Toward a coordination of inventory and distribution schedules for blood in disasters

The need for efficient blood supply is of more significance in the event of disasters, when there is a lack of coordination between distribution and inventory management. The recent earthquake in Kermanshah province in Iran is among such cases that confirmed the need for coordinating such schedules. In this respect, a two-stage stochastic programming (SP) approach is presented for planning supply of blood after disasters that can assist in inventory decisions under hybrid uncertainty, minimizing the shortage and wastages. The uncertainty stems from imprecise parameters and scenario variability, and a robust-fuzzy-stochastic programming (RFSP) approach is devised to hedge against the uncertainty. The perishability of blood, the substitutability of blood groups, and the age-based characteristic of blood are taken into account to make the model more reliable. The compromise programming is applied to solve the multi-objective model. The results illustrate that the RFSP model can make a reasonable trade-off between mean value, feasibility robustness, and optimality robustness, which results in a robust and reliable solution under disastrous conditions.     

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.     

Socio-economic evaluation model for sustainable solar PV panels using a novel integrated MCDM methodology: A case in Turkey

Due to the increasing awareness of environmental, social and economic factors, solar photovoltaic (PV) system planning requires strategic decision making process for socio-economic development in many countries. The main objective of this paper is to propose a new Multi-Criteria Decision Making (MCDM) approach that is flexible and practical to the decision makers (DMs) in governments for solar PV panel manufacturer evaluation based on qualitative and quantitative factors. Accordingly, a novel two-stage MCDM model integrating Analytic Hierarchy Process (AHP) and Multiplicative Multi-Objective Ratio Analysis (MULTIMOORA) methods under Interval Valued Pythagorean Fuzzy (IVPF) environment is presented, and applied to select the most appropriate solar PV panel manufacturer for solar power plants in Southeastern Anatolia Region of Turkey. As a result, the proposed novel Integrated IVPF-AHP&MULTIMOORA method produces consistent and reasonable results to select the most appropriate solar PV panel manufacturer for the solar power plants in the cities of Southeastern Anatolia Region of Turkey considering some socio-economic sustainable indicators such as cost, environmental, efficiency and technical indices. Furthermore, sensitivity analysis and comparative analysis are also applied to prove the robustness and verification of the results of the proposed approach.     

The undervaluation of corporate reputation as a supplier selection factor: An analysis of ingredient branding of complex products in the manufacturing industry

Prior research studies on supplier selection factors assess vendor reputation as a low ranked criterion. Reputation in these articles, however, only refers to the position in the industry, without considering the role of the final customer. Our results from a survey with 565 individuals suggest that the end user as a stakeholder should be also considered when analyzing a vendor’s prestige. We demonstrate that a supplier’s standing has negative as well as positive reputational effects on the buyer. Depending on the relevance of the purchased good as well as the reputation of the supplier and the buyer, low/high-ranked ingredients significantly decrease/increase the final customer’s perception of the buyer.     

Postponement strategy for international transfer of products in a global supply chain: A system dynamics examination

This paper contributes to research on “postponement” strategy in the context of a global production–distribution system of an automobile manufacturer. It proposes a model that integrates multiple considerations germane to global supply chains. Postponement is important in this context because it is necessary to consider international transfers and tariffs, and it is important to appropriately account for the impact of postponement on total costs. Consideration of several key variables such as shipping point, customs tariff, and cost differences between countries is essential to derive full benefits from postponement strategy in global supply chains. International transfer of goods among countries in global automobile industry is complex and dynamic because of the multitude of factors that must be considered. The paper develops insights regarding postponement strategy in global supply chains via a system dynamics simulation model. The model draws on the experiences of a Korean automobile manufacturer with operations in developing and developed countries.     

Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran

Balanced development of regions requires the fair distribution of facilities and services. Hence, it is necessary to find and estimate the development degree of regions for policy makers. This paper presents an integrated Common Weight Data Envelopment Analysis (CWDEA) – Principal Component Analysis (PCA) model to find out the development degree of provinces in Iran. First, 131 suitable indicators are selected and then, the indicators are classified in fourteen different classes. In classical DEA model, each Decision Making Unit (DMU) is free to set its weights to reach the efficient frontier. In this paper, to restrict flexibility in indicator weights, development degree of provinces in each class is calculated using CWDEA model. Since, the proposed CWDEA model is not capable of fully ranking of provinces with all indicators, hence, the development degrees generated by CWDEA model are considered as indicators of PCA and the final ranks are obtained using PCA model. The results of proposed CWDEA-PCA model show that Yazd, Semnan and Bushehr are top three provinces in Iran.