Models and Heuristics for the Tactical Berth Allocation Problem with Quay-Crane Assignment and Transshipment Costs

In the context of international sea-freight container transport, we study an integrated decision problem arising in container terminal management. We consider the integration of the Berth Allocation Problem (BAP), which consists of assigning and scheduling incoming ships to berthing positions, and the Quay Crane Assignment Problem (QCAP), which assigns to incoming ships a certain QC profile (i.e. number of quay cranes per working shift). BAP and QCAP are strictly correlated, since the QC profile assigned to the incoming ships affects their handling time and has thus an impact on the berth allocation. In particular, we solve this problem at the tactical decision level, with the intent of supporting the terminal in its negotiation process with shipping lines, as the number of quay cranes is usually bounded by contracts which are discussed months in advance. With our analytical tools, we aim to allow terminal managers to assign the right value to the QC profiles proposed to shipping lines, taking into account the impact on the terminal productivity. In addition to profile evaluation, the combined solution of BAP and QCAP optimizes the utilization of terminal resources. In this work, two mixed integer formulations are presented with a quadratic and a linearized objective function, respectively. The objective function aims, on the one hand, to maximize the total value of chosen QC profiles and, on the other hand, to minimize the housekeeping costs caused by transshipment flows between ships. Both models have been validated on instances based on real data provided by MCT, a transshipment container terminal in the south of Italy. Computation confirms that the problem is hardly solvable via exact methods, hence we introduce heuristic methods, in order to find good feasible solutions in a reasonable amount of time. The proposed heuristic algorithm is based on tabu search and decomposition: each iteration consists in two phases, one aimed at finding a feasible profile assignment, the other at finding a feasible solution of the restricted problem obtained by fixing profile variables. Computational results are presented and discussed

The Tactical Berth Allocation Problem (TBAP) with quay crane assignment and transshipment-related quadratic yard costs

International sea-freight container transport has grown dramatically over the last years and container terminals play nowadays a key-role in the global shipping network. Increasing competition and competitiveness among terminals require more and more efficiency in container handling operations, both in the quayside and the landside, in order to better utilize limited resources (such as cranes, trucks, berths, storage space, etc.) as well as minimize ship's turnaround time. Operations research methods are therefore worth being use for the optimization of terminal operations. We take into account two decision problems which are usually solved hierarchically by terminal planners: the Berth Allocation Problem (BAP), which consists of assigning and scheduling incoming ships to berthing positions, and the Quay Crane Assignment Problem (QCAP), which assigns to incoming ships a certain QC profile (i.e. number of quay cranes per working shift). These two problems are indeed strictly correlated: the QC profile assigned to the incoming ships affects their handling time and has thus an impact on the berth allocation. In this work, we aim to combine BAP with QCAP and analyze the resulting new integrated problem from the point of view of a transshipment terminal. We solve this problem at the tactical decision level, with the intent of supporting the terminal in its negotiation process with shipping lines, as the number of quay cranes is usually bounded by contracts which are discussed months in advance. Devised analytic tools and quantitative methods allow terminal managers to assign the right value to the QC profiles proposed to shipping lines, considering their impact on the terminal productivity. In addition to profile evaluation, the combined solution of these two problems optimizes the utilization of terminal resources. Alternative objectives are used for this purpose, such as the minimization of total distance covered to move containers, the minimization of ships turnaround time, etc. Starting from a collaboration with the transshipment terminal of Gioia Tauro in Italy, one of the busiest in Europe, we propose a new model for the Tactical Berth Allocation Problem (TBAP) with Quay Crane Assignment, which has been validated on real-world instances provided by the terminal, taking into account a time horizon up to one month. The objective function aims, on the one hand, to maximize the total value of chosen QC profiles and, on the other hand, to minimize the housekeeping costs caused by transshipment flows between ships. Preliminary results obtained through commercial software will be presented and further methodological approaches to the problem, such as decomposition techniques, will be outlined

Modeling and Solving the Tactical Berth Allocation Problem

In this paper we integrate at the tactical level two decision problems arising in container terminals: the berth allocation problem, which consists of assigning and scheduling incoming ships to berthing positions, and the quay crane assignment problem, which assigns to incoming ships a certain QC profile (i.e. number of quay cranes per working shift). We present two formulations: a mixed integer quadratic program and a linearization which reduces to a mixed integer linear program. The objective function aims, on the one hand, to maximize the total value of chosen QC profiles and, on the other hand, to minimize the housekeeping costs generated by transshipment flows between ships. To solve the problem we developed a heuristic algorithm which combines tabu search methods and mathematical programming techniques. Computational results on instances based on real data are presented and compared to those obtained through a commercial solver

