Journal of Quality Engineering and Production Optimization
Volume:5 Issue: 1, Winter-Spring 2020

One of the most attractive topics for industry and researchers in industrial engineering is the integration of decisions in the supply chain. There are some advantages in the integrated decisions compared to different decisions, such as decreasing the cost of distribution and On-Time delivery. An integrated production scheduling and distribution problem is discussed in this study. The main contribution of this paper is to study this problem from a multi-agent viewpoint. In this case, each agent has a set of customers with their jobs, and each agent has a specific objective. Here, a two-agent problem is discussed in which the first agent objective is the minimization of the total tardiness for jobs of the first agent and the second agent objective is to minimize the total cost of the distribution. For this problem, a mixed-integer linear programming formulation is developed. Due to the complexity of the original problem and its specific structure, a combinatorial Benders decomposition approach decomposes it to the master problem and sub-problem. It means some modifications have been applied to the classic version of Benders method. The results show the excellent performance of the algorithm in comparison with another exact method .

This study proposes a bi-objective optimization model to design a reliable biomass-biofuel supply chain, in which loading and unloading hubs and biorefineries can be encountered with disruption. For this purpose, the first objective function minimizes the total costs, and the second one minimizes the total times of recovery of disrupted facilities. Furthermore, due to the uncertain essence of the biomass supply chain, two efficient approaches, including robust optimization and congestion effect, are considered to overcome this challenge. Finally, several test problems are investigated to demonstrate the validity of the suggested mathematical model.

Statistical process control (SPC) is a leading method in monitoring process performance and detecting process deviations from goals, and measure progress in improving programs. Despite the widespread use of SPC in various processes, its capability has not yet been well studied in the values education process (VEP). Some challenges in using this method were: the lack of appropriate quantitative data for using in the SPC, invalid and untrusted data, the presence of different values that make it difficult to focus on values education, and choosing the proper process characteristic and control charts associated with it. In this paper, a framework is presented to resolve these challenges includes: extracting the quantitative data related to the values using event count items and check sheet, removing invalid data and its sources from the research process through statistical tests, prioritizing values based on four attributes related to values, and finally, measuring the value changes in students as Process characteristic. We used a modified deviation from the nominal (DNOM ) control chart to identify and analyze the VEP changes. The results of a case study at a school were quite promising. It increased team knowledge, helped decision-makers design and improved the VEP, and developed the SPC method capability in a new area.

As a traditional statistical quality control method, acceptance sampling plans are widely applied for quality assurance. In a sampling plan, with the aim of acceptance or rejection of a lot of martial, inspection is carried out to determine adherence to standards. Usually, it is assumed that the inspection of the items is error-free. In the present study, this assumption is relaxed. Using Bayesian inferences and considering inspection errors, three mathematical models are developed for the economic single-sampling plans. First, the model is developed based on Binomial distribution. Regarding the application of Poisson model in approximating Binomial distribution, the second model is developed based on Poisson distribution. The third model is presented considering Negative binomial (which is also known as Pascal Distribution). The models determine the sample size and acceptance number to minimize the expected inspection costs incurred during sampling. A numerical example is presented and sensitivity analyses are carried out.

 Every year, natural disasters (e.g., floods and earthquakes) threaten people's lives and finances. To cope with the damage of natural disasters, emergency resources (e.g., rescue teams) must be planned efficiently. Therefore, designing a decision support model to allocate and schedule rescue teams is necessary for the response phase of disaster management. The literature review shows that social aspects of disaster management have less been addressed by researchers, whereas this phenomenon must be incorporated into decision-making processes. The lack of timely relief implies a loss in people's welfare, which leads to social costs called deprivation cost or time. This study proposes a multi-objective mixed-integer programming model to assign and schedule the rescue teams considering different rescuers' capabilities, fatigue effects, and deprivation time. Due to the NP-Hardness of the proposed model, a hybrid approach based on the Lp-metric method and meta-heuristic algorithms are applied to solve the given problem. The results show that the developed algorithm can obtain high-quality solutions in a reasonable time.

In this paper, we develop a multi-period mathematical model involving economic and environmental considerations. A vehicle-routing problem is considered an essential matter due to decreasing the routing cost, especially in the concerned bioenergy supply chain. A few of the optimization model recognized the vehicle routing to design the bioenergy supply chain. In this study, a bi-objective mixed-integer linear programming (MILP) model is presented.  The economic objective function minimizes the transportation, capacity expanding, fixed and variable costs, and the locating routing cost in this problem. The proposed bi-objective model is solved through a Non-Dominated Genetic Algorithm (NSGA- II).  Furthermore, the small-sized problem is solved by the CPLEX solver and augmented ɛ-constraint method.

