Extended Ultimate-Pit-Limit Methodology for Optimizing Surface-to-Underground Mining Transition in Metal Mines

The transition from surface mining to underground is a critical issue for metal mines. The commonly cited procedure cored by ultimate-pit-limit (UPL) methodology is restricted to maximize the profit from both surface and underground mining, due to the absence of the integration of the profit from either of them. Under the target for such maximization, this study proposes a new optimization approach, which directly relates the design of open-pit limit and underground stopes, by equalizing the marginal profit from either surface or underground mining. The variation of the crown pillar size is involved in this approach. The proposed approach is applied to the Dagushan iron mine, and results show the total profit increased from 3.79 billion CNYs (original design by conventional UPL methodology) to 4.17 billion CNYs (optimal design by the proposed approach), by 9.91%. Moreover, the marginal profit from surface and underground mining, as well as total profit, of all possible designs of surface-to-underground mining transition in Dagushan iron mine is calculated to validate the proposed approach. When the marginal profits satisfy the criterion of the proposed approach, the maximum value of the total profit appears, and this demonstrates the proposed approach is robust to maximize the total profit in surface-to-underground mining transition. This work contributes to existing literature studies primarily from practical aspect, by providing a unified approach to optimize the transition from surface to underground mining.

Research on Dynamic Scheduling of Unbalanced Supply and Demand of the SROC Based on the Customers Priority Principle

The problem of imbalance between supply and demand in car-sharing scheduling has greatly restricted the development of car-sharing. This paper first analyzes the three supply and demand modes of car-sharing scheduling systems. Secondly, for the station-based with reservation one-way car-sharing problem (SROC), this article establishes a dynamic scheduling model under the principle of customer priority. The model introduces balance coefficients to predict the balance mode, and systematically rebalance the fleet networks in each period. In the case of meeting customer needs, the model objective function is to maximize the total profit and minimize the scheduling and loss costs. Then, in view of the diversity and uncertainty of scheduling schemes, a scheme information matrix is constructed. In the iterative process of genetic algorithm, individuals are selected and constructed according to the pheromone matrix, and evolution probability is proposed to control the balance between global search and local search of genetic algorithm. Finally, the data of Haikou City is used for simulation experiment.

The Role of the Discount Policy of Prepayment on Environmentally Friendly Inventory Management

Nowadays, more and more consumers consider environmentally friendly products in their purchasing decisions. Companies need to adapt to these changes while paying attention to standard business systems such as payment terms. The purpose of this study is to optimize the entire profit function of a retailer and to find the optimal selling price and replenishment cycle when the demand rate depends on the price and carbon emission reduction level. This study investigates an economic order quantity model that has a demand function with a positive impact of carbon emission reduction besides the selling price. In this model, the supplier requests payment in advance on the purchased cost while offering a discount according to the payment in the advanced decision. Three different types of payment-in-advance cases are applied: (1) payment in advance with equal numbers of instalments, (2) payment in advance with a single instalment, and (3) the absence of payment in advance. Numerical examples and sensitivity analysis illustrate the proposed model. Here, the total profit increases for all three cases with higher values of carbon emission reduction level. Further, the study finds that the profit becomes maximum for case 2, whereas the selling price and cycle length become minimum. This study considers the sustainable inventory model with payment-in-advance settings when the demand rate depends on the price and carbon emission reduction level. From the literature review, no researcher has undergone this kind of study in the authors’ knowledge.

An EOQ model for deteriorating items analyzing retailer’s optimal strategy under trade credit and return policy with nonlinear demand and resalable returns

This paper presents an EOQ model where demand is dependent upon time and selling price. In the proposed model of inventory, the retailer allows its unsatisfied customers to return their product whereas the manufacturer offers a full trade credit policy to the retailer. To make our model realistic, we have assumed that the product returned can be resold with the same selling price. Number of returns is a function of demand. In this proposed inventory model considering deterioration, the retailer does not fully reimburse its customers for the returned product. The primary purpose of this inventory model is to determine the optimal selling price, optimal order quantity, and optimal replenishment cycle length in order to maximize the retailer’s total profit earned per unit time. A numerical example is also presented and a sensitivity analysis is carried to highlight the findings of the suggested inventory model.

