A milk collection problem with blending

A milk collection problem with blending is introduced. A firm collects milk from farms, and each farm produces one out of three possible qualities of milk. The revenue increases with quality, and there is a minimum requirement at the plant for each quality. Different qualities of milk can be blended in the trucks, reducing revenues, but also transportation costs, resulting in higher profit. A mixed integer-programming model, a new cut, and a branch-and-cut algorithm are proposed to solve medium-sized instances. A three-stage heuristic is designed for large instances. Computational experience for test instances and a large-sized real case is presented.     

Optimization of postal express line network under mixed driving pattern of trucks

Details about the movement of trucks on postal express lines are investigated to improve the performances of mail distribution. A mixed driving pattern of trucks is introduced to minimize the transportation cost of a postal express line network with a service level requirement. We formulate this problem as a mixed p meeting depots location with shipment scheduling problem and build a MINLP model. A two-level tabu search procedure based on shipment grouping method is developed. Through a series of computational experiments and sensitivity analysis on different instances, some managerial insights of the network under mixed driving pattern are revealed.     

A heuristic algorithm for yard truck scheduling and storage allocation problems

The yard truck scheduling and the storage allocation are two important decision problems affecting the efficiency of container terminal operations. This paper proposes a novel approach that integrates these two problems into a whole. The objective is to minimize the weighted sum of total delay of requests and the total travel time of yard trucks. Due to the intractability of the proposed problem, a hybrid insertion algorithm is designed for effective problem solutions. Computational experiments are conducted to examine the key factors of the problem and the performance of the proposed heuristic algorithm.     

Loading optimization of palletized products on trucks

In this paper we analyze the application of an optimization model to solve problems of arranging products (packed in boxes) on pallets, and arranging loaded pallets on trucks. Initially the model is applied to solve thousands of randomly generated experiments. Then, in order to assess the effectiveness of the solutions in practice, the model is applied to two Brazilian case studies: a food company distribution center and a large wholesale distribution center. We also discuss the use of this approach for optimizing the sizes of packages, pallets and trucks. In particular, we analyze the performance of the Brazilian standard pallet (PBR), adopted by the Brazilian Association of Supermarkets (ABRAS) and recommended by the Brazilian Logistics Association (ASLOG), in comparison with other standard pallets. By examining not only the loading of products on pallets, but also the loading of pallets on trucks, we can obtain global utilization indices which are useful to evaluate the economical performance of unit load systems in the logistics chain of a company.     

Scheduling trucks in cross-docking systems: A robust meta-heuristics approach

In a cross-docking system, trucks must be scheduled to minimize the total flow time of the system. This problem is NP-hard, and this study proposes two hybrid meta-heuristics—hybrid simulated annealing and hybrid variable neighborhood search—to solve it by achieving the best sequence of truck pairs. The Taguchi method serves to reveal the best robustness of these algorithms. To demonstrate the effectiveness of the proposed methods, especially for large-sized problems, this study solves various test problems, and the computational results clearly reveal that the proposed methods outperform previous approaches.     

The liner shipping berth scheduling problem with transit times

In this paper speed optimization of an existing liner shipping network is solved by adjusting the port berth times. The objective is to minimize fuel consumption while retaining the customer transit times including the transhipment times. To avoid too many changes to the time table, changes of port berth times are only accepted if they lead to savings above a threshold value. Since the fuel consumption of a vessel is a non-linear convex function of the speed, it is approximated by a piecewise linear function. The developed model is solved using exact methods in less than two minutes for large instances. Computational experiments on real-size liner shipping networks are presented showing that fuels savings in the magnitude 2–10% can be obtained. The work has been carried out in collaboration with Maersk Line and the tests instances are confirmed to be representative of real-life networks.     

Negotiating truck arrival times among trucking companies and a container terminal

Congestion of trucks in port areas has created serious environmental and traffic problems in many countries. One of main reasons for congestion is the concentrated arrivals of road trucks at peak hours. A negotiation process for smoothing truck arrivals in peak hours among multiple trucking companies and a terminal is addressed. The negotiation process is to be used for the appointment system for road trucks in container terminals. This paper suggests a mathematical formulation for smoothing the peaks in arrivals considering the inconvenience of trucks from changing their arrival times and the waiting cost of trucks in peak hours. This study proposes a decentralized decision-making model to support the negotiation process between truck companies and the terminal operator. It gives an acceptable solution to every player involved as well as the near optimal solution to the entire system from the global point of view. Numerical experiments are performed to validate the approach in this study.     

