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Development, algorithmic solution and implementation of optimization models for the construction supply chain (Doctoral thesis)

Κουτσοκώστα, Ασπασία/ Koutsokosta, Aspasia

This work addresses the problem of optimal Construction Supply Chain (CSC) design and integration in deterministic and stochastic environments by providing a family of models for the optimization of a dynamic, multi-product, multi-site contractor-led CSC, consisting of three echelons. With the objective of minimizing the total CSC cost, optimal decisions are made on network design, production, inventory holding and transportation, while also considering discounts for bulk purchases, logistics centers, on-site shortages and an inventory-preparation phase. The models integrate the operations of temporal and project-based supply chains into a sustainable network with repetitive flows, large scope contracts, strategic alliances and economies of scale. This thesis begins with two deterministic mixed-integer linear programming models and then showcases the transitions to a chance-constrained programming model and a two-stage programming model, incorporating uncertainties with different types of probability distributions or scenarios, and even interdependent uncertainties – approaches that have not been explored extensively in the CSC context. These models allow for ‘here-and-now’ and/or ‘wait-and-see’ decisions depending on the nature of the problem and the risk-attitude of the decision maker. The novelty of this research lies in providing a flexible integrative optimization CSC tool that accounts for multiple CSC actors (suppliers and/or logistics centers), projects, products, time periods, operations, and different assumptions and decision-making environments. This research contributes to the fast-growing research field of CSC optimization by providing quantitative models and a rich discussion on modeling and solution approaches in light of the different solutions obtained with a spreadsheet software.This work addresses the problem of optimal Construction Supply Chain (CSC) design and integration in deterministic and stochastic environments by providing a family of models for the optimization of a dynamic, multi-product, multi-site contractor-led CSC, consisting of three echelons. With the objective of minimizing the total CSC cost, optimal decisions are made on network design, production, inventory holding and transportation, while also considering discounts for bulk purchases, logistics centers, on-site shortages and an inventory-preparation phase. The models integrate the operations of temporal and project-based supply chains into a sustainable network with repetitive flows, large scope contracts, strategic alliances and economies of scale. This thesis begins with two deterministic mixed-integer linear programming models and then showcases the transitions to a chance-constrained programming model and a two-stage programming model, incorporating uncertainties with different types of probability distributions or scenarios, and even interdependent uncertainties – approaches that have not been explored extensively in the CSC context. These models allow for ‘here-and-now’ and/or ‘wait-and-see’ decisions depending on the nature of the problem and the risk-attitude of the decision maker. The novelty of this research lies in providing a flexible integrative optimization CSC tool that accounts for multiple CSC actors (suppliers and/or logistics centers), projects, products, time periods, operations, and different assumptions and decision-making environments. This research contributes to the fast-growing research field of CSC optimization by providing quantitative models and a rich discussion on modeling and solution approaches in light of the different solutions obtained with a spreadsheet software.