Journal of Industrial and Systems Engineering
Volume:11 Issue: 4, Autumn 2018

In a supplier-retailer-buyer supply chain, the supplier frequently offers the retailer a trade credit of periods, and the retailer in turn provides a trade credit of periods to her/his buyer to stimulate sales and reduce inventory. From the seller’s perspective, granting trade credit increases sales and revenue but also increases opportunity cost (i.e., the capital opportunity loss during credit period) and default risk (i.e., the percentage that the buyer will not be able to pay off her/his debt obligations). Hence, how to determine credit period is increasingly recognized as an important strategy to increase seller’s profitability. Also, many products such as fruits, vegetables, high-tech products, pharmaceuticals, and volatile liquids not only deteriorate continuously due to evaporation, obsolescence and spoilage but also have their expiration dates. In this paper along with deterioration and expiration date, we consider shortages that are very rarely investigated by researches. Therefore, this paper proposes an economic order quantity model for the retailer where: (a) the supplier provides an up-stream trade credit and the retailer also offers a down-stream trade credit, (b) the retailer’s down-stream trade credit to the buyer not only increases sales and revenue but also opportunity cost and default risk, (c) deteriorating items not only deteriorate continuously but also have their expiration dates and (d) there is a shortage allowed in each time period. We then show that the retailer’s optimal credit period and cycle time not only exist but also are unique. Furthermore, we discuss several special cases including for non-deteriorating items. Finally, we run some numerical examples to illustrate the problem and provide managerial insights.

Healthcare systems help to improve the quality of working life. Therefore, high-quality services are essential in hospitals as the main party of healthcare systems. While there are limited resources in healthcare systems and hospitals, many factors may influence providing appropriate services. Therefore, finding and allocating rare resources of the hospitals to the most important factors can help to increase customer satisfaction. In this paper, a hybrid fuzzy multiple attribute decision-making approach is proposed to prioritize critical success factors (CSFs) of service quality of healthcare systems. The uncertainty of measuring qualitative factors has been modeled using fuzzy sets. The proposed hybrid approach consists of Fuzzy Delphi, Fuzzy DEMATEL, Fuzzy ANP, and Fuzzy VIKOR to find and prioritize the CSFs based on SEVQUAL method. The proposed hybrid approach has been applied to a real case study where its applicability and efficacy have been illustrated.

Taking into account the economic capabilities, competitive advantages and environment dynamics, this research presents a new physical flow optimization model by considering payment period, time value of money and exchange rate fluctuations. Considering the actual circumstances of the current case study in the global value chain, a single-objective non-linear mathematical model is presented for the role of the SIBA MOTOR Company. Owing to the non-linear nature of the presented model, it has been linearized through a heuristic method. Next, by using GAMS software, the model is solved by considering the assumption of the case study. Finally, a sensitivity analysis is performed on important parameters after obtaining the numerical results to assess them. Appropriate potential location of the factory, inventory of each period, CKDs and CBUs flow of vehicles, the amount of tax and the customs expenses, and each payment method’s share, are some of the outputs of the model. The results of the proposed model are appropriate for the Iranian automotive companies to participate more actively in the global value chains of the automotive industry.

This paper addresses a closed-loop supply chain network design problem, in which two different supply chains compete on retail prices by defining a price-dependent demand function. So, the model is formulated in a bi-level stochastic form to demonstrate the Stackelberg competition and associated uncertainties more precisely. Moreover, it is capable of considering random disruptions in the leader supply chain while incorporating the inventory, pricing, location and allocation decisions. Afterwards, having a contract with reliable suppliers is examined to resist the consequent results of disruption in the supply process. Additionally, the sharing strategy with new resilient distribution centers is used for tackling disruption risks at distribution centers. Furthermore, after integrating the proposed bi-level model into an integrated equivalent form by using the Karush–Kuhn–Tucker (KKT) transformation method, the conditional value at risk (CVaR) measure is used to handle the considered uncertainties. Finally, a real industrial case of a filter company is applied to obtain numerical results and the performance of the stochastic model is investigated by several test problems to arrive in helpful managerial insights.

The purpose of this study was to investigate designing a two-level hierarchical healthcare facilities network under human resource constraint. To this end, a mixed integer model has been proposed in which the location of facilities, optimal flow of patients between the levels of the network, capacity planning and the planning of the required human resources are considered as the most important decisions. The proposed model aimed to minimize the total costs including the costs for the establishment of facilities, the cost of setting up services in different facilities, the costs of non-fulfilled demand at the second level of the network and the travel costs for patients to receive a variety of services. In this model, some of the parameters were considered uncertain that in order to cope considered uncertainty credibility-based chance constraint programming method was used. Then, the proposed model was implemented for planning in the several districts of Sari city in Mazandaran province. Finally, sensitivity analysis was carried out on some parameters such as the maximum available human resources and the average number of referral of each patient zone to family physician centers. Results revealed that if the maximum available human resources increase by 50%, network costs will be considerably reduced since the shortage costs get zero.

This study proposes a novel mathematical model for redesigning existing relief logistics network including suppliers, distribution centers and demand nodes along with integrating the measures in preparedness and response phases, simultaneously. In order to improve the accessibility and connectivity, certain precautionary measures for strengthening and rehabilitation of the links have been taken into account in the preparedness phase. In addition, a new debris clearance scheduling model for blocked links is modeled in accordance with the rehabilitation strategies. To overcome the uncertainty in a predefined destruction scenario tree, a multi-stage stochastic programming has been applied in a real case study. The results obtained in the proposed model indicate that the redesigned network leads to better performance in dealing with evacuees’ requested relief as compared to the results obtained by the existing network. Moreover, the results clearly demonstrate the significant value of solutions determined by multi-stage stochastic programming.

