In the current socio-economic situation, smart products are essential for daily life. Energy is a very much related matter to smart products. To buy a smart product, people mostly care about that smart product’s energy consumption and the price. There is always a tug-of-war between the price of the product and the energy consumption of that product. An energy-efficient smart production system is described in this study where the production is variable, and in the out-of-control state, it produces defective products. For prevention of the out-of-control state, preventive maintenance and restoration are used within the smart production system. The rework policy helps to profit from the defective products, and the warranty policy helps to motivate the users. This model applies an improved strategy to the production process and develops a new product that needs to be marketed. Finally, this model plays a vital role in creating smart products with moderate energy consumption at a minimal cost. The mathematical model is a non-linear profit maximization problem that is solved both analytically and numerically. The classical optimization technique founds optimum solutions. Different numerical examples and sensitivity analysis with graphs are used to validate the mathematical model.

No abstract

The present study focuses on a single-vendor, single-buyer supply chain model for a single type of product with upgraded service provided to the buyer by the vendor. Vendors often increase their profit by providing a lower quality of a particular product. In this study, an advanced supply chain model is developed to increase service in the presence of an unreliable vendor and an online-to-offline (O2O) channeling system. The vendor provides lower quality items to the customer, even though they had committed to providing a certain quality product, in order to increase their profit. For more realistic results, demand is considered to be price-, quality-, and service-dependent. To advertise and sell the products, the manufacturer uses an online system, which the buyer also uses to choose and order the product, where the particular product is delivered to the customer by a third (offline) party; that is, the concept of an O2O retail channel is adopted to improve the service level of the supply chain management (SCM). To control the out-of-control state and improve the production quality, investment is used. Contrary to the literature, service is considered to be constrained, which makes the model more realistic. A classical optimization technique is used to solve the model analytically and a two-echelon supply chain model is obtained under the advanced O2O retail channel, along with optimized profit, shipment volume, selling price, ordering cost, service, back-ordered price discount, lead time, and safety factor values. Some numerical examples and a sensitivity analysis of the key parameters are provided, along with graphical representation, in order to validate the model.

No abstract

This paper considers an imperfect production system to obtain the optimal production run time and inspection policy. Contrary to the existing literature this model considerers that product inspection performs at any arbitrary time of the production cycle and after the inspection, all defective products produced until the end of the production run are fully reworked. Due to some misclassification during inspection, from the inspector's side two types of inspection errors as Type I and Type II are considered to make the model more realistic rather than existing models. Defective items, found by the inspector, are salvaged at some cost before being shipped. Non-inspected defective items are passed to customers with free minimal repair warranty. The model gives three special cases, where it is found that this model converges over the exiting literature. Some numerical examples along with graphical representations are provided to illustrate the proposed model with comparison with the existing models. Sensitivity analysis of the optimal solution with respect to key parameters of the model has been carried out and the implications are discussed.

The proposed model focuses on an imperfect production process (IPP) in which, during long-term production, the system may change to an “out-of-control” state from an “in-control” state and produce some imperfect products because of a long production run length. Brand image and industry reputation are affected by product defectiveness. To increase the profit of any industry and improve reputation and brand image, inspection of the production system is required. However, this inspection is subjected to human error, which negatively affects the assessment of production systems. Herein, an error-free inspection is performed with the help of an autonomation policy, in which each product is inspected via a machine instead of a human, facilitating an error-free inspection and converting the production system to a smart production system. Moreover, in reality, product demand cannot always be constant. Therefore, in this model, a selling-price-dependent demand is considered along with a variable production rate to enhance model applicability. Moreover, total system profit is optimized and optimal values for production run time, inspection scheduling, selling price, buffer inventory, and production rate are determined. Finally, for model validation, some numerical examples along with special cases are provided. The concavity of the optimal function is also proven through graphical illustration. The sensitivity of the key parameters of the presented model is explored and the significance is explained.

After an earthquake, the debris generated by it can hinder relief efforts, resulting devastating economic, environmental, and health problems. This paper reviews the condition of post‐earthquake situation in Bhutan and develops a mathematical model for improving the condition. Earthquake debris management system plays a vital role in managing the debris after the disaster, thus accelerating the early recovery of the affected regions. The modelling aims to optimize the disaster‐debris transportation cost along with selecting suitable debris dumping site. The outcomes improve the lifestyle of the people of the affected area simultaneously reducing the debris transportation cost and utilizing the reusable items. To analyse the phenomenon, the mathematical models are derived which would be validated through some real data collected from available literature on disaster management. Other than existing literature, the process of segregation of the reusable items is performed at the time of removal of debris from the affected regions which is preferable for comparatively small disaster and hilly areas like Bhutan. The study shows that the after disaster debris management cost can be reduced by determining a suitable TDMS and the loss of property can be saved up to a certain amount by reusing the undamaged or slightly damaged debris.

This paper describes a deteriorating inventory model with ramp-type demand pattern under stock-dependent consumption rate. The deterioration of the product is considered as probabilistic to make the research a more realistic one. The proposed model assumes partially backorder rate which follows a negative exponential with the waiting time. The effect of inflation and time value of money are incorporated into the model. The purpose of this study is to develop an optimal replenishment policy so that the total profit is maximized. We provide a simple solution procedure to obtain the optimal solutions. Numerical examples along with graphical representations are provided to illustrate the model. Sensitivity analysis of the optimal solution with respect to key parameters of the model has been carried out and the implications are discussed.