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We focus on a three-stage supply chain problem for fast moving consumer goods including a supplier, a manufacturer and customers. There are different orders over identical cycles, to be processed in production site. The problem is to find a joint cyclic schedule of raw material procurement and job scheduling minimized the total cost comprised of raw material ordering cost and holding cost, production cost, holding cost of finished products, tardiness cost and rejection cost. An integrated mixed integer programing model is proposed and optimal solution of some instances are provided by solving the model.

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In the face of budgetary limitations in organizations, identifying critical facilities for investing in quality improvement plans could be a sensible approach. In this paper, hierarchical facilities with specified covering radius are considered. If disruption happens to a facility, its covering radius will be decreased. For this problem, a bi-objective mathematical formulation is proposed. Critical facilities are equivalent to the facilities which attacking them causes the most reduction in the quality of the system performance. Consequently, this problem is studied in the interdiction problem framework. To solve the multi-objective model the weighting-sum approaches are applied. The first interdictor's objective function helps decision makers to identify the vulnerability of the system. Moreover, the second objective function may assist in minimizing the cost of applied quality improvement plans.

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In the past decade, fuel consumption and CO₂ emission have increased in the airline industry. Large CO₂ footprint has a damaging effect on the environment. Global concerns over this issue has made the airline industry to be greener. Most efforts of the green airline industry are improving the fuel consumption to reduce the CO₂ emission and its environmental damage. Here, we use cruise speed control to control the fuel consumption and CO₂ emission. Each aircraft has a different speed level needing a different fuel consumption. Service quality is studied besides the energy consumption. We investigate two objectives including total energy consumption (TEC) and passenger service level (PSL). TEC and PSL are conflicting in nature. We develop a mixed-integer nonlinear programming model to integrate schedule design, aircraft assignment and maintenance routing problems. We make use of the augmented ε-constraint method to solve the problem. To evaluate the model, a real data based on the Emirates airline flights is used. The results are compared using four different scenarios

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The current research provides a mixed integer nonlinear mathematical programming model for a company that operates with several stores and multiple products, in which demand for each customer is characterized using fuzzy logic by triangular numbers, while the replenishment policy of each store for any product is the popular economic order quantity (EOQ) model under backorder. The throughput, dispatch, and budget constraints are considered in the proposed EOQ model. The objective is to integrate a vendor selection problem and EOQ policy, in which a multi-sourcing strategy is considered. In the proposed strategy, the ordered value of each store for any product can be split between one or more vendors. As such, a set of selected vendors can replenish each store for each product. This research aims to answer the following question as follows: (i) which vendors are chosen; (ii) which store is allocated to the selected vendors for each product; (iii) what is the optimal value for the inventory decisions.
The aim is to reduce the total cost of the company, including costs related to the vendor selection decisions along with the inventory decisions. To solve the mathematical model, a novel and practical genetic algorithm (GA) is developed then the response surface methodology (RSM) is utilized to tune its parameters. At the end, some numerical instances under different categories are evaluated to explain the applicability of the proposed approach.


 

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The explosive growth of global e-commerce and the increasing complexity of last-mile logistics have made the strategic placement of smart lockers a critical concern in modern urban logistics systems. Conventional methods, which rely solely on Multi-Criteria Decision Making (MCDM) methods for obtaining solutions, suffer from several limitations when implemented in uncertain, significant, and multi-objective scenarios. This paper proposes a stochastic multi-objective optimisation model for the BWM, prioritising decision criteria, which is solved by combining a hybrid metaheuristic solution methodology. The proposed model optimizes both total cost and sustainability performance from economic, environmental, and social perspectives, as well as robustness to demand uncertainty. An empirical study using Babol City, Iran, is presented to test and demonstrate the proposed framework. Candidate locker location and demand areas were examined based on expert-elicited criteria weights, with the preparation of a multi-objective mixed-integer programming model. In order to alleviate the computation burden, a combined structure of NSGA-II and LNS (referred to as NSGA-II+LNS) was proposed, which outperforms classical evolutionary algorithms in terms of convergence into the Pareto frontier. Factual results indicate that factoring in economic affordability, accessibility, and environmental impact is key to optimal locker capacity design. Robust solutions under demand fluctuation can save up to 18% more on service reliability, providing strong deterministic answers. This article makes the following theoretical and practical contributions: (i) a novel sustainable-oriented, deterministic model for smart locker location is proposed; (ii) advanced metaheuristics are integrated with MCDM in urban logistics, whereas fewer studies have focused on integrating them; and (iii) policy implications are suggested not only to policymakers but also to logistics operators who want robust last-mile delivery strategies..