Young Hae Lee

Advanced planning and scheduling with outsourcing in manufacturing supply chain

This paper considers advanced planning and scheduling (APS) in which each customer order has a due date and outsourcing is available. We present a model for APS that requires an absolute due date with outsourcing in a manufacturing supply chain. In practice, planning and scheduling are interrelated and should be solved simultaneously with outsourcing to ensure that the due dates of customer orders are met. The proposed model considers alternative process plans for job types, with precedence constraints for job operations. Such a model has not been treated in the literature. The integrated states include: (a) selecting the best machine for each operation, (b) deciding the sequence of operations, (c) picking the operations to be outsourced, and (d) minimizing the makespan for the due date of each order. To solve the model, a genetic algorithm (GA)-based heuristic approach was developed. In numerical experiments, the GA-based approach efficiently solved the APS model, and produced the best process plans (operation sequence and machine selection with outsourcing) and schedules for all orders.

Vehicle routing scheduling for cross-docking in the supply chain

One of the most important factors in implementing supply chain management is to efficiently control the physical flow of the supply chain. Due to its importance, many companies are trying to develop efficient methods to increase customer satisfaction and reduce costs. In various methods, cross-docking is considered a good method to reduce inventory and improve responsiveness to various customer demands. However, previous studies have dealt mostly with the conceptual advantages of cross-docking or actual issues from the strategic viewpoint. It is also necessary, however, to considering cross-docking from an operational viewpoint in order to find the optimal vehicle routing schedule. Thus, an integrated model considering both cross-docking and vehicle routing scheduling is treated in this study. Since this problem is known as NP-hard, a heuristic algorithm based on a tabu search algorithm is proposed. In the numerical example, our proposed algorithm found a good solution whose average percentage error was less than 5% within a reasonable amount of time.

Production-distribution planning in supply chain considering capacity constraints

Analytic models have been developed to solve the integrated production-distribution problems in supply chain management (SCM). As one of the major constraints in analytic models, operation time has mostly been known or disregarded. However, in the real systems, due to the various kinds of uncertain factors such as unexpected delays, queuing, breakdowns, operation time in the analytic model cannot correctly represent the dynamic behavior of the consumption of real operation time. To solve this problem, in this paper, we propose a hybrid approach combining the analytic and simulation model. Operation time in the analytic model is considered as a dynamic factor and adjusted by the results from independently developed simulation model, which includes general production-distribution characteristics. We obtain the more realistically optimal production-distribution plans for the integrated supply chain system reflecting stochastic natures by performing the iterative hybrid analytic-simulation procedure.

Supply chain simulation with discrete-continuous combined modeling

Many simulation models have been built to facilitate the use of simulation in designing, evaluating, and optimizing supply chains. Simulation is preferred to deal with stochastic natures existing in the supply chain. Moreover, simulation has a capability to find a local optimum value within each component through the entire supply chain. Most supply chain simulation models have been developed on the basis of discrete-event simulation. Since supply chain systems are neither completely discrete nor continuous, the need for constructing a model with aspects of both discrete-event and continuous simulation is provoked, resulting in a combined discrete-continuous simulation. In this paper, architecture of combined modeling for supply chain simulation is proposed, which includes the equation of continuous portion in the supply chain and how these equations can be used in the supply chain simulation models. The simple example of a supply chain model dealing with the strategic level of the supply chain presented in this paper shows the possibility and the prospect of this approach.

A framework for measuring the performance of service supply chain management

Despite the increasing attention to the service supply chain management by both practitioners and academics, the performance measurement of service supply chains still remains unexplored. Most service firms realize that, in order to evolve an efficient and effective service supply chain, service supply chain management needs to be assessed for its performance. A literature review was conducted on performance measurement issues of service supply chains. This paper develops a framework of service supply chain performance measurement. Based on the strategic, tactical and operational level performance in a service supply chain, measures and metrics are discussed. The emphasis is on performance measures dealing with service supply chain processes such as demand management, customer relationship management, supplier relationship management, capacity and resource management, service performance, information and technology management and service supply chain finance. And to prioritize service supply chain performance measurement indicators to improve service supply chain performance, a methodology based on the extent fuzzy analytic hierarchy process is stressed. The developed framework of service supply chain performance measurement is applied to the hotel supply chain. The results of this study are useful both to practitioners in the service supply chain and to researchers carrying out further studies in the field.

