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In this study, a mixed-model assembly line (MMAL) balancing problem is applied in a make-to-order (MTO) environment. One of the important problems in MTO systems is identifying the control points, which is considered by designing a control system. Furthermore, the worker assignment problem is defined by considering abilities and operating costs of workers. The proposed model is solved in two stages. First, a multi-objective model by simultaneously minimizing the number of stations and the total cost of the task duplication and workers assignment is considered. The second stage is designing a control system to minimize the work in process.

To solve this problem, a non-dominated sorting genetic algorithm (NSGA-II) is introduced and the proposed model is compared with four multi-objective algorithms (MOAs).

The proposed model is compared with four MOAs, i.e. multi-objective particle swarm optimization, multi-objective ant colony optimization, multi-objective firefly algorithm and multi-objective simulated annealing algorithm. The computational results of the NSGA-II algorithm are superior to the other algorithms, and multi-objective ant colony optimization has the best running time of the four MOA algorithms.

With attention to workers assignment in a MTO environment for the MMAL balancing problem, the present research has several significant implications for the rapidly changing manufacturing challenge.

To the best of the authors’ knowledge, no study has provided for the MMAL balancing problem in a MTO environment considering control points. This study provides the first attempt to fill this research gap. Also, a usual assumption in the literature that common tasks of different models must be assigned to a single station is relaxed and different types of real assignment restrictions like resource restrictions and tasks restrictions are described.

This paper aims to present a novel bi-objective mathematical model for a production-inventory system under uncertainty.

This paper addresses agile and lean manufacturing concepts alongside with green production methods to design an integrated capacitated lot sizing problem (CLSP). From a methodological perspective, the problem is solved in three phases. In the first step, an FM/M/C queuing system is used to minimize the number of customers waited to receive their orders. In the second step, an effective approach is applied to deal with the fuzzy bi-objective model and finally, a hybrid metaheuristic algorithm is used to solve the problem.

Some numerical test problems and sensitivity analyzes are conducted to measure the efficiency of the proposed model and the solution method. The results validate the model and the performance of the solution method compared to Gams results in small size test problems and prove the superiority of the hybrid algorithm in comparison with the other well-known metaheuristic algorithms in large size test problems.

This paper presents a novel bi-objective mathematical model for a CLSP under uncertainty. The proposed model is conducted on a practical case and several sensitivity analysis are conducted to assess the behavior of the model. Using a queue system, this problem aims to reduce the items waited in the queue to receive service. Two objective functions are considered to maximize the profit and minimize the negative environmental effects. In this regard, the second objective function aims to reduce the amount of emitted carbon.

This paper aims to consider a multi-site production planning problem with failure in rework and breakdown subject to demand uncertainty.

In this new mathematical model, at first, a feasible range for production time is found, and then the model is rewritten considering the demand uncertainty and robust optimization techniques. Here, three evolutionary methods are presented: robust particle swarm optimization, robust genetic algorithm (RGA) and robust simulated annealing with the ability of handling uncertainties. Firstly, the proposed mathematical model is validated by solving a problem in the LINGO environment. Afterwards, to compare and find the efficiency of the proposed evolutionary methods, some large-size test problems are solved.

The results show that the proposed models can prepare a promising approach to fulfill an efficient production planning in multi-site production planning. Results obtained by comparing the three proposed algorithms demonstrate that the presented RGA has better and more efficient solutions.

Considering the robust optimization approach to production system with failure in rework and breakdown under uncertainty.