Amir Hossein Gharehgozli

Scheduling Twin Yard Cranes in a Container Block

Annually, millions of containers enter and exit the stacking area of a terminal. If the stacking operations are not efficient, long ship, train, and truck delays will result. To improve the stacking operations, new container terminals, especially in Europe, decouple the landside and seaside by deploying twin automated stacking cranes. The cranes cannot pass each other and must be separated by a safety distance. We study how to schedule twin automated cranes to carry out a set of container storage and retrieval requests in a single block of a yard. Storage containers are initially located at the seaside and landside input/output I/O points of the block. Each must be stacked in a specific location of the block, selected from a set of open locations suitable for stacking the storage container. Retrieval containers are initially located in the block and must be delivered to the I/O points. Based on the importance and acceptable waiting times of different modes of transport, requests have different priorities. The problem is modeled as a multiple asymmetric generalized traveling salesman problem with precedence constraints. The objective is to minimize the makespan. We have developed an adaptive large neighborhood search heuristic to quickly compute near-optimal solutions. We have performed extensive computational experiments to assess the performance of the heuristic including validation at a real terminal. It obtains near-optimal solutions for small instances. For large instances, it is shown to yield better solutions than CPLEX truncated after four hours, and it outperforms other heuristics from practice by more than 24% in terms of makespan. The average gaps between our heuristic and optimal solutions for relaxed problems are less than 3%.

An exact method for scheduling a yard crane

This paper studies an operational problem arising at a container terminal, consisting of scheduling a yard crane to carry out a set of container storage and retrieval requests in a single container block. The objective is to minimize the total travel time of the crane to carry out all requests. The block has multiple input and output (I/O) points located at both the seaside and the landside. The crane must move retrieval containers from the block to the I/O points, and must move storage containers from the I/O points to the block. The problem is modeled as a continuous time integer programming model and the complexity is proven. We use intrinsic properties of the problem to propose a two-phase solution method to optimally solve the problem. In the first phase, we develop a merging algorithm which tries to patch subtours of an optimal solution of an assignment problem relaxation of the problem and obtain a complete crane tour without adding extra travel time to the optimal objective value of the relaxed problem. The algorithm requires common I/O points to patch subtours. This is efficient and often results in obtaining an optimal solution of the problem. If an optimal solution has not been obtained, the solution of the first phase is embedded in the second phase where a branch-and-bound algorithm is used to find an optimal solution. The numerical results show that the proposed method can quickly obtain an optimal solution of the problem. Compared to the random and Nearest Neighbor heuristics, the total travel time is on average reduced by more than 30% and 14%, respectively. We also validate the solution method at a terminal.

A decision-tree stacking heuristic minimising the expected number of reshuffles at a container terminal

Reshuffling containers, one of the daily operations at a container terminal, is time consuming and increases a ship’s berthing time. We propose a decision-tree heuristic to minimise the expected number of reshuffles when arriving containers should be stacked in a block of containers with an arbitrary number of piles. The heuristic algorithm uses the optimal solutions of a stochastic dynamic programming model. Since the total number of states of the dynamic programming model increases exponentially, the model can only solve small-scale problems in a reasonable time. To solve large-scale problems, the heuristic uses the results of the exact model for small-scale problems to generate generalised decision trees. These trees can be used to solve problems with a realistic number of piles. The numerical experiments show the effectiveness of the algorithm. For small-scale problems, the trees can quickly make optimal decisions. For large-scale problems, the decision-tree heuristic significantly outperforms stacking policies commonly used in practice. Using the decision trees, we can compare the performance of a shared-stacking policy, which allows containers of multiple ships to be stacked on top of each other, with a dedicated-stacking policy. Shared-stacking appears to outperform dedicated-stacking.

State of the Practice: A Review of the Application of OR/MS in Freight Transportation

Freight transportation is an important part of the global supply chain. As distances shipped grow and supply chains become more complex and fragile, operations research OR can play an important role in improving the efficiency and robustness of supply networks. This article describes the state of the practice in OR and freight transportation, highlighting recent successful and widely used analytical techniques in oceanic transportation and port operations, and barge, freight rail, intermodal, truckload, less than truckload, and air freight transportation, as well as the use of OR techniques in third-party logistics.

A simulation study of the performance of twin automated stacking cranes at a seaport container terminal

This paper studies the effect of a handshake area on the performance of twin automated stacking cranes (ASCs) operating on top of a stack with transfer zones at both seaside and landside. The handshake area is a temporary storage location so that one crane can start a request and leave the container there for the other crane to complete the request. By testing settings with and without such a handshake area, the goal is to find robust rules which result in the best performance, measured as (1) the makespan to finish all requests and (2) the total waiting time of the cranes due to interference or nonconsecutive delivery of containers in the handshake area (blocking time). The effect of five decision variables on the performance are tested. The decision variables are (1) the way the requests are handled by the cranes (scheduling), (2) the storage location of the containers in the handshake area, (3) the location of the handshake area in the stack, (4) the size of the handshake area and (5) the number of handshake areas in the stack. For each decision variable, multiple heuristics are developed. The results indicate that settings without a handshake area outperform settings with a handshake area for virtually all instances tested when using the same scheduling heuristic. For both types of settings, the choice for a scheduling heuristic impacts the final performance the most. In this study, we opt for simple heuristics since container terminal operators prefer to avoid any complexity in coordinating and scheduling two ASCs for safety and simplicity reasons.

