The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

Realistic project scheduling and control are critical for running a profitable enterprise in the construction industry. Finance-based scheduling aims to produce more realistic schedules by considering both resource and cash constraints. Since the introduction of finance-based scheduling, its literature has evolved from a single-objective model to a multi-objective model and also from a single-project problem to a multi-project problem for a contractor. This study investigates the possibility of cooperation among contractors with concurrent projects to minimize financial costs. Contractors often do not use their entire credit and may be required to pay a penalty for the unused portions. Therefore, contractors are willing to share these unused portions to decrease their financing costs and consequently improve their overall profits. This study focuses on the partnering of two contractors in a joint finance-based scheduling where contractors are allowed to lend credit to or borrow credit from each other at an internal interest rate. We apply this approach to an illustrative example in which two concurrent projects have the potential for partnering. Results show that joint finance-based scheduling reduces the financing cost for both contractors and leads to additional overall profits. Our further analyses highlight the intricate dynamics impacting additional net profit, revealing optimal scenarios for cooperation in complex project networks.

This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.