Journal of Quality Engineering and Production Optimization
Volume:6 Issue: 2, Summer-Autumn 2021

This study evaluates a Set Covering Problem (SCP), an extension of the demand covering problem, with several potential applications. The original demand covering problem objective includes the selection of proper locations for a number of available facilities to cover the required demand. The SCP tries to minimize location cost satisfying a specified level of coverage. The SCP problems answer many location problems, e.g., the emergency services sector with alternative facilities that will cover the unavailability of the primary facility or recommender systems where it is desired to fulfill the demand by several available choices. We present a biclustering method to construct biclusters from the distance matrix where a bicluster depicts a subset of demand centers covered by a subset of facilities. According to experiments performed in this study, it is concluded that the proposed method provides high-quality solutions compared with an optimal solution attained from GAMS. Also, for larger problem instances, the proposed method provided solutions with higher quality than GAMS software when the computational time is limited to 1 Hour.

In this article, an approach to optimize opportunistic maintenance policies was presented to examine the use of opportunities created in preventive maintenance activities. After the operation, maintenance, and repair, a component never gets back to the status of a new one. Hence, assuming that the replacement case is not approached, a maintenance activity is referred to as an imperfect type. In this article, assuming the existence of the imperfect maintenance type, an opportunistic approach based on age threshold values of components is proposed. The maintenance activities in this research focus on the hybrid flow shop problem. Different threshold values are also introduced in this article for failure conditions for a machine. A harmony search algorithm is used to provide optimized values for this proposed approach. The simulation approach is used to calculate the average cost of maintenance. The cost analysis indicates that the proposed approach is better than the corrective policy widely in literature; otherwise, the proposed approach with about 25 percent saving is the best performance.

Supply chain management has significant economic and environmental effects, including strategic, tactical, and operational decisions. According to the need for further cost reduction and improving the process of the organization in the direction of customer demand, the concept of the supply chain has become increasingly important, and the organizations seek to expand this concept within their organizational framework. In this regard, efficient planning of products distribution in the supply chain considering disruption is very important. Thus, this study develops a multi-objective mixed-integer programming mathematical model to design a green multi-echelon closed-loop supply chain with the possibility of disruptions. Furthermore, the ε-constraint method is applied to solve and validate the proposed model in small-scale problems. On the other hand, a non-dominated sorting genetic algorithm is developed for solving large-sized problems. Results indicate that the proposed model has performed well in obtaining optimal solutions, and the proposed algorithm has an efficient performance.

Today, the concept of JIT production has usage in production management and inventory control widely. In such an environment, tardiness or earliness is essential. Therefore, scheduling tries to minimize the sum of earliness and tardiness, which represents customer satisfaction, as well as inventory control. Most studies in scheduling adopt the assumption that machines are continuously available during the planning horizon. But in the real world, some machines may be temporarily unavailable for reasons such as breakdowns or preventive maintenance activities. So, considering the unavailability as a constraint is necessary for scheduling problems in the JIT production system. In this study, the unavailability constraint has been investigated with two flexible modes on a single machine. In each period, the duration of unavailability corresponding to the continuous working time of the machine changes in a discrete manner and can adopt two different values. Since the objective function is irregular, unforced idleness may be useful, increasing the complexity of the problem. First, a binary integer mathematical programming model is presented. Due to the NP-Hardness of the problem under consideration, a genetic algorithm is proposed to solve the problem in large dimensions. To examine the performance of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), several problem instances are generated and solved, and the obtained results are compared with those obtained from solving the mathematical model with the GAMS software. The computational results indicate the proposed algorithm has a good performance with an average deviation of 0.87% and a reasonable computational time.

This paper presents an integrated hybrid optimization problem for production and maintenance scheduling within a comprehensive system using overall cost and reliability. The total cost consists of three parts: production costs, inventory costs, and workforce costs. This integration aims to simultaneously find the optimal value of the function in a period. Using mixed-integer linear programming, the optimal values ​​are minimized over a limited horizon in the various samples considered for different numbers of workers and machines. In order to evaluate the model in larger dimensions, the NSGA-II metaheuristic method has been used. Given that the error rate of the developed mathematical model with the results of the meta-heuristic method in small dimensions can be neglected, so this meta-heuristic method has been used to perform sensitivity analysis in larger dimensions of the problem. In general, the results of this paper provide valuable information about changes in the number of workers and machines simultaneously to prevent interruptions and save on production to managers and analysts in the field of production planning.

Monitoring Binomial regression profiles in Phase II is examined in this study for multistage manufacturing processes where the quality characteristic is binary. In these kinds of processes, the quality of the final product depends on the quality characteristic of the previous stages, which is referred to as the cascade property. The U statistic was used to diminish the effect of this property. Then, four approaches, such as T2 and MEWMA control chart, LRT, and LRT/EWMA method, have been used, and the performance of these methods have been evaluated using simulation and a numerical example by means of ARL. An actual case study was also used to investigate the effectiveness of monitoring methods in further depth. Studies reveal that the proposed schemes perform well.

