Production and shipment lot sizing in a vendor–buyer supply chain with transportation cost

Previous research on the joint vendor–buyer problem focused on the production shipment schedule in terms of the number and size of batches transferred between the two parties. It is a fact that transportation cost is a major part of the total operational cost. However, in most joint vendor–buyer models, the transportation cost is only considered implicitly as a part of fixed setup or ordering cost and thus is assumed to be independent of the size of the shipment. As such, the effect of the transportation cost is not adequately reflected in final planning decisions. There is a need for models involving transportation cost explicitly for better decision-making. In this study we analyze the vendor–buyer lot-sizing problem under equal-size shipment policy. We introduce the complete solution of the problem in an explicit and extended manner that has not existed in the literature. We incorporate transportation cost explicitly into the model and develop optimal solution procedures for solving the integrated models. All-unit-discount transportation cost structures with and without over declaration have been considered. Numerical examples are presented for illustrative purpose.     

Urban rapid transit network design: accelerated Benders decomposition

This paper presents an urban rapid transit network design model, which consists of the location of train alignments and stations in an urban traffic context. The design attempts to maximize the public transportation demand using the new infrastructure, considering a limited budget and number of transit lines. The location problem also incorporates the fact that users can choose their transportation mode and trips. In real cases, this problem is complex to solve because it has thousands of binary variables and constraints, and cannot be solved efficiently by Branch and Bound. For this reason, some algorithms based on Benders decomposition have been defined in order to solve it. These algorithms have been compared in test networks. The project has been supported by the research project 70029/T05, from the Spanish “Ministerio de Fomento” and the research project TRA2005-09068-C03-01, from the Spanish “Ministerio de Educación y Ciencia”.     

Adaptive Lasso estimators for ultrahigh dimensional generalized linear models

Generalized linear models have been more widely used than linear models which exclude categorical variables. The penalized method becomes an effective tool to study ultrahigh dimensional generalized linear models. In this paper, we study theoretical results of the adaptive Lasso for generalized linear models in terms of diverging number of parameters and ultrahigh dimensionality. The asymptotic results are examined by several simulation studies.     

A primal–dual algorithm for the economic lot-sizing problem with multi-mode replenishment

The classical economic lot-sizing problem assumes that a single supplier and a single transportation mode are used to replenish the inventory. This paper studies an extension of this problem where several suppliers and transportation modes are available. The decision-making process in this case involves identifying (i) the timing for an order; (ii) the choice of shipment modes; and (iii) the order size for each mode. The problem is defined as a network flow problem with multiple setups cost function and additional side constraints. This study provides an MIP formulation for the problem. We also provide an additional formulation of the problem by redefining its decision variables and show that the dual of the corresponding LP-relaxation has a special structure. We take advantage of the structure of the dual problem to develop a primal–dual algorithm that generates tight lower and upper bounds. Computational results demonstrate the effectiveness of the algorithm.     

An integrated framework for inventory management and transportation of refined petroleum products: Pipeline or marine?

We study the integrated problem of managing inventory of refined petroleum products, and their multi-modal (ships and pipeline) transportation between a refinery and the served distribution centers. It is important that the transportation decisions are driven not just by the inventory levels and customer demand, but also the environmental risks associated with different refined products. A bi-objective mixed integer linear programming optimization model (MILP) is proposed, where constituent components were independently developed and then interfaced to capture the complexity of the resulting integrated model. A time-based decomposition heuristic is also employed to solve the integrated problem. The proposed framework was used to study a number of problem instances generated using a realistic infrastructure in the United States, and the resulting analyses lead to the following inferences: pipeline is the preferred mode of transportation only when cost is the sole consideration; on the other hand, when environmental risks are considered marine is the preferred mode for most of the refined petroleum products, except for heavier oils; and, the proportion of traffic on the two modes is a function of the type and volume of products, and the number of vessels available at the start of the planning horizon.     

Outbound supply chain network design with mode selection,lead times and capacitated vehicle distribution centers

Most distribution network design models considered to date have focused on minimizing fixed costs of facility location and transportation costs. Measures of customer satisfaction driven by the operational dynamics such as lead times have seldom been considered. We consider the design of an outbound supply chain network considering lead times, location of distribution facilities and choice of transportation mode. We present a Lagrangian heuristic that gives excellent solution quality in reasonable computational time. Scenario analyses are conducted on industrial data using this algorithm to observe how the supply chain behaves under different parameter values.     