Heuristic estimation of container stacking and reshuffling operations under the containership delay factor and mega-ship challenge

ABSTRACT This paper addresses a highly researched area, the reshuffling problem in ports, using a new paradigm-modified containership service order in light of credit risk assessment. Container stacking and reshuffling operations can cause ship delays and additional risk. In deep-sea terminals, outbound containers are tightly stacked according to the retrieval sequence. Due to lack of space, terminals stack containers in multiple tiers. This means any delay in the arrival of a ship can impose extra handlings and reshuffling of containers delaying future cargo handling. This paper addresses the reshuffling problem with a concept similar to the credit scoring and rating of creditworthiness used in the banking industry. By utilizing this comparison to the banking credit risk concept, a heuristic estimation model is proposed that illustrates the side effects of unscheduled modifications in containership service order. Further, the mega-ship trend amplifies the reshuffling debate. Probability of delay, reshuffles given delay, and call size at delay are introduced as the three-point risk metrics of the model. Numerical simulations illustrate the functionality to develop terminal stacking strategies as well as emphasize the mega-ship phenomenon and its side effects on terminals (i.e. yard operation deadlock).

Collaborative solutions for inter terminal transport

Major ports contain multiple container terminals, sea terminals, train, truck and barge terminal, and empty container depots, operated by different companies. Port authorities try to streamline inter terminal container transport (ITT) within congested port areas by offering expensive common road and rail infrastructure. Alternatively, individual stakeholders can set up private or collaborative container transport systems. This paper develops a framework to analyse and determine feasibility conditions of a common ITT system in a port area, depending on total transport volumes. First, we develop a simulation model to evaluate the costs of transporting containers using different modes of transport including trucks, automated guided vehicles, and multi trailer systems. Next, the required number of vehicles per mode is determined for a given throughput and waiting time. The results of the simulation are used in a game-theoretic setting to determine the cost savings per stakeholder operating in a coalition. By comparing cost savings for all possible coalitions, it is possible to determine, for each stakeholder, the attractiveness of using a common system. We find the coalitions that result in the highest savings and compare them with the infrastructure cost required to realise them. We apply the method to determine the feasibility of a common ITT system for terminals in the Port of Rotterdam and show that it only pays off in case of high demand for container transports.

Polynomial Time Algorithms to Minimize Total Travel Time in a Two-Depot Automated Storage/Retrieval System

We sequence storage and retrieval jobs to minimize total travel time of a storage/retrieval ( S / R ) machine in a two-depot automated storage/retrieval system. These systems include storage systems with aisle-captive S/R machines and storage blocks with bridge cranes. The S/R machine must move retrieval unit loads from their current locations in the system to one of the two depots. In addition, it must move storage unit loads from given depots to given locations in the system. We model the problem as an asymmetric traveling salesman problem, which is in general (N-script)(P-script)-hard. We develop an algorithm to solve the problem in polynomial time, using the property that the system has two depots and the S/R machine always returns to one of the depots to pick up or deliver a load. Furthermore, we develop additional polynomial time algorithms for the following four special cases: (1) retrieval loads have to be delivered to given depots; (2) the system has one input depot and one output depot; (3) the system has a single depot; and (4) there are arbitrary S/R machine starting and ending locations. The computational results show the effectiveness of the proposed algorithms. Compared to first-come-first-served and nearest neighbor algorithms, commonly used in practice, the total travel time reduces on average by more than 30% and 15%, respectively.

Stacking outbound barge containers in an automated deep-sea terminal

In this paper, we study the stacking problem of outbound containers in a deep-sea container terminal. In such a terminal, outbound containers -to be transported by barge to the hinterland or other terminals within the port area- are stacked in a container stack. Reshuffling which is the process of removing interfering containers to access a desired container is one of the main challenges of such container terminals. In order to avoid reshuffling, container terminals commonly use a stacking policy in which each pile (column) accommodates the containers of the same barge with the same weight class, destination, etc. However, due to a limited number of containers per barge per weight class and destination, this policy might result in a low stack utilization. To address this issue, this study proposes an alternative stacking policy allowing different container types to share the same pile. We aim to minimize the total retrieval time of a set of containers. We show that the problem is strongly NP-complete. Thus, we propose a heuristic to quickly solve the problem and we compare the results with a lower bound. The results show that the proposed stacking heuristic can provide solutions with a gap of less than 10% with the lower bound for real-sized instances with high utilization. In addition, the results show that the stacking heuristic can reduce the total retrieval time up to 30% compared to often-used in practice stacking policy. Furthermore, in order to investigate the performance of the proposed stacking policy under different settings, we perform a sensitivity analysis by varying block configuration, number of barges, barge size, and barge arrival time window.