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Varied offspring memetic algorithm with three parents for a realistic synchronized goods delivery and service problem

  • Soft computing in decision making and in modeling in economics
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Abstract

In a competitive online retail market, orders for assembled products such as refrigerators, air conditioners, smart televisions, etc., attract significant attention due to their high gross merchandise value. Unlike other regular products, product delivery has a two-stage process—delivery of product components and assembly and installation of the final product—involving multiple parties that may be internal or external to the organization. Coordination of the above activities is essential to reduce customer dissatisfaction and to curb the various waiting or demurrage costs due to delayed arrivals of goods vehicles and traveling salesman. This paper attempts to model and solve such a realistic synchronized goods delivery and service problem against the online booking. In this model, one goods vehicle starts from the company’s storehouse with all the goods to be delivered and moves continually, dropping the goods at the specified locations. For service, a traveling salesman separately moves and uses the appropriate conveyances among the available ones at each node to reach the customers. This paper poses some interesting research questions to understand the requirements of separate tour paths for goods vehicles and traveling salesman along with appropriate conveyance for traveling salesman’s arrival. This is an NP-hard traveling salesman problem. For solving, a varied offspring memetic algorithm (VOMA) with modified probabilistic selection, varied offspring three-parent (i.e., surro-embryos) crossover and Fibo-generation-dependent mutation is developed and tested on some standard test functions to establish its superiority over the standard ones. VOMA implementation on the above proposed problem reveals the influence of unloading and service times, halt time and third-party outsourcing charges on the final optimum route design. Finally, the paper provides a structured decision-making framework for practitioners and showcases a case study by implementing VOMA in a similar problem context.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Nadia, 741249, WB, India

    Somnath Maji

  2. Operations Management Group, Indian Institute of Management Calcutta, D H Road, Joka, Kolkata, 700104, India

    Samir Maity & Sumanta Bsau

  3. Department of Information Technology, Maulana Abul Kalam Azad University of Technology, Nadia, 741249, WB, India

    Debasis Giri

  4. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapur, 721102, India

    Manoranjan Maiti

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  1. Somnath Maji
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  2. Samir Maity
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  4. Debasis Giri
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Correspondence to Samir Maity.

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Appendix A Input data

Appendix A Input data

Here, we have taken the distance matrix, transportation cost and time per unit distance, traveling cost and time per unit distance, predefined demand/requirement, unload time and cost, servicing time and cost at every node and three types of traveling salesman vehicle and only one type of goods vehicle are considered. Also values of the parameters for STSPwGDS are presented in Tables 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 and 22.

Table 15 Input data: demand/requirement of every node
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Table 16 Input data: unload time and cost of every node
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Table 17 Input data: servicing time and cost of every node
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Table 18 Input data: distance matrix for goods vehicle for M/S Sharma Furniture company (in km.)
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Table 19 Demand/requirement of every node for M/S Sharma Furniture company
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Table 20 Unload time and cost of every node for M/S Sharma Furniture company
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Table 21 Servicing time and cost of every node for M/S Sharma Furniture company
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Table 22 Parameter table of STSPwGDS
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Maji, S., Maity, S., Bsau, S. et al. Varied offspring memetic algorithm with three parents for a realistic synchronized goods delivery and service problem. Soft Comput 28, 4235–4265 (2024). https://doi.org/10.1007/s00500-023-09574-y

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