Combination of pricing and inventory policies for deteriorating products with sustainability considerations.

Economic, environmental, and social criteria are all being taken into consideration simultaneously when determining pricing policies or inventory levels in sustainable production management. The combination of pricing and inventory policies is an important source of leverage for the efficient management of perishable products. This paper, among the first studies, proposes the problem of devising optimal pricing and inventory management decisions simultaneously where the environmental and social criteria are contributed for perishable complementary products replenished and sold by the same company. This study considers two interrelated price-sensitive linear demand functions to consider the possibility of shortage with both budget and warehouse capacity constraints. Another contribution of the proposed model is to consider an upper bound for environmental pollution and a lower bound for job opportunities as the constraints to the model. As a complex optimization model, the challenge of complexity is addressed by a heuristic algorithm for finding an optimal solution. After an extensive analysis using numerical examples, some managerial insights are concluded from the results. One finding from these analyses confirms that the total capacity of the warehouse, the total available budget, carbon emissions, and variable job opportunities have a high impact on the optimal solution to find a balance between sustainability criteria for making pricing and inventory policies.

A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian Palm oil industry.

One of the major challenges of the supply chain managers is to select the best suppliers among all possible ones for their business. Although the research on the supplier selection with regards to green, sustainability or resiliency criteria has been contributed by many papers, simultaneous consideration of these criteria in a fuzzy environment is rarely studied. Hence, this study proposes a fuzzy decision framework to investigate the sustainable-resilient supplier selection problem for a real case study of palm oil industry in Malaysia. Firstly, the resilient-based sustainable criteria are localized for the suppliers' performance evaluation in palm oil industry of Malaysia. Accordingly, 30 criteria in three different aspects (i.e. general, sustainable and resilient) are determined by statistical tests. Moreover, a hyper-hybrid model with the use of FDEMATEL (fuzzy decision-making trial and evaluation laboratory), FBWM (fuzzy best worst method), FANP (fuzzy analytical network process) and FIS (fuzzy inference system), simultaneously is developed to employ their merits in an efficient way. In this framework, regarding the outset, the relationships among the criteria/sub-criteria are obtained by FDEMATEL method. Then, initial weights of the criteria/sub-criteria are measured by FBWM method. Next, the final weights of criteria/sub-criteria considering the interrelationships are calculated by FANP. Finally, the performance of the suppliers is evaluated by FIS method. To show the applicability of this hybrid decision-making framework, an industrial case of palm oil in Malaysia is presented. The findings indicate the high performance of the proposed framework in this concept and identify the most important criteria including the cost in general aspects, resource consumption as the most crucial sustainable criterion and agility as the most important resilient criterion.

Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty.

Today, an increase in the drought and water shortage all around the world is a challengeable threat for different governments and international committees. Water supply chain aims to manage the water consumption and to control the water shortage. Contrary to most of previous studies focused on the forward directions of the water supply chain, this paper also considers the reverse logistics known as a Closed-Loop Supply Chain (CLSC). Hence, an integrated Water Supply and Wastewater Collection System (WSWCS) under uncertainty is proposed. Furthermore, regarding the trend of sustainable development, the environmental dimensions and social benefits of the integrated WSWCS are contributed. To meet the standards of the sustainable development in developing countries, a case study in Iran is applied to a novel multi-objective stochastic optimization model based on triple bottom lines of sustainability. Having already been employed to similar optimization problems, the Social Engineering Optimizer (SEO) has been never applied in this research area. Another innovation of this study is to introduce an improved multi-objective SEO to solve this complicated model. Eventually, with regards to an extensive comparison contributing to the Pareto-based metrics and different sensitivities, some managerial implications are concluded as the main findings.

Cadmium and copper heavy metal treatment from water resources by high-performance folic acid-graphene oxide nanocomposite adsorbent and evaluation of adsorptive mechanism using computational intelligence, isotherm, kinetic, and thermodynamic analyses.

In this paper, folic acid-coated graphene oxide nanocomposite (FA-GO) is used as an adsorbent for the treatment of heavy metals including cadmium (Cd2+) and copper (Cu2+) ions. As such, graphene oxide (GO) is modified by folic acid (FA) to synthesize FA-GO nanocomposite and characterized by the atomic force microscopy (AFM), Fourier transform-infrared (FT-IR) spectrophotometry, scanning electron microscopy (SEM), and C/H/N elemental analyses. Also, computational intelligence tests are used to study the mechanism of the interaction of FA molecules with GO. Based on the results, FA molecules formed a strong π-π stacking, chemical, and hydrogen bond interactions with functional groups of GO. Main parameters including pH of the sample solution, amounts of adsorbent, and contact time are studied and optimized by the Response Surface Methodology Based on Central Composite Design (RSM-CCD). In this study, the equilibrium of adsorption is appraised by two (Langmuir and Freundlich and Temkin and D-R models) and three parameter (Sips, Toth, and Khan models) isotherms. Based on the two parameter evaluations, Langmuir and Freundlich models have high accuracy according to the R2 coefficient (more than 0.9) in experimental curve fittings of each pollutant adsorption. But, multilayer adsorption of each contaminant onto the FA-GO adsorbent (Freundlich equation) is demonstrated by three parameter isotherm analysis. Also, isotherm calculations express maximum computational adsorption capacities of 103.1 and 116.3 mg g-1 for Cd2+ and Cu2+ ions, correspondingly. Kinetic models are scrutinized and the outcomes depict the adsorption of both Cd2+ and Cu2+ followed by the pseudo-second-order equation. Meanwhile, the results of the geometric model illustrate that the variation of adsorption and desorption rates do not have any interfering during the adsorption process. Finally, thermodynamic studies show that the adsorption of Cu2+ and Cd2+ onto the FA-GO nanocomposite is an endothermic and spontaneous process.

A bi-objective home healthcare routing and scheduling problem considering patients' satisfaction in a fuzzy environment.

Home care services are an alternative answer to hospitalization, and play an important role in reducing the healthcare costs for governments and healthcare practitioners. To find a valid plan for these services, an optimization problem called the home healthcare routing and scheduling problem is motivated to perform the logistics of the home care services. Although most studies mainly focus on minimizing the total cost of logistics activities, no study, as far as we know, has treated the patients' satisfaction as an objective function under uncertainty. To make this problem more practical, this study proposes a bi-objective optimization methodology to model a multi-period and multi-depot home healthcare routing and scheduling problem in a fuzzy environment. With regards to a group of uncertain parameters such as the time of travel and services as well as patients' satisfaction, a fuzzy approach named as the Jimenez's method, is also utilized. To address the proposed home healthcare problem, new and well-established metaheuristics are obtained. Although the social engineering optimizer (SEO) has been applied to several optimization problems, it has not yet been applied in the healthcare routing and scheduling area. Another innovation is to develop a new modified multi-objective version of SEO by using an adaptive memory strategy, so-called AMSEO. Finally, a comprehensive discussion is provided by comparing the algorithms based on multi-objective metrics and sensitivity analyses. The practicality and efficiency of the AMSEO in this context lends weight to the development and application of the approach more broadly.