A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges.</p

Intelligent Mobility in Smart Cities

Smart Cities seek to optimize their systems by increasing integration through approaches such as increased interoperability, seamless system integration, and automation. Thus, they have the potential to deliver substantial efficiency gains and eliminate redundancy. To add to the complexity of the problem, the integration of systems for efficiency gains may compromise the resilience of an urban system. This all needs to be taken into consideration when thinking about Smart Cities. The transportation field must also apply the principles and concepts mentioned above. This cannot be understood without considering its links and effects on the other components of an urban system. New technologies allow for new means of travel to be built, and new business models allow for existing ones to be utilized. This Special Issue puts together papers with different focuses, but all of them tackle the topic of smart mobility

Measuring the Value of Time in Highway Freight Transportation

This research investigated several aspects of the value of time (VOT) in the trucking industry. This included examining the marginal monetary benefits and costs of reduced and prolonged freight transportation time on highways. First, a comprehensive survey estimated truckers’ perceived VOT by combining stated preference, utility theory, conditional logit modeling, and maximum likelihood function. From the data collected around major cities in Texas and Wisconsin, the truckers’ perceived VOT was estimated to be $54.98/vehicle/hour. Second, scenario-based simulation examined urban truckload operations, the purpose of which was to examine the fleet effect of individual vehicle delay on the carrier’s operation. Two of the most congested highway segments in Houston were used for the simulation, together with constrained delivery windows. The result showed that the scenario-based vehicle VOT varied from$79.81/vehicle/hour to $120.89/vehicle/hour. Third, VOT based on commodity delay only was examined in relationship to inventory management by assuming prolonged transportation time or freight delay. Delay of chemical products was ranked as the highest VOT at$13.89/truckload/hour, followed by food products at $7.24/truckload/hour. Finally, a continuous approximation technique was developed for fleet operations in the context of less-than-truckload deliveries. The trade-offs between travel time and roadway transportation cost were derived analytically and results were used to estimate fleet value of time. Ignoring time windows, the vehicle VOT for major distribution companies in Texas was estimated to be$15.50/vehicle/hour for highway trips and $22.00/vehicle/hour for local trips. To summarize, freight VOT is not only directly due to vehicles and drivers, but depends on fleet operations and supply chain management. The several approaches adopted in this research represent possible perspectives that need to be further examined. They each reveal a component of the entire shipping process that can be appropriately utilized to calculate the overall freight VOT in future studies. For example, an urgent delivery carrying chemical products can be estimated at a total congestion cost of$162.86/vehicle/hour. However, trips with different characteristics need to be treated individually andcarefully to avoid overestimation. It remains challenging tocombineall these different elements adequately to reach valid VOT for the trucking industry

A system dynamics &amp; emergency logistics model for post-disaster relief operations

Emergency teams&amp;rsquo; efficiency in responding to disasters is critical in saving lives, reducing suffering, and for damage control. Quality standards for emergency response systems are based on government policies, resources, training, and team readiness and flexibility. This research investigates these matters in regards to Saudi emergency responses to floods in Jeddah in 2009 and again in 2011. The study is relevant to countries who are building emergency response capacity for their populations: analysing the effects of the disaster, communications and data flows for stakeholders, achieving and securing access, finding and rescuing victims, setting up field triage sites, evacuation, and refuges. The research problem in this case was to develop a dynamic systems model capable of managing real time data to allow a team or a decision-maker to optimise their particular response within a rapidly changing situation. The Emergency Logistics Centre capability model responds to this problem by providing a set of nodes relevant to each responsibility centre (Civil Defence, regional/local authority including rescue teams, police and clean-up teams, Red Crescent). These nodes facilitate information on resource use and replenishment, and barriers such as access and weather can be controlled for in the model. The dynamic systems approach builds model capacity and transparency, allowing emergency response decision-makers access to updated instructions and decisions that may affect their capacities. After the event, coordinators and researchers can review data and actions for policy change, resource control, training and communications. In this way, knowledge from the experiences of members of the network is not lost for future position occupants in the emergency response network. The conclusion for this research is that the Saudi emergency response framework is now sufficiently robust to respond to a large scale crisis, such as may occur during the hajj with its three million pilgrims. Researchers are recommended to test their emergency response systems using the Emergency Logistics Centre model, if only to encourage rethinking and flexibility of perhaps stale or formulaic responses from staff. This may lead to benefits in identification of policy change, training, or more appropriate pathways for response teams

Forklift Routing Optimization in a Warehouse using a Clustering-based Approach

Order picking in a warehouse is considered to be a time-consuming and costly process that results in loss of profit for the management. Hence a warehouse management team is always looking to improve their picking process and increase their efficiency. In this research, a warehouse with narrow aisles is studied. The aisles are so narrow that a forklift is only allowed to traverse them in one direction thus making them unidirectional. The picking process is modeled first as an uncapacitated vehicle routing problem and then as a capacitated vehicle routing problem. The objective is to minimize the total travel distance. Since the Mixed Integer Programming model takes a long time to solve large instances, we develop a heuristic algorithm both for the uncapacitated and capacitated problems by combining two methodologies of heuristics and machine learning. The algorithm is able to solve the instances to near optimality quickly, finding practical solutions that could potentially be implemented into actual warehouses to reduce order picking time and hence, overall warehouse costs