Contagion processes on urban bus networks in Indian cities

Bus transportation is the most convenient and cheapest way of public transportation in Indian cities. Due to cost‐effectiveness and wide reachability, buses bring people to their destinations every day. Although the bus transportation has numerous advantages over other ways of public transportation, this mode of transportation also poses a serious threat of spreading contagious diseases throughout the city. It is extremely difficult to predict the extent and spread of such an epidemic. Earlier studies have focused on the contagion processes on scale‐free network topologies; whereas, real‐world networks such as bus networks exhibit a wide‐spectrum of network topology. Therefore, we aim in this study to understand this complex dynamical process of epidemic outbreak and information diffusion on the bus networks for six different Indian cities using SI and SIR models. We identify epidemic thresholds for these networks which help us in controlling outbreaks by developing node‐based immunization techniques. © 2016 Wiley Periodicals, Inc. Complexity 21: 451–458, 2016     

Optimization models for the single delay management problem in public transportation

Passengers travelling in public transportation networks often have to use different lines to cover the trip from their origin to the desired destination. As a consequence, the reliability of connections between vehicles is a key issue for the attractiveness of the intermodal transportation network and it is strongly affected by some unpredictable events like breakdowns or vehicle delays. In such cases, a decision is required to determine if the connected vehicles should wait for the delayed ones or keep their schedule. The delay management problem (DMP) consists in defining the wait/depart policy which minimizes the total delay on the network. In this work, we present two equivalent mixed integer linear programming models for the DMP with a single initial delay, able to reduce the number of variables with respect to the formulations proposed by the literature. The two models are solved by a branch and cut procedure and by a constraint generation approach respectively, and preliminary computational results are presented.     

Recurrent neural dynamic models for equilibrium and eigenvalue problems

Neural networks (NN) have been used in a number of interesting applications. In this paper, two neural dynamic models which belong to the class of recurrent neural networks (RNN) have been formulated for the solution of equilibrium and eigenvalue problems. The RNN is comprised of two layers, namely, variable layer and constraint layer, which correspond to the number of design variables in the problem. In addition, the recurrent connections and feed forward connections are used to represent the incremental values in the design parameters. The stability of the neural dynamic model for the equilibrium problem has been guaranteed using Lyapunov's function. Illustrative examples and results of the computer simulation of the neural dynamic model have also been presented.     

不同管制方式对基础设施产业效率的影响分析

基础设施产业具有自然垄断性,有必要对其实行管制。本文讨论了三种管制方式:价格管制、收益率管制、许可权投标管制,但不同的管制方式其效果也不尽相同。本文拟对三种不同的管制方式的管制效率进行比较分析,旨在说明在对基础设施产业进行管制时,目标不同选择的管制方式也应不同。     

Modelling dynamic network loading ontransportation networks through a continuous

The efforts spent by researchers in the last few years in traffic modelling have been focused on the modelization of dynamic behaviour of the several components making up a transportation system.In the field of traffic assignment, a large number of models and procedures have been proposed in order to perform Dynamic Network Loading (DNL), that is the reproduction of within-day variable link performances once a corresponding Origin/Destination (O/D) demand and users' choice model has been given. These models can be used both to evaluate traffic flows and, what is more relevant, to simulate the effects of regulation strategies on users' behaviour.

In this paper, after a brief review of the state of the art in this field, a continuous dynamic network loading model is proposed; it removes some of the drawbacks of other packet approach models proposed in literature and explicitly allows the en-route modification of the followed path. An algorithmic development of the model and a set of applications on text networks are also proposed.     

A randomized method for the identification of switched NARX systems

The identification of switched systems is a complex optimization problem that involves both continuous (parametrizations of the local models, a.k.a. modes) and discrete variables (model structures, switching signal). In particular, the combinatorial complexity associated with the estimation of the switching signal grows exponentially with the number of samples, which makes data segmentation (i.e. estimating the number and location of mode switchings, and the mode sequence) a challenging problem. In this work, we extend a previously developed randomized approach for the identification of switched systems to encompass the estimation of the switching locations. The method operates by extracting samples from a probability distribution of switched models, and gathering information from the associated model performances to update the distribution, until convergence to a limit distribution associated to a specific model. A suitable probability distribution is employed to represent the likelihood of a mode switching at a certain time, and the update process is designed to correct the switching locations and remove redundant switchings. The proposed algorithm has been compared to existing state-of-the-art methods and has been tested on various benchmark examples, to demonstrate its effectiveness.     

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+ε. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the ‘switching regression model’ and it is expressed with . Here l_i indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665–685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363–399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306–310].In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.     

Solving network manpower problems with side constraints

A number of authors have used goal-programming to solve manpower problems. Some of these authors have also formulated goal-programming manpower models in a network so as to take advantage of the rapid codes now available for the solution of capacitated transportation problems. In this paper, a number of methods of dealing with side constraints (those not included in the network formulation) are explored. Of particular interest are constraints for dealing with attrition and for expressing budgetary restrictions.     

