• 대한산업공학회지
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• 제40권4호
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• pp.435-441
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• 2014

This paper considers a single-product problem for inbound lot-sizing and outbound dispatching at a third-party warehouse, where the demand is dynamic over the discrete time horizon. Each demand must be delivered into the corresponding delivery time window which is the time interval characterized by the earliest and latest delivery dates of the demand. Ordered products are shipped by heterogeneous container types. Each container type has type-dependent carrying capacity and the unit freight cost depends on each container type. Total freight cost is proportional to the number of each container type used. Also it is assumed that related cost functions are concave and backlogging is not allowed. The objective of the paper is to simultaneously determine the optimal inbound lot-sizing and outbound dispatching plans that minimize total costs which include ordering, shipping, and inventory holding costs. The optimal solution properties are characterized for the problem and then a dynamic programming algorithm is presented to find the optimal solution.

• Industrial Engineering and Management Systems
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• 제11권3호
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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.

• Industrial Engineering and Management Systems
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• 제10권1호
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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.

• 대한산업공학회지
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• 제37권4호
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• pp.331-341
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• 2011

This paper considers an inbound lot-sizing and outbound dispatching problem for a single product in a thirdparty logistics (3PL) distribution center. Demands are dynamic and finite over the discrete time horizon, and moreover, each demand has a delivery time window which is the time interval with the dates between the earliest and the latest delivery dates All the product amounts must be delivered to the customer in the time window. Ordered products are shipped by multiple vehicle types and the freight cost is proportional to the vehicle-types and the number of vehicles used. First, we formulate a mixed integer programming model. Since it is difficult to solve the model as the size of real problem being very large, we design a conventional genetic algorithm with a local search heuristic (HGA) and an improved genetic algorithm called adaptive genetic algorithm (AGA). AGA spontaneously adjusts crossover and mutation rate depending upon the status of current population. Finally, we conduct some computational experiments to evaluate the performance of AGA with HGA.

• 산업경영시스템학회지
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• 제44권2호
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• pp.24-35
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• 2021

Due to increasing awareness on the treatment of end-of-use/life products, disassembly has been a fast-growing research area of interest for many researchers over recent decades. This paper introduces a novel lot-sizing problem that has not been studied in the literature, which is the service-parts lot-sizing with disassembly option. The disassembly option implies that the demands of service parts can be fulfilled by newly manufactured parts, but also by disassembled parts. The disassembled parts are the ones recovered after the disassembly of end-of-use/life products. The objective of the considered problem is to maximize the total profit, i.e., the revenue of selling the service parts minus the total cost of the fixed setup, production, disassembly, inventory holding, and disposal over a planning horizon. This paper proves that the single-period version of the considered problem is NP-hard and suggests a heuristic by combining a simulated annealing algorithm and a linear-programming relaxation. Computational experiment results show that the heuristic generates near-optimal solutions within reasonable computation time, which implies that the heuristic is a viable optimization tool for the service parts inventory management. In addition, sensitivity analyses indicate that deciding an appropriate price of disassembled parts and an appropriate collection amount of EOLs are very important for sustainable service parts systems.

• 대한산업공학회지
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• 제40권4호
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• pp.424-427
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• 2014

In this paper, we consider a production system in which the items are produced by batch. For a production planning of the system, we formulate a lot-sizing problem in which each production should be a multiple of a given unit batch and backlogging is allowed. We propose an optimal dynamic programming algorithm for the plan whose complexity is $O(T^2)$ where T is the maximum number of periods in a plan.

• 산업경영시스템학회지
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• 제32권3호
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• pp.29-39
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• 2009

In an environment of global competition, the success of a manufacturing corporation is directly related to how it plans and executes production in particular as well as to the optimization level of its process in general. This paper proposes a production planning algorithm for the Multi-Level, multi-item Capacitated Lot Sizing Problem (MLCLSP) in supply chain network considering back-order. MLCLSP corresponds to a mixed integer programming (MIP) problem and is NP-hard. Therefore, this paper proposes an effective algorithm, GRHS (GRASP-based Rolling Horizon Search) that solves this problem within reasonable computational time and evaluates its performance under a variety of problem scenarios.

• 한국경영과학회지
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• 제33권4호
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• pp.53-61
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• 2008

Recently, Ertogral et al.[2] suggested two models considering the transportation cost based on single-vendor single-buyer integrated production-inventory problem. Although their problem-solving algorithm guarantees solutions obtained are optimal, a limitation is revealed that its performance can be inefficient due to complete enumeration search in a certain range. In this paper, a more efficient algorithm in finding optimal solutions is suggested for the same problem suggested by Ertogral et at.[2]. Numerical examples are presented for illustrative purpose.

• 대한산업공학회지
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• 제22권2호
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• pp.231-245
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• 1996

Semiconductor manufacturing line includes several batch processes which are to be controlled effectively to enhance the productivity of the line. The key problem in batch processes is a dynamic batch sizing problem which determines number of lots processed simultaneously in a single botch. The batch sizing problem in semiconductor manufacturing has to consider delay of lots, setup cost of the process, machine utilization and so on. However, an optimal solution cannot be attainable due to dynamic arrival pattern of lots, and difficulties in forecasting future arrival times of lots of the process. This paper proposes an efficient batch sizing heuristic, which considers delay cost, setup cost, and effect of the forecast errors in determining the botch size dynamically. Extensive numerical experiments through simulation are carried out to investigate the effectiveness of the proposed heuristic in four key performance criteria: average delay, variance of delay, overage lot size and total cost. The results show that the proposed heuristic works effectively and efficiently.

• 대한산업공학회지
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• 제33권3호
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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.