Robust optimisation model for the cold food chain logistics problem under uncertainty

Behzadi, Golnar; Sundarakani, Balan; Mardaneh, Elham

doi:10.1504/ijleg.2013.058821

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…Regarding the food supply chain, RO plays a big role since various uncertainties have become the challenges in both demand and supply-side of the chain (Wan et al, 2016). One example of the studies in food supply chain involving uncertainties is given by Behzadi et al (2013). They propose a model to optimize cold food supply chain logistics involving demand uncertainty to maximize food safety with the minimum costs possible.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a food supply chain strategy during COVID-19 pandemic using an integrated Agent-Based Modelling and Robust Optimization

Achmad¹,

Chaerani²,

Perdana³

2021

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Designing a food supply chain strategy during COVID-19 pandemic using an integrated Agent-Based Modelling and Robust Optimization

Achmad¹,

Chaerani²,

Perdana³

2021

Heliyon

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“…Similarly, Castillo (2009) later developed a RO model for large scale system applications which explicitly incorporate the conflicting objectives of solution and model robustness. Additionally, Kanyalkar and Adil (2010) and La Torre (2003) applied RO in a multi-site procurement-production-distribution problem, while Behzadi et al (2013) applied the RO approach in the food industry to increase safety under uncertainty. It was found in a survey by Snyder (2006) on the literature of facility location problems under uncertainty that the models developed are mainly non-deterministic polynomial hard (NP-hard) models, as discussed by Rahmaniani et al (2013).…”

Section: Limitations Of Existing Solution Approachesmentioning

confidence: 99%

Robust facility location decisions for resilient sustainable supply chain performance in the face of disruptions

Sundarakani

Pereira

Ishizaka

2020

IJLM

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PurposeFacility location and re-location decisions are critical managerial decisions in modern supply chains. Such decisions are difficult in this environment as managers encounter uncertainty and risks. The study investigates establishing or moving distribution facilities in the global supply chain by considering costs, fulfilment, trade uncertainties, risks under environmental trade-offs and disruptive technologies.Design/methodology/approachThis paper combines the possibilities and probabilistic scenarios for a supply chain network by proposing the novel Robust Optimisation and Mixed Integer Linear Programming (ROMILP) method developed under the potential uncertainty of demand while considering the costs associated with a four-tier supply chain network. ROMILP has been solved in a real-time logistics environment by applying a case study approach.FindingsThe solution is obtained using an exact solution approach and provides optimality in all tested market scenarios along the proposed global logistics corridor. A sensitivity analysis examines potential facility location scenarios in a global supply chain context.Research limitations/implicationsLogistics managers can apply the ROMILP model to test the cost-benefit trade-offs against their facility location and relocation decisions while operating under uncertainty. Future research is proposed to extend the literature by applying data from the OBOR logistics corridor.Originality/valueThis study is the first to examine sustainable dimensions along the global logistics corridor and investigate the global container traffic perspective. The study also adds value to the Middle East logistics corridor regarding facility location decisions.

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“…This problem is solved by branch-and-bound (B&B) algorithms. Behzadi G et al [18] developed a robust optimization model for cold chain food logistics with uncertainty and customer time-windows Mathematical Problems in Engineering 3 restrictions; the model's objective is to maximize food safety and minimize transportation cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Cost-Optimization Problem with a Soft Time Window Based on an Improved Fuzzy Genetic Algorithm for Fresh Food Distribution

Wang

Yin

2018

Mathematical Problems in Engineering

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Given the increasing demand for fresh food quality, fresh food plants must manage not only product cost but more importantly the product quality. The transportation requirements for fresh food delivery have been continuously increasing. The purpose of this paper is to develop a method to ensure that fresh food can be delivered just in time and with minimum total cost while maintaining the quality of fresh food. Considering that fresh food plants need multiple trucks to deliver multiple products to numerous geographically dispersed customers, the delivery of fresh food is considered in two stages in our study. The first stage is cluster consumers; that is, we determine to which consumers each truck is responsible for delivery. The second stage, which is based on the consumer grouping results, develops a total cost model that includes the transportation, refrigerated, devalued, and penalty costs incurred during distribution. This model is used to determine the optimal route selection, the temperature control, and the average speed of each truck in distribution. This paper designs decision variables based on a customer's seven requirement attributes; it also proposes a fuzzy clustering method for grouping customers and improves a fuzzy genetic algorithm that is used to solve the proposed total cost model. The application of the proposed method is demonstrated using an example. The experimental results show that the proposed method has better performance than that of a traditional genetic algorithm. This research work provides an optimal distribution total cost decision method for the logistics managers. This research also provides an effective means to ensure the safety of fresh food.

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Robust optimisation model for the cold food chain logistics problem under uncertainty

Cited by 10 publications

References 15 publications

Designing a food supply chain strategy during COVID-19 pandemic using an integrated Agent-Based Modelling and Robust Optimization

Designing a food supply chain strategy during COVID-19 pandemic using an integrated Agent-Based Modelling and Robust Optimization

Robust facility location decisions for resilient sustainable supply chain performance in the face of disruptions

Cost-Optimization Problem with a Soft Time Window Based on an Improved Fuzzy Genetic Algorithm for Fresh Food Distribution

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