International Journal of Operational Research Impact Factor & Information

Publisher: Inderscience

Journal description

IJOR is a fully refereed journal generally covering new theory and application of operations research (OR) techniques and models that include inventory, queuing, transportation, game theory, scheduling, project management, mathematical programming, decision-support systems, multi-criteria decision making, artificial intelligence, neural network, fuzzy logic, expert systems, and simulation. New theories and applications of operations research models are welcome to IJOR. Modelling and optimisation have become an essential function of researchers and practitioners in a networked global economy. New theory development in operations research and their applications in new economy and society have been limited. In the information intensive society and economy, decisions are made based on the analysis of data available. Operations research techniques and models need to be integrated with computers for the purpose of analysis, optimisation and application in decision making. This development has led the researchers and practitioners to look for new operational research models and their applications in global economy and society. For this purpose, the modelling and optimisation have become a paramount important. IJOR will act as a platform to encourage further research in OR and MS theory and applications. Globalisation of market and operations places a tremendous pressure in making timely and accurate decisions using the analysis of data and more accurate information. This signifies the importance of developing suitable operations research techniques and models and their applications are a paramount important in the 21st century global society and economy.

Current impact factor: 0.00

Impact Factor Rankings

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Website International Journal of Operational Research website
Other titles International journal of operational research (Online), IJOR, Operational research
ISSN 1745-7653
OCLC 67616113
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details


  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author cannot archive a post-print version
  • Restrictions
    • 6 months embargo
  • Conditions
    • Cannot archive until publication
    • Author's pre-print and Author's post-print on author's personal website, institutional repository or subject repository
    • Publisher copyright and source must be acknowledged
    • Must link to journal webpage and /or DOI
    • Publisher's version/PDF cannot be used, unless covered by funding agency rules
    • Authors covered by funding agency rules, may post the Publisher's Version/PDF in subject repositories after a 6 months embargo
    • Reviewed 10/02/2014
    • Author's post-print equates to Inderscience's Proof
  • Classification

Publications in this journal

  • International Journal of Operational Research 09/2015; Forthcoming.
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    ABSTRACT: Uncertainty plays an important role in predicting the future earning of the assets in the financial market and it is generally measured in terms of probability. But in some cases, it would be a good idea for an investor to state the expected returns on assets in the form of closed intervals. Therefore, in this paper, we consider a portfolio selection problem wherein expected return of any asset, risk level and proportion of total investment on assets are in the form of interval, and obtain an optimum (best) portfolio. Such portfolio gives the total expected return and proportion of total investment on assets in the form of interval. The proposed portfolio model is solved by considering an equivalent linear programming problem, where all the parameters of the objective function and constraints as well as decision variables are expressed in form of intervals. The procedure gives a strongly feasible optimal interval solution of such problem based on partial order relation between intervals. Efficacy of the results is demonstrated by means of numerical examples.
    International Journal of Operational Research 07/2015;

  • International Journal of Operational Research 06/2015;
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    ABSTRACT: In this work, we consider a single server retrial queueing system with multiple working vacations and vacation interruption, where the regular busy server is subjected to breakdown due to the arrival of negative customers. After the completion of regular service the positive customers may demand re-service of the previous service without joining the orbit or may leave the system. When the orbit becomes empty at service completion instant for a positive customer; the server goes for multiple working vacations. The steady state probability generating function for the system size and orbit size are obtained by using the method of supplementary variable technique. Some important system performance measures and the mean busy period are obtained. The conditional stochastic decomposition law is shown for good for this model. Finally, the effects of various parameters on the system performance are analysed numerically
    International Journal of Operational Research 03/2015; (In press).
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    ABSTRACT: Four multiresponse optimization problems were simulated under the RSM framework to describe real-life situations and provide a fair basis to compare the performance of optimization criteria built on different approaches. Different response types, feasible regions, number of responses and variables as well as adverse variance conditions were considered in each problem. Performance metrics usefulness to take more informed decisions about solution selection is also illustrated. An unusual graphical representation of the results provides useful information about working abilities and performance of tested criteria.
    International Journal of Operational Research 03/2015; 23(1):15. DOI:10.1504/IJOR.2015.068742

  • International Journal of Operational Research 01/2015;
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    ABSTRACT: We consider an unscheduled rail network with stochastic demand for freight movement between stipulated origins and destinations. Freight orders arrive over time and each order is fulfilled as soon as a locomotive and a rake become available. After transporting an order to its destination, the locomotive and associated rake become available to move new shipments and the dispatcher must determine a suitable resource management policy at this juncture, i.e., whether to hold resources at the terminating junction, or deadhead them to be used at an alternate origin. The dispatcher must also determine if deadheading is to be undertaken reactively, after receiving a request from another station, or proactively, in order to pre-position rolling stock in anticipation of future demand. The amount of deadheading required also depends upon the system resource levels, e.g., rake and locomotive fleet capacities. The determination of the best fleet size and associated deadheading policy to guarantee a given service level (e.g., expected customer order flow time) is a complex operational problem. In this paper, we describe a simulation modeling approach to this problem. Our approach is illustrated using real data from the Indian Railway System, one of the largest freight carriers in the world.
    International Journal of Operational Research 01/2015; 24(3):329-355. DOI:10.1504/IJOR.2015.072232
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    ABSTRACT: Rail transit is a critical component of intermodal supply chain networks. We consider the problem of providing order delivery time quotations in an unscheduled rail network, where customers place orders requiring movement of freight between specified origins and destinations. Each order is fulfilled as soon as the required rolling stock, e.g., locomotive and rake, can be assembled at the origin. Estimating an order’s delivery time is a complex task, depending upon the available fleet capacity, deadhead policy and the level of congestion present in the network. In this paper, we develop an analytic approximation to estimate delivery time quotations for freight movements, along with required fleet capacity to meet a desired service level. The quality of the analytic approximation herein is validated using a real numerical example from the Indian Railway System, one of the largest freight carriers in the world.
    International Journal of Operational Research 01/2015; Forthcoming.
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    ABSTRACT: The school bus routing problem (SBRP) deals with finding optimal bus routes for carrying students from bus stops to school. Most previous literature on SBRP are mainly focused on models in which it is tried to include as many real-life objectives and constraints as possible. However, they have not considered uncertainty in some parameters in real cases. As a modeling methodology, combined with computational tools, robust optimization is employed to process optimization problems in which the data are uncertain and belong to some uncertainty set. In this paper, a robust optimization model for SBRP with uncertainty in times associated with moving from one bus stop to another is developed. In addition, a solution methodology based on simulated annealing algorithm, is presented for solving the proposed model. To evaluate the solution method and to illustrate its efficiency in real-life problems, a real-world case is studied.
    International Journal of Operational Research 01/2015;
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    ABSTRACT: Nowadays, defining new projects is significantly vital and necessary for many organizations and companies. The problem arise here is how to select an appropriate portfolio from a set of candidate projects. A good combination of projects can extensively promote the organizations in their competitive performance. Thus, the purpose of this study is to present a practical model in addition to some solution approaches to choose the best and proper project portfolios with the considerations of projects’ interactions, quantitative and qualitative criteria, and practical constraints. A linear formulation has been proposed which considers the interaction effects and integrates the number of selected projects, the segmentations, and the budgetary constraints into a single set of constraints. In order to solve the proposed model, a genetic algorithm and also a differential evolution algorithm are presented. Moreover, the efficiencies of these two algorithms are compared with an exact method using various numerical examples. Finally, through a case study the performance of the model is demonstrated.
    International Journal of Operational Research 12/2014; In press.