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

In this paper, we present a bi-objective mathematical model of location-routing problem with soft time windows for Perishable Products. The first objective is to minimize costs and the second objective is to maximize Products shelf-life. In order to validate the proposed model, some random numerical examples produced by using previous papers. The problem is coded in the GAMS environment and solved using Augmented Epsilon-Constraint method. Because of NP-hardness of location-routing problem, the NSGA-II meta-heuristic method has been proposed to solve large scale problems in a reasonable time. Computational results proves good performance of the proposed algorithm.

Supply chain design has a significant impact on its efficiency and effectiveness. Thus, The purpose of this study is to design a four echelon supply chain including manufacturing, plants, distribution centers, wholesalers and retailers for Perishable Products. The problem is modeled as a multi-objective mathematical modeling for strategic and tactical levels of decision-making in terms of the number, size and location of distribution centers and wholesalers, the product flow between different facilities, the inventory levels in distribution centers and also selecting the transportation vehicles. The objectives of the model is minimizing total cost and total travel time of product in supply chain by considering product life time. Regardings Products life time, varying perishability rates in different storage facilities and also different perishability rates for transportation facilities, are incorporated in the model, to cover the research gap in designing Perishable Products supply chain. Given to the multi-objective nature of the model, this study applies Normal Boundary Intersection (NBI) method which has significant superiority compared over other approaches like goal programming. GAMS 24 and CPLEX solver were used for solving in a case study in iran dairy industry.

One of the major challenges that manufacturing companies face today is the issue of addressing various aspects of Perishable Products in a supply chain environment. To address this issue, the integrated lot sizing problem for a Perishable product was investigated in the present work. The problem was modeled as a single-vendor multiple buyer system. A variant of the truckload discount scheme was applied, and the proposed model was formulated as a Mixed Integer Program (MIP). The traditional warehouses were replaced by 'cross-docks', and situations featuring bene ficial cross-docking are highlighted. The problem of fleet selection was addressed, and various strategies for minimizing the vendor cost were also highlighted for centralized and decentralized supply chains. Sensitivity analysis was then carried out on various input parameters such as setup cost at the plant, variable transportation cost, fixed transportation cost, setup cost per order, holding cost, and lost cost that underscore the significant impact of economies of scale in transportation on the total supply chain cost. The analysis of a lead time cost trade-off revealed that alternate modes of transportation that significantly reduce the lead time of transportation could be explored, thereby minimizing the total supply chain cost.

Decentralization allows channel members to make their decisions, independently. In this decentralized channels, members may have different power to make its decisions. Hence, decisions of each member may influence on the other members’ decisions. To model such situations, bi-level programming models are useful. In this study, a supply chain including one manufacturer and multiple competitive retailers are considered. The manufacturer produces several Perishable and substitutable Products. The problem is formulated as a multi-follower bi-level programming model. Since bi-level models are often NP-Hard, simulated annealing algorithm is applied to solve the proposed model. The numerical results of sensitivity analysis indicate that in the larger markets, even increasing in the prices leads to increase the profit of members. Also, when the consumers are price-sensitive, the manufacturer and the retailers must decrease their prices to attract the consumers and increase their profits, consequently.

In this study, we discuss a location-inventory-pricing model considering the capacity constraints of the warehouses, disruption, and multiple Perishable Products. We extend a model that assumes that warehouses may face disruption, failed warehouses cannot cover any service, and their customers are assigned to other warehouses. To decrease the risk of disruption, we examine the efficiency of markup pricing strategy and support services. The objective function of this MINLP is to maximize the total profit of warehouses. To solve this model, Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA) are used. To evaluate the recommended model, several sensitivity analyses are proposed. Finally, the results of numerical experiments implicate the high-performance of GOA in dealing with problems and achieving better results. According to the results, backup services and markup pricing strategies are very effective in reducing the damage caused by the disruption.

In this study, we discuss a location-inventory-pricing model considering the capacity constraints of the warehouses, disruption, and multiple Perishable Products. We extend a model that assumes that warehouses may face disruption, failed warehouses cannot cover any service, and their customers are assigned to other warehouses. To decrease the risk of disruption, we examine the efficiency of markup pricing strategy and support services. The objective function of this MINLP is to maximize the total profit of warehouses. To solve this model, Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA) are used. To evaluate the recommended model, several sensitivity analyses are proposed. Finally, the results of numerical experiments implicate the high-performance of GOA in dealing with problems and achieving better results. According to the results, backup services and markup pricing strategies are very effective in reducing the damage caused by the disruption.

location-inventory problem (LIP) is a significant issue in supply chain management (SCM), aiming to reduce and integrate the costs of inventory and location. Perishable-LIP (PLIP) includes Products, particularly those with a short expiration date, also known as Perishable items. This feature necessitates the supply chain to maintain high reliability and resilience to minimize costs faced with disruption risks. Implementing reliability and resilience in PLIP (R2-PLIP) requires methods such as lateral transshipment. These methods not only enhance the reliability and resiliency of the SC but also mitigate the risks associated with supply disruptions and demand fluctuations. Demand for Perishable Products is influenced by their expiration dates. By incorporating lateral transshipment, companies can ensure a more balanced inventory distribution. This study investigates the role of lateral transshipment in enhancing supply chain robustness. A multi-objective optimization model is developed, focusing on minimizing costs while maximizing resilience and service levels. The project aims to optimize the overall system efficiency. Additionally, the sensitivity analysis conducted in the research indicates that the shortage cost and the DC capacity each had the greatest variations in one of the objective functions. This research provides practical insights for designing resilient Perishable supply chains.

In any supply chain, distribution planning of Products is of great importance to managers. With effective and flexible distribution planning, mangers can increase the efficiency of time, place, and delivery utility of whole supply chain. In this paper, inventory routing problem (IRP) is applied to distribution planning of Perishable Products in a supply chain. The studied supply chain is composed of two levels a supplier and customers. Customers’ locations are geographically around the supplier location and their demands are uncertain and follow an independent probability distribution functions. The product has pre-determined fixed life and is to be distributed among customers via a fleet of homogenous vehicles. The supplier uses direct routes for delivering Products to customers. The objective is to determine when to deliver to each customer, how much to deliver to them, and how to assign them to vehicle and routes. The mentioned problem is formulated and solved using a stochastic dynamic programming approach. Also, a numerical example is given to illustrate the applicability of proposed approach.

Dependence of a product order cost to the order quantity is one of the practical and less surveyed assumptions of the literature of economic order quantity model. This assumptions will cause the goal function to be nonconvex and increases the complexity of the problem model. Furthermore, inventory management of the Products which are likely to be out of fashion or perished over time, has a great importance. In this regard, simulation of the inventory control model of Perishable Products has been considered in the present study via the stochastic demand of the product which depends on the time and price of the product. In addition, the dependence of the order cost to the order quantity and dependence of holding cost to the inventory level which are among the practical assumptions of the business world have been considered. These factors cause the usual mathematical solutions not to be able to solve the problem. Therefore, system dynamics have been used as a powerful, flexible, and practical solution to model and solve the problem. A numerical example is also presented to provide a better understanding of the simulation operation; and with the assistance of the optimization of the input variables (ReOrder Point, Order Quantity) the optimal amount of the objective function (Gross Cost Average) is reached. The results showed that the proposed replenishment policy can benefit the necessary decisions regarding inventory management and control of the Perishable Products.