MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

Optimizing a fuzzy multi-objective closed-loop supply chain model considering financial resources using meta-heuristic

This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this pape

Improving green supply chain performance with Operations Research

Due to increasing greenhouse gas emission as a consequence of the production activities in various industries, managing the supply chain has been a big concern between both scholars and practitioners. Green supplier selection and order allocation is among important topics that managers should pay attention to as the majority of the supply chain costs and emission level during production process depends on the procured material by suppliers. Also, investigating the emission abatement regulations, and interactions between regulator and manufacturers is one of the main concerns of supply chain managers that should be figured out. In the present study, green supply chain problems are taken into account for more investigations. First, a green supplier selection and order allocation model in a closed-loop supply chain considering both environmental and economical criteria, is studied. In this study, one of the carbon emission abatement schemes, cap-and-trade mechanism is proposed. The described problem is modeled as a multi-objective robust optimization (RO) model. Second, the cap-and-trade (C\&T) mechanism is further investigated. The goal of this investigation is to find the best strategy for supply chain parties to maximize their utility as well as minimize the carbon emission. To model the described problem, a stochastic three-player game theoretical model is developed. The results show that the developed models can effectively help decision makers select the most appropriate suppliers, allocate the proper amount of order to each selected supplier, and find optimal strategy of C\&T players. Also, the results show that the uncertainty control approaches used in the presented models are capable of handling the model uncertainties from different sources. Furthermore, this study shows that C\&T outperforms the penalty based systems in terms of the total utility of the supply chain. Moreover, the robustness of the results is proved by sensitivity analyses. Another area that is investigated in this study is the disruption effects on supply chain. Disasters and pandemics like COVID-19 can destroy industries by causing huge disruptions in their supply chains. To control these disruptions, decision-makers need to design resilient supply chains. This study proposes a multi-stage, multi-period resilient green supply chain design model considering six resilient strategies. Disruptions are taken into account in both downstream and upstream directions, causing the ripple effect and bullwhip effect, respectively. To control the mentioned disruptions, and handle uncertainties of parameter estimations, a two-stage stochastic optimization approach is applied. The objectives are to minimize the total cost of disruption and $CO_{2}$ emission considering the cap-and-trade mechanism as a government-issued emission regulation. The proposed decision-making framework and solution approach are validated using a numerical experiment followed by a sensitivity analysis. The results show the optimal structure of the supply chain and the best resilient strategies to mitigate the ripple effect. Moreover, the effect of a decrease in capacity of facilities on the optimal solution and the applied resilient strategies is investigated. This study provides managerial insights to help governments set the proper amount of cap and supply chain managers to predict the demand behaviour of essential and non-essential products in the event of disruptions