Release Time Scheduling and Hub Location for Next-Day Delivery

Cataloged from PDF version of article.Inspired by a real-life problem faced by one of the largest ground-based cargo companies of Turkey, the current study introduces a new facet to the hub location literature. The release time scheduling and hub location problem aims to select a specified number of hubs from a fixed set of demand centers, to allocate each demand center to a hub, and to decide on the release times of trucks from each demand center in such a way that the total amount of cargo guaranteed to be delivered to every potential destination by the next day is not below a threshold and the total routing cost is minimized. The paper introduces integer programming models to solve this problem in the special cases when the cargo uniformly arrives to each demand center during the day and the more realistic pattern of when the cargo arrivals exhibit a piecewise linear form. Several classes of valid inequalities are proposed to strengthen the formulations. Extensions with multiple service levels and discrete sets for release times are also discussed. Computational results show the computational viability of the models under realistic scenarios as well as the validity of the proposed problems in answering several interesting questions from the cargo sector’s perspective

Enriching the tactical network design of express service carriers with fleet scheduling characteristics

Express service carriers provide time-guaranteed deliveries of parcels via a network consisting of nodes and hubs. In this, nodes take care of the collection and delivery of parcels, and hubs have the function to consolidate parcels in between the nodes. The tactical network design problem assigns nodes to hubs, determines arcs between hubs, and routes parcels through the network. Afterwards, fleet scheduling creates a schedule for vehicles operated in the network. The strong relation between flow routing and fleet scheduling makes it difficult to optimise the network cost. Due to this complexity, fleet scheduling and network design are usually decoupled. We propose a new tactical network design model that is able to include fleet scheduling characteristics (like vehicle capacities, vehicle balancing, and drivers' legislations) in the network design. The model is tested on benchmark data based on instances from an express provider, resulting in significant cost reductions

Enriching the Tactical Network Design of Express Service Carriers with Fleet Scheduling Characteristics

Express service carriers provide time-guaranteed deliveries of parcels via a network consisting of nodes and hubs. In this, nodes take care of the collection and delivery of parcels, and hubs have the function to consolidate parcels in between the nodes. The tactical network design problem assigns nodes to hubs, determines arcs between hubs, and routes parcels through the network. Afterwards, fleet scheduling creates a schedule for vehicles operated in the network. The strong relation between flow routing and fleet scheduling makes it difficult to optimise the network cost. Due to this complexity, fleet scheduling and network design are usually decoupled. We propose a new tactical network design model that is able to include fleet scheduling characteristics (like vehicle capacities, vehicle balancing, and drivers’ legislations) in the network design. The model is tested on benchmark data based on instances from an express provider, resulting in significant cost reductions.express service carriers;freight transportation;tactical hub network design;integer programming;fleet scheduling;heuristics

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock

Development of natural gas and pipeline capacity markets in the United States

Deregulation of the U.S. natural gas industry has been under way since the late 1970s. The industry was deregulated to create competitive markets in natural gas and its pipeline transportation, in the expectation that competition would guide transactions toward a more efficient outcome. The author provides an overview of the deregulation process and its effect on the development and functioning of natural gas and gas transportation markets in the United States. He analyzes the trading of pipeline capacity in primary and secondary markets and the regulation of pipeline transportation, identifies mechanisms that pipeline companies use to coordinate bilateral transactions, and summarizes deregulation's main achievements in the U.S. natural gas industry. Industry achievements in the past 15 years show that expectations were not realistic. The United States enjoys a highly competitive interstate transportation market. Both markets have benefited from the deregulation of natural gas production and marketing and the liberalization of natural gas prices. Introducing open access to interstate pipelines and their unbundling from gas sales has allowed end users to participate in the efficiency gains in upstream markets. All this has contributed to declining retail prices for all major consumer categories. Deregulation is far from complete, however. Current regulation of interstate pipeline companies and the secondary transportation market does not promote efficient allocation of transportation contracts. Flexible pricing of transportation contracts should be introduced in both the primary and secondary transportation markets. But deregulation of retail markets remains the most important task and the bigger challenge facing industry regulators. Small-volume end users (such as residential or commercial customers) are captive to local distribution utilities, without access to competitive wholesale markets. All end users should be able to choose a natural gas supplier and receive natural gas at the minimum cost to society.Oil&Gas,Water and Industry,Markets and Market Access,Economic Theory&Research,Environmental Economics&Policies,Transport and Environment,Water and Industry,Oil Refining&Gas Industry,Oil&Gas,Carbon Policy and Trading

Integration planning of freight deliveries into passenger bus networks: exact and heuristic algorithms

With the increasing population living in cities, a growing number of small daily urban freight deliveries are performed, typically by private companies. Recently, more environmentally friendly urban logistics services have emerged to mitigate the negative effects of such activities. One example is the integration of freight deliveries into bus networks, traditionally dedicated to passenger transportation, to perform urban logistics activities within cities. In this paper, the integration of the freight delivery process into the urban bus passenger network is addressed where freight parcels are dropped by clients at bus hubs located outside the city center, transported by bus services from the hub to bus stops located in the city center, and delivered to the destination address by a last mile operator. Since bus vehicles supporting both passenger and freight flows need to be physically adapted, the aim is to support the decision-maker to select the minimum number of bus services that must be adapted for freight transportation. The optimization problem considers the freight demand uncertainty in terms of number of freight parcels, destination address, delivery time windows and last mile operator constraints which are modelled by a set of demand scenarios. An exact method based on an integer linear programming (ILP) and two heuristic algorithms based on a greedy randomized adaptive search procedure (GRASP) are proposed. The results show that the proposed optimization methods are efficient, giving valuable insights to stakeholders, in the fields of policy and practice, for the strategic decision of selecting the minimum number of buses to be physically adapted for freight transportation. In particular, the results show that all proposed optimization methods are of interest in practice since the type of problem instances for which each method is more efficient is clearly identified in the obtained computational results. Moreover, in the early stages of the integrated passenger and freight flows service, the impact on the required number of adapted bus services is mainly given by the last mile operator capacity of delivering freight from bus hubs to final parcel destinations, while the other factors (delivery time windows and distributions parcel destination addresses) do not have a significant impact on the required number of bus services.publishe

A Two-Tier Urban Delivery Network with Robot-based Deliveries

In this paper, we investigate a two-tier delivery network with robots operating on the second tier. We determine the optimal number of local robot hubs as well as the optimal number of robots to service all customers and compare the resulting operational cost to conventional truck-based deliveries. Based on the well-known p-median problem, we present mixed-integer programs that consider the limited range of robots due to battery size. Compared to conventional truck-based deliveries, robot-based deliveries can save about 70% of operational cost and even more, up to 90%, for a scenario with customer time windows