Jun-Gyu Kim

Advanced planning and scheduling based on precedence and resource constraints for e-plant chains

The e-plant chain is an extension of the integration beyond a production site by means of improved distribution management, electronic data interchange and coordination of multiple plants. The present paper proposes an advanced planning and scheduling model for the e-plant chain. The advanced planning and scheduling is the most important function when supporting flexible planning and scheduling in the e-plant chain. The problem is formulated as a mixed integer-programming model. The model includes the main features of the system including flexible operations’ sequences, resource requirements and alternative schedules. Since the problem is NP-hard, an intelligent search approach based on a genetic algorithm is developed. Numerical experiments show the proposed approach is satisfactory in its accuracy and efficiency.

A Tabu Search Algorithm using the Voronoi Diagram for the Capacitated Vehicle Routing Problem

We consider the capacitated vehicle routing problem that determines the routes of vehicles in such a way that each customer can be visited exactly once by one vehicle, starting and terminating at the depot while the vehicle capacity and the travel time constraints must be satisfied. The objective is to minimize the total traveling cost. Due to the complexity of the problem, we suggest a tabu search algorithm that combines the features of the exiting local search heuristics. In particular, our tabu search algorithm incorporates the method to reduce the neighborhoods using the proximity information of the Voronoi diagram corresponding to each problem instance. Computational experiments are done on the benchmark problems and the test results are reported.In this paper, we give an algorithm for the analysis and correction of the distorted QR barcode (QR-code) image. The introduced algorithm is based on the code area finding by four corners detection for 2D barcode. We combine Canny edge detection with contours finding algorithms to erase noises and reduce computation and utilize two tangents to approximate the right-bottom point. Then, we give a detail description on how to use inverse perspective transformation in rebuilding a QR-code image from a distorted one. We test our algorithm on images taken by mobile phones. The experiment shows that our algorithm is effective.

Disassembly scheduling with capacity constraints: minimizing the number of products disassembled

Abstract Disassembly scheduling, one of the important operational problems in disassembly systems, is the problem of determining the timing and quantity of disassembling used or end-of-life products to satisfy the demand of their parts or components over a given planning horizon. This paper focuses on the case of single product type without parts commonality while the resource capacity restrictions are considered explicitly. The problem is formulated as an integer programming model with the objective of minimizing the number of products disassembled, and a solution algorithm is suggested after deriving its optimal solution properties. Computational experiments are done on various test problems, and the results show that the algorithm suggested in this paper gives optimal solutions for the test problems within a very short amount of computation time.

Iterative algorithms for part grouping and loading in cellular reconfigurable manufacturing systems

A reconfigurable manufacturing system (RMS), one of state-of-the-art manufacturing system technologies, is the one designed at the outset for rapid changes in its hardware and software components in order to quickly adjust its production capacity and functionality in response to market or system changes. In this study, we consider a cellular RMS with multiple reconfigurable machining cells (RMCs), each of which has numerical control machines, a setup station, and an automatic material handling and storage system. Each machine within the RMC has an automatic tool changer and a tool magazine of a limited capacity. Two important operational problems, part grouping and loading, are considered in this study. Part grouping is the problem of allocating parts to RMCs, and loading is the problem of allocating operations and their cutting tools to machines within the RMC. An integer programming model is suggested to represent the two problems at the same time for the objective of balancing the workloads assigned to machines. Then, due to the complexity of the problem, we suggest two iterative algorithms in which the two problems are solved repeatedly until a solution is obtained. Computational experiments were done on various test instances and the results are reported.

Fast and meta-heuristics for common due-date assignment and scheduling on parallel machines

This study considers common due-date assignment and scheduling on parallel machines. The problem has three decision variables: assigning the common-due-date, allocating jobs to parallel machines, and sequencing the jobs assigned to each machine. The objective is to minimise the sum of due-date assignment, earliness and tardiness penalties. A mathematical programming model is presented, and then two types of heuristics are suggested after characterising the optimal solution properties. The two types of heuristics are: (a) a fast two-stage heuristic with obtaining an initial solution and improvement; and (b) two meta-heuristics, tabu search and simulated annealing, with new neighbourhood generation methods. Computational experiments were conducted on a number of test instances, and the results show that each of the heuristic types outperforms the existing one. In particular, the meta-heuristics suggested in this study are significantly better than the existing genetic algorithm.

