Introduction to Data Analytics and Emerging Real-World Use Cases

Data analytics is a rapidly emerging interdisciplinary research area that involves advances in engineering, computer science, statistics and operations research. This webinar is focused on introducing the foundation of data analytics and emerging real-world use cases of data analytics. This presentation will begin with a discussion of the mathematical and statistical modeling aspects of various levels of data analytics (i.e., descriptive, predictive and prescriptive). In this webinar, you will hear an overview of data analytics in real world problems ranging from healthcare analytics, retail analytics and financial analytics

Joint Requirement of Two Multi-skill Resource Types in Multi-period Multi-site Assignment Problem

A classic assignment problem determines how to assign resources to tasks in the best possible way. Over the past years, the classic assignment problem has been extended and more complicated assignment models have been proposed. A multi-period multi-site assignment problem is one extension of the classic assignment problem. The number of site and period are increased to more than one and the decision is extended to consider assigning resources to site while concerning tasks in each site and period. Most multi-period multi-site assignment models do not concern joint of resources for operation; however, in some real-life problems, there is a case in which joint of resources for doing tasks is required. In this study, we consider joint requirement of two multi-skill resource types in the multi-period multi-site assignment problem and propose the mathematical model and heuristic. The developed heuristic is separated into two parts. The first part is to create an initial solution by CPLEX In the second part, to improve solution, algorithms for allocating resources to sites and assigning resources to tasks are developed. The computational experiment is done for studying the characteristic of the proposed problem when joint requirement is added and also evaluating the efficiency of the developed algorithm. The result shows that the complexity of the problem highly depends on the ratio of task requiring one and two resource types and while other parameters are fixed except the number of resource, there is only one range of the number of resource that makes the problem complex. For the efficiency of the algorithm, the developed heuristic can find good solutions in a short time in all ranges of the number of resource in all test problems (average optimal gap of all test problems is 7.25%)

Dynamic Vehicle Routing Problem with Multiple Depots

Vehicle Routing Problems (VRPs) have been extensively studied and applied in many fields. Variants of VRPs have been proposed and appeared in researches for many decades. Dynamic Vehicle Routing Problem with Multiple Depots (D-MDVRP) extends the variation of VRPs to dynamism of customers by knowing the information of customers (both locations and due dates) at diverse times. An application of this problem can be found in food delivery services which have many service stores. The customer delivery orders are fulfilled by the scattered service stores where can be analogous to depots in D-MDVRP. In this example the information of all customer orders are not known at the same time depending on arrivals of customers. Thus the objective of this operation is to determine vehicle routing from service stores as well as dispatching time. This paper aims to develop the heuristic for D-MDVRP. The proposed heuristic comprises of two phases: route construction and vehicle dispatch. Routes are constructed by applying Nearest Neighbor Procedure (NNP) to cluster customers and select the proper depot, Sweeping and Reordering Procedures (SRP) to generate initial feasible routes, and Insertion Procedure (IP) to improve routing. Then the determination of dispatch is followed in the next phase. In order to deal with the dynamism, the dispatch time of each vehicle is determined by maximizing the waiting time to provide the opportunity to add more arriving customers in the future. An iterative process between two phases is adopted when a new customer enters the problem, and the vehicles are dispatched when the time becomes critical. From the computational study, the heuristic performs well on small size test problems in a shorter CPU time compared to the optimal solutions from CPLEX, and provides an overall average 8.36% Gap. For large size test problems, the heuristic is compared with static problems, and provides an overall average 3.48% Gap

An Application of Systems Thinking for Solving Inventory Management Problems

This study is motivated by the fact that inventory management problem is focused throughout the industrial firms nowadays as well as inventory management problems are not easy to be appropriately solved. According to the concept of systems approach can better give more effective result especially when applied to complex problems. In this paper, we propose a systems thinking framework for solving real-world inventory management problems. The systems thinking framework comes with three case studies to enhance better understanding of how to apply the framework with problems. This study aims to bring about the ideas for solving inventory management problems in more practical fashion. In addition to benefit the development of the proposed framework, the instructors and the industrial firms will gain the high-performing students or workers, who can get to the point of problem and can propose the reasonable solution to effectively dealing with problems. Moreover, this study can be used as a guideline for engineering educators to develop other meaningful methods in advances. We expect that if the case studies are collected much enough, the knowledge–based system for solving inventory management problems can be built for easy investigation to whom have the interest in solving inventory management problems

A Two-Step Tabu Search Heuristic for Multi-period Multi-site Assignment Problem with Joint Requirement of Multiple Resource Types

