Read e-book online Computational Logistics: 7th International Conference, ICCL PDF

By Ana Paias, Mario Ruthmair, Stefan Voß

ISBN-10: 3319448951

ISBN-13: 9783319448954

ISBN-10: 331944896X

ISBN-13: 9783319448961

This publication constitutes the refereed court cases of the seventh overseas convention on Computational Logistics, ICCL 2016, held in Lisbon, Portugal, in September 2016.

The 29 papers offered during this quantity have been rigorously reviewed and chosen for inclusion within the publication. they're geared up in topical sections entitled: box terminals and maritime transportation; intermodal delivery; place and routing; (general) logistics and provide chain management.

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Read or Download Computational Logistics: 7th International Conference, ICCL 2016, Lisbon, Portugal, September 7-9, 2016, Proceedings PDF

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Additional resources for Computational Logistics: 7th International Conference, ICCL 2016, Lisbon, Portugal, September 7-9, 2016, Proceedings

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Paias et al. ): ICCL 2016, LNCS 9855, pp. 18–34, 2016. 1007/978-3-319-44896-1 2 A MIP Based Local Search Heuristic for a Stochastic Maritime Inventory 19 and lower and upper limits are given for each port. Each port can be visited one or several times during the planning horizon depending on the size of the storage, the production or consumption rate, and the quantity loaded or unloaded at each port visit. The MIRP consists of designing routes and schedules for a fleet of ships in order to minimize the transportation and port costs, and to determine the quantities handled at each port call without exceeding the storage limits.

They ensure that the stock levels are within the corresponding limits and link the stock levels to the loading or unloading quantities. For each port i, the consumption/production rate, Ri , the minimum S i , the maximum S i and the initial Si0 stock levels, are given. We define the nonnegative continuous variables sim indicating the stock levels at the start of the mth visit to port i. The inventory constraints are as follows: si1 = Si0 + Ji Ri ti1 , ∀i ∈ N, sim = si,m−1 − Ji (17) qi,m−1,v + Ji Ri (tim − ti,m−1 ), A ∀(i, m) ∈ S : m > 1, v∈V (18) sim + TiQ qimv qimv − Ri v∈V sim − siμi + ∀(i, m) ∈ S A |Ji = −1, (19) ∀(i, m) ∈ S A |Ji = 1, (20) v∈V TiQ qimv ≥ S i , qimv + Ri v∈V ≤ Si, v∈V qi,μi ,v − Ri (T − tiμi ) ≥ S i , ∀i ∈ N |Ji = −1, (21) qi,μi ,v + Ri (T − tiμi ) ≤ S i , ∀i ∈ N |Ji = 1, (22) v∈V siμi − v∈V sim ≥ S i , sim ≤ S i , ∀(i, m) ∈ S A |Ji = −1, (23) A ∀(i, m) ∈ S |Ji = 1.

This is a complicating factor, considering the deck layout and ramp placement, which makes the stowage plans difficult to evaluate. The RSSP presented in [9] aims at deciding a deck configuration with respect to height, which optional/spot cargos to carry, and how to stow the vehicles carried during the voyage, given a predefined route. The authors in [9] propose a mixed integer programming (MIP) model and a heuristic method for solving this problem, where the objective is to maximize the sum of revenue from optional cargoes, minus the penalty costs incurred when having to move cargoes when performing the stowage along the route.

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Computational Logistics: 7th International Conference, ICCL 2016, Lisbon, Portugal, September 7-9, 2016, Proceedings by Ana Paias, Mario Ruthmair, Stefan Voß


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