1,800 research outputs found
Hybrid Meta-Heuristics for Robust Scheduling
The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.Meta-Heuristics;Multi-Objective Genetic Optimization;Robust Scheduling;Supply Networks
Hybrid Meta-Heuristics for Robust Scheduling
The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete
Clustering composite SaaS components in Cloud computing using a Grouping Genetic Algorithm
Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the componentsâ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA
Meta-heuristic based Construction Supply Chain Modelling and Optimization
Driven by the severe competition within the construction industry, the necessity of improving and optimizing the performance of construction supply chain has been aroused. This thesis proposes three problems with regard to the construction supply chain optimization from three perspectives, namely, deterministic single objective optimization, stochastic optimization and multi-objective optimization respectively. Mathematical models for each problem are constructed accordingly and meta-heuristic algorithms are developed and applied for resolving these three problems
Operational Research in Education
Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions
A case study of two-echelon multi-depot vehicle routing problem
The Vehicle Routing Problem (VRP) is a classic combinatorial optimization problem and a topic still studied for practical applications. Current research focuses on single echelon distribution systems such as distribution centers serving customers. However, in typical distribution, goods flows among regional distribution centers, local warehouses and customers, defined as a two-echelon network. The two-echelon multiple depot VRP problem is documented and applied to two stages illustrated by a small scale computational example. In the first stage, the simulated annealing algorithm is employed to determine the routes between local warehouses and final customers. For the second stage, trial-and-error is applied to obtain the number and location of regional distribution centers and the routes between regional distribution centers and local warehouses. Matlab is utilized to simulate annealing iterations and cost functions are analyzed. The convergence tendency of simulated annealing is depicted in figures by Matlab coding. Contributions include demonstration between the SA algorithm and a specific combinatorial optimization problem, and an application of the algorithm
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Strategic Technology Maturation and Insertion (STMI): a requirements guided, technology development optimization process
This research presents a Decision Support System (DSS) process solution to a problem faced by Program Managers (PMs) early in a system lifecycle, when potential technologies are evaluated for placement within a system design. The proposed process for evaluation and selection of technologies incorporates computer based Operational Research techniques which automate and optimize key portions of the decision process. This computerized process allows the PM to rapidly form the basis of a Strategic Technology Plan (STP) designed to manage, mature and insert the technologies into the system design baseline and identify potential follow-on incremental system improvements. This process is designated Strategic Technology Maturation and Insertion (STMI).
Traditionally, to build this STP, the PM must juggle system performance, schedule, and cost issues and strike a balance of new and old technologies that can be fielded to meet the requirements of the customer. To complicate this juggling skill, the PM is typically confronted with a short time frame to evaluate hundreds of potential technology solutions with thousands of potential interacting combinations within the system design. Picking the best combination of new and established technologies, plus selecting the critical technologies needing maturation investment is a significant challenge. These early lifecycle decisions drive the entire system design, cost and schedule well into production
The STMI process explores a formalized and repeatable DSS to allow PMs to systematically tackle the problems with technology evaluation, selection and maturation. It gives PMs a tool to compare and evaluate the entire design space of candidate technology performance, incorporate lifecycle costs as an optimizer for a best value system design, and generate input for a strategic plan to mature critical technologies. Four enabling concepts are described and brought together to form the basis of STMI: Requirements Engineering (RE), Value Engineering (VE), system optimization and Strategic Technology Planning (STP). STMI is then executed in three distinct stages: Pre-process preparation, process operation and optimization, and post-process analysis. A demonstration case study prepares and implements the proposed STMI process in a multi-system (macro) concept down select and a specific (micro) single system design that ties into the macro design level decision
Modeling Framework and Solution Methodologies for On-Demand Mobility Services With Ridesharing and Transfer Options
The growing complexity of the urban travel pattern and its related traffic congestion, along with the extensive usage of mobile phones, invigorated On-Demand Mobility Services (ODMS) and opened the door to the emergence of Transportation Network Companies (TNC). By adopting the shared economy paradigm, TNCs enable private car owners to provide transportation services to passengers by providing user-friendly mobile phone applications that efficiently match passengers to service providers. Considering the high level of flexibility, convenience, and reliability of ODMS, compared to those offered by traditional public transportation systems, many metropolitan areas in the United States and abroad have reported rapid growth of such services. This dissertation presents a modeling framework to study the operation of on-demand mobility services (ODMS) in urban areas. The framework can analyze the operation of ODMS while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed-integer program and an efficient decomposition-based methodology is developed for its solution. This solution methodology aims at solving the offline version of the problem, in which the passengersâ demand is assumed to be known ii for the entire planning horizon. The presented approach adopts a modified column generation algorithm, which integrates iterative decomposition and network augmentation techniques to analyze networks with moderate size. Besides, a novel methodology for integrated ride-matching and vehicle routing for dynamic (online) ODMS with ridesharing and transfer options is developed to solve the problem in real-time. The methodology adopts a hybrid heuristic approach, which enables solving large problem instances in near real-time, where the passengersâ demand is not known a priori. The heuristic allows to (1) promptly respond to individual ride requests and (2) periodically re-evaluate the generated solutions and recommend modifications to enhance the overall solution quality by increasing the number of served passengers and total profit of the system. The outcomes of experiments considering hypothetical and real-world networks are presented. The results show that the modified column generation approach provides a good quality solution in less computation time than the CPLEX solver. Additionally, the heuristic approach can provide an efficient solution for large networks while satisfying the real-time execution requirements. Additionally, investigation of the results of the experiments shows that increasing the number of passengers willing to rideshare and/or transfer increases the general performance of ODMS by increasing the number of served passengers and associated revenue and reducing the number of needed vehicles
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