Muhammad Nazim

Logistics Distribution Centers Location Problem under Fuzzy Environment

Logistics distribution centers location problem is concerned with how to select distribution centers from the potential set for minimizing cost and fulfill the demand. This paper aims at multi-objective optimization for three-echelon supply chain architecture consisting of manufacturer, distribution centers (DCs) and customers. The key design decisions considered are: the number and location of distribution centers, the quantity of products to be shipped from manufacturer to DCs and then from DCs to customers. The present study mainly investigates the proposed problem under fuzzy environment and the uncertain model is converted into a deterministic form by the expected value measure. The approximate best solution of the model is provided using fuzzy simulation. A numerical example is used to illustrate the effectiveness of the proposed model and solution approach.

Factors Which Affect the Sustainable Production of Cotton in Pakistan: A Detailed Case Study from Bahawalpur District

Pakistan is one of the most important cotton producing and consuming country in the World. The present study purpose is to explore the important factors affecting cotton production in Pakistan. A sample of cotton growing respondents was randomly selected from different villages of Punjab province. Some production function was employed to assess the effects of education, land fertilization, weather, land preparation, seed, irrigation, etc. On the productivity of cotton. Education, plant protection measures, fertilizer and land preparation were contributing towards higher cotton yield on the sampled respondents farms. So, there is a great need to educate the farmers on priority basis for adopting recommended practices. The extension staff can play a highly positive role in this regard. At the end, a detailed discussion is proposed to show the efficiency of the proposed model.

Production-Distribution Planning in Supply Chain Management Under Fuzzy Environment for Large-Scale Hydropower Construction Projects

In this chapter, production-distribution planning in construction supply chain management is presented under fuzzy environment. This chapter specifically addresses bi-level decision making problem with lower level corresponding to a plant planning problem, while the upper level to a distribution network problem. The main targets of the upper level are to minimize cost and maximize the satisfaction level for concrete mixing plants. The targets of the lower level are to maximize the profit and customer satisfaction level. The model is formulated in terms of fuzzy programming and the best solution is provided by using the fuzzy simulation and sensitive analysis is used to highlight the results. The Neelum Jhelum Hydropower Project is used as a real-world example to illustrate the effectiveness of the proposed approach.

Multi-objective Optimization Model for Supplier Selection Problem in Fuzzy Environment

Supplier selection decisions are typically multi-objectives in nature and it is an important component of production and logistics management for many firms. The present study mainly investigate a multi-objective supplier selection planing problem in fuzzy environment and the uncertain model is converted into deterministic form by the expected value measure (EVM). This paper aims at multi-objective optimization for minimizing cost and maximizing product quality level. For solving the multi-objective problem a weighted sum base genetic algorithm is applied and the best solution is provided using fuzzy simulation. Finally, a numerical example is used to illustrate the effectiveness of the proposed model and solution approach.

Multi Objective Production–Distribution Decision Making Model Under Fuzzy Random Environment

Today the most important concern of the managers is to make their firms viable in the competitive trade world. Managers are looking effective tools for decision making in the complex business world. This paper addresses a hierarchical multi objective production-distribution planing problem under fuzzy random environment. A mathematical model is presented to describe the purpose problem. To deal the uncertain environment, the fuzzy random variables are first transformed into trapezoidal fuzzy numbers, and by using the expected value operation, the trapezoidal fuzzy numbers are subsequently defuzzified. For solving the multi-objective problem a weighted sum base genetic algorithm is applied. Finally, the result of a numerical example are presented to demonstrate the practical and efficiency of the optimized model.

