This paper focuses on how to minimize the total passenger waiting time at stations by computing and adjusting train timetables for a rail corridor with given time-varying origin-to-destination passenger demand matrice...
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This paper focuses on how to minimize the total passenger waiting time at stations by computing and adjusting train timetables for a rail corridor with given time-varying origin-to-destination passenger demand matrices. Given predetermined train skip-stop patterns, a unified quadratic integerprogramming model with linear constraints is developed to jointly synchronize effective passenger loading time windows and train arrival and departure times at each station. A set of quadratic and quasi-quadratic objective functions are proposed to precisely formulate the total waiting time under both minute-dependent demand and hour-dependent demand volumes from different origin-destination pairs. We construct mathematically rigorous and algorithmically tractable nonlinear mixed integer programming models for both real-time scheduling and medium-term planning applications. The proposed models are implemented using general purpose high-level optimization solvers, and the model effectiveness is further examined through numerical experiments of real-world rail train timetabling test cases. (C) 2015 Elsevier Ltd. All rights reserved.
A warranty distribution network provides aftersales warranty services to customers and resembles a closed-loop supply chain network with specific challenges for reverse flows management like recovery, repair, and refl...
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A warranty distribution network provides aftersales warranty services to customers and resembles a closed-loop supply chain network with specific challenges for reverse flows management like recovery, repair, and reflow of refurbished products. We present here a nonlinear and nonconvex mixedintegerprogramming model for the design of the warranty distribution network of a semiconductor company which is operated by an out-sourced third party logistics service provider. The application of the model to the real-life case provides an improved distribution network flow and rearranged warehouse and recovery locations, and resulted in weekly cost savings of 3.4% for the considered item. (C) 2015 Elsevier Ltd. All rights reserved.
Increasing the environmental concerns with legislations and responsibilities are forcing enterprises to take an attention at the impact of their supply chain operations on the environment. An optimally designed supply...
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Increasing the environmental concerns with legislations and responsibilities are forcing enterprises to take an attention at the impact of their supply chain operations on the environment. An optimally designed supply chain network is required to provide economic, social and environmental requirements in good flows actualizing between suppliers, manufacturers and customers. A nonlinear mixed integer programming model is proposed with the aimed of optimally determining consumed fuel, emitted CO2, driver and transportation costs in this study. However, real world supply chain operations are occurred in a highly dynamic and imprecise environment. Thus, a fuzzy mathematical programming approach is applied to cope with uncertainty in the capacities and demands. The proposed fuzzy model is validated and also compared with different fuzzy mathematical programming approaches through an example, where its applicability is demonstrated for managerial insights.
Today, the requirement of reverse supply chain (RSC) optimization takes more attention due to environmental and competitive factors. However, increasing attention and existing uncertainty in RSC also increases the dif...
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Today, the requirement of reverse supply chain (RSC) optimization takes more attention due to environmental and competitive factors. However, increasing attention and existing uncertainty in RSC also increases the difficulties for decisions of production/distribution planning. Therefore, considering strategic and tactical decisions together under fuzziness is being essential. This paper presents a fuzzy programming approach to the integration of RSC optimization (strategic level) and disassembly line balancing (DLB) (tactical level) problems. The aim of this study is to apply fuzzy modeling to optimize a RSC that involves customers, collection/disassembly centers and plants while balancing the disassembly lines in disassembly centers, simultaneously. Two types of fuzzy mathematical programming models with different aggregation operators are used. Finally, accuracy and applicability of the model is illustrated and a comparison of fuzzy approaches is done via a hypothetical example.
This paper describes an integrated model that jointly optimizes the strategic and tactical decisions of a closed-loop supply chain (CLSC). The strategic level decisions relate to the amounts of goods flowing on the fo...
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This paper describes an integrated model that jointly optimizes the strategic and tactical decisions of a closed-loop supply chain (CLSC). The strategic level decisions relate to the amounts of goods flowing on the forward and reverse chains. The tactical level decisions concern balancing disassembly lines in the reverse chain. The objective is to minimize costs of transportation, purchasing, refurbishing, and operating the disassembly workstations. A nonlinear mixed integer programming formulation is described for the problem. Numerical examples are presented using the proposed model. (C) 2013 Elsevier Ltd. All rights reserved.
In this paper, we consider a general class of nonlinearmixed discrete programming problems. By introducing continuous variables to replace the discrete variables, the problem is first transformed into an equivalent n...
