Cheol Min Joo

Genetic algorithms for door-assigning and sequencing of trucks at distribution centers for the improvement of operational performance

In a supply chain, cross docking is one of the most innovative systems for improving the operational performance at distribution centers. By utilizing this cross docking system, products are delivered to the distribution center via inbound trucks and immediately sorted out. Then, products are shipped to customers via outbound trucks and thus, no inventory remains at the distribution center. In this paper, we consider the scheduling problem of inbound and outbound trucks at distribution centers. The aim is to maximize the number of products that are able to ship within a given working horizon at these centers. In this paper, a mathematical model for an optimal solution is derived and intelligent genetic algorithms are proposed. The performances of the genetic algorithms are evaluated using several randomly generated examples.

Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability

We study unrelated parallel machine scheduling with setup and production availability.We derive a mixed integer programming model for the problem.We propose hybrid genetic algorithms (HGAs) with three dispatching rules.We evaluate the performances of the GAs using randomly generated examples. This article considers the unrelated parallel machine scheduling problem with sequence- and machine-dependent setup times and machine-dependent processing times. Furthermore, the machine has a production availability constraint to each job. The objective of this problem is to determine the allocation policy of jobs and the scheduling policy of machines to minimize the total completion time. To solve the problem, a mathematical model for the optimal solution is derived, and hybrid genetic algorithms with three dispatching rules are proposed for large-sized problems. To assess the performance of the algorithms, computational experiments are conducted and evaluated using several randomly generated examples.

Genetic algorithms for single machine scheduling with time-dependent deterioration and rate-modifying activities

In this paper, the integration of two emerging classes of scheduling problems, the class of scheduling problems with time-dependent deterioration and the class of scheduling problems with rate-modifying activities, are addressed. The scheduling problems have been studied independently. However, the integration of these classes is motivated by human operators of tasks who have fatigue while carrying out the operation of a series of tasks. This situation is also applicable to machines that experience performance degradation over time due to mal-position or mal-alignment of jobs, abrasion of tools, and scraps of operations, etc. It requires maintenance in order to sustain acceptable production rates. We consider the single machine scheduling problem with time-dependent deterioration and multiple RMAs. A mathematical model for an optimal solution to minimize the makespan is derived and genetic algorithms are proposed. The performance of the genetic algorithms is evaluated using randomly generated examples.

Parallel machine scheduling problem with ready times, due times and sequence-dependent setup times using meta-heuristic algorithms

This article considers a parallel machine scheduling problem with ready times, due times and sequence-dependent setup times. The objective of this problem is to determine the allocation policy of jobs and the scheduling policy of machines to minimize the weighted sum of setup times, delay times and tardy times. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through comparison with optimal solutions using several randomly generated examples.

Block transportation scheduling under delivery restriction in shipyard using meta-heuristic algorithms

Special vehicles called transporters are used to deliver heavy blocks from one plant to another in shipyards. Because of the limitation on the number of transporters, the scheduling of transporters is important for maintaining the overall production schedule of the blocks. This paper considers a scheduling problem of block transportation under a delivery restriction to determine when and by which transporter each block is delivered from its source plant to its destination plant. The objective of the problem is to minimize the penalty times that can cause delays in the overall block production schedule. A mathematical model for the optimal solution is derived, and two meta-heuristic algorithms based on a genetic algorithm (GA) and a self-evolution algorithm (SEA) are proposed. The performance of the algorithms is evaluated with several randomly generated experimental examples.

Ant colony optimisation with random selection for block transportation scheduling with heterogeneous transporters in a shipyard

This paper considers a scheduling problem of heterogeneous transporters for pickup and delivery blocks in a shipyard assuming a static environment where all transportation requirements for blocks are predetermined. In the block transportation scheduling problem, the important issue is to determine which transporter delivers the block from one plant to the other plant and when, in order to minimise total logistic times. Therefore, the objective of the problem is to simultaneously determine the allocation policy of blocks and the sequence policy of transporters to minimise the weighted sum of empty transporter travel times, delay times, and tardy times. A mathematical model for the optimal solution is derived and an ant colony optimisation algorithm with random selection (ACO_RS) is proposed. To demonstrate the performance of ACO_RS, computational experiments are implemented in comparing the solution with the optimal solutions obtained by CPLEX in small-sized problems and the solutions obtained by conventional ACO in large-sized problems.