Revenue management policies for the truck rental industry

In this paper, we consider the problem of managing a fleet of trucks with different capacity to serve the requests of different customers that arise randomly over time. The problem is formulated via dynamic programming. Linear programming approximations of the problem are presented and their solutions are exploited to develop partitioned booking limits and bid prices policies. The numerical experiments show that the proposed policies can be profitably used in supporting the decision maker.     

The geography of freight-related accidents in the era of E-commerce: Evidence from the Los Angeles metropolitan area

Due to the burgeoning demand for freight movement in the era of e-commerce, freight related road safety threats have been growing in both urban and suburban areas, despite the improved general traffic safety over the past decades. The empirical evidence on how freight trucks related crashes are distributed across neighborhoods and correlated to spatially varying factors is, however, highly limited. This article uses data from the Los Angeles region in 2018 to analyze the spatial patterns of freight trucks related traffic crashes and examines the major factors that contribute to those patterns using spatial econometric models. Maps show that freight trucks related crashes are highly associated with major freight generators but less clustered than the overall traffic crashes. Results from the spatial Durbin model indicate that access to freight generators, economic attributes, land uses, road infrastructure, and road network variables all contribute to the spatial distribution of freight trucks related crashes. The findings could help transport planners understand the dynamics of freight trucks related traffic safety and develop operational measures for mitigating the impacts of growing goods movement on local communities.     

Truck movement across the Canada–US border: The effects of 9/11 and other factors

The objective of this study is to isolate the effects of the terrorist attacks of 9/11 and the subsequent stringent border security regime on the levels of truck movement across the Canada–US border. A time series analysis, within an econometric system that rendered stationarity and used the seemingly unrelated regression (SUR) estimator, was performed to model these movements. The analysis represents a natural experiment on the variability of cross-border truck movements in the aftermath of 9/11. Annual truck crossing data over the period 1972–2011, disaggregated by the country of registration (US and Canada) and Canadian jurisdictions (provinces) were used in the analysis. The results indicate that in general, 9/11 have had a negative effect on the cross-border movement of Canadian and US trucks, but the impacts were more pronounced in the case of the US trucks. Other interesting results include an increase in the number of US trucks during the period that followed the last US financial crisis. The results also illustrate the importance of GDP in explaining the observed truck movements across the Canada–US border.     

An exact solution approach for vehicle routing and scheduling problems with soft time windows

A new column generation based exact optimization approach for the vehicle routing and scheduling problem with semi soft time windows (VRPSSTW) is presented. Elementary shortest path problem with resource constraints and late arrival penalties is solved as a subproblem, which rises from the Dantzig–Wolfe decomposition method. Exact solutions of VRPSSTW and hard time windows variant are compared on Solomon’s benchmark instances as well as on an instance based on Tokyo road network. It was found that the VRPSSTW solution results in fewer routes thus overall costs are reduced and late arrival penalties contribute only a small fraction to total cost.     

A polynomial-time heuristic for the quay crane double-cycling problem with internal-reshuffling operations

One of great challenges in seaport management is how to handle containers under reshuffling, called reshuffles. Repositioning reshuffles in a bay (internal reshuffling) can improve the efficiency of quay cranes and help ports to reduce ship turn-around time. This paper studies the quay crane double-cycling problem with internal-reshuffling operations, and presents a fast solution algorithm. To reduce the number of operations necessary to turn around a bay of a vessel, the problem is first formulated as a new integer program. A polynomial-time heuristic is then developed. The analysis is made on the worst-case error bound of the proposed algorithm. Results are presented for a suite of combinations of problem instances with different bay sizes and workload scenarios. Comparisons are made between our algorithm and the start-of-the-art heuristic. The computational results demonstrate that our model can be solved more efficiently with CPLEX than the model proposed by Meisel and Wichmann (2010), and the proposed algorithm can well solve real-world problem instances within several seconds.     