A case study on period vehicle routing in a refuse collection system

Period vehicle routing, a multi-period extension of the capacitated vehicle routing problem, is the problem of determining a service combination of each customer as well as the vehicle routes in each period of the planning horizon while satisfying the restrictions on the vehicle capacity and the travel distance (time). The problem can typically be found in refuse collection systems in which end-of-life products or wastes are collected and moved to the facilities where further treatment is taken care of. In this study, we report a case study on the problem for the objective of minimising the fleet size, i.e. the maximum number of vehicles simultaneously required over the planning horizon. To solve the problem, we adopt the two-stage heuristic in which an initial solution is obtained by assigning a service combination to each collection point, and then it is improved by changing the service combination assigned to each collection point. Computational experiment was done on the case data and significant improvement over the conventional method is reported.

Loading algorithms for flexible manufacturing systems with partially grouped unrelated machines and tooling constraints

This paper considers the loading problem for flexible manufacturing systems with partially grouped machines, i.e., machines are tooled differently, but multiple machines can be assigned to each operation. Loading is the problem of allocating operations and their associated cutting tools to machines for a given set of parts. As an extension of the existing studies, we consider unrelated machines, i.e., processing time of an operation depends on the speed of the machine where it is allocated. Also, we consider the practical constraints associated with cutting tools: (a) tool life restrictions; and (b) available number of tool copies. An integer linear programming model is suggested for the objective of balancing the workloads assigned to machines. Then, due to the complexity of the problem, we suggest two-stage heuristics in which an initial solution is obtained and then it is improved. The heuristics were tested on some test instances, and the results are reported.

Capacitated disassembly scheduling: minimizing the number of products disassembled

Disassembly scheduling is the problem of determining the timing and quantity of disassembling used products to satisfy the demands of their parts or components over the planning horizon. This paper focuses on the case of single product type without parts commonality while the resource capacity restrictions are explicitly considered. The problem is formulated as an integer program for the objective of minimizing the number of products to be disassembled, and an optimal algorithm, after deriving the properties of the problem, is suggested. Computational experiments are done on randomly generated test problems, and the results are reported.

Common Due-Date Assignment and Scheduling on Parallel Machines with Sequence-Dependent Setup Times

This paper considers common due-date assignment and scheduling on parallel machines. The main decisions are: (a) deter-mining the common due-date; (b) allocating jobs to machines; and (c) sequencing the jobs assigned to each machine. The objective is to minimize the sum of the penalties associated with common due-date assignment, earliness and tardiness. As an extension of the existing studies on the problem, we consider sequence-dependent setup times that depend on the type of job just completed and on the job to be processed. The sequence-dependent setups, commonly found in various manufacturing systems, make the problem much more complicated. To represent the problem more clearly, a mixed integer programming model is suggested, and due to the complexity of the problem, two heuristics, one with individual sequence-dependent setup times and the other with aggregated sequence-dependent setup times, are suggested after analyzing the characteristics of the problem. Computational experiments were done on a number of test instances and the results are reported.

Common Due-Date Assignment and Scheduling with Sequence-Dependent Setup Times: a Case Study on a Paper Remanufacturing System

In this paper, we report a case study on the common due-date assignment and scheduling problem in a paper remanufacturing system that produces corrugated cardboards using collected waste papers for a given set of orders under the make-to-order (MTO) environment. Since the system produces corrugated cardboards in an integrated process and has sequence-dependent setups, the problem considered here can be regarded as common due-date assignment and sequencing on a single machine with sequence-dependent setup times. The objective is to minimize the sum of the penalties associated with due-date assignment, earliness, and tardiness. In the study, the earliness and tardiness penalties were obtained from inventory holding and backorder costs, respectively. To solve the problem, we adopted two types of algorithms: (a) branch and bound algorithm that gives the optimal solutions; and (b) heuristic algorithms. Computational experiments were done on the data generated from the case and the results show that both types of algorithms work well for the case data. In particular, the branch and bound algorithm gave the optimal solutions quickly. However, it is recommended to use the heuristic algorithms for large-sized instances, especially when the solution time is very critical.