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In the oil and gas industry, adopting policies that can reduce the negative environmental effect is vital. Environmentally Sustainable Supply Chain Management (ESSCM) is an approach to carrying out Supply Chain Management (SCM) in an eco‐friendly manner and according to environmental requirements. There are different environmental policies that companies can apply based on their resource availability. Therefore, this study aims to evaluate the impact of hard dimensions on Environmentally Adaptive (EA) and Mitigated Adverse Eco‐Effect (MAE) policies in the oil and gas industry. To rank the data, Bayesian Best‐Worst Method (BWM) and Ordinal Priority Approach (OPA) have been applied. Cause‐and‐effect relationships are then calculated by employing the Decision‐Making Trial and Evaluation Laboratory (DEMATEL) technique. The results indicate that the ranking of the hard dimensions varies based on the companies' business policies and their new product/technology development projects. In other words, the findings of this research demonstrate that 'innovation' is the crucial dimension in companies that are focussed on developing eco‐friendly products while 'technologies for cleaner production' is the most important dimension in the companies attempting to reduce destructive consequences on the environment. In both types of the company policies, 'lean manufacturing', 'total quality management', and 'institutional pressures' are the key dimensions for a successful implementation of ESSCM while the least important dimensions include 'supplier relationship management', 'green purchasing', and 'green logistics'. The findings of this research can assist the decision‐makers in the oil and gas sector in prioritising and identifying the interrelationship of the dimensions that significantly impact the ESSCM. [ABSTRACT FROM AUTHOR]

Two-stage data envelopment analysis (TsDEA) models evaluate the performance of a set of production systems in which each system includes two operational stages. Taking into account the internal structures is commonly found in many situations such as seller-buyer supply chain, health care provision and environmental management. Contrary to conventional DEA models as a black-box structure, TsDEA provides further insight into sources of inefficiencies and a more informative basis for performance evaluation. In addition, ignoring the qualitative and imprecise data leads to distorted evaluations, both for the subunits and the system efficiency. We present the fuzzy input and output-oriented TsDEA models to calculate the global and pure technical efficiencies of a system and sub-processes when some data are fuzzy. To this end, we propose a possibilistic programming problem and then convert it into a deterministic interval programming problem using the α-level based method. The proposed method preserves the link between two stages in the sense that the total efficiency of the system is equal to the product of the efficiencies derived from two stages. In addition to the study of technical efficiency, this research includes two further contributions to the ancillary literature; firstly, we minutely discuss the efficiency decompositions to indicate the sources of inefficiency and secondly, we present a method for ranking the efficient units in a fuzzy environment. An empirical illustration is also utilised to show the applicability of the proposed technique. [ABSTRACT FROM AUTHOR]

• Contrast among two inventory management strategies: linear and fixed policies. • Minimizing logistics cost and improve customers experience level simultaneously. • Finding optimal values of decision variables (flexibility and safety stock level). • Replacing fixed strategy by linear one, enhances the overall performance up to 17%. • Backup supply cost variation has great impact on linear policy performance. To this day, sales & operations planning (S&OP) remains one of the most critical challenges of supply chain management in terms of improving customers' experience level and managing supply chain costs. Nowadays the synchronization of sales essentials and industrial restrictions in an uncertain environment is critical for all global companies facing complexity in supply chain networks and operations systems. Sales and supply chain structures in build-to-order industries generally have cross-functional and conflicting objects that are not easily comparable. The current research is an extension of an innovative S&OP model within a more dynamically complex system in terms of demands uncertainty and unreliability of backup supplies. The promoted model is utilized for more efficient management and integration of industrial restrictions and sales essentials in global supply chain networks in terms of demand and supply coordination and responsiveness toward customers' needs. Accordingly, this research has applied a simulation-based optimization approach to balance the trade-off between logistics cost and customers' experience level as the main objectives. Two classes of strategies are offered for handling parts' replenishment and flexibility to the sales structure. Initially, the proposed model evaluates two main inventory strategies' performances. Thereafter, the optimized values of flexibility degree and safety stock fraction for corresponding inventory strategies are calculated by using a multi-objective simulation-optimization method. The investigation begins with an overall assessment of the system's performance. Furthermore, it details the alterations among the strategies' key performance indicators (KPIs) as system parameters' function. [ABSTRACT FROM AUTHOR]

Scheduling the supply chain in an integrated manner is a quite fundamental subject in supply chain management. To consider due-dates with production and distribution times at the same time could lead to reduced costs and, thereby, increased profitability. Integrating the problems dealing with due-date assignment, production/distribution times, and routing, while incorporating environmental considerations, could not only increase the cohesion among different decision-making levels and reduce costs in the long run, it would also contribute to the improvement of environmental conditions and benefit the world population. In this study, the integrated supply chain scheduling problem features assignment of due dates, batch delivery, assignment to multiple heterogeneous vehicles based on their capacity, and delivery of customer orders in time-windows. The objective is to minimize distribution cost, fixed and variable fuel costs, the carbon emitted by the vehicles, total delivery tardiness, and customer dissatisfaction. In this model, the customers are categorized into five clusters based on the length of their association, how recently they began interacting, how often they interact, and the monetary value of their interaction with the organization. The members of these clusters are called core customers, potential customers, new customers, lost customers, and resource-consumption customers. A mixed integer non-linear programming model is introduced for this problem which is solved using three multi-objective metaheuristic algorithms: Multi-Objective Particle Swarm Optimization, Non-dominated Sorting Genetic Algorithm II, and Multi Objective Ant Colony Optimization. A number of performance criteria and statistical tests are used to evaluate the algorithms. • Present a GSC multi-objective integrated scheduling of production and distribution. • Develop a mixed integer non-leaner programming formulation to this problem. • Consider Heterogeneous fleet vehicle routing problem and batch delivery for this problem. • Minimizing total distribution costs, carbon emission and customer dissatisfaction. • The orders are delivered to the customers in multiple time-windows. [ABSTRACT FROM AUTHOR]