• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.79-88
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• 1989

This characteristics of process industries are high capital intensity, relatively long and sequence dependent setup times, and extremely limited capacity resources. The lot sizing, sequencing and limited capacity resources factors must he considered for production scheduling in these industries. This paper presents a mixed integer programming model for production scheduling. The economic trade offs between capacitated lot sizing flow shop scheduling and sequence dependent setup times also be compared with SMITH-DANIELS's model. As a results, it is shown that this paper has lower total cost, more efficient throughput than SMITH-DANIELS's model.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.98-103
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• 2006

In this research, the single machine capacitated lot-sizing and scheduling problem with sequence- dependent setup costs and setup times (CLSPSD) is considered. This problem is the extension of capacitated lot-sizing and scheduling problem (CLSP) with an additional assumption on sequence-dependent setup costs and setup times. The objective of the problem is minimizing the sum of production costs, inventory holding costs and setup costs satisfying customers' demands. It is known that the CLSPSD is NP-hard. In this paper, the MIP formulation is presented. To handle the problem more efficiently, a conceptual model is suggested, and one of the well-known meta-heuristics, the simulated annealing approach is applied. To illustrate the performance of this approach, various instances are tested and the results of this algorithm are compared with those of the CLPEX. Computational results show that this approach generates optimal or nearly optimal solutions.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.74-83
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• 2011

In this paper, we consider a new lot-sizing and scheduling problem (LSSP) that minimizes the sum of production cost, setup cost and inventory cost. Setup carry-over, setup overlapping, state dependent setup time as well as demand splitting are considered. For this LSSP, we develop a mixed integer programming (MIP) model, of which the size does not increase even if we divide a time period into a number of micro time periods. Also, we develop an efficient heuristic algorithm by combining a decomposition scheme with a local search procedure. Test results show that the developed heuristic algorithm finds a good quality (in practice, even better) feasible solution using far less computation time compared with the CPLEX, a competitive MIP solver.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.3
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• pp.373-380
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• 2007

In this paper, we consider a new lot-sizing and scheduling problem (LSSP) that minimizes the sum of production cost, setup cost and inventory cost. Setup carry-over and overlapping as well as demand splitting are considered. Also, maximum number of setups for each time period is not limited. For this LSSP, we have formulated a mixed integer programming (MIP) model, of which the size does not increase even if we divide a time period into a number of micro time periods. Also, we have developed an efficient heuristic algorithm by combining decomposition scheme with local search procedure. Test results show that the developed heuristic algorithm finds good quality (in practice, even better) feasible solutions using far less computation time compared with the CPLEX, a competitive MIP solver.

• Industrial Engineering and Management Systems
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• v.11 no.3
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• pp.288-298
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• 2012

Lot sizing and shipment scheduling are two interrelated decisions made by a manufacturing plant and a third-party logistics distribution center. This paper analyzes a dynamic inbound ordering problem and shipment problem with a freight container cost, in which the order size of multiple products and single container type are simultaneously considered. In the problem, each ordered product placed in a period is immediately shipped by some freight containers in the period, and the total freight cost is proportional to the number of containers employed. It is assumed that the load size of each product is equal and backlogging is not allowed. The objective of this study is to simultaneously determine the lot-sizes and the shipment schedule that minimize the total costs, which consist of production cost, inventory holding cost, and freight cost. Because the problem is NP-hard, we propose three meta-heuristic algorithms: a simulated annealing algorithm, a genetic algorithm, and a new population-based evolutionary meta-heuristic called self-evolution algorithm. The performance of the meta-heuristic algorithms is compared with a local search heuristic proposed by the previous paper in terms of the average deviation from the optimal solution in small size problems and the average deviation from the best one among the replications of the meta-heuristic algorithms in large size problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.77-86
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• 2012

We consider the capacitated lot-sizing and scheduling problem for a paper remanufacturing system that produces several types of corrugated cardboards. The problem is to determine the lot sizes as well as the sequence of lots for the objective of minimizing the sum of setup and inventory holding costs while satisfying the demand and the machine capacity over a given planning horizon. In particular, the paper remanufacturing system has sequence-dependent setup costs that depend on the type of product just completed and on the product to be processed. Also, the setup state at one period can be carried over to the next period. An integer programming model is presented to describe the problem. Due to the complexity of the problem, we modify the existing two-stage heuristics in which an initial solution is obtained and then it is improved using a multi-pass interchange method. To show the performances of the heuristics, computational experiments were done using the real data, and a significant amount of improvement is reported.

• IE interfaces
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• v.9 no.2
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• pp.3-17
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• 1996

Productivity improvement is one of the most challenging problem facing the motor industry. This paper deals with the lot sizing and production scheduling problems of the side frame press shop in a domestic truck manufacturing company. The problems can not be solved simultaneously due to the computational complexity. Thus, we present a heuristic method which solves the two problems sequentially with the objective of maximizing the press utilization while maintaining a minimum inventory level. A micro-computer-based software is developed for easy implementation of the heuristic in the shop floor level.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.506-512
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• 2006

In this paper, we propose a lot-sizing and scheduling problem that seeks to minimize the sum of production cost and inventory cost over a given planning horizon while considering idle state of a machine in a batch production system. For this problem, we develop an integer programming model. And, we develop an efficient 2-phase heuristic algorithm to find a high quality feasible solution within reasonable time bounds. In the first phase, we seek to minimize the production cost by assigning batches to machines. Then, in the second phase, we find a production sequence of batches that reduces the inventory cost, while considering adding or deleting idle states between batches. Computational results show that the developed heuristic algorithm finds excellent feasible solutions within reasonable time bounds. Also, we could significantly reduce the total cost consisting of production cost and inventory cost by using the developed heuristic algorithm at a real manufacturing system that produces zinc alloys.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.110-117
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• 2009

This paper introduces a case study for efficient generation of production schedules in a tube manufacturing system. The considered scheduling problem consists of two sub problems : lot sizing for a job and Job sequencing. Since these problems require simulation optimization in which the performance measures are obtained by simulation execution, the trade-off between solution quality and computation time is an important issue. In this study, the optimal lot size for every product type is determined from simulation experiments. Then, target production quantity for each product type is transformed to several jobs such that a Job consists of determined lot size. To obtain the good solution for a Job sequence in a reasonable time, a number of alternatives are generated from heuristic rules developed by intuition and analysis of the considered system, and a job sequence is selected from simulation experiments.

• Management Science and Financial Engineering
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• v.11 no.1
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• pp.63-78
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• 2005

Disassembly scheduling is the problem of determining the ordering and disassembly schedules of used or end-of-life products while satisfying the demand of their parts or components over a certain planning horizon. This paper considers the case of the assembly product structure for the cost-based objective of minimizing the sum of purchase, setup, inventory holding, and disassembly operation costs. To represent and solve the problem optimally, this paper presents an integer programming model, which is a reversed form of the multi-level lot sizing formulation. Computational experiments on an example derived from the literature and a number of randomly generated test problems are done and the results are reported.