Woon-Seek Lee

Scheduling parallel batch processing machines with arbitrary job sizes and incompatible job families

Motivated by a bottleneck operation in an MLCC (multi-layer ceramic capacitor) production line, we study the scheduling problem of parallel batch processing machines in which a number of jobs can be processed simultaneously in a machine as a batch. Volumes of the jobs are different from each other and each job belongs to the family in which all jobs have the same processing time. In this situation, we analyse three kinds of problems whose performance measures are makespan, total completion time, and total weighted completion time, respectively. Since these problems are known to be NP-hard, we propose a number of heuristics and design genetic algorithms for the problems. Through some computational experiments, we evaluate the performances of the heuristic algorithms proposed, including the genetic algorithms for each of three problems.

Transporter scheduling based on a network flow model under a dynamic block transportation environment1

This paper considers a transporter scheduling problem under dynamic block transportation environment in shipbuilding. In dynamic situations, there exist the addition, cancellation or change of block transportation requirements, sudden breakdowns, and maintenance of transporters. Some blocks are available to be picked up at a specific time during the planning horizon while some other blocks need to be delivered before a specific time. These requirements cause two penalty times: 1) delay times incurred when a block is picked up after a required start time, and 2) tardy times incurred when a block shipment is completed after a required delivery time. The blocks are located at different areas in the shipyard and transported by transporters. The objective of this paper is to propose a heuristic algorithm based on a network flow model which minimize the weighted sum of empty transporter travel times, delay times, and tardy times. Also, a rolling scheduling algorithm is proposed for dynamic block transportation environment. The performance of the proposed heuristic algorithms are evaluated through a simulation experiment.

Decompositions of T-generalized transformation semigroups

We introduce several decompositions of T-generalized transformation semigroups and investigate some of algebraic properties of them.

A genetic algorithm for dynamic inbound ordering and outbound dispatching problem with delivery time windows

This article considers an inbound ordering and outbound dispatching problem for a single product in a third-party warehouse, where the demands are dynamic over a discrete and finite time horizon, and moreover, each demand has a time window in which it must be satisfied. Replenishing orders are shipped in containers and the freight cost is proportional to the number of containers used. The problem is classified into two cases, i.e. non-split demand case and split demand case, and a mathematical model for each case is presented. An in-depth analysis of the models shows that they are very complicated and difficult to find optimal solutions as the problem size becomes large. Therefore, genetic algorithm (GA) based heuristic approaches are designed to solve the problems in a reasonable time. To validate and evaluate the algorithms, finally, some computational experiments are conducted.

Production batch sizing at multi-product bottleneck machines

In common manufacturing systems, there exist bottleneck machines that limit the capacity of the entire system. This paper addresses a batch sizing problem at the bottleneck machines in which various product types are processed. Koo et al. (2006) have presented a batch sizing model at single-product bottleneck machines. This paper is an extension to the previous research work where multiple products are taken into explicit considerations in batch sizing. We introduce a linear search algorithm to find optimal throughput rate and batch size at the same time. Numerical examples are provided to see how the proposed method works.

An Adaptive Genetic Algorithm for a Dynamic Lot-sizing and Dispatching Problem with Multiple Vehicle Types and Delivery Time Windows

Graduate School of Management of Technology, Pukyong National University, Busan, 608-739, KoreaDepartment of Systems Management and Engineering, Pukyong National University, Busan, 608-739, KoreaThis paper considers an inbound lot-sizing and outbound dispatching problem for a single product in a third- party 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.

Scheduling two-machine flow shop with a batch processing machine

This paper deals with the two-machine flow shop scheduling problem in which the first machine is a batch processing machine (BPM) that can process a number of jobs simultaneously, while the second machine is a discrete processing machine (DPM) that processes jobs one by one. To minimize makespan of the system, we present a mixed integer programming formulation for the problem. Using this formulation, we show that an optimal solution for small problem can be obtained by a commercial optimization software. However, since the problem is NP-hard and the size of real problems is usually large, we propose a number of heuristic algorithms including genetic algorithm to solve practical big-sized problems in a reasonable computational time. To verify the performances of the algorithms, we compare them with lower bound for the problem. From the results we obtained, some of the heuristic algorithms show very good performances.

Performance Analysis of a New Work Assignment in Assembly Cells

This paper addresses the work assignment problems in assembly cells. The assembly cells are known to be more flexible and productive than the traditional assembly line. To maximize the utilization of resources in assembly cells, it is important to have the line balanced. This paper presents a dynamic work assignment method where each worker performs assembly operations on a product until the next worker downstream takes it over. This pull-type assignment method balances the assembly cell in an autonomous way. The performance of the new assignment method is examined and compared with existing assignment methods.

Genetic Algorithms for a Multi-product Dynamic Lot-sizing and Dispatching Problem with Delivery Time Windows and Multi-vehicle Types

This paper analyzes a multi-product inbound lot-sizing and outbound dispatching problem with multi-vehicle types in a third-party logistics distribution center. The product must be delivered to the customers within the delivery time window and backlogging is not allowed. Replenishing orders are shipped by several types of vehicles with two types of the freight costs, i.e., uniform and decreasing, are considered. The objective of this study is to determine the lot-size and dispatching schedules to minimize the total cost with the sum of inbound and outbound transportation and inventory costs over the entire time horizon. In this study, we mathematically derive a mixed-integer programming model and propose a genetic algorithm (GA1) based on a local search heuristic algorithm to solve large-scale problems. In addition, we suggest a new genetic algorithm (GA2) with an adjusting algorithm to improve the performance of GA1. The basic mechanism of the GA2 is to provide an unidirectional partial move of items to available containers in the previous period. Finally, we analyze the results of GA1 and GA2 by evaluate the relative performance using the gap between the objective values of CPLEX and the each algorithm.

Transporter Scheduling under Dynamic Block Transportation Environment

This paper considers a transporter scheduling problem under dynamic block transportation environment in shipbuilding. In dynamic situations, there exist the addition or cancellation of block transportation requirements. The transportation of the blocks in the shipyard has some distinct characteristics. Some blocks are available to be picked up at a specific time during the planning horizon while some other blocks need to be delivered before a specific time. These requirements cause two penally times: 1) delay time incurred when a block is picked up after a required start time, and 2) tardy time incurred when a block shipment is completed after the required delivery time. The blocks are located at different areas in the shipyard. The objective of this paper is to propose heuristic algorithms which minimize the weighted sum of empty transporter travel times, delay times, and tardy times. Four heuristic algorithms for transporter scheduling are proposed and their performance is evaluated through computational experiments.