8 research outputs found

    On a paradox in linear plus linear fractional transportation problem

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    A paradoxical situation arises in a linear plus linear fractional transportation problem (LPLFTP), when value of the objective function falls below the optimal value and this lower value is attainable by transporting larger amount of quantity. In this paper, a new heuristic is proposed for finding initial basic feasible solution for LPLFTP and a sufficient condition for the existence of a paradoxical solution is established in LPLFTP. Two numerical examples are proposed for explanation of the algorithms

    Linear fractional transportation problem with varying demand and supply

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    In this paper, we investigate the transportation problem with fractional objective function when the demand and supply quantities are varying. A set of mathematical programs is obtained to determine the objective value. Due to varying economic policies in the global world, it is hard to specify the supply and demand quantities for transportation problem. A transportation problem with fractional objective function is based on a network structure consisting of a finite number of nodes and arcs attached to them. After the derivation, we obtained the result in range, where the total transportation cost would appear. In addition to allowing for simultaneous changes in supply and demand values, the total cost bounds are calculated directly. This methodology would be very beneficial in the decision making. A numerical example has given in this paper for the support of theory. </p

    A New Approach for More-For-Less Paradox in the Linear Fractional Transportation Problem

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    The more-for-less (MFL) situation occurs in the linear fractional transportation problem (LFTP) when we are able to increase total shipment for less (or same cost) total cost. In this paper we develop a simple step by step procedure to solve MFL situation in LFTP. The main advantage of the algorithm is that it never changes the initial basis. This method is based on optimal solution of LFTP and theory of shadow prices. At the end, a numerical example is discussed to explain the proposed algorithm.

    Urban Odyssey: “Pioneering multimodal routes for Tomorrow's smart cities”

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    Getting around in modern cities has become a daily puzzle for both residents and travelers. As cities increasingly evolve into complex hubs of innovation and development, the demand for efficient transportation solutions has never been higher. In the multifaceted field of urban transportation, cost-effective and efficient mobility remains a high priority. This paper looks at how cities are planning better ways for people to travel and move within the growing scope of multimodal transportation, a paradigm shift beyond traditional single-mode transit systems. Our approach to improving urban transport revolves around a few key principles: integration, innovation, and collaboration. Integration means bringing together different modes of transportation – like buses, trains, bikes, and even new technologies like ride-sharing services to make it easier for people to switch between them. This transformative approach not only aims to reduce congestion and reduce environmental footprints but also prioritizes user experience while ensuring a harmonious blend of convenience, sustainability, and accessibility. We use new technology and ideas to ensure that travel is easy, quick, and good for the environment. Looking at new trends and examples from around the world, this overview shows how cities are shaping a better future for everyone's daily commute. In this paper, we use Lingo software to solve a numerical example related to multi-modal transportation, demonstrating practical solutions for real-world implementation. Through innovation, we can find creative solutions to urban transportation challenges. Additionally, public transportation enhancements, such as the introduction of synchronized bus routes and electric vehicle charging stations, underline the commitment to sustainability and inclusivity. In the dynamic urban transportation landscape, cities will revolutionize our approach to providing cost-effective and efficient mobility solutions
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