交通流非参数回归模型

交通流宏观参数流量、速度和占有率在交通工程和管理中具有非常重要的作用 ,对这三者关系的刻划反映了道路本身特性和交通流的规律。到目前为止 ,基本上采用线性或非线性的参数模型来描述 ,此类模型在应用中具有一定的局限性 ,并交通变量时间序列进行预测 ,数据拟合表明 ,选择适当的核函数或邻近数以及窗宽 ,可以达到比较满意的效果     

Sugar cane transportation in Cuba,a case study

In this study the authors present a mixed integer linear programming model to solve the problem of cost minimization of sugar cane removal and its transport from the fields to the sugar mill at operational level. The complexity of the problem is basically determined by the system approach which results in the generation of a great number of variables and constraints that refer to the following operational dimensions: (a) need for continuous supply to the sugar mill; (b) cutting means used in cane harvesting; (c) transportation vehicles and (d) providing routes, which are characterized by the existence of storage facilities at the beginning of the railroads. The results demonstrate the model is not only useful to minimize transportation cost, but also for scheduling daily cane road transport and harvesting quotas of cutting means.     

Integrated Production and Distribution Planning for Södra Cell AB

In this paper we consider integrated planning of transportation of raw material, production and distribution of products of the supply chain at Södra Cell AB, a major European pulp mill company. The strategic planning period is one year. Decisions included in the planning are transportation of raw materials from harvest areas to pulp mills, production mix and contents at pulp mills, distribution of pulp products from mills to customer via terminals or directly and selection of potential orders and their levels at customers. Distribution is carried out by three different transportation modes; vessels, trains and trucks. We propose a mathematical model for the entire supply chain which includes a large number of continuous variables and a set of binary variables to reflect decisions about product mix and order selection at customers. Five different alternatives regarding production mix in a case study carried out at Södra Cell are analyzed and evaluated. Each alternative describes which products will be produced at which pulp mills.     

The NATO strategic commanders' assessment of requirements for strategic sea and airlift

With the end of the Cold War and the adoption of a new NATO strategic concept, NATO nations now emphasize the use of mobile, flexible, and multinational military forces to be deployed for a myriad of crisis response operations outside the normal NATO treaty area. One critical shortfall for a number of NATO members has been the ready availability of strategic sea and airlift assets. Most NATO nations rely on the civilian transport market to provide dry cargo vessels and wide-body cargo aircraft. The two NATO strategic commands, Supreme Headquarters Allied Powers Europe (SHAPE) and Headquarter Supreme Allied Command Atlantic (SACLANT), have been assessing the requirements for sea and airlift and have proposed to nations a variety of measures to improve their movement and transport capability. SACLANT staff has been developing the operations research methods to assist in this assessment. The current paper conveys the methodology and the mathematical programming model used in this analysis, using ship, aircraft, and port availability constraints. Furthermore, the paper presents a framework of the modeling and simulation capability of the NATO commands and agencies in the movement and transportation area. Additionally, the paper provides an example of the use of simulation models in follow-up activities such as high-level seminars in which national military force planners and movement and transport staff participate. During these events, participants are provided with the results from a number of transportation simulation models, in an attempt to steer the decision making process in NATO.     

Tree-Structured Mixed-Effects Regression Modeling for Longitudinal Data

Tree-structured models have been widely used because they function as interpretable prediction models that offer easy data visualization. A number of tree algorithms have been developed for univariate response data and can be extended to analyze multivariate response data. We propose a tree algorithm by combining the merits of a tree-based model and a mixed-effects model for longitudinal data. We alleviate variable selection bias through residual analysis, which is used to solve problems that exhaustive search approaches suffer from, such as undue preference to split variables with more possible splits, expensive computational cost, and end-cut preference. Most importantly, our tree algorithm discovers trends over time on each of the subspaces from recursive partitioning, while other tree algorithms predict responses. We investigate the performance of our algorithm with both simulation and real data studies. We also develop an R package melt that can be used conveniently and freely. Additional results are provided as online supplementary material.     

Integrated supply chain planning based on a combined application of operations research and optimal control

In operations research (OR), remarkable advancements have been achieved in the domain of supply chain (SC) planning for the last two decades. In recent years, the works on SCs have been broadened to integrate production and transportation planning along with covering the SC dynamics and execution control. Based on a combination of fundamental results of the modern optimal program control (OPC) theory with the optimization methods of OR, an original integrated model of production and transportation planning in the SC is developed. It is shown explicitly how to distribute static and dynamics variables and constraints among the OR and OPC models and interconnect static elements in optimization linear programming model with corresponding dynamic elements in OPC model. This makes it possible to integrate SC production and transportation planning and execution taking into account process non-stationarity caused by structure dynamics. In doing so, the developed framework contributes to the advancing decision-making support for supply chain management (SCM). Conventionally isolated SCM problems may be considered from a different viewpoint and new integrated problems may be revealed, stated and solved due to the mutual enriching of OR and OPC techniques.