Optimizing site layout and material logistics planning during the construction of critical infrastructure projects

Planning the site layout of construction projects is a crucial task that has a significant impact on construction cost, productivity, and safety. It involves the positioning and dynamic relocation of temporary facilities that are needed to support various construction activities on site such as offices, storage areas, workshops, and parking areas. Due to the complexity of the site layout planning problem, construction managers often perform this task using previous experience, ad-hoc rules, and first-come-first-serve approach which leads to ambiguity and even to inefficiency. Accordingly, a number of site layout planning models have been developed over the past three decades to support this important planning task.;First, two novel optimization models are developed that are capable of generating global optimal solutions of dynamic site layout planning in order to minimize resources travel and facilities relocation costs while complying with various site geometric constraints. The first model, DSLP-GA, is implemented using Genetic Algorithms while the second model, DSLP-ADP, is formulated using Approximate Dynamic Programming. The performance of these two models is evaluated using two examples to illustrate their capabilities in generating global optimal plans solutions for dynamic site layout planning problems.;Second, a novel model of construction logistics planning (CLP) is developed to enable the integration and simultaneous optimization of critical planning decisions of material procurement and material storage on construction sites. The performance of the developed CLP model is evaluated using an application example that illustrates the model capabilities in: (1) generating optimal procurement decisions that minimize ordering, financing, and stock-out costs while considering site space availability; and (2) generating optimal layout decisions that minimize layout costs while complying with material storage space needs as well as imposed operational and safety geometric constraints.;Third, an innovative multi-objective optimization model for congested construction logistics planning (C2LP) is developed to help planners in utilizing interior building spaces and generating optimal logistics plans that minimize total logistics cost while minimizing the adverse impacts of interior material storage on project schedule. Interior building space is represented as a set of non-identical rooms that can be defined based on project architectural drawings, while exterior space is modeled as a grid of locations with planner-specified fixed spacing. C2LP model utilizes Genetic Algorithms to generate optimal solutions that represent optimal tradeoffs between the two conflicting objectives of minimizing total logistics costs and project schedule criticality.;Fourth, a prototype automated multi-objective optimization system for construction logistics planning is implemented to support construction planners in generating optimal plans of material logistics and site layout. The relational database module is designed to store and integrate project spatial, temporal, and logistics input data considering their interdependencies in order to eliminate data inconsistencies. The user interface module is designed to facilitate data input and reporting of generated optimal material logistics plans.;Fifth, a multi-objective optimization framework is developed to enable construction planners of critical infrastructure projects to plan and optimize the implementation of site physical security systems and layout planning in order to minimize construction security risks and overall site costs. The framework is developed in four main phases: (1) risk identification and system modeling phase to identify security threats, attackers, and targets as well as site and security system geometric representation; (2) security lighting optimization phase to generate optimal tradeoff designs of fence and area lighting systems that consider the conflicting objectives of maximizing lighting performance while minimizing its system cost; (3) security-cost optimization phase to generate optimal site security systems that quantifies and simultaneously minimizes construction security risks and overall site cost; and (4) performance evaluation phase to test and analyze the performance of the proposed framework. (Abstract shortened by UMI.)...