Optimizing a sustainable inventory-routing problem in tomato agri-chain considering postharvest biological behavior

Subject Areas : Mathematical Optimization

Shima Shirzadi ¹ (School of Industrial Engineering, South Tehran Branch, Islamic Azad university, Tehran, Iran)
Vahidreza Ghezavati ² (School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Reza Tavakkoli-Moghaddam ³ (University of Tehran)
Sadoullah Ebrahimnejad ⁴ (School of Industrial Engineering, Islamic Azad university, Karaj Branch, Karaj, Iran)

Received: 2021-04-04 Accepted : 2021-07-06 Published : 2021-04-01

Keywords: Sustainable Development, Fresh agricultural products, Fair pricing, Postharvest biological behavior, Inventory routing problem,

Abstract :

A new mixed-integer multi-objective mathematical model is developed to optimize sustainable inventory-routing decisions in products agri-chain. The first aim is to optimize the network total revenue besides noticing logistics decisions related to the distribution and collection of perishable products. Also, the economical, social and environmental factors have been integrated in the proposed model. The first objective function considers some traditional terms and novel issues (e.g., postharvest biological behavior of agricultural products), which is related to deviation from ideal quality (customer's dissatisfaction) and the costs of expired products. Because old products have significant environmental impacts and require recycling, the reverse logistics framework is used to collect and bring products back to recycling. A function is applied to compute the level of deviation from suitable maturity and customer's dissatisfaction costs. A numerical example is analyzed to indicate the model's applicability by applying the ε-constraint methodology to show the opposite pattern between the two objectives. Results show that a lower level of accidents leads to lower revenue or higher costs of the supply chain. Remarking the NP-hardness of the presented model, two multi-objective meta-heuristic algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Dragonfly Algorithm (MODA) are used to explore near-optimal solutions for medium and large-sized problems. Results show a better performance of the NSGA-II. Furthermore, the sensitivity analysis is presented and explained in four parts to show the trend of the proposed model by fluctuations on important parameters.

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