red deer algorithm

This paper discusses the modeling and solution of a flexible flow shop scheduling problem with forward and reverse flow (FFSP-FR). The purpose of presenting this mathematical model is to achieve a suitable solution to reduce the completion time (Cmax) in forward flow (such as assembling parts to deliver jobs to the customer) and reverse flow (such as disassembling parts to reproduce parts). Other important decisions taken in this model are the optimal assignment of jobs to each machine in the forward and reverse flow and the sequence of processing jobs by each machine. Due to the uncertainty of the important parameters of the problem, the Fuzzy Jiménez method has been used. The results of the analysis with CPLEX solver show that with the increase in the uncertainty rate, due to the increase in the processing time, the Cmax in the forward and reverse flow has increased. GA, ICA and RDA algorithms have been used in the analysis of numerical examples with a larger size due to the inability of the CPLEX solver. These algorithms are highly efficient in achieving near-optimal solutions in a shorter time. Therefore, a suitable initial solution has been designed to solve the problem and the findings show that the ICA with an average of 273.37 has the best performance in achieving the near-optimal solution and the RDA with an average of 31.098 has performed the best in solving the problem. Also, the results of the T-Test statistical test with a confidence level of 95% show that there is no significant difference between the averages of the objective function index and the calculation time. As a result, the algorithms were prioritized using the TOPSIS method and the results showed that the RDA is the most efficient solution algorithm with a utility weight of 0.9959, and the GA and ICA are in the next ranks. Based on the findings, it can be said that industrial managers who have assembly and disassembly departments at the same time in their units can use the results of this research to minimize the maximum delivery time due to the reduction of costs and energy consumption, even though there are conditions of uncertainty

An efficient design of a water supply and wastewater collection system is significantly important to tackle the natural uncertainty of this system and the sustainable development goals in developing countries like Iran. To address the natural uncertainty in the water supply and the challenge of global warming, this design must be robust and this motivates a robust optimization. To consider the sustainability criteria, this design should cover all economic, environmental and social impacts. Hence, this study develops innovative solutions based on recent and traditional metaheuristic algorithms for a robust and sustainable water supply and wastewater collection system. Red deer algorithm (RDA) and Keshtel algorithm (KA) as the recent algorithms, are employed. These recent algorithms are compared with the state-of-the-art methods like genetic algorithm (GA) and particle swarm optimization (PSO). An application of our model and algorithms, is tested on a case study in North Khorasan province. After performing some analyses on the performance of our algorithms and sensitivities on the model, a discussion is provided to conclude managerial insights and findings for practitioners in the applied system.

Nowadays, there is much attention for planning of container terminals in the global trade centers. The high cost of quay cranes motivates both scholars and industrial practitioners especially in the last decade to develop novel optimization models to address this dilemma. This study proposes a coordinated optimization model to cover both Quay Crane Scheduling Problem (QCSP) and Quay Crane Assignment Problem (QCAP) as among the first attempts in this area. Another main contribution of this paper is to apply a recent nature-inspired algorithm called Red Deer Algorithm (RDA). The RDA revealed its performance for a variety of combinatorial optimization problems in different real-world applications. This is the first attempt in the literature to employ this recent metaheuristic to solve the proposed Coordinated Quay Crane Scheduling and Assignment Problem (CQCSAP). Finally, an extensive comparison discussion is considered to reveal the main benefits of the proposed optimization model and solution algorithm.

Nowadays, cross-docking is one of the main concepts in supply chain management in which products received to a distribution center by inbound trucks which are directly to lead into outbound trucks with a minimum handling and storage costs as the main cost of a cross-docking system. According to the literature, several metaheuristics and heuristics are attempted to solve this optimization model. In this regard, this study utilizes three recent nature-inspired metaheuristics among the first studies in this area. Red Deer Algorithm (RDA), Virus Colony Search (VCS) and Water Wave Optimization (WWO) are three novel nature-inspired algorithms proposed recently to employ their applications in engineering problems. The used algorithm’s parameters were selected by Taguchi method to enhance the efficiency of algorithms. The outputs of the proposed algorithms are assessed with each other in different criteria along with statistical analyses and the results yielded by prior works. The results demonstrate that RDA showed a competitive performance compared with mixed other existing algorithms.

Nowadays, in global free market, third-party logistics providers (3PLs) are becoming increasingly important. Hence, this study aims to develop the freight consolidation and containerization problem, which consists of loading items into containers and then shipping these containers to different warehouse they are delivered to their final destinations. In order to handle the proposed problem, this research not only uses the traditional and recent algorithms, but also the two new hybridized methods are introduced in order to strengthen the advantages of recent ones. In this regard, this study considers the two important phases in meta-heuristic to develop new ones. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms’ parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, the impacts of the rise in the problem size on the performance of the proposed algorithms are investigated.