The study of product family and its design as well as issues related to supply chain is as fascinating discussion, and its modeling and optimization consider as a challenge for industries and businesses. In this paper, using a consolidated approach, a comprehensive model in the Mixed Integer Linear Program (MILP) dominant is proposed to concurrent optimization of product family and its supply chain network design by considering reverse logistics. In the proposed model, different levels of bill of material, including components, sub-assemblies, sub-sub-assemblies and finished products is considered while there is possibility of substitution at all levels. The supply chain network, includes 5 levels consist of suppliers, factories, distribution centers, customers and recycling centers. To solve low complexity instances in the view of products design and supply chain network structure, CPLEX solver has been applied. To solve high complexity instances, a heuristic method based on linear programming rounding has been developed, which caused a considerable reduction in solving time with an acceptable gap.

Regarding the sustainability considerations in the health care centers is critical to improve the quality of services, to decrease the costs and environmental issues. Of approaches that health care centers can use to achieve sustainability is sustainable supply chain management that improves environmental, social and economic effects of the organizations. In this research, after a review on the literature related to the sustainability in health care centers and sustainable supply chain management as well as interview with experts in these fields, 15 barriers of implementing sustainable supply chain management were determined in the health care centers. In addition, in another interview with experts, the relations and order of the importance of 15 identified barriers were determined according to interpretive structural modeling approach. The research results indicate that in addition to the Lack of an institutional support for integration, coordination and communication that is the most important barrier in implementing sustainable supply chain management in health care centers, the Lack of top management commitment to initiate sustainability efforts, the lack of knowledge among the members of supply chain, the lack of skilled human resources and Unawareness among society about social practices are also considered as other barriers in implementing sustainable supply chain management in the health care centers.

This article studies flexible job-shop scheduling problem (FJSSP) considering three objective functions. The objectives are minimizing maximum completion time (Cmax), the maximum machine workload (Wmax), and the total workload (WT). After presenting the mathematical model of the problem, a genetic algorithm called Reference Group Genetic Algorithm(RGGA) is used to solve the problem. RGGA implements the reference group concept in the sociology to the genetic algorithm. The term " reference group" is credited to sociologist Robert K. Merton. Three standard data sets are used to evaluate the performance of RGGA. On the first data set, RGGA is compared to six algorithms in the literature, on the second data set RGGA is compared to four algorithms in the literature, and on the third data set RGGA is compared to three algorithms in the literature. Moreover, RGGA is compared with optimum solution in small size problem. Results show the superiority of RGGA in comparison to other algorithms.

Delivering on time is an essential factor for the survival of factories in a competitive environment, which requires planning in the supply chain. Therefore, with the correct planning in the supply chain scheduling, it can leads to reduce the cost, lower prices, customer satisfaction and ultimately leads to competitive advantage for organizations. This study considers the scheduling of production and transportation in two-stage Multi-Site supply chain (MS-SC) regarding geographical zoning and using exclusive suppliers (MSZ-SC). The first stage contains suppliers with different production speeds and ability to produce a particular production. The second stage is composed of vehicles, each of which may have a different speed, capacity and setup time. In fact, 5 major factors that are able to be seen as a value for owners are considered. We have presented a mathematical model for scheduling of this supply chain, and then the model coded by Dynamic Genetic Algorithm (DGA), which is an improved version of genetic algorithm, with MATLAB software. Covering the wide range of problems, 648 random problems are solved reaching reasonable achievement. Experimental results from both model with and without zoning, clearly show that proposed model offers better performance in critical variables. In fact, managers are able to use this planning regarding geographical zoning and exclusiveness to gain competitive advantage by time and consequently cost reduction. Better operation abilities in tow-stage proposed model that is clearly shown, certainly, lead to have merits on managerial decision making.

This paper presents a multi-stage model for accurate prediction of demand for dairy products (DDP) by the use of artificial intelligence tools including Multi-Layer Perceptron (MLP), Adaptive-Neural-based Fuzzy Inference System (ANFIS), and Support Vector Regression (SVR). The innovation of this work is the improvement of artificial intelligence tools with various meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Cultural Algorithm (CA). First, the best combination of factors with can affect the DDP is determined by solving a feature selection optimization problem. Then, the artificial intelligent tools are improved with the goal of making a prediction with minimal error. The results indicate that demographic behavior and inflation rate have the greatest impact on dairy consumption in Iran. Moreover, PSO still exhibits a better performance in feature selection in compare of newcomer meta-heuristic algorithms such as IWO and CA. However, IWO shows the best performance in improving the prediction tools by achieving an error of 0.008 and a coefficient of determination of 95%. The final analysis demonstrates the validity and reliability of the results of the proposed model, as it supports the simultaneous analysis and comparison of the outputs of different tools and methods.

In this paper, we study a supplier selection and order allocation problem with in a multi-objective and fuzzy environment. Transportation costs and quantity discount are taken into account in the problem. We assume four common objectives as total costs, on-time delivery rate, defective rate, and purchasing value. We utilize a max-min approach such that the min-operator finds the fuzzy decision that simultaneously satisfies all the fuzzy objectives. Then the maximizing decision is determined to be the maximum degree of membership for the fuzzy decision. We use the non-linear S-shape membership functions to express the vague aspiration levels of the DM’s objective. According to the defined fuzzy membership functions and applying Bellman–Zadeh’s maximization principle, the fuzzy multi objective model is transformed into a single objective model. A genetic algorithm is applied to solve the multi objective fuzzy supplier selection and order allocation problem. Computational results are presented using numerical examples.