential set of cost-effective control strategies for reducing ozone. These targeted control strategies are specified for different types of emission sources in various time periods and locations. Three MILP models, a static model, a sequential model, and a dynamic model, are studied in this research. These different MILP models allow decision makers to study how the targeted control strategies change under different circumstances. Meanwhile, two types of auxiliary variables are considered as supplemental control strategies in the optimization if the current set of control strategies is unable to reduce ozone to complywith the 8-h ozone standard. Results fromthe differentmodels can provide decisionmakers with information concerning how the effectiveness of the control strategies varies with daily emission patterns and meteorology.
This work proposes a methodology for directional overcurrent protection coordination in interconnected transmission systems considering a possible network contingency state. The methodology uses the short-circuit data...
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This work proposes a methodology for directional overcurrent protection coordination in interconnected transmission systems considering a possible network contingency state. The methodology uses the short-circuit data of the current network topology;however, the maximum load current data in the protection section of each relay is obtained considering the n-1 criterion, already foreseeing the disconnection of some network line. Thus, if a line gets disconnected, other protective devices will not improperly actuate by the redistribution of load currents in the network. The objective is to propose an adaptive protection scheme to redo the coordination for each topological change in the network. To this end, this work considers a smart grid environment with a supervisory system with communication capability between this and the remote devices. To obtain the optimal performance, the coordination problem, originally non-linear and non-convex, is linearized, allowing its formulation as a mixed integer linear programming problem. The methodology is applied to the 8-bus test system in 3 different cases and the 30-bus test system. Results show that the optimal coordination is obtained in a fast computational processing time, showing the suitability of the methodology for real-time application.
The bi-objective double-floor corridor allocation problem (bDFCAP) investigated here explores the effective placement of given departments in a double-floor space to minimise the overall flow cost and the corridor len...
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The bi-objective double-floor corridor allocation problem (bDFCAP) investigated here explores the effective placement of given departments in a double-floor space to minimise the overall flow cost and the corridor length objectives. Within each floor, departments are arranged in two parallel rows on opposite sides along a central corridor without overlapping. In this study, the bDFCAP is formulated as a mixed integer linear programming model, which has improved the performance over the previous one. Thereafter, a genetic algorithm with a variable neighbourhood search technique is designed and employed to solve the bDFCAP in a more effective manner. This technique is utilized to improve the local search capability by adaptively transforming between a deep-searching strategy and broad-searching strategy, and the superior performance of the proposed method is proven through comparisons with two other algorithms in current literature. Besides, the state-of-the-art lower bounds of several benchmark instances are updated.
In this paper, we consider a multi-attribute technician routing and scheduling problem motivated by an application for the maintenance and repair of electronic transaction equipment. This problem is aimed at routing t...
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In this paper, we consider a multi-attribute technician routing and scheduling problem motivated by an application for the maintenance and repair of electronic transaction equipment. This problem is aimed at routing technicians to perform tasks at different customer locations so as to maximize the total gain associated with served customers, minus the operations costs (total travelled distance and overtime of the technicians). At the same time, a number of constraints must be satisfied like technician skills, breaks, maximum distance, multiple time windows and parts inventory. A mixed integer linear programming model is proposed to address this problem, which is then solved with a commercial solver. The computational results explore the difficulty of the problem along various dimensions and underline its inherent complexity.
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