Simulation-Based Optimization of Logistics Decisions under Horizontal Collaboration Following the Can-Order Policy


  • Shaza Hammoud1 (American University of Sharjah, United Arab Emirates)
  • Rami As'ad1 (American University of Sharjah, United Arab Emirates)
  • Mohamed Ben-Daya1 (American University of Sharjah, United Arab Emirates)
  • Moncer Hariga1 (American University of Sharjah, United Arab Emirates)

In today's competitive and environmentally conscious business landscape, companies constantly seek more efficient ways to conduct their daily operations. Horizontal Logistics Collaboration (HLC), in which firms at the same supply chain level share resources such as trucks and information, has proven effective in achieving synchronized deliveries, optimizing transport equipment usage, and reducing carbon footprint. This study implements HLC between two neighboring companies ordering different products from the same supplier. The study adopts the can-order policy, employing three threshold values to define each company's ordering policy and potential joint orders. To better reflect real-world operational aspects, a simulation-based optimization approach is employed, allowing experimentation with various realistic scenarios. The developed model assumes stochastic demand and lead time for both companies and assesses the benefits of HLC from both economic and environmental standpoints, one at a time. Computational experiments consistently demonstrate cost savings through collaboration, especially when both companies are similar with low unit holding costs. From an environmental standpoint, adopting the collaborative model can reduce carbon emissions by up to 27%, particularly when both companies are identical and have low demand and low products' weight. Statistical analysis using paired t-tests confirms the significant differences in cost and carbon emissions after implementing HLC.

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