In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale.

Production scheduling is a crucial task in the manufacturing process. In this way, the managers must decide the job's production schedule. However, this task is not simple, often requiring complex software tools and specialized algorithms to find the optimal solution. In this work, a multi-objective optimization model was developed to explore production scheduling performance measures to help managers in decision-making related to job attribution under three simulations of parallel machine scenarios. Five important production scheduling performance measures were considered (makespan, tardiness and earliness times, number of tardy and early jobs), and combined into three objective functions. To solve the scheduling problem, three multi-objective evolutionary algorithms are considered (Multi-objective Particle Swarm Optimization, Multi-objective Grey Wolf Algorithm, and Non-dominated Sorting Genetic Algorithm II), and the set of optimum solutions named Pareto Front, provided by each one is compared in terms of dominance, generating a new Pareto Front, denoted as Final Pareto Front. Furthermore, this Final Pareto Front is analyzed through an automatic bio-inspired clustering algorithm based on the Genetic Algorithm. The results demonstrated that the proposed approach efficiently solves the scheduling problem considered. In addition, the proposed methodology provided more robust solutions by combining different bio-inspired multi-objective techniques. Furthermore, the cluster analysis proved fundamental for a better understanding of the results and support for choosing the final optimum solution.

The issue of integrated scheduling and sequencing operation of unloading and loading equipment in container ports has been one of the most important issues concerning time efficiency. In addition, with the emergence of green harbor concepts, the inclusion of criteria for minimizing energy consumption, fuel and emission reduction are among the other issues that have been noticed by planners in the field of energy efficiency. Furthermore, due to the complexity and scope of activities of a container terminal, uncertainty in operational parameters such as transportation time, time of readiness and entry of work into the system and the velocity of the transportation fleet are inevitable in this operational environment. Therefore, this research with the aim of sharing trucks among loading and unloading equipment, proposes a robust multi-objective integer programming model for the synchronized scheduling of truck operations with other handling equipment to decrease the fuel consumption of trucks and the flow time of containers, considering the uncertainty in operational parameters as fuzzy numbers. To find the Pareto solutions for this model, the ε-Constraint technique is employed. Finally, the performance of the model in deterministic and uncertain modes is evaluated, compared and analyzed employing the inputs gathered from Shahid Rajaei port. The findings demonstrate that using this model will result in a substantial decrease in both fuel consumption and flow time of containers in comparison to the current procedure. Additionally, results will demonstrate the extent to which the terminal's fuel and time consumption will increase under conditions of uncertainty in operational parameters when the optimal plans derived from the robust model are implemented.

This paper addresses the sustainable closed-loop supply chain (SCLSC) design problem regarding selecting a supplier under total quantity discount with demand uncertainty and logistic flow uncertainty. The proposed model considers the three pillars of sustainability: the economic, environmental, and social realms. The model deals with the costs incurred by products-related manufacturing and minimizes the carbon dioxide emissions resulting from different manufacturing processes, as well as the attendant rate of injuries among the workers. Python edition 2019-07 software with the SCIPY solver was used to solve the model, using a sequential least squares programming algorithm (SLSQP) to obtain optimal solutions. A numerical study was conducted to validate the model. A sensitivity analysis was conducted to address the effects of both types of uncertainty on the optimal solution. It was found that the effect of a high rate of demand uncertainty is more severe than the effect of the uncertainty of the flow logistics in the reverse direction since the former generated a lower value of the optimal solution than the worst-case scenario generated by the uncertainty budget. Moreover, the higher the weight of environmental and social objectives, the higher the proportion of recycled products from the total production. This study proposes a robust optimization model for an SCLSC that considers two types of uncertainty: the uncertainty budget that is used for the logistics flow in the reverse direction for refurbished and redesigned products and the box of uncertainty that is used to address the demand uncertainty.

Advanced production systems usually are complex in nature and aim to deal with multiple performance measures simultaneously. Therefore, in most cases, the consideration of a single objective function is not sufficient to properly solve scheduling problems. This paper investigates the multi-objective mixed no-idle flowshop scheduling problem. The addressed optimization case is minimizing makespan subject to an upper bound on total completion time. To solve this problem, we proposed a two-stage iterated greedy and a multi-objective constructive heuristic. Moreover, we developed a new multi-objective improvement procedure focusing on increasing the performance of the developed methods in solving the addressed problem. and a new initialization procedure. We performed several computational tests in order to compare our developed methods with the main algorithms from similar scheduling problems in the literature. It was revealed that the proposed approaches give the best results compared with other state-of-the-art performing methods.

