The advancement in wireless communication is fueling the growth of innovative antenna array designs toward cost-effective and performance-oriented solutions. This paper proposed unconventional methods to design antenna arrays for multi-pattern synthesis without using attenuators or phase shifters. A low-cost alternative is proposed with “Time-modulation”-based antenna array capable of electronic scanning and beam steering. Here, “Time” is utilized as a fourth-dimensional (4D) array parameter, and that is why “Time-modulated” arrays are also called as 4D antenna arrays. The idea is to control the high-speed switch attached with each antenna periodically to produce desirable current and phase tapering. This article expanded the “Time-modulation” concept to synthesize multiple radiation patterns like monopulse patterns, scanned beam patterns, shaped beam patterns, and cosecant-squared beam patterns for multifunctional radar systems. Suitable time schemes are developed to generate the narrowband sum–difference patterns useful for monopulse radars. Simultaneous scanned beam patterns are also proposed for narrowband communication. Furthermore, to address the wideband applications, shaped flat-top beam patterns and cosecant-squared beam patterns are also proposed. In this regard, 20- and 16-element “Time-modulated” linear array antennas are developed, and the parameters of the arrays are controlled by suitably designed objective functions with quasi-Newton method (QNM)-based memetic optimization method. For this purpose, first a well-known genetic algorithm is adopted to search the potential trust regions in the exploration stage and QNM is used for fine-tuning. Furthermore, the Broyden’s good method-based direction-updating equation is used with QNM to improve the performance.

The aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper.

The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.

The tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to be completed), as well as the tools to be loaded/unloaded at each step to process these jobs, such that the total number of tool switches is minimized. Different exact and heuristic methods have been defined to deal with this problem. In this work, we focus on memetic approaches to this problem. To this end, we have considered a number of variants of three different local search techniques (hill climbing, tabu search, and simulated annealing), and embedded them in a permutational evolutionary algorithm. It is shown that the memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).

This article deals with the vehicle routing problem with timewindows (VRPTW). This problem consists in determining a least-costset of trips to serve customers during specific time windows. Theproposed solution method is a memetic algorithm (MA), a geneticalgorithm hybridised with a local search. Contrary to most paperson the VRPTW, which minimize first the number of vehicles, ourmethod is also able to minimize the total distance travelled. Theresults on 56 classical instances are compared to those of thebest metaheuristics. The efficiency of the MA is similar for theclassical criterion, but it becomes the best algorithm availablefor the total distance, being much faster and improving 20best-known solutions.

We present in this paper a new multiobjective memetic algorithm scheme called MEMOX. In current multiobjective memetic algorithms, the parents used for recombination are randomly selected. We improve this approach by using a dynamic hypergrid which allows to select a parent located in a region of minimal density. The second parent selected is a solution close, in the objective space, to the first parent. A local search is then applied to the offspring. We experiment this scheme with a new multiobjective tabu search called PRTS, which leads to the memetic algorithm MEMOTS. We show on the multidimensional multiobjective knapsack problem that if the number of objectives increase, it is preferable to have a diversified research rather using an advanced local search. We compare the memetic algorithm MEMOTS to other multiobjective memetic algorithms by using different quality indicators and show that the performances of the method are very interesting.