The current research has considered the design of the four-level supply chain of perishable goods, including manufacturing factories, distribution centers, wholesalers, and retailers, in conditions of uncertainty in important parameters. The aim is to make strategic and tactical decisions, including the location, number, and size of distribution centers and wholesalers, stock levels in stocking centers, determining the flow of goods between facilities at different supply chain levels, and choosing the type of means of transporting goods between facilities. This is achieved through a three-objective mathematical model. The goals include minimizing the expected total cost in the supply chain, achieving the shortest travel time of goods in the chain, and at the same time minimizing the amount of deviation from customer demand. The presented model tries to pay attention to environmental uncertainty and consider different operational scenarios, as well as the possible approach in important parameters. This takes into account the product life cycle, the different rate of spoilage of the goods in different storage facilities, the different capacity of the facilities in different scenarios, and considering different methods of product transportation with different rates of product spoilage. All of this aims to cover the lack of previous research in the field of perishable goods supply chain design. Considering the multi-objective nature of the model and the need to create flexibility in decision-making for decision-makers, this research uses Normal Boundary Intersection (NBI), which allows decision-makers to choose the most optimal solution according to the importance of different goals. GAMS 24 software and MILP solver were used to solve the mathematical model.
Materials and Methods
This study presents a multiobjective model for designing a four-echelon supply chain (SC) in the strategic and tactical levels for fixed lifetime perishable products. The targeted SC levels include production plants, distribution centers (DC), wholesalers, and retailers. The locations of the plants and retailers are predetermined, while the locations of DCs and wholesalers will be selected from potential locations. The elaborated model seeks to minimize the total cost and product transportation time in the SC and minimize expected demand deviation as well. The Normal Boundary Intersection (NBI) method is employed for solving the model, and GAMS software is used to determine the optimal values of decision variables.
This study utilizes a case study of an Iranian broad dairy company that produces eleven product groups. Data for the study were collected from historical company records and expert interviews. According to the opinions of the experts, three different operational scenarios have been extracted, and the data related to each scenario, especially the customer demand, has been estimated according to historical data as well as the corrective opinions of the managers. The results of the proposed mathematical programming model showed that changes in demand did not have unexpected effects on the values of the objective function and did not change the general trend of the answer to the problem. On the other hand, changes in the percentage of perishability of the product had far less impact on the values of the objective functions as well as the membership function. The overall result is normal, and as a result, in general, these changes represent the stability of the model against the fluctuations of important parameters. A comparison of optimal results and reality reveals that the examined SC needs a redesign of its DCs and wholesalers' locations, and hybrid transportation methods should be used.
Supply chain design (SCD) of fixed lifetime perishable products at the strategic and tactical levels is indeed an important issue. By considering the research gap, this study developed a multi-objective and multi-level model for SCD of fixed lifetime perishable products, and new concepts such as varying perishability rates in storage and transportation facilities are considered. On the other hand, with regard to environmental uncertainty, important parameters such as demand and capacity of facilities are considered as probable parameters. Adding environmental and social factors as new objectives, hybrid transportation methods, and horizontal interactions in the same SC levels can be considered for model development. In order to solve the proposed model, NBI has been used, which has significant advantages compared to other solution methods. By turning the answer of the optimization model into a kind of decision-making problem, this technique gives flexibility to the decision-maker to choose the best solution for their supply chain design according to the weight of each goal. Also, the decision-maker can redesign and increase the adaptability of the supply chain by changing the important parameters of the problem over time.