This paper considers a multi-period, multi-product inventory-routing problem in a two-level supply chain consisting of a distributor and a set of customers. This problem is modeled with the aim of minimizing bi-objectives, namely the total system cost (including startup, distribution and maintenance costs) and risk-based transportation. Products are delivered to customers by some heterogeneous ...

During the last decade, the stringent pressures from environmental and social requirements have spurred an interest in designing a reverse logistics (RL) network. The success of a logistics system may depend on the decisions of the facilities locations and vehicle routings. The location-routing problem (LRP) simultaneously locates the facilities and designs the travel routes for vehicles among ...

The present work focuses on evolving the multiple light-in-weight topologies of compliant mechanism tracing user defined path. Therefore in this paper, the bi-objective set is formulated first on the optimization frame-work in which the helper objective of maximum diversity is introduced with the primary objective of minimum weight of elastic structures. Thereafter, the evolutionary algorithm (...

-In this paper, a fast dynamical multi-objective evolutionary algorithm (DMOEA) based of the principle of the minimal free energy in thermodynamics, was developed to solve mechanical component design problems. Its performance was compared with the ε -constraint method and NSGA-II proposed by Deb et al. Simulation results show that the proposed evolutionary approach produces excellent solutions ...

Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality co...

This paper considers a scheduling problem of a set of independent jobs on unrelated parallel machines (UPMs) that minimizesthe maximum completion time (i.e., makespan or ), maximum earliness ( ), and maximum tardiness ( ) simultaneously. Jobs have non-identical due dates, sequence-dependent setup times and machine-dependentprocessing times. A multi-objective mixed-integer linear programmi...

Antenna design problems often require the optimization of several conflicting objectives such as gain maximization, sidelobe level (SLL) reduction and input impedance matching. Multiobjective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. An efficient algorithm is Generalized Differential Evolution (GDE3), which is a multi-objective extension of ...

49 Any real-world optimization problem involves several objectives. Chemical engineering is no exception. Chemical processes, such as distillation (Figure 1), refinery operations, polymerization, etc., involve a number of process parameters which are to be set for achieving certain properties in the final product. Often such a process is modelled using a number of differential and/or algebraic ...

The present work aims to evolve the multiple topologies of path generating compliant mechanisms. The trade-off solution’s based topologies are developed by simultaneously minimization of weight and supplied input energy to elastic structures. The functional aspect of these compliant mechanisms is accomplished by imposing the constraints on precision points to generate the user-defined path. The...

Evolutionary algorithms (EAs) have been systematically developed to solve mono-objective, multi-objective and many-objective optimization problems, in this order, over the past few decades. Despite some efforts in unifying different types of mono-objective evolutionary and non-evolutionary algorithms, there does not exist many studies to unify all three types of optimization problems together. ...