Journal of Quality Engineering and Production Optimization
Volume:5 Issue: 2, Summer-Autumn 2020

This study integrates the problem of locating and routing electric and conventional vehicles besides considering greenhouse gases emission. This problem is a subset of the problems of locating and routing and the green routing problem in which a combination of electric and conventional vehicles is used. The advantage of this model is the aid of the utilization of electric vehicle technology to reduce the elimination of greenhouse gases. This model can be used in the design of the transport and logistics system of organizations and companies. Many models have been developed and applied concerning electric vehicles. However, this type of composition and its use is subject to environmental requirements to reduce greenhouse gas emissions. We also assumed the capability of recharging and battery replacement in the model. The model for different samples was solved using GAMS software and a multi-objective particle swarm optimization (MOPSO) algorithm. Besides, the impact of increasing the tax on greenhouse gas (GHG) emissions was tested on electric vehicle usage, amount of GHG emissions, and system costs. The results show that the model can be used to design the transport and logistics systems of organizations to impose the least charges besides emitting the least greenhouse gases.

The scheduling and batch delivery problem has been studied by many researchers as one of the classic operation sequence problems. However, this study attempted to address this problem by adding order acceptance determination and sequence-dependent setup. In this study, a mathematical model has been presented in Marun Petrochemical Company considering the number of orders received for production to maximize the organization's profit by selecting the order and planning the production based on the order received. Therefore, to realize this goal in the small production space, the mathematical model was coded in GAMS software and solved using the CPLEX solving method. Then, to investigate the larger scale of the model under study, the validation of 23 generated problems was carried out by the exact solution in GAMS software and genetic algorithm in MATLAB software, and in the end, the comparative evaluation was performed. The evaluation showed that the meta-heuristic solution on a small scale has a small deviation from the exact solution, and the mathematical model is solved in a proper time by the meta-heuristic algorithm. As the problem's size grows up, the exact solution loses its efficiency in terms of time, and the application of the exact solution algorithm to solve the model becomes inadequate. The genetic algorithm achieves an acceptable solution in a proper time with reasonable deviation. So, this algorithm can replace the exact solution properly.

One of the most critical problems in managing the car manufacturing factories' final assembly line is Car Sequencing Problem (CSP). The optimal permutation of car models is determined in a mixed model assembly line by solving this problem. In real-world cases, the unforeseen occurrence of disturbances like shortage or delay in feeding required parts to the assembly line cause to stir up an initially planned sequence. In this situation, the car resequencing problem is another challenge that should be solved. This study treats the car resequencing problem with an intermediate buffer before the final assembly line to rearrange the given initial sequence. Two objective functions are considered: (1) minimizing the ratio constraint violations (definitive objective of the car sequencing problem), and (2) minimizing work in process that remained in painted body storage (PBS) buffer. For this problem, a mathematical model as MIP is developed. Since this problem is discussed as intensely NP-hard, a new hybrid algorithm is proposed based on NSGAII and VNS to solve the medium and large scales. The numerical experiments are used according to sample problems in CSP Lib. to run the mathematical model and evaluate the developed approach's performance. The computational results show that the proposed method has a good effect on minimizing two objective functions in solving the medium and large-sized problems.

In decision-making situations, the opinions expressed by decision-makers (DMs) are often vague. Using linguistic variables expressed in intuitionistic fuzzy numbers is a more realistic approach to describing DMs’ judgments. The paper aims to develop a Group Decision Making (GDM) methodology based on the data envelopment analysis (DEA) method with intuitionistic fuzzy information. This method is utilized once a set of Decision-Making Units (DMUs) need to be ranked based on their efficiencies over a set of input and output measures considering DMs’ weights in an intuitionistic fuzzy environment. In the proposed method, concerning the input and output measures, each DM utilizes membership and non-membership degrees to determine the degrees of satisfiability and non-satisfiability of each DMU, respectively. Besides, a new technique is presented to determine the DMs’ weights. Different values of a DMU's efficiency obtained by individual DMs are converted into an aggregated efficiency based on the DMs’ weights. Finally, the extended DEA method is used to rank the DMUs based on their efficiencies. A case study on a production company is done for illustration and verification of the proposed approach.

The volume of international maritime trade has increased significantly in recent years, and the growth is expected to be continued with similar rates.  To meet the growing demand, it is necessary to improve productivity with minimal investment in container terminals infrastructures. Iranian Rajaee port is the main gateway of Iran import and export and is chosen as the case study of this research. Rajaee port has two container terminals with different depths. Currently, these two container terminals are working separately by two different operators. To meet the growing demand for maritime trade, these two container terminals could share their resources based on a policy presented in this study. This research has developed a berth allocation policy where demand could be driven from a container terminal to an adjacent container terminal with an additional cost. The goal of the Original Container Terminal is to minimize the total cost of vessel services. Several numerical examples are presented to evaluate the effectiveness of the new berth allocation policy. The results show that the proposed berth allocation policy offers significant cost savings over high demand periods.

This paper designs an optimization model for the emergency department of a hospital, considering related costs, nursing staff satisfaction, and waiting time for several diseases concerning the number of staff in each shift. This study's primary purpose is to minimize the related costs, maximize nursing staff satisfaction, and allocate nursing staff to working shifts in the emergency department. In the first stage, a simulation model is constructed based on the emergency department's status with ARENA 14 software. Then, the model is investigated under three different scenarios. In the second stage, mixed-integer programming is proposed to minimize the costs, nursing staff satisfaction and optimally allocate nurses to various shifts. Furthermore, the generalized center method is used to solve the model by converting the multi-objective model to a single-objective one. In Tabriz, Iran, Imam Reza hospital is considered our case study investigated by simulation and MIP models. Finally, the results of simulation and mathematical models demonstrate that six new nurses should be added to the emergency department.

