Three simple metaphor-less optimization algorithms are developed in this paper for solving the unconstrained and constrained optimization problems. These algorithms are based on the best and worst solutions obtained during the optimization process and the random interactions between the candidate solutions. These algorithms require only the common control parameters like population size and number of iterations and do not require any algorithm-specific control parameters. The performance of the proposed algorithms is investigated by implementing these on 23 benchmark functions comprising 7 unimodal, 6 multimodal and 10 fixed-dimension multimodal functions. Additional computational experiments are conducted on 25 unconstrained and 2 constrained optimization problems. The proposed simple algorithms have shown good performance and are quite competitive. The research community may take advantage of these algorithms by adapting the same for solving different unconstrained and constrained optimization problems.

In the classical vehicle routing problem with backhauls (VRPB) the customers are divided into two sets; the linehaul and backhaul customers, so that the distribution and collection services of goods are separated into different routes. This is justified by the need to avoid the reorganization of the loads inside the vehicles, to reduce the return of the vehicles with empty load and to give greater priority to the customers of the linehaul. Many logistics companies have special responsibility to make their operations greener, and electric vehicles (EVs) can be an efficient solution. Thus, when the fleet consists of electric vehicles (EVs), the driving range is limited due to their battery capacities and, therefore, it is necessary to visit recharging stations along their route. In this paper the electric vehicle routing problem with backhauls (EVRPB) is introduced and formulated as a mixed integer linear programming model. This formulation is based on the generalization of the open vehicle routing problem considering a set of new constraints focussed on maintaining the arborescence condition of the linehaul and backhaul paths. Different charging points for the EVs are considered in order to recharge the battery at the end of the linehaul route or during the course of the backhaul route. Finally, a heuristic initialization methodology is proposed, in which an auxiliary graph is used for the efficient coding of feasible solutions to the problem. The operation and effectiveness of the proposed formulation is tested on two VRPB instance datasets of literature which have been adapted to the EVRPB.

The purpose of this paper is to present the relief operations (RO), responding to a sudden, natural, national disaster (SNND) as a multi-mode resource-constrain multi-project scheduling problem (MRCMPSP). A conceptual framework at a strategic level is constructed and the Colombian RO for an earthquake response is shown as an illustrative case. We concluded that RO can be addressed as a MRCMPSP and that for Colombian case, it is a convenient way to board it. Addressing RO as a MRCMPSP allows managers to implement different project scheduling tools successful in other contexts.

Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems.

We propose and develop a new G-optimality criterion using the concept of weighted optimality criteria and certain additional generalizations. The goal of the weighted G-optimality is to minimize a weighted average of the maximum scaled prediction variance in the design region over a set of reduced models. A genetic algorithm (GA) is used for generating the weighted G-optimal exact designs in an experimental region for mixtures. The performance of the proposed GA designs is evaluated and compared to the performance of the designs produced by our genetic algorithm and the PROC OPTEX exchange algorithm of SAS/QC. The evaluation demonstrates the advantages of GA designs over the designs generated using exchange algorithm, showing that the proposed GA designs have better model-robust properties and perform better than the designs generated by the PROC OPTEX exchange algorithm.

During the past two decades, there has been an increase on maritime freight traffic particularly in container flow. Thus, the Berth Allocation Problem (BAP) can be considered among the primary optimization problems encountered in port terminals. In this paper, we address the Dynamic Berth Allocation Problem (DBAP) in a conventional layout terminal which differs from the popular discrete layout terminal in that each berth can serve multiple vessels simultaneously if their total length is equal or less than the berth length. Then, a Modified Sailfish Optimizer (MSFO) meta-heuristic based on hunting sailfish behavior is developed as an alternative for solving this problem. Finally, computational experiments and comparisons are presented to show the efficiency of our method against other methods presented in the literature in one hand. We also discuss the productivity of a container terminal based on different scenarios which can happen.

Transnational corporations, which operate in competitive global marketplaces, have to build the best possible intra-supply chain model for meeting, on time, clients’ need for multiple products with the requisite quality. Since fabrication capacity is always limited, the introduction of the external provider option can assist in leveling utilization, smoothing manufacturing schedules, eliminating overtime usage, and shortening the length of the fabrication cycle. Seeking to support intra-supply chain planning, this research aims to provide a concurrent decision on rotation cycle length and delivery frequency for a multi-item vendor-buyer incorporated type of intra-supply chain system with an external provider and rework. First, a model is built to represent this hybrid inventory replenishing problem. Then, renewal reward theory, mathematical derivation, and Hessian matrix equations are utilized to arrive at the expected total cost of the model, as well as the best policies for both cycle time and distribution. Last, the applicability and sensitivity analyses of our results are exhibited by a numerical demonstration. The insights obtained from this study about critical system-related information, such as the individual and joint impacts of the variation in outsourcing and reworking-related features on the system’s optimal operating policy and various performance parameters, will offer crucial help to the managerial functions of planning and decision making in firms using this realistic multi-item hybrid intra-supply chain system.

The processing time of the machine is assumed fixed in several studies. In many real industrial applications, the processing time is affected by learning and forgetting effects. This research proposes a scheduling approach to support a manufacturing system under learning/forgetting effect. The approach is supported by a Multi-Agent System to perform the scheduling activities in a quasi-real-time and in general manufacturing systems. A simulation environment is developed to test the proposed approach and the results are compared with a benchmark model for evaluating several performance measures of the manufacturing system. The simulation results highlight how the proposed approach improves all the performance measures under different conditions of inter-arrival time, learning and forgetting rates. A complete Analysis of the Variance highlights the main effects on the performance measures to support the decision maker of the manufacturing system.

In this paper, an optimization model for the Charging Station Location Problem of Electric Vehicles for Freight Transportation CSLP-EVFT is presented. This model aims to determine an optimal location strategy of Electric Vehicle Charging Stations EVCSs and the routing plan of a fleet of electric vehicles under battery driving range limitation, in conjunction with the impact on the power distribution system. Freight transportation is modeled under the mobility patterns followed by the Capacitated Vehicle Routing Problem CVRP for contracted fleet, and Shortest Path SP problem for subcontracted fleet. A linear formulation of the power flow is used in order to consider the impact on the electric grid. Several costs are examined, i.e., EVs routing, installation and energy consumption of EVCSs, and energy losses. Although uncertainties related to temporal variation of some aspects (number of customers and their demands, fleet size, power network nodes and routes) are not addressed, the proposed model represents a useful approach to evaluate multiple scenarios or to be introduced within stochastic optimization. Instead, the mathematical model is studied under the variation of EVs travel range that accounts for the advance of battery technology and sensitivity analysis. Additionally, the problem is reduced to a mixed integer non-linear mathematical model, which is linearized by using multivariable Taylor’s series.

In this study, several series of experiments on turning process of AISI 12L14 free cutting steel characterized by its self-lubrication and the high percentage of lead in its composition were performed to rate the influence of cutting conditions (Vc, f and ap) on the machining performance such as surface roughness, cutting force, cutting power and material removal rate. A computer generated optimal design of experiment based on the I-optimality criteria along with analysis of variance was created to study the characterizations in turning of this steel, and desirability function was utilized for the optimization. The global optimization, combined high surface quality and productivity with low cutting power consumption, gave 12 optimal setting points provided high desirability values. The obtained correlation for surface roughness, cutting force, material removal rate and cutting power were 99.4%, 95.5%, 99.7% and 94.3%, respectively.