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Enhanced supply selection for better initial basic feasible solutions in transportation problems.
Rihan Farih Bunyamin1, Bilqis Amaliah1, Ahmad Saikhu1
1Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.
A new method, Rihan-Bilqis-Saikhu Method (RBSM), improves initial solutions for the Transportation Problem (TP). RBSM offers higher accuracy and efficiency in finding optimal distribution costs compared to existing approaches.
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Area of Science:
- Operations Research
- Optimization Techniques
- Supply Chain Management
Background:
- The Transportation Problem (TP) is a key optimization model for logistics.
- Generating an accurate Initial Basic Feasible Solution (IBFS) is critical for TP efficiency.
- Existing IBFS methods like SSM, VAM, BCE, JHM, and TOCM-MT may not yield optimal low-cost solutions.
Purpose of the Study:
- To introduce and evaluate the Rihan-Bilqis-Saikhu Method (RBSM) for solving the TP.
- To address the limitations of current IBFS methods in producing cost-effective initial solutions.
- To enhance the efficiency and accuracy of the TP optimization process.
Main Methods:
- The proposed Rihan-Bilqis-Saikhu Method (RBSM) is a heuristic modification of the Supply Selection Method (SSM).
- RBSM incorporates total cost-supply considerations for each cell during allocation.
- It refines reallocation rules for surplus allocations to rows with shortages.
Main Results:
- RBSM was tested on 42 diverse cases (published, random, and real-world).
- RBSM achieved optimal solutions in 85.71% of test cases (36 out of 42).
- The method demonstrated the lowest average deviation (0.58%) from optimal costs compared to other methods.
Conclusions:
- RBSM significantly improves the accuracy and efficiency of finding initial feasible solutions for the TP.
- The method's integration of cost-supply and refined reallocation rules contribute to its superior performance.
- RBSM offers a reliable and effective approach for optimizing transportation logistics across various scenarios.

