# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 1 Introduction  
In today's world, economies and commodity markets swing rapidly; personal, organizational, and business networks are becoming more interconnected, instrumented, and intelligent [1]. One of the important aspects of the business world is supply chain management. It works towards satisfying customers' requirements through the efficient use of resources resulting in reducing cost and increasing profit margin for companies [2]. With a highly competitive global market, supply chains have to be more responsive to customers' needs. Supply chain represents all stages of a process; usually, researchers focus on materials that are manufactured to become an end product; however, the supply chain covers a wider range that extends beyond physical assets. For instance, looking at the banking sector, their main supply chain is of financial data that needs to be processed using available resources which are software and hardware processors. One of the main instruments used in business networks for the management of the financial data supply chain is data batch processing (DBP). It plays a critical role in daily operations carried out in most organizations in different business fields. Data batch processing can be defined as the execution of data files as input batches using the available resources and gather the resulted files as output batches while satisfying files priorities, predecessors constraints, and time constraints [3, 4]. The execution is carried out on the mainframe computer that runs at a scheduled time or on an as-needed basis without user interaction and minimal or no interaction with a computer operator [3, 4]. The main feature of data batch processing is its ability to handle an enormous amount ofdata files which makes it attractive and essential to many organizations that are constantly dealing with heavy business activities [5]. It contributes to the major part of the workload on mainframe computers that will often run a large number of business workflows with complex interrelations, requiring careful scheduling and prioritizing to ensure that all batch jobs run in the correct order and meet strict deadlines [3; 4; 6]. Our research considers the case of processing end-of-day (EOD) operations which include highly important and frequently used activities and services such as: preparing customers' bank statements; credit card processing bills; fraud detection data; merging the day's transactions into master files; sorting data files for optimal processing; providing daily, weekly, monthly, and annual reports; invoices and bank statements; performing periodic payroll calculations; and applying interest to financial accounts [4]. Banks seek to elevate their level of customer service by optimizing their data supply chain scheduling. This supply chain consists of the bank, a service provider that arranges the processing of data, and processors providing company that rents and leases processors and software to service providers. Also, in our research, data supply chains adopt the technique of lot streaming which is a technique that splits a given data job, each consisting of similar files, into tasks to allow overlapping of successive operations in processing systems thereby reducing the processing makespan. The proposed work addresses the financial data supply chain scheduling problem from an operational perspective by considering the scheduling at an individual job level. The problem can be viewed as a single-stage supply chain scheduling problem where jobs are arranged to be processed by mainframe processors that can be modeled as a series of flow shop machines. Processors are leased and rented from manufacturing and supplying company as needed by a third-party company that performs the data scheduling. Data arrives raw and unprocessed from banks to a service provider which needs to manage scheduling them for processing as per the available resources (software and hardware) in the most efficient way.