In this research, an integrated inventory problem is formulated for a single-vendor multiple-retailer supply chain that works according to the vendor managed inventory policy. The model is derived based on the economic order quantity in which shortages with penalty costs at the retailers` level is permitted. As predicting customer demand is the most important problem in inventory systems and th...

Due to the immensity of phylogenetic tree space for large data sets, researches must rely on heuristic searches to infer reasonable phylogenies. By designing meta-searches which appropriately combine a variety of heuristics and parameter settings, researchers can significantly improve the performance of heuristic searches. Advanced language constructs in the open-source PSODA project—including ...

Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. In this paper we put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief ove...

This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain opt...

Local search, in either best or first admissible form, generally suffers from poor solution qualities as search cannot be continued beyond locally optimal points. Even multiple start local search strategies can suffer this problem. Meta-heuristic search algorithms, such as simulated annealing and tabu search, implement often computationally expensive optimisation strategies in which local searc...

Nature Inspired Meta-Heuristic algorithms are proved to be beneficial for solving real world combinatorial problems such as minimum spanning tree, knapsack problem, process planning problems, load balancing and many more. In this research work, existing meta-heuristic approaches are discussed. Due to astonishing feature of echolocation, bat algorithm has drawn major attention in recent years an...

This paper discusses an approach to generate test data for path coverage based testing using Genetic Algorithms, Differential Evolution and Artificial Bee Colony optimization algorithms. Control flow graph and cyclomatic complexity of the example program has been used to find out the number of feasible paths present in the program and it is compared with the actual no of paths covered by the ev...

The main purpose of this paper is to show a data mining-based approach to tackle the problem of tuning the performance of a meta-heuristic search algorithm with respect to its parameters. The operational behavior of typical meta-heuristic search algorithms is determined by a set of control parameters, which have to be fine-tuned in order to obtain a best performance for a given problem. The pri...

Deep learning (DL) is a type of machine learning that mimics the thinking patterns of a human brain to learn the new abstract features automatically by deep and hierarchi‐ cal layers. DL is implemented by deep neural network (DNN) which has multihidden layers. DNN is developed from traditional artificial neural network (ANN). However, in the training process of DL, it has certain inefficiency d...