An Empirical Study on Port Choice Behaviors of Shippers in a Multiple-Port Region

2009 ◽  
Vol 43 (3) ◽  
pp. 71-77 ◽  
Author(s):  
Chien-Chang Chou
Keyword(s):  
International Trade ◽  
Mathematical Programming ◽  
Empirical Study ◽  
Programming Model ◽  
Transportation Costs ◽  
Programming Models ◽  
Mathematical Programming Model ◽  
Logistics System ◽  
Mathematical Programming Models ◽  
Port Choice

AbstractIn the international trade cargo logistics system, the port choice of the shipper is seen to depend not only on transportation costs, but also on the value of the cargoes being shipped. In many previous studies, researchers have assumed that the ultimate aim of shippers when making port choices was to minimize inland freight costs. They then used that assumption to develop mathematical programming models for port choices. In practice, however, when making decisions about port choices, shippers always focus on total logistics costs. In other words, shippers not only aim to minimize the inland freight costs but also consider the frequency of ship callings. Thus, in this paper, a mathematical programming model for port choice of shippers, which not only considers inland freight costs but also takes into account the frequency of ship callings, is proposed and tested using a Taiwanese port case. The results show that the model proposed in this paper can be used to explain the actual port choice behaviors of Taiwanese shippers accurately.

scholarly journals Effective Optimisation of the Patient Circuits of an Oncology Day Hospital: Mathematical Programming Models and Case Study

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 62
Author(s):  
Adrián González-Maestro ◽  
Elena Brozos-Vázquez ◽  
Balbina Casas-Méndez ◽  
Rafael López-López ◽  
Rosa López-Rodríguez ◽  
...  
Keyword(s):  
Mathematical Programming ◽  
Programming Model ◽  
Stochastic Approach ◽  
Programming Models ◽  
Day Hospital ◽  
Mathematical Programming Model ◽  
Waiting Room ◽  
Mathematical Programming Models

In this paper, we first use the information we have on the patients of an oncology day hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this, we propose a deterministic mathematical programming model in such a way that we minimise the duration of the waiting room stays of the total set of patients and taking into account the restrictions of the circuit. Secondly, we will look for a solution to the same problem under a stochastic approach. This model will explicitly consider the existing uncertainty in terms of the different times involved in the circuit, and this model also allows the reorganisation of the schedules of medical appointments with oncologists. The models are complemented by a tool that solves the problem of assigning nurses to patients. The work is motivated by the particular characteristics of a real hospital and the models are used and compared with data from this case.

scholarly journals Empirical Testing of a Farm Firm Growth Theory

1970 ◽  
Vol 2 (1) ◽  
pp. 175-179
Author(s):  
Frederick J. Rafeld ◽  
Edgar T. Shaudys
Keyword(s):  
Mathematical Programming ◽  
Firm Growth ◽  
Programming Model ◽  
Social Institutions ◽  
Agricultural Economics ◽  
Programming Models ◽  
Growth Theory ◽  
Economic Factors ◽  
Empirical Testing ◽  
Mathematical Programming Models

Researchers in agricultural economics have become increasingly concerned with the effects of structural and technical changes in agriculture upon the size of the farm firm. These researchers not only want to understand firm growth in order to make suggestions for necessary changes in social institutions but also to advise the managers of farm firms.Recent farm firm growth research studies were conducted using empirically based mathematical programming models to explore growth and to test hypotheses concerning the influence of various economic factors upon growth. For examples, see. Growth in these studies is a function of the assumptions of the particular programming model.

scholarly journals A Mathematical Programming Model for Vegetable Rotations

1985 ◽  
Vol 17 (1) ◽  
pp. 169-176 ◽  
Author(s):  
Wesley N. Musser ◽  
Vickie J. Alexander ◽  
Bernard V. Tew ◽  
Doyle A. Smittle
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Crop Production ◽  
Programming Model ◽  
Crop Rotations ◽  
Programming Models ◽  
Vegetable Production ◽  
Model Design ◽  
Mathematical Programming Model ◽  
Large Numbers

AbstractRotations have historically been used to alleviate pest problems in crop production. This paper considers methods of modeling rotations in linear programming models for Southeastern vegetable production. In such models, entering each possible crop rotation as a separate activity can be burdensome because of the large numbers of possible rotational alternatives. Conventional methodology for double crop rotations reduces the number of activities but must be adapted to accommodate triple crop rotational requirements in vegetable production. This paper demonstrates these methods both for a simple example and an empirical problem with numerous rotation alternatives. While the methods presented in this paper may have computational disadvantages compared to entering each rotation as a separate activity, they do have advantages in model design and data management.

