Abstract
In this paper, we devoted a design under uncertainty of a four-echelon supply chain network including multiple suppliers, multiple plants, multiple distributors and multiple customers. The proposed model is a bi-objective mixed integer linear programming which considers several constraints and aims to minimize the total costs including the procurement, production, storage and distribution costs as well as to maximize on-time deliveries (OTD). To bring the model closer to real-world planning problems, the objective function coefficients (e.g. procurement cost, production cost, inventory holding and transport costs) and other parameters (e.g., demand, production capacity and safety stock level), are all considered triangular fuzzy numbers. Besides, a hybrid mathematical model-based on credibility approach is constructed for the problem, i.e., expected value and chance constrained models. Moreover, to build the crisp equivalent model, we use different property of the credibility measure. The resulted crisp equivalent model is a bi-objective mixed integer linear programs (BOMILP). To transform this crisp BOMILP into a single objective mixed integer linear programs (MILP) model, we apply three different aggregation functions. Finally, numerical results are reported for a real case study to demonstrate the efficiency and applicability of the proposed model.
Highlights
Given the current trends in globalization, many companies are increasingly sourcing, producing and marketing their products around the world
Our research is in line with this trend, with the aim of studying the problem of integration of functions such as procurement, production, storage and distribution of the supply chain network
This work is motivated by the problem of integrating procurement, production and distribution functions of a supply chain network which has several geographically dispersed suppliers, production sites, distribution centers (DC) and customers
Summary
Given the current trends in globalization, many companies are increasingly sourcing, producing and marketing their products around the world. Several researchers and practitioners focus on integrating the various supply chain functions to increase flexibility, to improve cycle times and to reduce costs. According to [1], any supply chain planning that relies on deterministic conditions risks losing its durability They mention that in some cases, it is not enough for the company to consider usual parameters such as demand, prices or other parameters such as random variables, but undesirable events such as Cyber Security Threats and natural disasters. The main contributions of this paper can be summarized as follows: (i) introduction, in third section, of a MILP formulation for integrated procurement, production and distribution problem within the supply chain network, (ii) consideration of different sources of uncertainty such as costs, demand, production capacity and safety.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
