Reconfigurable Dynamic Cellular Manufacturing System: A New Bi-Objective Mathematical Model

Multi-objective parallel machine scheduling problem with job deterioration and learning effect under fuzzy environment

Green vessel scheduling in liner shipping: Modeling carbon dioxide emission costs in sea and at ports of call

A bi-objective mathematical model toward dynamic cell formation considering labor utilization

Cell formation problem mainly addresses how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand are changed in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customers' requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage are considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NP-hard class of problems. Sensitivity analysis is used to show the validity of the proposed model. Besides, numerical examples are used both, for small-sized and large- sized instances to show the efficiency of the method in finding near optimal solution.

A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment

Optimization issues are often defined statically, assuming constant environmental conditions.However, in many real-world scenarios, problem environments are dynamic and continuously changing.Thus we need optimization algorithms that could solves those issues in dynamic environments as well.Dynamic optimization problems are change(s) that may occur through the time.Such environments are characterized by uncertainty, temporal changes, and structural complexities, which makes the optimization process a significant challenge.In addressing these challenges, evolutionary algorithms have emerged as one of the most effective approaches for solving dynamic optimization problems (DOPs).Among these algorithms, the Imperialist Competitive Algorithm (ICA), designed based on swarm intelligence and competition among imperialist countries, has garnered considerable attention due to its capability in solving static optimization issues.In this research, Imperialist Competitive Algorithms, inspired by the historical and political processes of colonization and assimilation, have been known as one of the efficient evolutionary algorithms.These algorithms face numerous challenges when dealing with dynamic problems , including reduced population diversity, performance degradation in conditions of rapid environmental changes, and limitations in optimal convergence.These cases indicate the need to develop improved and more adaptable versions of these algorithms.Using concepts such as memory, population clustering, and repulsion mechanisms, this algorithm has been able to maintain population diversity at all stages while increasing the speed of convergence in the face of environmental changes.The key feature of the proposed algorithm is the use of memory to store previous optimal solutions, a clustering mechanism to manage population diversity, and repulsion to prevent unnecessary accumulation of solutions in specific regions.Nevertheless, ICA exhibits poor performance in dynamic environments because it lacks mechanisms to maintain diversity, quick adaptation to environmental changes, and new optima track.This study presents an improved version of ICA aimed at overcoming these limitations.The proposed algorithm incorporates a combination of a memory mechanism and a clustering strategy to enhance its adaptability to environmental changes and preserve diversity within the population.The memory mechanism stores information about previous optima and utilizes it under appropriate conditions to accelerate the optimization process.forclustering method is used for clustering.Clustering in the proposed method ensures that diversity is maintained for the population during the execution of the algorithm.In this study, our goal is to solve problems that change the environment in a global way.That means, the fitness of all points in the environment changes.By testing just one point in the environment and comparing the fitness obtained with its previously stored value, we can detect a change in the environment.On the other hand, the clustering strategy, particularly the k-means technique, to maintain maintain population diversity and prevents the convergence of solutions to specific regions.Together, these two components create a balance between exploration and exploitation, thereby improving the algorithm's performance in dynamic environments.To evaluate the performance of the proposed algorithm, the Moving Peaks Benchmark (MPB) was used as a standard metric.Due to its capability to simulate complex and diverse changes in dynamic environmentsparticularly in Branke's second scenario-MPB is one of the most recognized tools for assessing the performance of dynamic optimization algorithms.The proposed algorithm was evaluated alongside advanced algorithms such as FTmPSO (TMO), RAmQSO-s4, RmNAFSA-s4, TFTmPSO, RFTmPSO, mQSO10 (5+5q), FMSO, CellularPSO, Multi-SwarmPSO, mCPSO, AmQSO*, FTMPSO, almPSO, and CDEPSA.Experimental results demonstrated that the proposed algorithm outperformed other methods in areas such as convergence speed, adaptability to environmental changes, and population diversity preservation.A key feature of the proposed algorithm is its ability to retain identified optima even after environmental changes.Additionally, the use of the k-means clustering technique has ensured that the algorithm effectively avoids excessive focus on specific regions and maintains population diversity while facing complex environmental changes.Another advantage of this algorithm is its scalability in handling dynamic optimization issues with high dimensions and complexities.These findings indicate that the proposed algorithm is not only effective in laboratory settings but also suitable for real-world applications with fast and dynamic changes.