An assignment problem has been extensively studied and applied in many industries. Variations of assignment problem have been proposed and appeared in literatures for many years. This paper extends the variation of assignment problem in the dimension of task and resource by proposing the joint requirement of multiple resource types in a multi-period multi-site assignment problem. This specific characteristic is that there are many multi-skill resource types and tasks require joint requirement of more than one resource type for operation. An application of this model can be found in healthcare industry, especially in clinic networks or hospital networks, which have many service locations, have many resource types such as doctors, nurses or medical equipments and definitely require more than one resource type for operations. This paper proposes a two-step Tabu search heuristic for multi-period multi-site assignment problem with joint requirement of multiple resource types. The specified neighborhood strategy, short-term memory and long-term memory are designed to suitable for the problem and to produce an efficient move to better solutions. From computational study, solutions from Tabu search algorithm are compared with optimal solutions from CPLEX and the result shows that, for small size problems, most solutions are close to optimal solutions, for medium size problems, the algorithm can provide good solutions in a short time comparing with CPLEX, and for large size problem, four out of five solutions from the proposed algorithm are better than solutions from CPLEX in a limit of time

The Review of Mark Planning Problem

Mark planning is one of the most important planning processes in garment industry. The major function is to generate a set of markers which is used as cutting guidelines or cutting templates in a cutting process. In other words, this process can be seen as a planning step of a cutting process which determines what sequence and how many parts will be cut. For the academic point of view, mark planning has been of interests for more than 15 years. Many papers were published with various objective functions, problem scenarios, and constraints. Therefore, a summarization of these papers representing key contents of each paper will be useful for researchers who are interested in this type of problem. The purpose of this paper is to introduce an exhaustive review of mark planning papers which is composed of three sections: the relevant background, the summary of papers, and the relationship between objective functions which is cost combinations and major garment characteristics. In conclusion, future researches should go forward the trend of integration between this process and other relevant processes in the production chain, e.g. cut scheduling, assembly planning, marker making

The Can-Order Policy for One-Warehouse N-Retailer Inventory System: A Heuristic Approach

We study an application of the can-order policy in one-warehouse n-retailer inventory system, and propose a heuristic approach for setting the appropriate inventory policy. On the can-order policy, an order is triggered when a retailer's inventory position reaches its must-order level. Then other retailers are examined whether their inventory reaches their can-order level, and if so they are filled by this order as well. Warehouse fulfills all involved retailers' inventory to their order-up-to levels. The can-order policy is not only able to save the total system-wide cost from joint replenishment, but it is also simple to use. Computer simulation is utilized to preliminarily study and to determine the best-known solution. We propose a heuristic approach utilizing decomposition technique, iterative procedure, and golden section search to obtain the satisfying total system-wide cost. This can save our computational time to find the appropriate inventory policy setting. We found that the proposed heuristic approach performs very well with the average cost gap less than 2% comparing to the best-known solution. It also provides satisfactory computational time from the reduced search space. Thus, the can-order policy can be very useful for such system

Novel Price Sensitive Demand Model with Empirical Results

Demand of commoditized products –for example drinking water and generic pharmaceutical products – depends mainly on price. Therefore, pricing strategy is very important especially for competing suppliers with common retailer. However, although many literatures were studied in pricing strategy, but almost all of those studies did not expand to use empirical data which is very important for applying the findings to real business.     To find the point that both manufacturers and a retailer can maximize their own profit in a supply chain, Game theory principle is used. This paper introduces a new parameter concept, the competition intensity of pricing degree, which is an important parameter that we incorporate in our linear price sensitive demand model in order to make the result more applicable. Moreover, we include a case study to show how the finding can be valuable tools for deciding on pricing policy. Keywords: Demand Model, Competition intensity of price degree, Bargaining Power, Game Theory, Pharmaceutica

A Hierarchical Analytic Process Framework for Manufacturing System Improvement

This paper proposes a hierarchical analytic process framework to facilitate practitioners to determine improvement directions for a manufacturing system efficiently and systematically.  The proposed framework analyzes current state performances of a manufacturing system by applying the knowledge of science of manufacturing systems which describe relationship control factors, performance measures, and improvement objectives. Then, concrete directions for improvements are suggested. The analysis process embeds the concept of diagnostic tree which makes it an easy-to-handle framework. Under the diagnostic tree concept, it decomposes the high-level business goal into successively low-level activities to give more comprehensive areas of improvement.  The proposed framework comprises of three key elements:  Operation Performance Measures (OPMs), Diagnostic tree (D-Tree), and Action guidelines. The OPMs are used in the D-Tree to determine improvement objectives.  Then the Action guidelines suggest how to adjust control factors in a manufacturing system according to each improvement objective.  The proposed diagnostic framework is demonstrated by Promodel simulation of a case study.   The simulation model includes physical resources, flow lines, WIP, and replenishment signals of the case. By following the analytic process in the framework, the performance measures have shown improvements according to action guidelines and the result of improvements meets the requirement of a factory in the case study