The paper aims at obtaining an optimal route of a fleet of oil-delivery tankers from a source to a number of service stations. The distance between any two stations and demand(s) for each station are given. The objective is to find an optimal route undertaken by tankers such that the requirements of stations are met, the total distance travelled by the Tankers and the backload of the Tankers is to be minimized under the considerations. Also, the Tanker should visit a pair of stations exactly once. Further, a tanker does not supply the requirements of the stations partially. Here the tanker carries back the residue to the depot the available amount of oil will be considered as Backload or undelivered oil when the Tanker returns to depot (source station) or the maximum utilization of the capacity of Tankers in a trip schedule. Dantzig and Ramser—[4] investigated the problem of “The Truck Dispatching Problem” without the minimum backload and obtained a near optimal solution with the dynamic programming approach. For obtaining the optimal solution of the same problem with minimum backload, we proposed the Lexicographic Search approach. The algorithm is tested using C-language and the computational details are also reported, observed that it takes less computational time for higher size of problems.

The oil tankers dispatching problem

Objective of the study: We aim to propose, and test an integrative framework for actors’ orchestration in the dairy sector based on a data science perspective to drive innovation.
Methodology/Approach: To achieve this aim, a systematic literature review and content analysis were conducted, then, an integrative framework was developed and empirically tested. Subsequently, incremental adjustments in the framework’s data structure were performed based on the interviews conducted through the focus group were taken into consideration. Increasing the framework assertiveness and adaptability in different contexts.
Originality/Relevance: The article delves into the world of dairy production, focusing on the key players in the milk and its derivatives sector. It emphasizes the crucial role of data in driving innovation and value co-creation among these actors. By collecting and displaying the relevant data, each player can unlock new opportunities for growth and collaboration within the value chain. The data science principle forms the bedrock of this process, as it enables a deeper understanding of the data flow and facilitates effective information-sharing across the right value chain actors.
Main results: A framework that encompasses key data that can be shared among actors, to foster information exchange and long-term relationships, promoting transparency in the process, which raises a sense of trustworthiness, responsibility, and innovation.
Theoretical/Methodological contributions: The study involves the development of an integrative framework that is constructed based on insights gathered from the systematic literature review and content analysis. This framework serves as a comprehensive and cohesive structure for understanding the utilization of data flow to connect stakeholders in the dairy sector. It highlights the key elements necessary for successful actors' relationships and outlines the potential impact on value co-creation and the generation of valuable information. Empirical contributions are made through the testing and validation of this framework with a diverse range of stakeholders in the dairy sector. Through focus groups involving multiple players within the industry, valuable insights are gathered, and the framework is tested. The empirical findings validate the applicability and effectiveness of the framework, ensuring its practicality and relevance.
Social/management contributions: The framework elevates the knowledge level about the dairy sector and its practices among stakeholders. It amplifies the potential for innovation within the sector, serves as a guiding tool that encourages stakeholders to explore new avenues for enhancing the customer experience. The social and management contributions are evident in its ability to empower stakeholders with knowledge, inspire innovation, and elevate the overall customer experience in the dairy sector.

An integrative framework to driver innovation among dairy actors

Multi Phase Methodology for Solving the Multi Depot Vehicle Routing Problem with Limited Supply Capacity at the Depots

Solving Vehicle Routing Problem Using a Hybridization of Gain-Based Ant Colony Optimization and Firefly Algorithms

Vehicle Routing Problem is one of the classical NP hard and combinatorial optimization problems that has been a spark of interest in the operation research domain. Though many variations of classical VRP are being developed, there is still the need for developing algorithms to improve solutions for VRP. A hybrid gain-based ant colony optimization-firefly algorithm (GACO-FA) has been proposed to deal with VRP. A global search is initially performed using the gain-based ant colony optimization, and subsequently local search for promising solution is done in the fine-tuned search space using firefly algorithm. The strengths of GACO and weakness of FA are aptly managed with a finer trade-off between them. The proposed GACO-FA is compared with best-known solutions and existing algorithms for performance analysis using the benchmark dataset. Analysis has been performed using measures like route cost, standard deviation, and percentage variation in length. The results have also been statistically verified for their significance. 

This paper describes a computer-aided custom designed support system to reduce milk transportation costs. The model was developed in co-operation with Uruguay National Cooperative of Milk Producers (Conaprole). The vehicle routing model contains three inter-communicating parts: a) a Geographic Information System (GIS), b) external and internal interfaces, and c) a Vehicle Routing solver that includes heuristics inspired on the strategy of “cluster first, route second”. The distance and time matrices are calculated directly on the cartography The first official digital map of Uruguayan roads was digitised according to the model requirements. As a result of the system implantation, large benefits are reported in haulage savings and in improved efficiency in milk collection management. The solution cost depends on the choice of clustering algorithm and on problem topology.

A Vehicle Routing System Supporting Milk Collection

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A Vehicle Routing System Supporting Milk Collection