Cutting stock problem is a problem generally encountered in many manufacturing industries such as the furniture, clothing, glass production, leather, paper, textile, metals industries amongst others most especially du...
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Cutting stock problem is a problem generally encountered in many manufacturing industries such as the furniture, clothing, glass production, leather, paper, textile, metals industries amongst others most especially during mass production. The problem usually arises during roll slitting whereby a large role is to be cut into smaller pieces. The challenge may be further compounded due to constraints and different variants that arises from product customization, process and machinery as well as customer requirements and quality issues. To address this problem, this study develops a linearintegerprogramming technique and provides a practical guided approach. First mathematical formulation (having objective function and constraints) comprising of a list of q orders, that requires p j pieces was formulated and a list of all possible cuts combination of configuration and patterns were derived. The linearinteger program was solved in the MATLAB 2022 environment. The results indicated that the large rolls can be cut into the desired length, number, patterns and configuration in a time effective manner using the developed linearintegerprogramming technique. The results further show that for the total wastes generated was 24 for the optimal solutions. This number of wastes can be considered to be minimal considering the volume of demand and patterns generated from the stock material. Furthermore, 6 out of the 7 pattern optimal solutions generated zero waste. This study provides theoretical and empirical findings that can assist the manufacturing industries in minimizing time and cost variables while addressing stock cutting problems.
Railway passenger transportation plays a fundamental role in China, reasonable revenue is the guarantee of railway's regularly development such as equipment replacement, technology enhancement, etc. Although many ...
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Railway passenger transportation plays a fundamental role in China, reasonable revenue is the guarantee of railway's regularly development such as equipment replacement, technology enhancement, etc. Although many studies on the railway revenue models have been conducted, there is a lack of effective modeling which considers the multiple trains and the multiple levels of seats in real operation. Aiming to improve the revenue of railway transportation industry, this paper proposes a new optimization method for the train seat inventory control problem with the consideration of the multiple trains and the multiple levels of seats. As the in-depth research of this problem, an integerlinearprogrammingmodel is formulated which aims to maximize the total revenue of rail industry. The commercial software MATLAB with CPLEX solver is employed to obtain the approximate optimal solutions. The effectivess and performance of the proposed approaches are testified by two examples implemented on a simple railway corridor and Wuhan-Guangzhou high-speed railway corridor. Moreover, sensitivity analysis experiments are given to explore the impact on the revenue if the model parameters are changed.
This paper presents a linear integer programming model to plan the production for multiple products. The production is planned using information shared across a supply chain. The advantage of information sharing has b...
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ISBN:
(纸本)9780769535623
This paper presents a linear integer programming model to plan the production for multiple products. The production is planned using information shared across a supply chain. The advantage of information sharing has been proved from research that it can make the whole supply chain globally visualized and yet decreasing in variability on the supply chain. The objective function of the proposed model is to minimize the total costs involved including the production costs, the distribution costs, the setup costs of manufacturing, and the fixed costs of distributing. From previous research, one more restriction which is limitation of distribution centers has been added to make the model more realistic. Numerical results verify that this model can be used to generate feasible and optimal plans for multiple products across the supply chain.
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