Journal of Quality Engineering and Production Optimization
Volume:6 Issue: 1, Winter-Spring 2021

Resource-constrained multi-project scheduling problem (RCMPSP) arises in many project-based organizations, including construction and civil engineering companies. Numerous heuristic and meta-heuristic approaches have been proposed for a project scheduling problem with limited resources. In this paper, a twofold constructive genetic algorithm is proposed for the resource-constraint multiple projects scheduling problem, which benefits from a number of priority and several auxiliary rules which are fed into a serial schedule generation scheme (SGS), where auxiliary rules are used to break the tie situations where several activities have equal priority values. Numerical standard problems in different sizes are retrieved from the multi-project scheduling problem LIBRARY (MPSPLIB) website, and the numerical results are analyzed in different scenarios. Then, the genetic algorithm is used to improve the results where its parameters are tuned via Taguchi design of experiments (DOE). The results of this study showed that the performance of the proposed approach has significantly improved the solution of several problem instances and registered in the MPSPLIB.

An order-picking system (OPS) is essential to meet customer needs and orders in inbound logistics. Failure to perform the process properly results in high costs and customer dissatisfaction. To properly execute the OPS and to be able to meet the needs of customers with maximum efficiency, different aspects of the order picking process should be carefully considered. Research works have been conducted to improve OPS. This article attempted to review study components, study methods, and results in internal logistics while focusing on OPS. We also examined the composition of different aspects of OPS, including cost, equipment, warehousing, assignment, routing, batching, sequencing, and tardiness in 157 articles performed from 1958 to 2020 and studies on the process of time (Makespan). The reviewed studies were classified based on the simultaneous use of batching variables, assignment, routing, sequencing, and tardiness, the methods used in the research work, and finding research gaps for future investigations to clarify the relevance and application of routing, assignment, batching, sequencing, and tardiness aspects in the order picking process.

This research aims to optimize cost and demand-satisfaction in a 3-level supply chain management for a portfolio of projects at EPC companies, including vendors (for product procurement), warehouses as distribution centers, and projects as demand zones. In contrast, it reduces the costs of transporting and warehousing and increases demand satisfying for projects. We utilize the Multi-objective Particle Swarm Optimization (MOPSO) meta-heuristic algorithm to solve this model and the decision-making of vendors and warehouses. Besides, it leads to demand and storage allocation and monitoring of product flow between these levels of the portfolio supply chain.

This paper takes a predictive scheduling approach to deal with machine disruption and uncertain job processing times in single-machine systems. A two-dimensional scale is proposed based on robustness and stability. The expected total realized tardiness of jobs and the expected sum of absolute deviation between the planned and realized job completion times are respectively considered as robustness and stability measures. Considering the total tardiness as a robustness measure includes due dates, the customer satisfaction enhancement level is achievable. We propose a novel heuristic to deal with such an NP-hard problem. Computational results show the proposed method's superiority in satisfying customers and staff and increasing systems accountability, especially in large-size problems over the common methods in the literature.

This study aimed to analyze the behavior of effective factors on safety management to reduce incidents in the pelletizing industry using FMEA and the system dynamics approach. Risk assessment for the Sechahon pelletizing plant was performed using the FMEA method. A total of 625 risks were identified, of which 286 are high-risk RPNs and need to be scrutinized. These risks were categorized according to their nature into five categories of physical, exposure to dust, ergonomics, emergencies, and psychosocial risks. The results of our study show that decreasing the number of incidents in "risk reduction scenario" leads to the reduction of risk at the early durations of the study. Hence it is a high-priority policy in comparison with others. The technology improvement scenario shows an incremental growth trend, but the current situation scenario can decrease the number of incidents.

A multi-objective mathematical model is proposed to design a humanitarian logistics network under uncertain conditions. Three objective functions are considered to formulate this problem. The first one minimizes the total costs of logistics activities, the second minimizes the maximum overload of local distribution centers, and the third minimizes the maximum accident loss throughout the distribution of relief items. What is more, different simultaneous decisions are determined, including facility location-allocation, service sharing, relief distribution, truck routing, transferring service, and the evacuation of victims. Owing to the fact that the planning of humanitarian logistics problems is encountered with miscellaneous uncertain factors, such as demand, supply, costs, and capacities of facilities, a robust optimization approach is employed to tackle these challenges. Furthermore, a number of numerical instances are provided to illustrate the validity of the proposed mathematical model.

corresponding to the advent of technology, manufacturers are taking into consideration novel means of selling their respective products directly to the customer. Thus, the present paper aims to find the optimal pricing strategy for a supply chain, including a single manufacturer and two retailers. In the proposed model, the manufacturer decides whether to consider the channel for his/her direct sales on the market. Three scenarios are considered: Lack of cooperation between retailers, cooperation between retailers with fixed-price sales, and cooperation between retailers with various sales prices. Stackelberg game is used to examine scenarios in which the manufacturer is a leader, and two retailers pose as followers. In case of no cooperation between retailers, the results demonstrate a higher rate of profit for the manufacturer, while the total profit of the retailers is greater in the event of cooperation, using different sale prices. Finally, a numerical example is provided in order to demonstrate the effectiveness of the aforementioned three scenarios in supply chain models.

