Balancing cost and responsiveness: Evaluating operational and geographic scenarios to advance sustainable supply chains with reusable packaging

Reusable packaging systems are increasingly recognized as a cornerstone of circular and sustainable supply chains; however, their operational management remains underexplored. Despite the growing emphasis on reusable packaging systems, most prior research has focused on design innovation, material substitution, or life-cycle assessment rather than on logistics optimization. Yet, the transportation and redistribution of reusable packages involve unique challenges — such as synchronizing delivery of full packages with pickup of empty ones, managing vehicle capacity, and responding to uncertain customer demands — that directly affect system responsiveness, cost efficiency, and environmental performance. Consequently, there is a pressing need for operational models that capture these dynamics and determine the conditions under which reusable systems can achieve both economic and environmental sustainability. This study addresses this gap by modeling reusable packaging logistics within the framework of the Dynamic Vehicle Routing Problem with Backhauls (DVRPB). Specifically, it examines how key operational factors — customer participation rate, vehicle capacity, degree of demand dynamism, reception time frame for online requests, and decision frequency — affect cost efficiency and system responsiveness in both urban and rural contexts. The adopted approach constructs an initial static routing plan for delivering full packages and picking up empty ones whose requests were placed the day before operations start, while dynamically inserting new pickup requests as they arise during the day. This approach mirrors real-world practices used by carriers such as UPS and Amazon, balancing computational tractability with operational realism. The findings offer actionable insights for designing responsive, cost-effective, and sustainable reusable packaging networks. • Apply a dynamic vehicle routing problem with backhauls for reusable packages. • Solve the problem on real-world transportation networks. • Include unmet requests as lost opportunity costs into the model. • Assess how geographical setting influences system efficiency and responsiveness. • Assess how operational factors influence system efficiency and responsiveness.