Mathematical Programming Models for Classification

2014 ◽  
pp. 1503-1516 ◽  
Author(s):  
Minghe Sun
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Programming Model ◽  
The Other ◽  
Programming Models ◽  
Mixed Integer ◽  
Classification Models ◽  
Classification Analysis ◽  
Mathematical Programming Models ◽  
Multi Class Classification

Mathematical programming models for discriminant and classification analysis are presented. Specifically, linear programming and mixed integer programming approaches are discussed. For each approach, two-class classification models and multi-class classification models are discussed. The emphasis is on the formulations of these mathematical programming models rather than on their performances. Two illustrative examples, one for two-class and the other for multi-class classification, are used to demonstrate the formulations of these mathematical programming models. An example is used to demonstrate the formulation after a mathematical programming model is presented.

A mathematical programming approach for walking-worker assembly systems

2014 ◽  
Vol 34 (1) ◽  
pp. 56-68 ◽  
Author(s):  
Emre Cevikcan
Keyword(s):  
Mathematical Programming ◽  
Task Allocation ◽  
Programming Model ◽  
Programming Models ◽  
Assembly System ◽  
Programming Approach ◽  
Assembly Systems ◽  
Mathematical Programming Model ◽  
Content Type ◽  
Mathematical Programming Approach

Purpose – It has become increasingly critical to design and maintain flexible and rapid assembly systems due to unpredictable and varying market conditions. The first stage of developing such systems is to restructure the existing assembly system. After designing the manufacturing system, efforts should be made for capacity adjustments to meet the demand in terms of allocating tasks to workers. Walking-worker assembly systems can be regarded as an effective method to achieve flexibility and agility via rabbit chase (RC) approach in which workers follow each other around the assembly cell or line and perform each task in sequence. In this paper, a novel mathematical programming approach is developed with the aim of integrating RC in assembly processes. Therefore, this study is thought to add value to industrial assembly systems in terms of effectively raising engineering control for task allocation activities. Design/methodology/approach – Two consecutive mathematical models are developed, since such a hierarchical approach provides computational convenience for the problem. The initial mathematical programming model determines the number of workers in each RC loop for each segment. In addition, the number of stations and the distribution of station times in the segments is essential. Therefore, the succeeding mathematical programming model generates stations in each segment and provides convenience for the workflow in RC loops. The output of mathematical programming models are the parameters of simulation model for performance assessment. Findings – The effectiveness of the proposed approach was validated by an application in a real-life chair production system. The application resulted in performance improvements for labour requirement (12.5 per cent) and production lead time (9.6 per cent) when compared to a classical assembly system design (CASD) where one stationary worker exists in each station. In addition, it is worth to note that RC leads to a reduced number of workers for a considerable number (39.4 per cent) of test problems. What is more, input as well as output factors have been determined via discriminant analysis and their impacts to the utilization of RC were analyzed for different levels. Practical implications – This study is thought to add value to the industry in terms of effectively providing convenience during production planning and task allocation in assembly lines and cells. Originality/value – To the best knowledge of the author, optimization models for RC considering a real industrial application have not yet been developed. In this context, this paper presents an approach which models RC by the use of mathematical programming in manual assembly processes to address this research gap. The contribution of the paper to the relevant literature is the development of hierarchical mixed integer linear programming models to solve RC problem for the first time.

scholarly journals Price Endogenous Mathematical Programming Models and Trade Analysis

