multi-objective meta-heuristic algorithms

In recent years, the growing industrial and economic competition, environmental concerns, and governmental pressures on manufacturers regarding waste management have underscored the significance of designing a reverse supply chain and closed-loop network. Simultaneously, the potential for profit arising from product recycling has further emphasized the importance of these systems. This research focuses on developing a four-stage closed-loop network model for the supply chain, taking into account the uncertainty of product recycling rates. The primary objective of this study is to provide an integer linear programming model aimed at minimizing supply chain costs and customer service time under uncertain conditions. The supply chain model is designed by integrating queuing theory and product recycling system optimization. A critical aspect of this research involves modeling the uncertainty in the return rate of consumer products in the closed-loop supply chain, with the aim of developing a robust approach to address this issue. Additionally, the performance of the proposed model in the paper industry is evaluated, and a sensitivity analysis is conducted with respect to the decision variables using two metaheuristic algorithms: the Multiple Objective Harmony Search and the Non-dominated Sorting Genetic Algorithm.

Home Healthcare provides a wide range of home care services from prevention to rehabilitation and primary care to professional nursing services. This research presents a two-objective mathematical model based on the mixed integer linear programming approach for the home healthcare routing and scheduling problem (HHCRSP) with the objectives of minimization the nurses’ travel costs and the maximal working time difference among nurses. Considering the multimodal transportation, several initial health centers and one final health center and patient and nurse time windows are important features of the studied mathematical model. Small-sized problems have solved by using the Ɛ-constraint method on GAMS software. Also, due to the NP-hardness of the problem, MOPSO and NSGA-II algorithms have used to solve the medium and large-sized problems. The statistical results showed that the NSGA-II performed better than the MOPSO for medium and large sizes of problems in both MID and NOS performance metrics. Overall, Results of the performance metrics on different sizes of problems indicate the efficient and effective performance of NSGA-II in solving the understudied problem.

At the time of natural disasters occurrence, prompt responding and providing required items to affected areas is the most urgent priority. Moreover, given a stochastic nature of the crisis severity and the number of the affected areas,effective planning is a crucial task. In this study, two major steps in the disaster management cycle,namely preparation and response phases, are formulated using a multi-objective mathematical model under uncertainty. In the preparation phase, the optimum location of relief distributions, medical centers and inventories of relief goods to storage items received from suppliers are determined. Also, in the second step or response phase, the amount of relief goods transported from supply points to relief distribution centers and from these centers to affected areas as well as the number of injured people transferred to medical centers and hospitals through ambulances and aerial transportationare determined. Moreover, regarding the problem nature, its key parameters (e.g., demand and the number of injured people) are considered to be uncertain. Furthermore, given that the failureof facilities in both supplier and distributor sections can adverselyaffect their service provision, this issueisconsidered in the model. To efficiently solve the model, the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective grey wolf optimizer (MOGWO) areused. Comparison of the results obtained from the proposed meta-heuristics with the exact solution method indicates that these algorithms can provide acceptable solutions in a reasonable amount of computational time.

The goal of the dynamic facility layout problem is to find the best layout for facilities at a multi period planning horizon so that the total cost of material handling and relocating the facilities is minimized. One of the most important aspects of this issue is to consider the transporters and minimizing material handling time. This paper developed a bi-objective mathematical model which is able to simultaneously minimize the material handling costs between facilities and the cost of rearrangement facilities and material handling time. Due to probabilistic characteristic of the transporters, such as the time of handling operation and existence of the failure, calculating the time required to carry the material using analytical relationships is impossible.Therefore, this paper uses the simulation approach and artificial neural networks. In this approach, a lot of scenarios are generated by combining of variables. Each scenario shows the location of the facilities and how to perform transportation operations in each period. Then each of these scenarios is implemented through computer simulation and simulation results are considered as the response variable. Finally, using input and response variable, an artificial neural network is trained so that it can accurately estimate the time of carrying out the transportation operations. Given that the above problem is a NP-hard; this paper proposes a new Multi-Objective meta heuristic algorithm to optimize the problem and compares the performance of the proposed algorithm with existing algorithms in literature.