The sustainable supplier selection (SSS) problem is an integral part of project procurement management. In this paper, a new extended grey relational analysis (GRA) based on the complex proportional assessment (COPRAS) is applied for SSS problems for using the merits of these two methods simultaneously. Furthermore, a new multi-objective optimization model (MOOM) is developed to obtain the objective weights of criteria. Moreover, to illustrate the uncertainty of real SSS problems and derived uncertainty of experts’ judgments, grey numbers are employed. To reduce the reliance on the experts, a new MOOM is developed for criteria’ weights determination. Finally, the performance of the introduced method is demonstrated by solving a numerical example.

Nowadays, the lack of energy regarding increasing population growth and increasing consumption to meet industries' needs is one of the major problems worldwide, especially in developing countries. This paper attempts to model multiple energy hubs to promptly meet customers' needs and prevent shortages and pay extra costs during peak periods by storing energy in regular periods. The system has several energy hubs with different equipment that will be used according to the customers' needs. Various maintenance policies have been defined to achieve the optimum conditions based on cost and capacity available by solving the model to increase the quality of service and the equipment's high efficiency. Due to uncertainties in customer demand and different maintenance policies, two-staged stochastic optimization has been used based on the scenario. Model solution results show that concerning the defined costs, each hub's interior equipment has better performance with a six-month maintenance policy and the hub input equipment has better performance with a monthly maintenance policy.

Because of the high costs for the delivery, manufacturers are generally needed to dispatch their products in a batch delivery system. However, using such a system leads to some adverse effects, such as increasing the number of tardy jobs. The current paper investigates the two-machine flow-shop scheduling problem where jobs are processed in series on two stages and then dispatched to customers in batches. The objective is to minimize the batch delivery cost and tardiness cost related to the number of tardy jobs. First, a mixed-integer linear programming model (MILP) is proposed to explain this problem. Because the problem under consideration is NP-hard, the MILP model cannot solve large-size instances in a reasonable running time. Some metaheuristic algorithms are provided to solve the large-size instances, including BA, PSO, GA, and a novel Hybrid Bees Algorithm (HBA). Using Friedman and Wilcoxon signed-ranks tests, these intelligent algorithms are compared, and the results are analyzed. The results indicate that the HBA provides the best performance for large-size problems.

The municipal concrete waste production has grown recently due to the urban population's considerable increment. With the advances in technology, SWM (solid waste management) has been a significant challenge for many countries worldwide. Therefore, in this paper, a multi-objective mixed-integer linear programming model with three objectives, maximizing job opportunities and minimizing costs and carbon emissions, is extended under uncertainty. A fuzzy goal programming approach is applied to deal with uncertain parameters and solve the proposed multi-objective model. A case study is employed for waste management in Tehran's fifteen urban areas to demonstrate the proposed model's efficiency. Ultimately, the model is solved using the CPLEX solver of GAMS software, and a sensitivity analysis is performed to assess the results.

Evaluating the sustainability in the crop supply chain plays an important role in creating an efficient supply chain and increasing food safety in developing countries. Therefore, in the present study, an efficient network of greenhouses, agricultural lands, processing plants, and agricultural distribution centers (farmer’s markets) have been designed with regards to the sustainability dimension and with the purpose of minimizing the costs, minimizing the greenhouse gas emissions, and maximizing employment. The uncertainty has been identified as an inevitable part of studies in some parameters such as Brix, deterioration rate, employment rate, ideal yield, and water consumption rate. Also, the use of agricultural methods and techniques such as crop rotation policy, the use of nurtured seedling, and the consideration of Brix are among the issues that have been considered to increase the efficiency of the products and consequently to increase the economic efficiency. The model proposed with augmented ε-constraint method has been solved, and its efficiency has been investigated by presenting a case study in Iran. The results of the model solution indicate that the use of different planting policies, the consideration of Brix index, and effective attention to crop rotation policy has led to higher productivity of agricultural lands, production of higher quality products by expending lower prices and preserving environmental resources such as soil and water; so that the water consumption is reduced by 65.258%. Furthermore, the proposed model increases employment in the region and reduces unemployment by 11.2%

The process of designing and redesigning supply chain networks is subject to multiple uncertainties. Given the growing environmental pollution and global warming caused by societies' industrialization, this process can be completed when environmental considerations are also taken into account in the decisions. In this study, an integrated four-level closed-loop supply chain network, including factories, warehouses, customers, and disassembly centers (DCs) is designed to fulfill environmental objectives in addition to economic ones. The reverse flow, including recycling and reprocessing the waste products, is considered to increase production efficiency. Also, the different transportation modes between facilities, proportional to their cost and greenhouse gas emissions, are taken into account in the decisions. A random cost function and chance constraints are presented firstly to handle the uncertainties in different parameters. After defining the random constraints using the chance-constraint programming approach, a deterministic three-objective model is presented. The developed model is solved using the GAMS software and the goal attainment (GA) method. Also, the effect of the priority of the goal, uncertain parameters, and confidence level of chance constraints on objective function values has been carefully evaluated using different numerical examples.