2006 ◽  
Vol 38 (2) ◽  
pp. 249-253 ◽  
Author(s):  
Thomas H. Spreen
Keyword(s):  
Mathematical Programming ◽  
Programming Model ◽  
Programming Models ◽  
Firm Level ◽  
Typical Application ◽  
Software Packages ◽  
Mathematical Programming Models ◽  
Trade Analysis ◽  
Econometric Approach ◽  
Level Analysis

Takayama and Judge introduced the price endogenous mathematical programming model as an alternative to the traditional econometric approach to sector-level policy analysis. McCarl and Spreen provided a review of price endogenous mathematical programming models. In that paper, they showed how price endogeneity can be introduced into a standard firm-level linear programming model. The introduction of price endogeneity allows expansion of the firm-level specification to a market-level analysis. At the time of publication of McCarl and Spreen, however, the application of price endogenous mathematical programming models was limited by the availability of software packages that could directly solve such models. The typical application used linear supply and/or demand relationships, which resulted in a quadratic programming (QP) specification. The advent of MINOS in the 1980s and then its incorporation into GAMS has lifted the computation constraint. In the present day, numerous price endogenous models have been developed. I can lay claim to six such models.

Integer Programming Models for Sales Territory Alignment to Maximize Profit

1976 ◽  
Vol 13 (4) ◽  
pp. 426-430 ◽  
Author(s):  
Andris A. Zoltners
Keyword(s):  
Integer Programming ◽  
Mathematical Programming ◽  
Recent Article ◽  
Programming Model ◽  
Solution Procedure ◽  
Programming Models ◽  
Mathematical Programming Model ◽  
Heuristic Solution ◽  
Linear Integer Programming

A recent article described a mathematical programming model and heuristic solution procedure to realign sales territories. This report presents two linear integer programming models for sales territory alignment to maximize profit. Emphasis is placed on the development of models which are easy to implement.

An Application of a Positive Mathematical Programming Model to Analyse the Impact of Agricultural Policy Measures in the Spanish Agricultural Sector

2011 ◽  
pp. 175-198
Author(s):  
Lucinio Asensio ◽  
Rosario Gómez de Barreda ◽  
Miguel Ruiz ◽  
José-Luis Miguel de Diego ◽  
Elvira Miqueleiz
Keyword(s):  
Mathematical Programming ◽  
Agricultural Policy ◽  
Agricultural Sector ◽  
Programming Model ◽  
Programming Models ◽  
Limited Information ◽  
Agricultural Policies ◽  
Mathematical Programming Models ◽  
Positive Mathematical Programming ◽  
The Impact

In agricultural economics, one of the greatest weaknesses in mathematical programming models for the evaluation of agricultural processes is the calibration of the model in a base year. The reason for this is that it is extremely difficult, if not impossible, to introduce all the variables affecting farmers’ decisions in the models and thus obtain reliable results. This chapter presents a method for calibrating mathematical programming models using limited information. From the mathematical programming properties, by using the dual form of the original model, this methodology allows the model results to reproduce the situation existing in a baseline situation of the unit (farm, region) modelled. This method, called Positive Mathematical Programming, is currently being used in a great number of analyses of new agricultural policies. In this chapter it is applied to analyse the impact of recent measures of the European Common Agricultural Policy in Spain.

scholarly journals A Mathematical Programming Model to Determine Objective Weights for the Interval Extension of TOPSIS

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Hai Shen ◽  
Lingyu Hu ◽  
Kin Keung Lai
Keyword(s):  
Mathematical Programming ◽  
Input Data ◽  
Programming Model ◽  
Multiple Criteria Decision Making ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Topsis Method ◽  
Data Set ◽  
Interval Extension ◽  
Interval Valued

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method has been extended in previous literature to consider the situation with interval input data. However, the weights associated with criteria are still subjectively assigned by decision makers. This paper develops a mathematical programming model to determine objective weights for the implementation of interval extension of TOPSIS. Our method not only takes into account the optimization of interval-valued Multiple Criteria Decision Making (MCDM) problems, but also determines the weights only based upon the data set itself. An illustrative example is performed to compare our results with that of existing literature.

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