Transhipment is an important logistics strategy that helps to improve supply chain efficiency and reduce transportation costs. It enables cargo to be transported to multiple destinations using different modes of transportation, such as ships, trains, trucks, and planes. This can help to reduce the overall transportation time and cost, as well as improve inventory management and distribution. In addition to its use in logistics and transportation, transhipment can also be used in other industries such as manufacturing, where it can be used to transfer raw materials or finished products between different facilities or production lines. This research paper examines the role of transhipment in improving the efficiency of spare part delivery systems to the PMPML depots from central workshop Swargate. PMPML has 12 depots in total (including central workshop). In many industries, the supply chain for spare parts is complex, with multiple suppliers, warehouses, and service centres involved. Transhipment, or the transfer of inventory between locations, can help to reduce lead times and improve inventory availability. In this paper, we analyze the impact of transhipment on key performance metrics such as order fulfilment, inventory turnover, and transportation costs. We also discuss the challenges associated with implementing transhipment in spare part delivery systems, including coordination between different parties, data sharing, and system integration.

In open travelling salesman subset-tour problem (OTSSP), the salesman needs to traverse a set of k (≤n) out of n cities and after visiting the last city, the salesman does not necessarily return to the central depot. The goal is to minimize the overall traversal distance of covering k cities. The OTSSP model comprises two types of problems such as subset selection and permutation of the cities. Firstly, the problem of selection takes place as the salesman’s tours do not contain all the cities. On the other hand, the next problem is about to determine the optimal sequence of the cities from the selected subset of cities. To deal with this problem efficiently, a hybrid nearest neighbor technique based crossover-free Genetic algorithm (GA) with complex mutation strategies is proposed. To the best of the author’s knowledge, this is the first hybrid GA for the OTSSP. As there are no existing studies on OTSSP yet, benchmark instances are not available for OTSSP. For computational experiments, a set of test instances is created by using TSPLIB. The extensive computational results show that the proposed algorithm is having great potential in achieving better results for the OTSSP. Our proposed GA being the first evolutionary-based algorithm that will help as the baseline for future research on OTSSP.