Journal of Industrial Engineering and Management Studies

Abstract In this paper, a bi-level optimization model is proposed for managing the supply chain of deteriorating products utilizing cold plasma technology. The model aims to simultaneously optimize the profitability of the manufacturer and retailer, reduce product waste, and lower carbon emissions. Cold plasma plays a pivotal role in extending product shelf life and reducing deterioration rates, which subsequently contributes to waste reduction and increased profitability. Moreover, this technology aids in minimizing greenhouse gas emissions throughout the supply chain and is considered a sustainable environmental solution. The model's results demonstrate that employing cold plasma leads to a significant reduction in waste and enhances both the manufacturer’s and retailer’s profits. The analysis further reveals that supply chain management strategies must adapt in response to varying deterioration rates. Based on these insights, companies should progressively shift towards sustainability and waste reduction, benefiting not only the environment but also their profitability. Implementing practical solutions and adopting efficient strategies can yield positive impacts on the overall profitability and efficiency of the supply chain. This paper investigates the economic and environmental aspects of employing cold plasma technology in the supply chain of deteriorating products and provides actionable recommendations for its practical implementation.

Abstract This paper examines the joint optimization of production and maintenance planning for a single-machine deteriorating system. To achieve optimal performance and meet customer demand at the lowest cost, manufacturing companies need to carefully plan production and maintenance, considering various factors such as time, cost, output levels in any period and its impact on machine deterioration. In this research, we attempt to plan the production and maintenance process for a single-machine single-product system over multi-period. The machine has two operational states during production and gradually deteriorates as it ages. Maintenance operations restore the machine to healthy state and reduce the probability of producing defective products. We model the problem using the Markov decision process and employ the value iteration algorithm to determine the optimal policy, i.e., the best actions to take at each decision epoch. We evaluate the model's effectiveness by solving a numerical example and analyzing how changes in different parameters affect the results. The findings reveal the relationship between various parameters and the average cost rate. Changes in the mentioned rate due to changes in setup cost and the probability of producing conforming products are almost uniform without any drastic fluctuations. If the production cost of each item exceeds a certain threshold, the company's obligations are not enforceable.

Abstract In this paper, a new model for hybrid flow shop scheduling is presented in which after the production is completed, each job is held in the warehouse until it is sent by the vehicle. Jobs are charged according to the storage time in the warehouse. Then they are delivered to customers by means of routing vehicles with limited and equal capacities. The problem’s goal is finding an integrated schedule that minimizes the total costs, including transportation, holding, and tardiness costs. At first, a mixed-integer linear programming (MILP) model is presented for this problem. Due to the fact that the problem is NP-hard, a hybrid metaheuristic algorithm based on PSO algorithm and GA algorithm is suggested to solve the large-size instances. In this algorithm, genetic algorithm operators are used to update the particle swarm positions. The algorithm represents the initial solution by using dispatching rules. Also, some lemma and characteristics of the optimal solution are extracted as the dominance rules and are integrated with the proposed algorithm. Numerical studies with random problems have been performed to evaluate the effectiveness and efficiency of the suggested algorithm. According to the computational results, the algorithm performs well for large-scale instances and can generate relatively good solutions for the sample of investigated problems. On average, PGR performs better than the other three algorithms with an average of 0.883. To significantly evaluate the differences between the algorithms’ solutions, statistical paired sample t-tests have been performed, and the results have been described for the paired algorithms.

Abstract In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.

Abstract In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the second one maximizes the machines reliability through minimizing machines failure. In addition, some routes are considered to produce each part based on operational requirements. An appropriate route is selected respect to the costs and operational time. Some parameters are considered under uncertainty in two categories. The first category such as demand is dependent on market condition and the uncontrolled competitive environment. The second one includes some parameters for production system and machines that are directly related to plans organized by production management. A robust optimization approach is used to deal with parameters uncertainty to produce feasible and optimal solutions. Furthermore, for validation and implementation of results in real world, a case study is investigated. Computational results show that the robust model reports better values for objective functions compared to the scenario-based model. In fact, Pareto-front which are resulted by robust model are dominated by scenario-based models’ Pareto front.  Sensitivity analyses on main parameters of the problem are performed to drive some managerial insights that help corresponding decision makers to provide suitable and homogenous decisions in a production system.