Nowadays, different important indicators besides price on product sales and durability of manufacturers on the market have been considered. This paper considers the demand, cost, competitive pricing behavior, substitutability, and quality in the proposed model under two competitive manufacturers and one standard retailer. Each competitive manufacturer can sell a product directly (D) or indirectly (I) to the customer. So, we develop three scenarios for delivering the manufacture's product to the end customer. In scenario DD, two manufacturers sell directly. However, in scenario II, they sell through the familiar retailer to the end customer (indirectly), and in scenario ID, one of the manufacturers sells directly, but the other sell indirectly. Finally, some numerical examples are given to illustrate the effectiveness of the proposed scenarios in the model. Numerical examples show that the total profit of scenario DD is less than the total profit of Scenario ID. When two manufacturers' products' substitution rate is close to one, each player's total profit in scenario II is greater than the other two scenarios.

 Reviewing related studies to supply chain networks shows that various factors influence the supply chain network's optimal design. Demand changes in market zones, changes in customers' locations, various competitive conditions in different regions, network costs fluctuations, and different disruption pose risks to the supply chain network in terms of optimal condition. In such conditions, to help the network return to its optimality, the network needs to be redesigned. In this respect, this paper has formulated a multi-period multi-product mathematical model for redesigning the warehouses considering backorder shortage. In this regard, decisions such as the construction of new facilities, closing non-optimal facilities, increasing the active facilities' capacity, the optimal amount of order/shortage in each active warehouse in each period have been investigated. Due to the inherent uncertainty in real-world business, parameters related to cost and demand are considered uncertain. Then, to deal with uncertain parameters in the proposed model, Jimenez's possibilistic programming has been used. The obtained results show that redesigning the supply chain network by setting up new facilities/closing non-optimal facilities can significantly reduce inventory and transportation costs and shortages.

Pharmaceutical companies need to take advantage of adequate profits to obtain sufficient funds for playing major roles in the competitive market. The purpose of this research is to predict the price of medicine and the volume of production, taking the producer’s profit, the prices of the raw materials, and qualities into consideration. System thinking is employed to develop the cause and effect diagram and system dynamics for preparing the model for simulation and trend analysis. The simulation was carried out using VENSIM software on the amoxicillin capsule as a case study. When the government increases the marginal profit percentage for producers and with high-quality raw materials used by the producer, the companies` profits, production volume, and medicine quality will increase. Sensitivity analysis indicates that our pharmaceutical production company can better deal with the pharmacies, producers, and suppliers. This means that with a two percent lower profit margin for the pharmacy industry, the final medicine price would come down to 40105 from 40601, which is a one percent reduction to the base price suggested by simulation originally. This article makes a significant contribution to the Pharmaceutical field and hence to the patients and health industry.

In this paper, the contribution and application of queuing theory in food supply chain management were reviewed. Although many published articles have studied food supply chain management, none of them have focused on the application of queuing theory. This paper first briefly compares queueing theory with other operations research methods and explains the reason for choosing this mathematical method. This review proposes an innovative procedure (based on content analysis) for categorizing food supply chain issues in different areas with queueing theory. We also perform additional content and descriptive analyses to identify trends in the extant literature on food supply chain management and queuing theory application. The results of our investigations are presented based on five perspectives: food supply chain management, decision level, type of food product, queuing system, modeling methodology. Our studies show that extensive gaps are dealing with the topic of this study. Also, a structured summary of each article is presented to provide comprehensive guidelines in this research area. Also, the challenges of this hybrid field were explored, and topics were suggested for future research. The purpose of this paper is to provide sufficient information for researchers who want to use queuing theory in various areas of food supply chain management.

This paper presents a scenario-based supply chain network design (SCND) model in the case of disruption occurrence with a single product type. The proposed supply chain (SC) comprises three echelons, including manufacturers, distribution centers (DCs), and customers. Two kinds of DCs, namely reliable and unreliable DCs, are considered in the presented model. Disruption affects unreliable DCs and causes the loss of a portion of their capacity. Thus, an unreliable DC capacity in each period is assumed to be a positive variable specified based on its capacity in the previous period. There are different investment levels for establishing each DC, which affects the amount of capacity loss due to disruption. Two resilience strategies, DC capacity fortification and inventory keeping are considered to reduce the effects of disruption on SC, and the results under each strategy are investigated. The outcomes are investigated by presenting numerical examples, and the advantages of the proactive manner versus reactive manner are shown. Finally, sensitivity analysis is done on disruption related parameters to show how parameters influence the model outputs.

The concept regarding green supply chain management developed as a response in order to increase public awareness from environmental stability which regularly holds both the environment and supply chain management. In the past few years, green supply chain management has become a challenging issue in advanced operations research. In a manufacturing company, selecting an appropriate supplier for their long-term growth prospects by the supply chain managers not only proceed with the business strategy but also help the company remain in a competing position. Considering multiple criteria group decision-making (MCGDM) problem in order to solve the green supplier selection (GSS) includes many unmeasurable and contradictory criteria. This research presents a new group decision approach via interval-valued 2-tuple linguistic preferences and compromises solution to appraise the GSP. The falsification and loss of information which happens formerly in the probabilistic linguistic information process are avoided. Next, a real application is provided via the introduced approach under uncertain conditions regarding the recent literature in the manufacturing industry. Finally, for validation, sensitivity and comparative analyses and a discussion on the effectiveness and benefits of the introduced method are completed and provided.