Research on Factors Influencing the Sugarcane Production in Pakistan: A Case Study of Punjab

Sugarcane is the one of the most important cash crop and industry of Pakistan and it is an important sugarcane producing countries in the world but, the Punjab province of Pakistan has highest share in sugarcane production. Like growers of other crops, sugarcane growers of the Punjab province are also facing economic, technical and social problems, but high rate of inputs are the primary problems of sugarcane growers. The efficacy checked by using Cobb-Douglas production function, Marginal value of product and allocated efficacy are calculated. The results show that the coefficient of multiple determination indicated that 93 % variation in the cost of inputs is explained by all variables and the regression model is well fitted. For this purpose, Data were collected during the period 2009–2010. Data were collected from 400 sugarcane growers from 5 districts of Punjab province. The purpose of this study is to explore and mechanisms for a progression to sustainable development of agriculture sector in Pakistan.

Multi-Objective Inventory Planning under Stochastic Availability of Complement in Fuzzy Environment

Effective inventory planing is instrumental in reducing costs and leadtime. In this paper, a multi-objective inventory planing model is proposed with imprecise demand, lead time and inventory costs. An inventory policy is proposed to minimize the costs using man–machine interaction. The fuzzy parameters of leadtime, inventory costs and demand are expressed through linear non-linear membership functions. The fuzzy parameters are first transformed into corresponding interval numbers and then following the interval mathematics, objective function of average cost is changed into respective multi-objective functions. An interactive fuzzy decision making method is used to minimize these functions and solve for Paretooptimum solutions. The proposed model is illustrated numerically and the results are presented in tabular forms.

Supplier Evaluation and Selection Using Restricting Weights Method in Fuzzy Environment

Data envelopment analysis (DEA) is a method for supplier selection. Weight restrictions allow for the integration of managerial preferences in terms of relative importance levels of various inputs and outputs. In some situations there are some factors which play both input and output roles as well. The purpose of this research is to propose a method for selecting the best suppliers in the presence of weight restrictions and dual-role factors. This study shows the supplier selection process through a DEA model, while allowing for the incorporation of decision makers preferences and considers multiple factors which simultaneously play both input and output roles. The proposed model does not demand exact weights from the decision maker. This study presents a robust model to solve the multiple-criteria problem. A numerical example certifies the application of the proposed method.

A Bi-level Multi-objective Optimization Model of Multiple Items for Stone Industry Under Fuzzy Environment

Traditionally, stone industry is produced essential materials for the construction industry but stone industry is always debated as a high emission industry for stone dust and waste water. This emission has an adverse impact on environment, humans, agriculture and ground water. This paper focuses on how to optimize the stone industry. The government is considered as the leader level which will make a strategy to plan the exploring amount of every stone plant and sustainable development in stone industry to create employment opportunity and economic growth. The stone plants are considered as the lower-level decision-makers which optimize their objective functions under the constraint of leader. The stone plants have individual objectives of maximizing the profit and produce different product according to the demand constraints under the limited exploring amount. Due to the lack of historical data, some emission coefficients are considered as fuzzy numbers according to experts advices. Therefore, a bi-level multi-objective optimization model with possibilistic and predetermined constraints under the fuzzy environment is developed to control the pollution and get sustainable development in stone industry. For some special fuzzy coefficients, the equivalent model is obtained. At the end, a practical case is proposed to show the efficiency of the proposed model.

A Bi-level Multiobective Optimization Model for Risk Management to Utilize Wastes in Stone Industry under Fuzzy Environment

Control and management of waste materials created during stone production is one of the most important problems of stone industry and modern societies today. Waste materials which are different depending on properties of structure and construction technique generally consist of materials such as concrete, brick, stone, briquette, wood, metal, glass, gypsum, plastic, ceramic [1]. This paper is about how to optimize the stone industry. The plant considered as the leader level will make a strategy to utilize the wastes amount for waste department. The waste department considered as the follower level will make a decision to produce different stone products under the utilizing constraint. Due to the lack of historical data, some emission coefficients are considered as fuzzy numbers.Therefore, a bi-level multiobjective optimization model with possibilistic constraints are developed to get the maximum profit and control the pollution. At the end, a case study is proposed to show the efficiency of the proposed model.