Cost and Benefit Analysis for Rubber Product Transportation

Srisawadi, Sasitorn; Paoprasert, Naraphorn; Wattanawongsakun, Prasit; Lerspalungsanti, Sarawut; Srisurangkul, Chadchai; Pitaksapsin, Narong; Neamsup, Yongyuth

doi:10.32738/ceppm.201310.0040

Cited by 2 publications

(2 citation statements)

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“…Constraint (6) ensures that whenever the vehicle enters farm i, it must leave farm i. Constraint (7) provides the transportation route among the farms which are assigned to the same procurement center. Constraints (8) and (9) are capacity constraints related to vehicle capacity and sub-tour elimination. Finally, constraint (10) is a binary variable.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…This cost can be divided across two stakeholders, such that a farmer pays 10.53 Baht/Kg and the middlemen pay 1.02 Baht/Kg (assuming passing through two middlemen). The transportation cost for farmer is 1.29 Baht/Kg from the total cost 10.53 Baht/Kg, and only 0.23 Baht/Kg for the middlemen [8]. Considering transportation in rubber logistics, direct shipment from farmer to middleman is still a weak point that occurs in the collection process, and results in high transportation costs when there are a greater number of routes and vehicles used.…”

Section: Introductionmentioning

confidence: 99%

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A New Optimization Technique for the Location and Routing Management in Agricultural Logistics

Theeraviriya

Pitakaso

Sethanan

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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This paper aims to solve the location and routing problem (LRP) in the agricultural sector with the objective function of fuel cost minimization. Many farmers may have problems when transporting and selling products because of high costs and unfair prices. The proper location of standardized procurement centers and suitable routes will relieve farmers’ problems. This paper includes a realistic constraint that a farm can be visited to collect product more than once. A mathematical model was formulated to be solved by Lingo software, but when the problem size was larger, Lingo was unable to solve the problem within a reasonable processing time. The variable neighborhood strategy adaptive search (VaNSAS) is proposed to solve this LRP. The main contributions of this paper are a real case study problem and the first introduction of VaNSAS. Furthermore, the different combinations of the solution approach are proposed to prove which combination is the best algorithm. The computational results show that VaNSAS can find the solutions for all problem sizes in much less processing time compared to Lingo. In medium and large-sized instances, the VaNSAS can reduce processing times by 99.91% and 99.86%, respectively, from solutions obtained by Lingo. Finally, the proposed VaNSAS has been deployed in a case study problem to decide the best locations and transportation routes with the lowest fuel cost.

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Section: Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%