On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded

Decision trees usefully represent sparse, high dimensional and noisy data. Having learned a function from this data, we may want to thereafter integrate the function into a larger decision-making problem, e.g., for picking the best chemical process catalyst. We study a large-scale, industrially-relevant mixed-integer nonlinear nonconvex optimization problem involving both gradient-boosted trees and penalty functions mitigating risk. This mixed-integer optimization problem with convex penalty terms broadly applies to optimizing pre-trained regression tree models. Decision makers may wish to optimize discrete models to repurpose legacy predictive models, or they may wish to optimize a discrete model that particularly well-represents a data set. We develop several heuristic methods to find feasible solutions, and an exact, branch-and-bound algorithm leveraging structural properties of the gradient-boosted trees and penalty functions. We computationally test our methods on concrete mixture design instance and a chemical catalysis industrial instance

Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems

In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mechanism with a random local search, which is a derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling the inequality constraints of the problem relies on selective conditions that impose a sufficient reduction either in the constraints violation or in the objective function value, when comparing two points at a time. The hybrid EM method is tested on a set of benchmark engineering design problems and the numerical results demonstrate the effectiveness of the proposed approach. A comparison with results from other stochastic methods is also included

Network Interdiction under Uncertainty

We consider variants to one of the most common network interdiction formulations: the shortest path interdiction problem. This problem involves leader and a follower playing a zero-sum game over a directed network. The leader interdicts a set of arcs, and arc costs increase each time they are interdicted. The follower observes the leader\u27s actions and selects a shortest path in response. The leader\u27s optimal interdiction strategy maximizes the follower\u27s minimum-cost path. Our first variant allows the follower to improve the network after the interdiction by lowering the costs of some arcs, and the leader is uncertain regarding the follower\u27s cardinality budget restricting the arc improvements. We propose a multiobjective approach for this problem, with each objective corresponding to a different possible improvement budget value. To this end, we also present the modified augmented weighted Tchebychev norm, which can be used to generate a complete efficient set of solutions to a discrete multi-objective optimization problem, and which tends to scale better than competing methods as the number of objectives grows. In our second variant, the leader selects a policy of randomized interdiction actions, and the follower uses the probability of where interdictions are deployed on the network to select a path having the minimum expected cost. We show that this continuous non-convex problem becomes strongly NP-hard when the cost functions are convex or when they are concave. After formally describing each variant, we present various algorithms for solving them, and we examine the efficacy of all our algorithms on test beds of randomly generated instances

Integration of process design and control: A review

There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs)

Plantwide Control System Design for IGCC Power Plants with CO2 Capture

In this paper, a systematic approach to design the control system of a commercial-scale integrated gasification combined cycle (IGCC) power plant with CO2 capture is considered. The control system design is developed with the objective of optimizing a desired scalar function while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances. Various objective functions can be considered for the control system design such as maximization of profit, maximization of the power produced, or minimization of the auxiliary power consumed in the plant. The design of such a control system can make the IGCC plant suitable to play an active role in the smart grid era by enabling operation in the load-following mode as demand for electricity from the grid fluctuates over time. In addition, other penalty functions such as emission penalties for CO2 or other criteria pollutants can be considered in the control system design.;The control system design is performed in two stages. In the first stage, a top-down analysis is used to generate a list of controlled, manipulated, and disturbance variables considering a scalar operational objective and other process constraints. In this section, innovative methods devised for primary and secondary controlled variable selection will be discussed.Exploiting these results, the second stage uses a bottom-up approach for simultaneous design of the control structure and the controllers. In this section, a novel means of control structure design has been proposed.;In this research, the proposed two-stage control system design approach is applied to the IGCC\u27s acid gas removal (AGR) process which uses the physical solvent Selexol(TM) to selectively remove CO2 and H2S from the shifted syngas. Aspen Plus DynamicsRTM is used to develop the AGR process model while MATLABRTM is used to perform the control system design. This work has shown the proposed design procedure for plantwide control yields an optimal control structure. Additionally, the methods proposed in this work for primary and secondary controlled variable selection yield controlled variables which balance economic and control performance. Finally, the method proposed for control structure design has been found to yield a control structure that balance the control performance with controller complexity

Multi-objective genetic algorithm for robust flight scheduling

Master'sMASTER OF ENGINEERIN

IMPLEMENTATION OF GENETIC ALGORITHM BASED ON JAVASCRIPT IN OBJECT ROUTING SHORTEST TOUR ON TIMOR ISLAND

The purpose of this study is to apply a genetic algorithm using a programming language that can be used to determine the shortest route to tourist attractions in  Timor Island, East Nusa Tenggara. The application of genetic algorithms expected to be able to obtain the shortest route information that is most effective, the route that do not take a long time for tourists, and the presentation of route information for tourists. The method used is genetic algorithm to build the shortest route system for tourist attractions in Timor Island. The results of the system application shown that the optimization of travel routes that have been built can provide convenience in choosing tourist travel routes in Timor Island. Route optimization can help the process of making routes from the first location of departure and the attractions to be visited. The system is also used to assist domestic tourists in getting to know the attractions of Timor Island for solutions the shortest route

Rail-freight crew scheduling with a genetic algorithm

peer reviewedThis article presents a novel genetic algorithm designed for the solution of the Crew Scheduling Problem (CSP) in the rail-freight industry. CSP is the task of assigning drivers to a sequence of train trips while ensuring that no driver’s schedule exceeds the permitted working hours, that each driver starts and finishes their day’s work at the same location, and that no train routes are left without a driver. Real-life CSPs are extremely complex due to the large number of trips, opportunities to use other means of transportation, and numerous government regulations and trade union agreements. CSP is usually modelled as a set-covering problem and solved with linear programming methods. However, the sheer volume of data makes the application of conventional techniques computationally expensive, while existing genetic algorithms often struggle to handle the large number of constraints. A genetic algorithm is presented that overcomes these challenges by using an indirect chromosome representation and decoding procedure. Experiments using real schedules on the UK national rail network show that the algorithm provides an effective solution within a faster timeframe than alternative approaches