Genetic Algorithm with an Effective Dispatching Method for Unrelated Parallel Machine Scheduling with Sequence Dependent and Machine Dependent Setup Times

This paper considers a unrelated parallel machine scheduling problem with ready times, due times and sequence and machine-dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of machines to minimize the total tardy time. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, a genetic algorithm using an effective dispatching method is proposed. The performance of the proposed genetic algorithm is evaluated using several randomly generated examples.

Rule-based meta-heuristics for integrated scheduling of unrelated parallel machines, batches, and heterogeneous delivery trucks

Display Omitted We study an integrated scheduling for unrelated parallel machines, batches, and trucks.We derive a mixed integer programming model (MIP) for the problem.We propose novel meta-heuristics using a single-stage genetic algorithm (GA) framework.The meta-heuristics include machine-assignment rules, batching rules, and truck-assignment rules.We evaluate the performances of the rule-based meta-heuristics using randomly generated examples. In this article, we deal with an integrated scheduling for unrelated parallel machines, batches, and heterogeneous delivery trucks. In a manufacturing plant, jobs ordered by customers are manufactured by one of several unrelated parallel machines. Then, they are grouped and delivered to the respective customers by heterogeneous trucks with different capacities and travel times. The objective of the problem is to simultaneously determine the machine schedule, batching, and truck-delivery schedule to minimize the make span of the entire process. To solve this problem, we derive a mathematical model to obtain the optimal solution, and we propose rule-based meta-heuristics using single-stage GA framework. Through randomly generated instance examples, the performances of the proposed meta-heuristics are compared.

Stepwise structural verification of cyclic workflow models with acyclic decomposition and reduction of loops

Abstract Existence of cycles (or loops) is one of the main sources that make the analysis of workflow models difficult. Several approaches of structural verification exist in the literature, but how to verify cyclic workflow models efficiently in a comprehensible form remains an open research question. Thus, a novel structural verification approach for cyclic workflow models by means of acyclic decomposition and reduction of loops is introduced in this paper with the following contributions. First, acyclic decomposition of natural loops, further enhanced by reduction of nested loops, enables existing verification techniques, normally dealing with acyclic models, to handle workflow models with natural loops. Second, instantiation of an irreducible loop into natural loops, altogether with reduction of concurrent loop entries, enables the proposed approach to handle workflow models with irreducible loops. Last, diagnostic information, provided by the proposed approach, helps stakeholders correct and improve their workflow models. Two examples are provided to show that the proposed approach is systematic and practical. In addition, a prototype of the proposed approach is developed. Its execution result shows that, while providing diagnostic information, the proposed approach can handle workflow models with arbitrary cycles effectively.

Scheduling Trucks in Multi-Door Cross Docking Systems: An Adaptive Genetic Algorithm with a Dispatching Rule

One of the most important operational management problems of a cross docking system is the truck scheduling problem. Cross docking is a logistics management concept in which products delivered to a distribution center by inbound trucks are immediately sorted out, routed and loaded into outbound trucks for delivery to customers. The truck scheduling problem in a multi-door cross docking system considered in this paper comprises the assignment of trucks to dock doors and the determination of docking sequences for all inbound and outbound trucks in order to minimize the total operation time. A mathematical model for optimal solution is derived, and the genetic algorithms (GAs) and the adaptive genetic algorithms (AGAs) as solution approaches with different types of chromosomes are proposed. The performance of the meta-heuristic algorithms are evaluated using randomly generated several examples.