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A Decomposition-Based Pricing Method for Solving a Large-Scale MILP Model for an Integrated Fishery

机译：一种基于分解的综合渔业大规模MILP模型定价方法

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We study the integrated fishery planning problem (IFP). In this problem, a fishery manager must schedule fishing trawlers to determine when and where the trawlers should go fishing and when the trawlers should return the caught fish to the factory. The manager must then decide how to process the fish into products at the factory. The objective is to maximize profit. We have found that IFP is difficult to solve. The initial formulations for several planning horizons are solved using the AMPL modelling language and CPLEX with branch and bound.The IFP can be decomposed into a trawler-scheduling subproblem and a fish-processing subproblem in two different ways by relaxing different sets of constraints. We tried conventional decomposition techniques including subgradient optimization and Dantzig-Wolfe decomposition, both of which were unacceptably slow. We then developed a decomposition-based pricing method for solving the large fishery model, which gives excellent computation times. Numerical results for several planning horizon models are presented.
机译：我们研究综合渔业计划问题（IFP）。在此问题中，渔业经理必须安排捕捞拖网渔船以确定拖网渔船何时何地去捕鱼，以及拖网渔船何时应将捕获的鱼还给工厂。然后经理必须决定如何在工厂将鱼加工成产品。目的是使利润最大化。我们发现，IFP很难解决。使用AMPL建模语言和带有分支和界限的CPLEX解决了多个规划视野的初始公式.IFP可以通过放宽不同的约束以两种不同方式分解为拖网渔船调度子问题和鱼类加工子问题。我们尝试了包括次梯度优化和Dantzig-Wolfe分解在内的常规分解技术，这两种方法均速度缓慢。然后，我们开发了一种用于分解大型渔业模型的基于分解的定价方法，该方法可提供出色的计算时间。给出了几个规划层位模型的数值结果。

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