Models for technology choice in a transit corridor with elastic demand

We present two optimization models for a transit line under the assumption that the demand is elastic and can be approximated by a linear function of fare and passenger travel time components. These models can be used to strategically evaluate technology choices. We study the effect of demand elasticity on the technology choice by analytic and numerical comparison with some fixed demand models. We assume a range of objective functions having as two extrema the maximization of operator’s profit and the maximization of social welfare. We show both analytically and numerically that accounting for demand elasticity does not change the conclusions that can be derived by an equivalent fixed demand model. This invariance holds for a broad range of objective functions in the elastic case. The significant difference between the two objective function extrema lies in the proportions of captured demand

SVM-Based Multiple Instance Classification via DC Optimization

A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, while the labels of the points inside the bags are unknown. We focus on the binary classification case, where the objective is to discriminate between positive and negative bags using a separating surface. Adopting a support vector machine setting at the training level, the problem of minimizing the classification-error function can be formulated as a nonconvex nonsmooth unconstrained program. We propose a difference-of-convex (DC) decomposition of the nonconvex function, which we face using an appropriate nonsmooth DC algorithm. Some of the numerical results on benchmark data sets are reported

Modeling and solving the Tactical Berth Allocation Problem

In this paper we integrate at the tactical level two decision problems arising in container terminals: the berth allocation problem, which consists of assigning and scheduling incoming ships to berthing positions, and the quay crane assignment problem, which assigns to incoming ships a certain quay crane profile (i.e. number of quay cranes per working shift). We present two formulations: a mixed integer quadratic program and a linearization which reduces to a mixed integer linear program. The objective function aims, on the one hand, to maximize the total value of chosen quay crane profiles and, on the other hand, to minimize the housekeeping costs generated by transshipment flows between ships. To solve the problem we developed a heuristic algorithm which combines tabu search methods and mathematical programming techniques. Computational results on instances based on real data are presented and compared to those obtained through a commercial solver.Berth allocation Quay crane assignment Container terminal management

Laparoscopic Ultra-radical Lymph Node Debulking Using Yasargil Clamps for Gynecological Malignancies: Results from a Large, Multicenter, Retrospective Analysis

Resection of bulky lymph nodes in gynecologic oncology is a challenging procedure. Considering the risk of intraoperative vascular injury, a technique to avoid severe complications is mandatory. In this study, we aimed to analyze the feasibility of laparoscopic ultraradical lymph node debulking using Yasargil clamps in patients with gynecologic cancer with bulky lymph node metastases

Concordance of Radiological, Laparoscopic and Laparotomic Scoring to Predict Complete Cytoreduction in Women with Advanced Ovarian Cancer

Objective: To identify the best method among the radiologic, laparoscopic and laparotomic scoring assessment to predict the outcomes of cytoreductive surgery in patients with advanced ovarian cancer (AOC). Methods: Patients with AOC who underwent pre-operative computed tomography (CT) scan, laparoscopic evaluation, and cytoreductive surgery between August 2016 and February 2021 were retrospectively reviewed. Predictive Index (PI) score and Peritoneal Cancer Index (PCI) scores were used to estimate the tumor load and predict the residual disease in the primary debulking surgery (PDS) and interval debulking surgery (IDS) after neoadjuvant chemotherapy (NACT) groups. Concordance percentages were calculated between the two scores. Results: Among 100 eligible patients, 69 underwent PDS, and 31 underwent NACT and IDS. Complete cytoreduction was achieved in 72.5% of patients in the PDS group and 77.4% in the IDS. In patients undergoing PDS, the laparoscopic PI and the laparotomic PCI had the best accuracies for complete cytoreduction (R0) [area under the curve (AUC) = 0.78 and AUC = 0.83, respectively]. In the IDS group, the laparotomic PI (AUC = 0.75) and the laparoscopic PCI (AUC= 0.87) were associated with the best accuracy in R0 prediction. Furthermore, radiological assessment, through PI and PCI, was associated with the worst accuracy in either PDS or IDS group (PI in PDS: AUC = 0.64; PCI in PDS: AUC = 0.64; PI in IDS: AUC = 0.46; PCI in IDS: AUC = 0.47). Conclusion: The laparoscopic score assessment had high accuracy for optimal cytoreduction in AOC patients undergoing PDS or IDS. Integrating diagnostic laparoscopy in the decision-making algorithm to accurately triage AOC patients to different treatment strategies seems necessary