A time-period choice model for road freight transport in Flanders based on stated preference data

This paper presents one of the first models explaining the choice of time-period in road freight transport. Policies that would shift some fraction of the trucks from peak to earlier and later periods will contribute to the reduction of congestion. Therefore there is an increasing interest in modelling the time-period sensitivity of road freight transport to changes in travel time and cost by period. The model developed here is based on a stated preference survey amongst receivers of goods in Flanders and was implemented in the strategic freight transport model of the Flemish authorities.     

Dispatching trucks for drayage operations

We propose a novel model for dispatching trucks given the constraints and sources of uncertainty that arise in drayage operations. The proposed model is designed to minimize the expected cost and is generally applicable to cases including different distributions of random parameters. Numerical examples illustrate this robustness of the model, as well as the potential for reducing the drayage cost by increasing the available storage capacity and permitted number of terminal truck entries. Mathematical results derived within this paper (e.g. expected dwell time) can be used more generally in analyzing transfers in transportation networks under stochastic conditions.     

Robust planning and disruption management in roll-on roll-off liner shipping

This paper presents different strategies for handling disruptions in fleet deployment in roll-on roll-off liner shipping, which basically consists of assigning a fleet of vessels to predefined voyages at minimum cost. A new mathematical model of the problem is presented, including a set of robust planning strategies, such as adding slack and rewarding early arrivals. To solve real-life instances a rolling horizon heuristic is proposed. A computational study, where we also propose some recovery planning strategies, is conducted, and simulation results show that adding robustness significantly reduces the actual cost of the plan and the total delays of the voyages.     

Arc routing problems to restore connectivity of a road network

After a disaster, restoring accessibility in the affected area is critical for response operations. We study two arc routing problems for clearing blocked roads. The first problem minimizes the time to reconnect the road network, while the second maximizes the total benefit gained by reconnecting network components within a time limit. For each problem, we develop a mixed integer programming formulation and two versions of a heuristic algorithm. We conduct computational experiments on Istanbul data and instances adapted from the literature. The heuristics achieve near-optimal or optimal solutions quickly in most of the tested instances.     

A rolling planning horizon heuristic for scheduling agents with different qualifications

This paper presents an optimization model for the tour scheduling problem for agents with multiple skills and flexible contracts in check-in counters at airports. The objective is to minimize the total assignment costs subject to demand fulfillment and labor regulations. In order to solve this problem we develop a rolling planning horizon-based heuristic. Our heuristic is robust and provides near-optimal schedules within reasonable computation time for real-world cases, although the parameter selection is important to its performance. In addition, we discuss the impact of the skill distribution on the scheduling costs for several instances.     

A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation

This paper focuses on the planning of road restoration efforts during disaster response and recovery. The primary objective is to maximize network accessibility for all locations in the area during the restoration process so that survivors are evacuated and road side debris is removed as soon as possible. We propose a dynamic path based mathematical model that identifies criticality of blockages and clears them with limited resources. This model is more efficient than link based models and can solve restoration problems for realistic size networks within reasonable time. Algorithm performance is demonstrated using two instances based on districts in Istanbul.     

Multi-objective design of an organ transplant network under uncertainty

We propose a novel multi-period location–allocation model for the design of an organ transplant transportation network under uncertainty. The model consists of a bi-objective mathematical programming model that minimizes total cost and time, including waiting time in the queue for the transplant operation, while considering organs’ priorities. A fuzzy multi-objective programming based approach is presented to solve the small and medium size problems to optimality. For larger problems, we propose two meta-heuristics based algorithms. Lower bounds, and several numerical examples with managerial insights are discussed. A real case-study is provided, and the existing and the proposed optimal solutions are compared.     

Heuristics for the integration of crane productivity in the berth allocation problem

In this paper, the combined problem of berth allocation and crane assignment in container terminals is investigated. The proposed problem formulation includes important real world aspects such as the decrease of marginal productivity of quay cranes assigned to a vessel and the increase in handling time if vessels are not berthed at their desired position at the quay. To solve the problem a construction heuristic, local refinement procedures, and two meta-heuristics are presented. These methods perform well on a set of real world like instances. The results emphasize the important role of quay crane productivity in berth planning.