Integrating sustainability into production scheduling in hybrid flow-shop environments

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop foors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid fow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers’ fexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversifcation, and quality metrics, providing more versatile and better solutions for production and energy efciency

Optimization of pipe spool fabrication shop scheduling using genetic algorithm

Spool fabrication shop is an intermediate phase in the piping process for construction projects. The delivery of pipe spools at the right time in order to be installed in the site, is very important. Therefore, effective scheduling and control of the fabrication shop has a direct effect on the productivity and successfulness of the whole construction projects. This research developed a genetic algorithm (GA) in order to generate a feasible and near-optimal schedule for the operational level of pipe spool fabrication shop based on the concepts and methods of job shop scheduling problems. In the proposed algorithm, an improved chromosome representation is used to conveniently represent a schedule for the fabrication shop. Operation-based global selection and Operation-based local selection are designed to generate high-quality initial population in the initialization stage. To adapt to the special chromosome structures and the characteristics of the problem, precedence order-based crossover (POX), two-point crossover, and uniform crossover are used. In addition, different mutation operators for operation sequence part and machine assignment part of the chromosome are used. The data which consist of operations processing time, and dimension of spools and stations are collected from an industrial fabrication shop. The proposed algorithm is applied by using the collected data to obtain a feasible and near-optimal schedule for the operational level of pipe spool fabrication shop. The results showed that the productivity of the fabrication shop by using the proposed algorithm for scheduling fabrication processes has increased to 178 percent

Escaping saddle points in constrained optimization

In this paper, we study the problem of escaping from saddle points in smooth nonconvex optimization problems subject to a convex set C. We propose a generic framework that yields convergence to a second-order stationary point of the problem, if the convex set C is simple for a quadratic objective function. Specifically, our results hold if one can find a ρ-approximate solution of a quadratic program subject to C in polynomial time, where ρ < 1 is a positive constant that depends on the structure of the set C. Under this condition, we show that the sequence of iterates generated by the proposed framework reaches an (ε, γ)-second order stationary point (SOSP) in at most O(max{ε- 2 , ρ -3 γ -3 }) iterations. We further characterize the overall complexity of reaching an SOSP when the convex set C can be written as a set of quadratic constraints and the objective function Hessian has a specific structure over the convex set C. Finally, we extend our results to the stochastic setting and characterize the number of stochastic gradient and Hessian evaluations to reach an (ε, γ)-SOSP.United States. Defense Advanced Research Projects Agency. Lagrange ProgramUnited States. Office of Naval Research. Basic Research Challeng

SOLVING A HYBRID JOB-SHOP SCHEDULING PROBLEM WITH SPACE CONSTRAINTS AND REENTRANT PROCESS BY A GENETIC ALGORITHM: A CASE STUDY

In this paper, several concepts and techniques of job-shop scheduling are adjusted and applied to the real-life scheduling problem at a pipe spool fabrication shop. A genetic algorithm (GA) is developed to create a feasible and active schedule for the operational level of pipe spool fabrication with the aims of minimizing completion time of all spools, i.e. makespan. In the proposed algorithm, an enhanced solution coding is used to represent a schedule for the fabrication shop. To generate high quality initial population, we designed an Operation order-based Global Selection (OGS). This operator is taken into account both the operation processing times and workload of machines while it is assigning machines to the operations The precedence preserving order-based crossover (POX) and uniform crossover are used appropriately, and an intelligent mutation operator is carried out in reproduction phase. The proposed algorithm is applied on the benchmark data set taken from the literature, in which its results demonstrated efficiency and effectiveness of the algorithm. After that, the proposed algorithm is implemented with the collected data from an industrial fabrication shop. The results showed that by using GA for scheduling the fabrication processes, the productivity of the spool fabrication shop with particular constraints has increased by 83 percent

Solving a capacitated p-median location allocation problem using genetic algorithm: a case study

Facility location-allocation problems have various applications in private and public sectors. A capacitated p-median problem is considered in this work which is computationally NPHard. The primary goal of this paper was to determine a set of p-facilities location in which all demand points are allocated and its average distance traveled from the customers' location to the selected p-facilities is minimized. In addition, the model also considered supplier's allocation for p facilities. A real world case study has been addressed, and genetic algorithm which consists of crossover and mutation operators was proposed in order to solve the problem. Computational results for different values of p were generated, and finally the optimum solution based on minimum cost was reported

Direct Runge-Kutta discretization achieves acceleration

© 2018 Curran Associates Inc..All rights reserved. We study gradient-based optimization methods obtained by directly discretizing a second-order ordinary differential equation (ODE) related to the continuous limit of Nesterov's accelerated gradient method. When the function is smooth enough, we show that acceleration can be achieved by a stable discretization of this ODE using standard Runge-Kutta integrators. Specifically, we prove that under Lipschitz-gradient, convexity and order-(s + 2) differentiability assumptions, the sequence of iterates generated by discretizing the proposed second-order ODE converges to the optimal solution at a rate of O(N−2 s+1 s ), where s is the order of the Runge-Kutta numerical integrator. Furthermore, we introduce a new local flatness condition on the objective, under which rates even faster than O(N−2) can be achieved with low-order integrators and only gradient information. Notably, this flatness condition is satisfied by several standard loss functions used in machine learning. We provide numerical experiments that verify the theoretical rates predicted by our results

Sialadenoma papilliferum of the hard palate: A rare case report

Sialadenoma papilliferum (SP) is a rare benign salivary gland tumor with unclear cell origin. This report presents a new case of SP of the hard palate occurring in a 50-year-old female. The lesion was completely excised, and the microscopic features were consistent with SP. The knowledge of this rare entity contributes to proper diagnosis and prevents unnecessary radical surgery and treatment

Solving an industrial shop scheduling problem using genetic algorithm

Spool fabrication shop is an intermediate phase in the piping process for construction projects. The delivery of pipe spools at the right time in order to be installed in the site is very important. Therefore, effective scheduling and controlling of the fabrication shop has a direct effect on the productivity and successfulness of the whole construction projects. In this paper, a genetic algorithm (GA) is developed to create an active schedule for the operational level of pipe spool fabrication. In the proposed algorithm, an enhanced solution coding is used to suitably represent a schedule for the fabrication shop. The initial population is generated randomly in the initialization stage and precedence preserving order-based crossover (POX) and uniform crossover are used appropriately. In addition, different mutation operators are used. The proposed algorithm is applied with the collected data that consist of operations processing time from an industrial fabrication shop. The results showed that by using GA for scheduling the fabrication processes, the productivity of the spool fabrication shop has increased by 88 percent