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In this paper, we consider a general class of nonlinearmixed discrete programming problems. By introducing continuous variables to replace the discrete variables, the problem is first transformed into an equivalent nonlinear continuous optimization problem subject to original constraints and additional linear and quadratic constraints. Then, an exact penalty function is employed to construct a sequence of unconstrained optimization problems, each of which can be solved effectively by unconstrained optimization techniques, such as conjugate gradient or quasi-Newton methods. It is shown that any local optimal solution of the unconstrained optimization problem is a local optimal solution of the transformed nonlinear constrained continuous optimization problem when the penalty parameter is sufficiently large. Numerical experiments are carried out to test the efficiency of the proposed method.
In this article, optimal design under the restriction of pre-determined budget of experiment is developed for the Pareto distribution when the life test is progressively group censored. We use the maximum-likelihood m...
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In this article, optimal design under the restriction of pre-determined budget of experiment is developed for the Pareto distribution when the life test is progressively group censored. We use the maximum-likelihood method to obtain the point estimator of the Pareto parameter. We propose two approaches to decide the number of test units, the number of inspections, and the length of inspection interval under limited budget such that the asymptotic variance of estimator of Pareto parameter is minimum. A numerical example is given to illustrate the proposed method. Some sensitivity analysis is also studied.
In this paper a novel Branch and Bound (B&B) algorithm to solve the transmission expansion planning which is a non-convex mixedintegernonlinearprogramming problem (MINLP) is presented. Based on defining the opt...
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ISBN:
(纸本)9781479928026
In this paper a novel Branch and Bound (B&B) algorithm to solve the transmission expansion planning which is a non-convex mixedintegernonlinearprogramming problem (MINLP) is presented. Based on defining the options of the separating variables and makes a search in breadth, we call this algorithm a B&BML algorithm. The proposed algorithm is implemented in AMPL and an open source Ipopt solver is used to solve the nonlinearprogramming (NLP) problems of all candidates in the B&B tree. Strategies have been developed to address the problem of non-linearity and non-convexity of the search region. The proposed algorithm is applied to the problem of long-term transmission expansion planning modeled as an MINLP problem. The proposed algorithm has carried out on five commonly used test systems such as Garver 6-Bus, IEEE 24-Bus, 46-Bus South Brazilian test systems, Bolivian 57-Bus, and Colombian 93-Bus. Results show that the proposed methodology not only can find the best known solution but it also yields a large reduction between 24% to 77.6% in the number of NLP problems regarding to the size of the systems.
The single-vehicle cyclic inventory routing problem (SV-CIRP) consists of a repetitive distribution of a product from a single depot to a selected subset of customers. For each customer, selected for replenishments, t...
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The single-vehicle cyclic inventory routing problem (SV-CIRP) consists of a repetitive distribution of a product from a single depot to a selected subset of customers. For each customer, selected for replenishments, the supplier collects a corresponding fixed reward. The objective is to determine the subset of customers to replenish, the quantity of the product to be delivered to each and to design the vehicle route so that the resulting profit (difference between the total reward and the total logistical cost) is maximised while preventing stockouts at each of the selected customers. This problem appears often as a sub-problem in many logistical problems. In this article, the SV-CIRP is formulated as a mixed-integer program with a nonlinear objective function. After a thorough analysis of the structure of the problem and its features, an exact algorithm for its solution is proposed. This exact algorithm requires only solutions of linear mixed-integer programs. Values of a savings-based heuristic for this problem are compared to the optimal values obtained for a set of some test problems. In general, the gap may get as large as 25%, which justifies the effort to continue exploring and developing exact and approximation algorithms for the SV-CIRP.
In this paper, a progressive type I group-censoring life test for Burr XII distribution is considered. We use the maximum likelihood method to obtain the point estimators of the Burr XII parameters. The approximate co...
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In this paper, a progressive type I group-censoring life test for Burr XII distribution is considered. We use the maximum likelihood method to obtain the point estimators of the Burr XII parameters. The approximate confidence intervals for the parameters of Burr XII distribution are also obtained. We use modified algorithm proposed by Kus et al. [20] to decide the number of test units, number of inspections, and length of inspection interval under a restricted budget of experiment such that the determinant of the asymptotic variances-covariance of estimators of parameters is minimum. A numerical example is presented to illustrate the proposed approach. The sensitivity analysis is also investigated.
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