Goal Programming and Monte Carlo Simulation for Optimizing Inbound Scheduling in Resource-Constrained Warehouses
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Keywords
Goal Programming, Monte-Carlo Simulation, Time Efficiency, inbound scheduling, SMEs
Abstract
Resource efficiency lies at the heart of logistics performance, with unloading operations in storage facilities serving as a critical determinant of overall productivity. In less developed regions, the widespread reliance on basic rules-based systems such as FIFO often proves inadequate for handling operational complexities, leading to bottlenecks and inefficiencies. Small and medium-sized enterprises (SMEs), constrained by limited resources, are compelled to optimize existing infrastructure rather than invest in costly upgrades. To address this challenge, the present study introduces a goal-oriented programming model designed to assign trucks to loading docks within specific time slots, thereby enhancing time efficiency. The model evaluates performance across four key metrics: waiting time, loading time, overtime, and equity. By leveraging goal programming, numerical prioritization of these objectives becomes possible, enabling flexible adjustments to meet operational needs. Furthermore, Monte Carlo simulation (MCS) is employed to incorporate variability into the dataset and assess model robustness under real-world uncertainty. Experimental results reveal that the proposed approach consistently outperforms traditional systems, delivering significant improvements in time efficiency. These findings highlight the potential of goal programming as a practical solution for planning in resource-constrained environments. The resulting model offers an adaptive, reliable framework that warehouse managers can implement without incurring substantial infrastructure costs.
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Andreas Handojo, Petra Christian University
Andreas Handojo is an associate professor and head of field studies at the Department of Informatics in Petra Christian University, Indonesia. He received his Doctor of Industrial Engineering degree in Supply Chain and Logistics Management; and master’s degree in management of technology from the Sepuluh Nopember Institute of Technology, Indonesia. He achieved his bachelor’s degree in electrical engineering at Petra Christian University. His research interests are machine learning related to engineering and industrial application.
Tanti Octavia, Petra Christian University
Tanti Octavia is an associate professor specializing in Supply Chain and Inventory Management. She achieved a master's degree in industrial engineering and management from the Asian Institute of Technology, Thailand and a bachelor's degree in the industrial engineering department from Petra Christian University, Indonesia. She actively works in research and publishing, providing her expertise to help create effective operating methods across diverse sectors. Her research interests are industrial engineering related to supply chain and inventory management.
Demetrio F. Edryan, University of British Columbia
Demetrio Franklyn Edryan is an undergraduate student at the University of British Columbia, Canada majoring in computer science. He has an extensive background in mathematics and software engineering, with notable achievements in the mathematics national Olympiad. He is passionate about exploring analytical approaches that make use of technological advancements to solve problems in industry. His research interests are primarily on the field of data science and cybersecurity.