This paper investigates the configuration of a closed-loop supply chain (CLSC) network, which involves suppliers, a single manufacturer, customers, collection/disassembly centers, disposal centers, a single recovery center and subcontractors. Due to the importance of green issues in the proposed supply chain, the efforts mainly focus on the suitable parts that form more durable and more sustainable products, reduce costs and help the environmental protection. To do so, an integrated framework is introduced which consists of a multi-attribute decision making (MADM) method and a multi-objective mathematical model that determines the material flow in the dynamic consecutive time segments of the network. This flow consists of parts and products which pass through the extant or potential facilities so that ultimately organize a CLSC network. Thereafter, a numerical example is examined by the augmented ε-constraint method and the results are demonstrated in a specific time horizon as an efficient solution. The results show the effects of time horizon in finding different solutions for each time segment. Furthermore, it shows how subcontractors can facilitate the flow of materials.

In recent decays, there has been an extensive improvement in technology and knowledge; hence, human societies have started to fortify their urban environment against the natural disasters in order to diminish the context of vulnerability. Local administrators as well as government officials are thinking about new options for disaster management programs within their territories. Planning to set up local disaster management facilities and stock pre-positioning of relief items can keep an urban area prepared for a natural disaster. In this paper, based on a real-world case study for a municipal district in Tehran, a multi-objective mathematical model is developed for the location-distribution problem. The proposed model considers the role of demand in an urban area, which might be affected by neighbor wards. Integrating decision-making process for a disaster helps to improve a better relief operation during response phase of disaster management cycle. In the proposed approach, a proactive damage estimation method is used to estimate demands for the district based on worst-case scenario of earthquake in Tehran. Since such model is designed for an entire urban district, it is considered to be a large-scale mixed integer problem and hence, a genetic algorithm is developed to solve the model.

One of the primary concerns on supply chain management is to handle risk components, properly. There are various reasons for having risk in supply chain such as natural disasters, unexpected incidents, etc. When a series of facilities are built and deployed, one or a number of them could probably fail at any time due to bad weather conditions, labor strikes, economic crises, sabotage or terrorist attacks and changes in ownership of the system. The objective of risk management is to reduce the effects of different domains to an acceptable level. To overcome the risk, we propose a reliable capacitated supply chain network design (RSCND) model by considering random disruptions risk in both distribution centers and suppliers. The proposed study of this paper considers three objective functions and the implementation is verified using some instance.

One of the primary issues in line balancing problems is the uncertainty associated with the processing times. There are different reasons for having uncertain processing times such as task deterioration, failure in machines, etc. On the other hand, there are different objectives, such as cycle time, number of workstations in an assembly line balancing. In this paper, we present a multi-objective decision making assembly line balancing which minimizes different objectives such as cycle time and number of workstations. The resulted problem is formulated based on Lp-norm mixed integer programming and a meta-heuristic approach is also presented to solve the resulted model. The problem formulation is solved for some test examples and the results are discussed under different conditions.

Modern hospital operations need more advanced optimization methods due to the factors such as the variation in patient demand, limited resources, and complicated workforce regulations. The initial research suggested a combined Linear and Mixed-Integer Linear Programming (LP/MILP) approach to the joint optimization of patient admissions, bed/OR utilization, and nurse scheduling. The model unified the operational costs and the staff preferences into a single weighted objective, thereby showing the very significant resource utilization and scheduling satisfaction improvements. We have extended the framework from its original version and in this extended work we are going to demonstrate how the nurse scheduling component is fashioned into an actual multi-objective optimization problem. Rather than addressing the problem via a single weighted aggregation, two opposing targets, minimizing overall operational cost and maximizing nurse preference satisfaction, are treated openly. Moreover, we introduce the Adaptive ε-Constraint method that allows us to take advantage of the division between coarse ε sweep and local refinement to produce a well-distributed approximation of the Pareto frontier. The progressive method not only addresses the clustering problem that has appeared in the naive ε sweeps but also creates a continuous and varied set of solutions that are not dominated by any other solution. With the extended model that utilizes synthetic but realistic nurse, demand, and preference data, a variety of feasible scheduling policies with obvious trade-offs between cost and employee satisfaction are provided. The Pareto frontier offers intermediate solutions which are able to achieve large increases in preference satisfaction at the expense of only negligible increments in operational costs when compared to the baseline cost-minimal and preference-maximal schedules. The findings emphasize the usefulness of multi-objective decision support in hospital practice and also prove that through the direct representation of staff preferences, it is possible to have even distributions of working time without losing the effectiveness of operations. On the whole, the extension demonstrates that the original "smart hospital" model is enriched and the decision-making process for the administrators is more flexible with the inclusion of multi-objective optimization, thus resulting in the enhancement of both efficiency and health of the staff.