Exact and approximate solutions of the container ship stowage problem

Avriel, Mordecai; Penn, Michal

doi:10.1016/0360-8352(93)90273-z

Cited by 74 publications

(37 citation statements)

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“…Imai and Miki (1989) considered the minimization of container loading-related rearrangements. Avriel and Penn (1993) formulated the SSP problem as a binary integer linear programming model and argued that exact algorithms solving the integer programming model were too slow even after some preprocessing. Ambrosino and Sciomachen (1998) derived some rules for determining a better ship stowage configuration using a constraint satisfaction approach.…”

Section: Ship Storage Planningmentioning

confidence: 99%

Ship Route Schedule Based Interactions Between Container Shipping Lines and Port Operators

Wang

Alharbi

Davy

2014

International Series in Operations Research &Amp; Management Science

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This chapter examines a practical tactical liner ship route schedule design problem, which involves the interaction between container shipping lines and port operators. When designing the schedule, the availability of each port in a week, i.e., port time window, is incorporated. As a result, the designed schedule can be applied in practice without or with only minimum revisions. We assume that each port on a ship route is visited only once in a round-trip journey. This problem is formulated as a nonlinear non-convex optimization model that aims to minimize the sum of ship cost, bunker cost and inventory cost. In view of the problem structure, an efficient dynamic-programming based solution approach is proposed. First, a lower bound of the number of ships is determined, and then we enumerate all possible numbers of ships. Given the number of ships, we can construct a space-time network that discretizes the time and represents the design of schedule. The optimal schedule in such a space-time network can be obtained by dynamic programming. The algorithm stops when the lower bound is not smaller than the optimal total cost of the best solution obtained. The proposed solution method is tested on a trans-Pacific ship route.

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Section: Ship Storage Planningmentioning

confidence: 99%

Ship Route Schedule Based Interactions Between Container Shipping Lines and Port Operators

Wang

Alharbi

Davy

2014

International Series in Operations Research &Amp; Management Science

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“…. , p. Note that in Avriel and Penn (1993) a 0/1 Linear Programming model is given for the stowage of a single rectangular bay, knowing in advance, as in our case, the number of containers to be loaded.…”

Section: A 0/1 Model For the Optimal Stowage Of Each Ship Partitionmentioning

confidence: 99%

“…Dubrovsky et al (2002) use a GA for minimizing the number of container movements in the stowage planning problem, while being able to include with appropriate constraints some ship stability criteria; the authors significantly reduce the search space using a compact and efficient encoding scheme. Avriel and Penn (1993) and Avriel et al (1997) focus on stowage planning considering the problem of minimising the number of unproductive shifts (temporary unloading and reloading of container at a port before their destination ports in order to access containers below them for unloading). Martin et al (1988) develop a heuristic algorithm that in solving the planning problem takes into account the transtainer quay cranes, with the aim of minimising their global longitudinal movement time and the total number of shifts in the successive ports.…”

Section: Introduction and Problem Definitionmentioning

confidence: 99%

A decomposition heuristics for the container ship stowage problem

2006

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In this paper we face the problem of stowing a containership, referred to as the Master Bay Plan Problem (MBPP); this problem is difficult to solve due to its combinatorial nature and the constraints related to both the ship and the containers. We present a decomposition approach that allows us to assign a priori the bays of a containership to the set of containers to be loaded according to their final destination, such that different portions of the ship are independently considered for the stowage. Then, we find the optimal solution of each subset of bays by using a 0/1 Linear Programming model. Finally, we check the global ship stability of the overall stowage plan and look for its feasibility by using an exchange algorithm which is based on local search techniques. The validation of the proposed approach is performed with some real life test cases.

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“…Moura A and Oliveira J et al [13] proposed a MIP model optimizing total transportation cost with shipping line in consideration. Amone In-Bay Plan, Avriel M and Penn M [14] proposed a MIP model minimizing reshuffles. Proposed algorithm can solve small scale problems.…”

Section: Introductionmentioning

confidence: 99%