Delivering Perishable products to customers as soon as possible and with the minimum cost has been always a challenge for producers and has been emphasized over recent years asthe global market becoming more competitive. In this studya multi-objective mix integer non-linear programming model is proposed to maximize both profits of a distributer and the total freshness of the several products to be delivered to customers with respect to their demands and with consideration of different soft time windows for each customer, heterogeneous distribution fleet and customer selection option for the distributer. The proposed model is solved with TH method. The two genetic algorithm and simulated annealing algorithm are used to solve large-sized problems. Finally, their results are compared to each other when the optimization software becomes unable of solution representation.

The current article examines the trends and performance of research on the types of effects of the COVID-19 epidemic and the different management strategies of Perishable goods supply chain disruptions during 2 years (2020-2022) using bibliometric and network analysis based on the data set from the Scopus database using VOSviewer software. The analysis designates that Kumar A. has been the most influential author in Perishable goods supply chain research during the COVID-19 pandemic, and India has contributed the most in this field. The most repeated keywords were resilience, food supply, and Perishable food supply chain. Citation analysis indicated that resilience and sustainability are the mainstream of research in articles with high citations. The network analysis also disclosed that resilience and sustainability are the key themes in the clusters. Furthermore, strategies for managing disruption during the COVID-19 pandemic were extracted from the highly-cited articles, which are presented in this article.

In the competitive market of Perishable cargoes, determining the price of the product and making opportunities for customers to accelerate the sale of goods through discounts is crucial. Over the life of Perishable goods, generally its value reduced to the customer, in this situation, to encourage the purchase, policies such as a discount or reduced price sales policies can be effective. Literature has not provided a model for determining the optimal time to announce a price reduction. While early or late prices announcement could reduce profit, the aim of this paper is to analyze such a model at the level of an enterprise. In the modeling, we assumed that by announcing price discount, tangible changes occur in demand, and demand is a function of price and time. The demand rate in the discount time is decreasing in the beginning of the time and then declining over time. The purpose of the model is determining the optimal price, discount time and order size to maximize the total profit in a single period. After modeling, concavity of the profit function is considered and optimal pricing and discounts are exclusive. Then, a heuristic algorithm derived from the literature was used in order to determine the optimal price, the optimal discount time and the optimal order quantity.

Due to the rapid development of technology in recent years, market competition and customer expectations have increased more than ever. In this situation, it is vital for businesses survival to determine the appropriate policy for inventory control, pricing, and routing, and decisions regarding each of them are often made separately. If the products have a Perishable nature, it will be more important to determ ine the above policies. For integration of the decision-making concerning the three key components of the supply chain, i. e. pricing, routing, and inventory control, two mathematical models were developed for a two-echelon supply chain of Perishable items with direct shipment, where fixed lifetime is assumed in one model and random lifetime in another, so that profit is maximized. The proposed mathematical model was solved using the CPLEX solver package of the GAMS software for specification of the optimal policy of the supply chain. The results demonstrated that the CPU time needed for solving the mathematical model for Perishable items with random lifetime was less than that for fixed lifetime, while the value of the objective function for products with fixed lifetime was greater than that for products with random lifetime.

Sustainability in the supply chain means pushing the supply chain to focus on social, economic and environmental aspects, and addressing the existing problems in the traditional supply chain. Considering the importance of evaluating supply chain networks, especially in the field of Perishable commodities, this paper aimed to design a mathematical model for the reverse supply chain of Perishable goods, taking into account the sustainable production system. In this research, four objective functions were considered to maximize profitability and the level of satisfaction with the use of technology, minimize costs and measure environmental impacts. The results of the implementation of the proposed model for a manufacturing company show that objective functions are sensitive to demand, so the change in demand changes the objective functions, in particular the profitability function.

This paper develops a bi-objective optimization model for the integrated production-distribution planning of Perishable goods under uncertainty. The first objective seeks to maximize the profit in a specific supply chain with three levels: plants, distribution centers, and in the last level, customers. Since transportation is one of the major pollution sources in a distribution problem, the second objective is to minimize their emission. In the considered problem, the decisions of production, location, inventory, and transportation are made in an integrated structure. In developing the demand function, the effect of the product freshness and the price is formulated. Besides, to encourage customers, three strategies, including perished product return, discount, and credit policies, are proposed. Also, robust optimization is utilized to cope with the operational uncertainty of some cost parameters. To prove the applicability of this research and the feasibility of the environmental aspect, a case study is conducted. Finally, the numerical computations on the case study provide a trade-off between the environmental and economic goals and indicate a 37.5 percent increase in the profit using the developed model.

Recently, following the raise in expense pressures led to lower economic growth, an increasing number of manufacturers have begun to investigate eventuality of handling returned product in a more cost-effective and proper procedure. Significance of Reverse Logistics (RL) is becoming greater due to various governmental, societal, and environmental reasons. Number of papers present in the literature on RLs is a well index of its importance. In some industries, appropriately collected returned products could be used as raw material for another product, increasing Supply Chain (SC) profits and reducing the waste. Since, Perishable goods have a limited shelf-life, they can be reusable if they are collected before they reach a critical time. Accordingly, in the present study, a Mixed Integer Linear Programming (MILP) model was introduced for a network of closed-loop SC with recycling of returned Perishable goods, involving suppliers, producers, retailers, together with collection and disposal centers, in a multi-product, multi-period, and multi-level basis. To do this, a case study was performed on milk and yogurt products of a company in dairy industry. The model was solved and analyzed using GAMS software. Results obtained from assessment of the model indicated that, timely collection of Perishable goods and their use in production of new products reduces total costs of Perishable SC network.

Short life cycle products, especially food products, require a certain type of supply chain management due to their particular specifications such as perishability. On the other hand, the food distribution also requires special considerations and imparts more complexity compared with the distribution of other goods because in food distribution the quality of the food delivered to the customer should be considered as well as transportation costs. Therefore, in this paper, a new mathematical model is developed for integrating decisions regarding food supply and distribution under conditions of uncertainty (vehicles’ travel time) with aims to minimize purchase and transportation costs and maximize customer satisfaction. Customer satisfaction relies upon the quality of the food delivered to the customers. The multi-objective model proposed in this paper is NP-hard. Hence, a developed version of NSGA-II called Multi-Objective Time Travel to History (MOTTH) algorithm, inspired from the idea of traveling through history, is proposed to solve the problem. In order to validate the performance of the proposed algorithm, the results of MOTTH algorithm are compared with the results obtained from an exact augmented epsilon-constraint method. Furthermore, a comparison is provided between the NSGA-II and MOTTH algorithms, the results of which indicate the superiority of the MOTTH metaheuristic algorithm.

The stockpiling or storage of commodities plays a very important role in commercial and marketing services. This process by managing the time intervals between production and consumption, brings new synergy for goods and yields expected profits for producers. Such process by diminishing the wastes and increasing opportunity cost for products in different periods of time leads to credibility and incentive in market factors. It also amounts to prudent and efficient decision making in the field of production, consumption and distribution. Furthermore, this process by bringing necessary and proper adjustments in consumption and price level leads to food and economic security. The purpose of this paper is , by taking into account the special characteristics of Perishable agricultural products, to review and analyse the factors which determine the level and magnitude of storage. To accomplish the task, first the theoretical background and literature are briefly reviewed and a simple model is introduced and at the next stage the model is extended to include other concepts such as rational expectations, uncertainty and cost per unit of storage