The present research formulates unequal area stochastic dynamic facility layout problems for minimizing the total cost of material handling while considering their upper bound. In the proposed model, the area and shape of departments are considered to be stable during the implementation of an algorithm, and product demands are normally distributed with a known expected value and variance. Since these problems are NP-hard, thus particle swarm optimization (PSO) was employed, and a theoretical problem instance was presented to evaluate the efficiency and effectiveness of the algorithm. The findings confirmed the efficiency and validity of the proposed algorithm.

Vertical cooperative advertisement is one of the market strategies in which the manufacturer pays for a part of the supplier's local advertisements. Considering the quality level of products in the model and demand function is necessary and exciting. In this paper, a two-level supply chain with one manufacturer and one supplier, and one complementary manufacturer is developed, which advertisement costs share among manufacturer and retailer. It means that we have a cooperative advertisement in this model. The demand in this paper has uncertainty, and retailers order products from the manufacturer with uncertainty. This supply chain and distribution channel with uncertainty in retailers' demand, products quality level, and considering complementary price and qualities is one of the most practical models in this field. The main aim of these models is to give insights to managers and decision-makers by investigating the effect of the participation rate and some of the important parameters on other essential variables and profit functions and display the relationship between this rate and the trend of the variables. There are two non-cooperative games, including manufacturer Stackelberg and supplier Stackelberg. The results are obtained from these games, and the trends are demonstrated.

Nowadays, the importance of supply chain management (SCM) includes timely and efficient decisions at strategic, tactical, and operational levels while addressing economic and environmental aspects. Meanwhile, designing an optimal supply chain to produce and distribute perishable items is of specific significance because of its prominent role in the human food pyramid. Delivery time of these products plays an important role which can directly affect customer satisfaction and is known as one of the main challenges. We try to develop a novel bi-objective model to configure a closed-loop supply chain network (SCN) for such products considering economic and environmental issues. Furthermore, according to the bi-objectiveness of our suggested model, the ε-constraint approach (EC) is employed to validate the model in small-scale instances. The obtained results showed that the model has an appropriate efficiency in solving the problems. Eventually, managerial insights are presented using the sensitivity analysis method for key parameters of the problem. It is demonstrated that the objective functions are so sensitive to the demand parameter where 20% increase and 10% decrease in this parameter lead to the most significant changes in the 1st and 2nd objective functions, respectively.

Control chart is one of the useful tools of statistical process control, which monitor the processes over time. In most of the designed control charts, it is assumed that the measurement errors do not exist in the measurement system, while this assumption is usually violated in practice as well. The presence of measurement errors leads to poor performance of the control charts. In this paper, a multivariate exponentially weighted moving average control chart is designed by considering measurement errors in Phase II. To decline the effect of measurement errors on the performance of the proposed control chart, multiple measurements method is applied. Also, sensitivity analysis about the effect of the number of measurements on the ARL performance of the proposed control chart is conducted. Note that different scenarios for the variance-covariance matrix are considered in simulation studies, including Case 1. Uncorrelated case with equal variances. Case 2. Negatively correlated case with equal variances. Case 3. Uncorrelated case with unequal variances. Case 4. Positively correlated case with unequal variances. Moreover, the performance of the proposed control chart is compared with the performance of Hotelling's T2 control chart. Results show the admissible performance of the proposed method in decreasing the effect of measurement errors.

Natural disasters and their destructive effects on life and property are the most important issues these days.  Implementation time of the relief operation and limitations associated with the ground infrastructure in disaster situations is the main priority of humanitarian logistics. So the necessity of using Unmanned Aerial Vehicles (UAVs) to reduce the service time is evident. On the Other hand, considering the deprivation cost function as an appropriate objective function can improve existing problems in disaster situations. This paper proposes a hybrid vehicle-drone routing model in the post-disaster phase. Multi trucks and multi drones with complementary capabilities are applied. Trucks are restricted to travel to the nodes with undamaged road networks, known as LD nodes. Drones can fly from the trucks to the nodes with damaged roads, known as DN nodes. They are used multiple times from each truck at LD nodes. The objective is to minimize the deprivation cost as a function of the deprivation time of both types of nodes. So by minimizing the deprivation cost, the vehicles' arrival times are minimized, and better routes are selected for vehicles. The effect of the population rate of the affected area on the route determination also is considered by the deprivation cost function. Finally, the validation of the proposed model is tested by solving it in GAMS software. Some examples are solved to show how applying the deprivation cost can improve the selecting routes by this model.