A stochastic dairy transportation problem considering collection and delivery phases

Huang, Kuancheng; Wu, Kun-Feng; Ardiansyah, Muhammad Nashir

doi:10.1016/j.tre.2018.01.018

Cited by 17 publications

(6 citation statements)

References 21 publications

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“…([14, 17], [13,18]) ( [12,16], [10,17]) S 2 ([14, 16], [13,18]) ( [17,21], [16,22]) Table 4: Transportation cost from S 1.1 and S 1.2 to D 1 and D 2 (in $). [6,11]) ( [9,12], [8,14]) S 1.2 ([8, 11], [6,12]) ( [6,9], [4,11]) [3,9]) ( [4,8], [3,9]) Table 6: Transportation cost from S 1 and S 2 to S 1.1.1 (in $). S 1.1.1 S 1 ([10, 13], [9,14]) S 2 ([11, 13], [10,16]) Table 7: Transportation cost from S 1.1 and S 1.2 to S 1.1.1 (in $).…”

Section: Numerical Examplementioning

confidence: 99%

“…[6,11]) ( [9,12], [8,14]) S 1.2 ([8, 11], [6,12]) ( [6,9], [4,11]) [3,9]) ( [4,8], [3,9]) Table 6: Transportation cost from S 1 and S 2 to S 1.1.1 (in $). S 1.1.1 S 1 ([10, 13], [9,14]) S 2 ([11, 13], [10,16]) Table 7: Transportation cost from S 1.1 and S 1.2 to S 1.1.1 (in $). S 1.1.1 S 1 ([7, 10], [6,12]) S 2 ([8, 10], [7,14]) Table 8: Transportation cost from S 1 and S 2 to S 1.1 and S 1.2 (in $).…”

Section: Numerical Examplementioning

confidence: 99%

“…Murphy [25] developed a TP, described its advantages for the dual of TP and gave its implications for land-use and transport planning. Huang et al [9] solved a stochastic dairy TP with considering collection and delivery phases. Maity et al [16] analyzed an MMTP and its application to artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

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Solving the multi-modal transportation problem via the rough interval approach

et al. 2022

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This research studies a transportation problem to minimize total transportation cost under the rough interval approximation by considering the multi-modal transport framework, referred to here as the rough Multi-Modal Transportation Problem ( MMTP ). The parameters of MMTP are rough intervals, because the problem is chosen from a real-life scenario. To solve MMTP under a rough environment, we employ rough chance-constrained programming and the expected value operator for the rough interval and then outline the main advantages of our suggested method over those existing methods. Next, we propose an algorithm to optimally solve the problem and present a numerical example to examine the proposed technique. Finally, the solution is analyzed by our proposed method with rough-chance constrained programming and expected value operator.

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Section: Numerical Examplementioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

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Solving the multi-modal transportation problem via the rough interval approach

et al. 2022

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“…Singh et al (2019) [19] formulated an uncertain threedimensional transportation problem with multi-objective decision-making and proposed a solution methodology based on chance-constraint programming techniques. Huang et al (2018) [20] developed a stochastic programming model to handle milk collection and delivery process with maximum route duration limitation, external cooling facility options, and travel time uncertainty.…”

Section: Related Researchmentioning

confidence: 99%

Two-Stage Stochastic Programming for the Refined Oil Secondary Distribution With Uncertain Demand and Limited Inventory Capacity

Zhang

2020

IEEE Access

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In recent years, more and more oil companies adopt initiative delivery mode to make the refined oil secondary distribution scheme. In this work, we focus on the optimization problem of refined oil secondary distribution based on the initiative distribution mode considering stochastic demand and the limited inventory capacity of each petrol station. We present a two-stage stochastic programming model that determines the replenishment quantity of each petrol station based on its existing stock and the available supply quantity of each oil depot, as well as transportation schedule. When the uncertainty in demand can be captured via a finite set of scenarios, the two-stage stochastic programming model is transformed into an equivalent deterministic mixed integer programming model that can be efficiently solved by CPLEX solver. The effectiveness of the two-stage stochastic programming model is verified by simulation on extensive computer-generated instances. To solve practical problems with a large number of scenarios, we propose a method to reduce the problem scale by merging similar scenarios. We demonstrate that compared to the optimal solution obtained from the model with all scenarios, the gap corresponding to the model with merged scenarios is always less than 1%. The results of the sensitivity analysis show that an increase in the inventory capacity leads to a decrease in the total cost within a certain range. The results of this study can help companies making refined oil secondary distribution plan. INDEX TERMS Refined oil secondary distribution, initiative distribution mode, transportation, stochastic demand, limited inventory capacity, two-stage stochastic programming.

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“…Some authors estimate that transportation costs can reach up to 30% of the final price of the product (Apte and Viswanathan, 2000). The concern with transportation costs is even more important in developing countries that lack appropriate infrastructure, which leads to higher logistics costs (Huang et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

How can mixed integer linear programming assist dairy manufacturers by integrating supply decisions and production planning?

Emidio

Lima

Leal

et al. 2021

JADEE

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PurposeThe dairy industry needs to make important decisions regarding its supply chain. In a context with many available suppliers, deciding which of them will be part of the supply chain and deciding when to buy raw milk is key to the supply chain performance. This study aims to propose a mathematical model to support milk supply decisions. In addition to determining which producers should be chosen as suppliers, the model decides on a milk pickup schedule over a planning horizon. The model addresses production decisions, inventory, setup and the use of by-products generated in the raw milk processing.Design/methodology/approachThe model was formulated using mixed integer linear programming, tested with randomly generated instances of various sizes and solved using the Gurobi Solver. Instances were generated using parameters obtained from a company that manufactures dairy products to test the model in a more realistic scenario.FindingsThe results show that the proposed model can be solved with real-world sized instances in short computational times and yielding high quality results. Hence, companies can adopt this model to reduce transportation, production and inventory costs by supporting decision making throughout their supply chains.Originality/valueThe novelty of the proposed model stems from the ability to integrate milk pickup and production planning of dairy products, thus being more comprehensive than the models currently available in the literature. Additionally, the model also considers by-products, which can be used as inputs for other products.

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A stochastic dairy transportation problem considering collection and delivery phases

Cited by 17 publications

References 21 publications

Solving the multi-modal transportation problem via the rough interval approach

Solving the multi-modal transportation problem via the rough interval approach

Two-Stage Stochastic Programming for the Refined Oil Secondary Distribution With Uncertain Demand and Limited Inventory Capacity

How can mixed integer linear programming assist dairy manufacturers by integrating supply decisions and production planning?

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