This paper provides a mathematical model and a bi-phase heuristic algorithm for the uniform parallel machines scheduling problem to maximize benefits and the number of jobs processed before their due dates as the weighted objective function. In the first phase of this heuristic, named “the neighborhood combined dispatching rules algorithm” (NCDRA), an initial sequence by the segmentation of the dispatching rules (DRs) is generated. Then, the output sequence is segmented, and required efforts are made to derive a sequence combined with these rules to improve the objective. The second phase involves a local search in which operators such as swapping, insertion, and reversion are concurrently implemented there on. The proposed algorithm is examined on four classes of problems with 50, 100, and 1000 jobs on 5, 10, and 50 machines, respectively. Results obtained by NCDRA and a Simulated Annealing (SA) algorithm developed on problem instances indicate that the NCDRA provides high-quality results on objective function for solving problems in different scales.

One of the topics in the world today is related to the production and development of infrastructure projects under the supervision and control of governments, in which the government has no operational role and acts as an observer. This type of production is called BOT, which brings both the risk and the project's profit to the private sector company. In such projects, before concluding a contract, the government gives a concession to a private company for a certain period to deliver the completed project, and on the other hand, it assigns the risks in the project to the investing company in full. In this research, the risks in BOT projects are investigated and using a proposed approach under the conditions of intuitive fuzzy uncertainty and multi-period systems, the weights of these indicators are calculated, and the strategies in this research are ranked. Finally, a case study is presented to construct a highway project, and the efficiency of the proposed method compared to a traditional method is measured. Meanwhile, the performance of the proposed approach is analyzed by eliminating contributions such as the last aggregation concept, criteria weights determination, experts’ weights computations. Moreover, a sensitivity analysis is provided to represent the robustness and sensitiveness of the main parameters.

Logistics makes up one of the significant parts of humanitarian organizations. Regarding the natural disasters’ increasing growth, coordination and cooperation in the logistics sector get more and more critical in order to minimize costs and enhance relief effectiveness. Thus, the current study proposes a decentralized multi-commodity and multi-period mathematical model for disaster relief commodities’ location and distribution. The major players of the research are the relief warehouses and the third-party logistics (3PL) organizations. These two players interact through a coordination mechanism, which keeps going until the time no shortage pops up in the system. The involved innovations encompass considering the simultaneous location, inventory, and distribution of aid supplies and relief provision outsourcing and relief goods’ transportation services to 3PL companies. The proposed HACO-VNS hybrid approach-based model has been solved for a case study in Tehran. The results indicate that as the demand increases, the number of established distribution centers increases. Besides, the budget increase leads to the reduction of the relief commodities’ shortage. Moreover, consequently, the present study extracted results that have been made accessible for disaster management practices.

This research focuses on the response phase of disaster management to plan debris clearance and relief distribution operations. For this purpose, a mathematical model is proposed under fairness concern and split delivery, in which various decisions such as facility location, vehicle routing, and scheduling are considered. Due to the uncertainty in different parameters such as demand for relief items, the amount of debris, costs, and service times, a robust optimization approach is employed to handle the uncertainties of the parameters. Finally, in order to illustrate the applicability and validity of the proposed model, a real case study is investigated.

Supply chain field has always been an aspiration for competitiveness in manufacturing organizations. Any organization’s conditions can be judged based on several criteria, such as robustness, rapid reconfiguration, lead time compression, etc. These criteria effectively determine the kind of supply chain strategy; it should be noted that this strategy varies in different markets and industries. Therefore, considering an appropriate strategy for the supply chain is an essential issue for most managers. Hence, this study, using a fuzzy expert system, shows how to select the best supply chain strategy. Three popular strategies, i.e., lean, agile, and leagile, are the main elements in this research. In addition, five applicable criteria are applied for selecting a supply chain strategy. A fuzzy expert system based on if-then rules was designed to connect these criteria to three strategies and select an appropriate supply chain strategy. Hence, criteria and supply chain strategies are taken to be input and output of this expert system, respectively, which lead to faster decision-making in the selection of supply chain strategy.

Healthcare industries create hazardous waste (HCW), which can become a danger to the health of society. HCW disposal management is one of the main challenges for urban organizations and healthcare systems. Meanwhile, the HCW disposal location is a wrapped flow due to the contention of different alternatives, criteria, and government principles related to the HCW disposal. In this regard, a new multi-criteria decision-making (MCDM) approach is introduced under the intuitionistic fuzzy (IF) conditions to evaluate the importance degrees of criteria and decision-makers (DMs) by computing their weights and coping with uncertain situations. A new integrated weighting criteria method is provided based on aggregating the subjective and objective criterion weight. The subjective weight is gathered from an expert person and objective weight is computed from ordered weight averaging (OWA) method. Afterward, the DM weights are computed based on similarity measure approach. Also, a new ranking approach is introduced with an ideal and anti-ideal distance-based method. These methods are utilized under IF condition. Finally, a case study from the recent literature is applied to validate the proposed approach. This case determines that the proposed method has a high performance to take the appropriate decision.