2016 Optimization Days
HEC Montréal, Québec, Canada, 2 — 4 May 2016
MA9 Supply Chains
May 2, 2016 10:30 AM – 12:10 PM
Location: Pricewaterhouse Coopers
Chaired by Matthieu Gruson
4 Presentations
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10:30 AM - 10:55 AM
Towards a regional logistical center: Design and Management
Our project aims to develop a profit maximisation model in order to identify the conditions ensuring the success of a forest regional logistic center comprising sorting yard operations and transportation coordination. A sensitivity analysis is also conducted to identify the factors affecting the most the profitability of such a center.
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10:55 AM - 11:20 AM
The selection of harvest areas and wood allocation problem – Multi-objective optimization
The tactical level of planning in forest management involves the selection of harvesting areas over a horizon of several years and allocation of logs to specific mills in order to fulfill certain demand. At this level, forest managers need to include several optimization criteria to fulfill new sustainable forest management policies. This study proposes a decision support system based on multi-objective optimization to enable an efficient tactical planning process of wood supply chain at the forest management unit level. Preliminary results from applying the model to a case study in the province of Québec will be presented and discussed.
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11:20 AM - 11:45 AM
Analysis of machine flexibility in lot sizing problems
In this paper, we study the value of machine flexibility in lot sizing models, analyse several machine flexibility configurations and determine what the best flexibility configuration would be for a given budget in order to balance the benefits and cost of machine flexibility.
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11:45 AM - 12:10 PM
An analysis of service level constraints in deterministic lot sizing
We address the lot sizing problem with backlogging under deterministic demand. We use a reformulation of the problem and compare several types of service level constraints, order management policies as well as backlogging and backordering costs. The results of extensive computational experiments lead to interesting insights in the properties of the optimal solutions under different settings.