PurposeEnvironmental awareness is increasing among people in developing countries. In this regard, companies should consider ecological goals in addition to financial goals. Since the food industry is recognised as one of the largest emitters of CO2, profit and ecological objectives are optimised in radio-frequency identification (RFID) based closed-loop supply chain in the food industry in this paper.Design/methodology/approachBased on the literature, companies with a green entrepreneurial orientation (GEO) can turn ecological problems into opportunities using their proactiveness. In this regard, a new mixed-integer non-linear mathematical model is presented for optimising a new multi-product RFID-based closed-loop supply chain with a GEO in the food industry. The case study in this paper is Ofogh-e Kourosh company which is located in Iran. The GAMS software is used to code this model.FindingsThe optimum number of new products and materials flow was found among the closed-loop supply chain entities. Some factors as price, quality and warranty of products were considered, and the number of reopening of facilities if needed was set. The optimum node for RFID installation was found.Originality/valueThe paper presents a multi-objective mathematical model for optimising a multi-product RFID-based closed-loop supply chain with a GEO in the food industry. In addition, this paper gives insights into how can model this type of supply chain considering ecological and financial attributes.

Airline networks are becoming increasingly complex, particularly at mega-hub airports characterized by high transit volumes. Effective schedule design and fleet assignment are critical for an airline, as they directly influence passenger connectivity and profitability. This study addresses the challenge of introducing a new route from a mega-hub to a new destination, while maintaining the existing flight network and leveraging arrivals from spoke airports to ensure connectivity. First, a mixed-integer nonlinear mathematical model was formulated to produce a global optimal solution at a lower time granularity, but it became computationally intractable at higher granularities due to the exponential growth in constraints and variables. Second, a genetic algorithm (GA) was employed to demonstrate scalability and flexibility, delivering near-optimal, high-granularity schedules with significantly reduced computational time. Empirical validation using real-world data from 37 spoke airports revealed that, while the exact model minimized waiting times and maximized profit at lower granularity, the GA provided nearly comparable profit at higher granularity. These findings guide airline managers seeking to optimize passenger connectivity and cost efficiency in competitive global markets.

A bi-objective multi-period series-parallel inventory-redundancy allocation problem with time value of money and inflation considerations

In this paper a new model is developed to deal with a simultaneous dynamic cell formation and worker assignment problem (SDCWP). Part routing flexibility and machine flexibility and also promotion of workers from one skill level to another are considered. The proposed model is formulated as a single objective nonlinear integer programming which is converted to a linear one. The objective function consists of two separate components. The first part of the objective function is related to machine-based costs such as production cost, intercell material handling cost, machine costs in the planning horizon. The second part is related to human issues and consists of hiring cost, firing cost, training cost and salary. It is the first time that worker assignment and dynamic cell formation are considered simultaneously. To verify the performance of the proposed model, some numerical examples are presented. Computational and sensitivity analysis results imply the significance of SDCWP.

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing

Solving a dynamic cell formation problem using metaheuristics

Flow shop scheduling problem has a wide application in the manufacturing and has attracted much attention in academic fields. From other point, on time delivery of products and services is a major necessity of companies’ todays; early and tardy delivery times will result additional cost such as holding or penalty costs. In this paper, just-in-time (JIT) flow shop scheduling problem with preemption and machine idle time assumptions is considered in which objective function is minimizing the sum of weighted earliness and tardiness. A new non-linear mathematical model is formulated for this problem and due to high complexity of the problem meta-heuristic approaches have been applied to solve the problem for finding optimal solution. The parameters of algorithms are set by Taguchi method. Each parameter is tested in three levels. By implementation of many problems with different sizes these levels are determined .The proposed model is solved by three meta-heuristic algorithms: genetic algorithm (GA), imperialist competitive algorithm (ICA) and hybrid of GA and ICA. To evaluate the performance of the proposed algorithms many test problems have been designed. The Computational results indicate the superiority of the performance of hybrid approach than GA and ICA in finding thebest solution in reasonable computational time.

A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training

The optimal configuration of flexible workshop resources is critical to production efficiency, while disturbances pose significant challenges to the effectiveness of the configuration. Therefore, this paper proposes a hybrid-driven resource dynamic configuration model and an improved Imperialist Competitive Algorithm hybrid Neighborhood Search (IICA-NS) that incorporates domain knowledge to allocate resources in flexible workshops. First, a hybrid-driven configuration framework is proposed to optimize resource configuration strategies. Then, in the revolutionary step of the Imperialist Competitive Algorithm (ICA), the bottleneck heuristic neighborhood structure is adopted to retain the excellent genes in the imperial so that the updated imperial is closer to the optimal solution; And a population invasion strategy is proposed further to improve the searchability of the ICA algorithm. Finally, the simulation experiments are carried out through production examples on flexible workshop production cases, and the proposed algorithm is applied. Compared with traditional ICA, genetic algorithm (GA), particle swarm optimization algorithm (PSO), moth-flame optimization (MFO) and sparrow search algorithm (SSA), the proposed method and algorithm effectively solve flexible workshops’ resource dynamic configuration problems.