A shape-based block layout approach to facility layout problems using hybrid genetic algorithm

In this paper, a shape-based block layout (SBL) approach for solving facility layout problem with unequal-areas and fixed-shapes is presented. The SBL approach employs hybrid genetic algorithm to find good solution. The existing algorithms for the problem of assigning positions to unequal-area and fixed or approximated shape departments within a given building area can produce solutions with some drawbacks, which require extensive manual revision to create practical layouts and produce irregular building shapes and too much unusable spaces. The objective function of SBL approach minimizes total material handling cost and maximizes space utilization. Experimental results show that the SBL approach is able to improve solution and it can create more practical layout than that of existing approaches.

Integrated process planning and scheduling in a supply chain

This paper deals with the integration of process planning and scheduling, which is one of the most important functions in a supply chain to achieve high quality products at lower cost, lower inventory, and high level of performance. Solving the problem is essential for the generation of flexible process sequences with resource selection and for the decision of the operation schedules that can minimize makespan. We formulate a mixed integer programming model to solve this problem of integration. This model considers alternative resources: sequences and precedence constraints. To solve the model, we develop a new evolutionary search approach based on a topological sort. We use the topological sort to generate a set of feasible sequences in the model within a reasonable computing time. Since precedence constraints between operations are handled by the topological sort, the developed evolutionary search approach produces only feasible solutions. The experimental results using various sizes of problems provide a way to demonstrate the efficiency of the developed evolutionary search approach.

Group multi-criteria supplier selection using combined grey systems theory and uncertainty theory

Developing framework for reducing purchasing risks associated with suppliers.Combination of grey system theory and uncertainty theory is used.It neither requires any probability distribution nor fuzzy membership function.It selects the most appropriate suppliers and allocates optimal purchase quantity. Supplier selection in supply chain is critical strategic decision for organizations success and has attracted much attention of both academic researchers and practitioners. Supplier selection problem consists of stochastic and recognitive uncertainties. However, the requirement of large sample size and strong subject knowledge to build suitable fuzzy membership function restrict the applicability of probability and fuzzy theories in supplier selection problem. In response, this study proposed a new tool for supplier selection. In this paper, we applied the combination of grey system theory and uncertainty theory which neither requires any probability distribution nor fuzzy membership function. The objective of this paper is to develop framework for reducing the purchasing risks associated with suppliers. The proposed supplier selection method not only selects the most appropriate supplier(s) but also allocate optimal purchase quantity under stochastic and recognitive uncertainties. An example is shown to highlight the procedure of the proposed model at the end of this paper.

Optimal production-distribution planning in supply chain management using a hybrid simulation-analytic approach

Production-distribution planning is the most important activity in supply chain management (SCM). To solve this planning problem, either analytic or simulation approaches have been used. However these two approaches have their own demerits in problem solving. We propose a hybrid approach which is a specific problem solving procedure combining analytic and simulation methods to solve production-distribution problems in supply chains. The machine capacity and distribution capacity constraints in the analytic model are considered as stochastic factors and are adjusted by the proposed specific process according to the results from an independently developed simulation model which includes general production-distribution characteristics.

Integrated machine tool selection and operation sequencing with capacity and precedence constraints using genetic algorithm

In this paper, an integrated machine tool selection and sequencing model is proposed. The model determines machine visiting sequences for all part types, such that the total production time for the production order is minimized and workloads among machine tools are balanced. The model is formulated as a 0-1 integer programming. To solve the model, a genetic algorithm approach based on a topological sort technique is developed. To demonstrate the efficiency of the proposed GA approach on the integrated machine tool selection and sequencing problem, a number of numerical experiments using various size problems are carried out. The numerical experiments show that the proposed GA approach is efficient to this problems.