An Inventory Model for Deteriorating Items with Stock Dependent Demand and Partial Backlogging

In this paper, we formulate a deteriorating inventory model with stock-dependent demand Moreover, it is assumed that the shortages are allowed and partially backlogged, depending on the length of the waiting time for the next replenishment. The objective is to find the optimal replenishment to maximizing the total profit per unit time. We then provide a simple algorithm to find the optimal replenishment schedule for the proposed model. Finally, we use some numerical examples to illustrate the model. Keywords- Inventory, Deteriorating items, Stock dependent demand, Partial backlogging

Control of Non-instantaneous Degrading Inventory under Trade Credit and Partial Backlogging

Inventory management is an extremely difficult task. It has become usual practice for a provider during the last few decades to provide a retailer with a credit term. In this article, a non-instantly degradable products inventory system is built with a price-sensitive demand and a Weibull credit term allocation reduction rate. Some backlogged deficiencies are permitted. The aim is to maximize the total profit in this study by taking three cases into account. Numerical examples, graphical representations and sensitivity analysis demonstrate the application of the approach developed in this study.

OPTIMIZATION OF PROCESSED FISHERY PRODUCTS ON TELUK SANTONG VILLAGE USING LINEAR PROGRAMMING METHOD

This study aims to optimize processed fishery products in Teluk Santoni Village with a constraint factor: Fish Resources (SDI) availability. The analytical tool used in this research is linear programming, maximizing the profit of processed fishery products with limitations on the availability of human resources, production capacity, and total production costs. Based on the results of data analysis with the help of the Linear Program Solver (LiPS) software, it was found that to obtain maximum profit, the strategy that needs to be done is to produce crabs 10/3 times the average amount, 2.5 times salty, empek-empek and shrimp paste, produced two times the usual production amount and did not produce shredded fish and fish crackers. The total profit obtained from the optimization is Rp. 18.85 million per month. KEYWORDS: optimization, processed fishery products, linear programming

Two Level Trade Credit Policy Approach in Inventory Model with Expiration Rate and Stock Dependent Demand under Nonzero Inventory and Partial Backlogged Shortages

In present real life situations, the stock and expiration date directly impact on the demand of an item. In this context, this research work develops an inventory model for stock and expiration rate-dependent demand under a two-level trade credit policy. Specifically, the following three situations are studied: (i) trade credit policy without zero ending inventory; (ii) trade credit policy with zero ending inventory; (iii) trade credit policy with partial backlogged shortages. The proposed inventory model is formulated as a non-linear constrained optimization problem. Some theoretical results are derived, and an algorithm is stated in order to solve the proposed inventory model. The main objective of the inventory model is to determine the optimal cycle length, the optimal ending inventory level, and the optimal number of units displayed which maximize the total profit. Some numerical examples are solved. Finally, a sensitivity analysis is done with the aim to see the impacts of a variation of the input parameters on the decision variables and the total profit.

Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model

<div>Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from the web over a specified time interval and predicting their future prices for a specified forecast horizon, and forecasts the future stock prices. The model is deployed for making buy and sell transactions based on its predicted results for 70 important stocks from seven different sectors listed in the National Stock Exchange (NSE) of India. The profitability of each sector is derived based on the total profit yielded by the stocks in that sector over a period from Jan 1, 2010 to Aug 26, 2021. The sectors are compared based on their profitability values. The prediction accuracy of the model is also evaluated for each sector. The results indicate that the model is highly accurate in predicting future stock prices.</div>

Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model

<div>Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from the web over a specified time interval and predicting their future prices for a specified forecast horizon, and forecasts the future stock prices. The model is deployed for making buy and sell transactions based on its predicted results for 70 important stocks from seven different sectors listed in the National Stock Exchange (NSE) of India. The profitability of each sector is derived based on the total profit yielded by the stocks in that sector over a period from Jan 1, 2010 to Aug 26, 2021. The sectors are compared based on their profitability values. The prediction accuracy of the model is also evaluated for each sector. The results indicate that the model is highly accurate in predicting future stock prices.</div>