The mobile supply chain (MSC) is a new development that aims to help companies implement these ideas. In MSCs, production, distribution, and delivery of a product family is performed by a mobile factory (MF), which can be carried by truck and shared among different production sites. In this paper, a real-world application of mobile factories in the chemical industry is studied. Critical production assets (e.g. reactor) are carried by truck and can produce a product family locally. For this purpose, a mixed-integer mathematical model is developed to optimize the logistics costs of MSCs, concerning retailer/job-order allocation, production scheduling, and MF routing. The proposed optimization model is overly complex due to the three NP-hard sub-problems. Therefore, a neighborhood search and an evolutionary algorithm are developed to solve the problem in large-scale data sets. The experimental results show that the proposed algorithms can find a near-optimal solution in a reasonable time.