Repair and Replacement Strategy for Optimizing Cost and Time of Warranty Process using Integer Programming

Warranty is an assurance issued by a company as the manufacturer to guarantee that its product is damage-free within a specified period. The warranty process is usually carried out when a complaint or damage regarding the product is received. The warranty process consists of two decisions that the company establishes to handle the process. The occurring problem is in the warranty process; there is not any standard established to determine the cost to incur for the warranty process. In this research, integer programming method was used to do optimization on repair and replacement strategy in warranty process. Before doing optimization, mathematical model must be created. Using that mathematical model, the results show that the costs of the warranty process decrease by 16.97%, while the time increases by 13.9%. So, with this method company will be increase the profit

Time and cost optimization of business process RMA using PERT and goal programming

As a company engaged in the distribution of wireless devices, Return Material Authorization (RMA) is a mandatory service that must be owned by the company. RMA is a part of the process of returning a product to receive a refund, replacement, or repair during the product’s warranty period. The business process of RMA used functionally design, which is not considered to be effective in terms of time and cost for the company of 2880 minutes for IDR 348242. In this research, Project Evaluation and Review Technique (PERT) was used to select the optimal traces; to optimize time and cost, the researcher used goal programming. This technique allows the creation of a controlled and orderly activity because the schedule and budget of a job are predetermined prior to the implementation. The optimization results show that the process time is reduced by 50% and the cost is reduced by almost 55% which show improvement on the performance of the business processes

"Integrating Optimization and Strategic Conservation to Achieve Higher Efficiencies in Land Protection"

Strategic land conservation seeks to select the highest quality lands given limited financial resources. Traditionally conservation officials implement strategic conservation by creating prioritization maps that attempt to identify the lands of highest ecological value or public value from a resource perspective. This paper describes the history of using optimization in strategic conservation and demonstrates how the combination of these approaches can significantly strengthen conservation efforts by making these programs more efficient with public monies.Mathematical Programming, Conservation Optimization, Cost Effectiveness Analysis, Strategic Conservation

Optimal scope of supply chain network & operations design

The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are significant and that has attracted considerable research attention since the late 1990s. This doctoral thesis focuses on developing manageable and realistic optimization models for solving four contemporary and interrelated supply chain network and operations design problems. Each requires an integrated decision-making approach for advancing supply chain effectiveness and efficiency. The first model formulates the strategic robust downsizing of a global supply chain network, which requires an integrated decision-making on resource allocation and network reconfiguration, given certain financial constraints. The second model also looks at the strategic supply chain downsizing problem but extends the first model to include product portfolio selection as a downsizing decision. The third model concerns the redesign of a warranty distribution network, which requires an integrated decision-making on strategic network redesign and tactical recovery process redesign. The fourth model simultaneously determines the operational-level decisions on job assignment and process sequence in order to improve the total throughput of a production facility unit

A NEW HYBRID MODEL BASED DECISION SUPPORT SYSTEM FOR SUPPLIER SELECTION PROCESS: A CASE STUDY FROM PURCHASING DEPARTMENT, UTP

Decision making process is a huge and crucial activity that must be given high attention by decision makers and managers as it affects all business strategies in organizations. Computer Based Decision Support System (DSS) is built and developed to assist decision makers in the activity of decision making process. DSS includes different components that integrate together out of which the most important part is the model based system. As a result of the rapidly increasing and sustainable needs of organizations, suppliers have become essential to any business. On the other hand, decision makers and managers face challenges when they are about to select suppliers due to the strong competitiveness among suppliers, obstacles that they will face when poor decisions are made, and many other reasons. Evaluating and selecting suppliers has been considered as the most critical and important process among the whole purchasing processes. However most of the existing models that have been proposed to support supplier selection decisions have various shortcomings. All the drawbacks of these models will be discussed during this research in details which indicates the urgent need for new suitable model. This research intends to develop a new hybrid model base DSS for supplier selection process that can guarantee better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable supplier. The proposed model depends upon linear weightage model and Analytic Hierarchy Process (AHP) approach. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose. Finally, DSS software will be developed to utilize the proposed model to assist supplier selection decisions

A bi-objective robust inspection planning model in a multi-stage serial production system

International audienceIn this paper, a bi-objective mixed-integer linear programming (BOMILP) model for planning of an inspection process used to detect nonconforming products and malfunctioning processors in a multi-stage serial production system is presented. The model involves two inter-related decisions: 1) which quality characteristics need what kind of inspections (i.e., which-what decision) and 2) when the inspection of these characteristics should be performed (i.e., when decision). These decisions require a trade-off between the cost of manufacturing (i.e., production, inspection and scrap costs) and the customer satisfaction. Due to inevitable variations in the manufacturing systems, a global robust BOMILP (RBOMILP) is developed to tackle the inherent uncertainty of the concerned parameters (i.e., production and inspection times, errors type I and II, misadjustment and dispersion of the process). In order to optimally solve the presented RBOMILP model, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is combined with the Taguchi and Monte Carlo methods. The proposed model and solution algorithm are validated through a real industrial case from a leading automotive industry in France