• Journal of the Korean Operations Research and Management Science Society
• /
• v.41 no.1
• /
• pp.87-98
• /
• 2016

A single machine scheduling problem for jobs with stochastic processing time is considered in this study. Shortest processing time (SPT) sequencing according to the expected processing times of jobs is optimal for schedules with minimal expected mean flow time when all the jobs arrive to be scheduled and their expected processing times are known. However, SPT sequencing according to the expected processing time may not be optimal for the minimization of the mean flow time when the actual processing times of jobs are known. This study evaluates the complexity of SPT sequencing through a comparison of the mean flow times of schedules based on the expected processing times and actual processing times of randomly generated jobs. Evaluation results show that SPT sequencing according to the expected flow time exhibits chaotic variation to the optimal mean flow time. The relative deviation from the optimal mean flow time increases as the number of jobs, processing time, or coefficient of variation increases.

• Management Science and Financial Engineering
• /
• v.18 no.1
• /
• pp.1-12
• /
• 2012

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.

• Management Science and Financial Engineering
• /
• v.19 no.1
• /
• pp.29-36
• /
• 2013

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.

• Industrial Engineering and Management Systems
• /
• v.9 no.2
• /
• pp.70-79
• /
• 2010

Makespan and cost minimization are two important factors in project investment. This paper considers a multi-mode resource-constrained project scheduling problem with the objective of minimizing costs, subject to a deadline constraint. A number of studies have focused on minimizing makespan or resource availability cost with a specified deadline. This problem assumes a fixed cost for the availability of each renewable resource per period, and the project cost to be minimized is the sum of the variable cost associated with the execution mode of each activity. The presented memetic algorithm (MA) consists of three features: (1) a truncated branch and bound heuristic that serves as effective preprocessing in forming the initial population; (2) a strategy that maintains two populations, which respectively store deadline-feasible and infeasible solutions, enabling the MA to explore quality solutions in a broader resource-feasible space; (3) a repair-and-improvement local search scheme that refines each offspring and updates the two populations. The MA is tested via ProGen generated instances with problem sizes of 18, 20, and 30. The experimental results indicate that the MA performs exceptionally well in both effectiveness and efficiency using the optimal solutions or the current best solutions for the comparison standard.

• Journal of Korean Institute of Industrial Engineers
• /
• v.37 no.3
• /
• pp.248-257
• /
• 2011

Cross docking is a logistics management concept in which items delivered to a terminal by inbound trucks are immediately sorted out, routed and loaded into outbound trucks for delivery to customers. Two main advantages by introducing a cross docking terminal are to consolidate multiple smaller shipment into full truck load and remove storage and order picking processes to save up logistics costs related to warehousing and transportation costs. This research considers the scheduling problem of trucks in the cross docking terminals with multi-door in an inbound and outbound dock, respectively. The trucks sequentially deal with the storage process at the one of inbound doors and the shipping process at the one of the outbound doors. A mathematical model for an optimal solution is derived, and genetic algorithms with two different chromosome representations are proposed. To verify performance of the GA algorithms, we compare the solutions of GAs with the optimal solutions and the best solution using randomly generated several examples.

• Industrial Engineering and Management Systems
• /
• v.13 no.2
• /
• pp.193-202
• /
• 2014

Patients' satisfaction while receiving medical service is affected by whether or not their preferences can be met, including time and physician preference. Due to scarcity of medical resource in China, efficient use of available resources is urgently required. To guarantee the utilization ratio, the scheduling decisions are made after all booking information is received. Two integer models with different objectives are formulated separately, maximizing the degree of satisfaction and revenue. The optimal value of the two models can be considered as the bound of corresponding objectives. However, it is improper to implement any of the extreme policies. Because revenue is a key element to keep the hospital running and satisfaction degree is related to the hospital's reputation, neither the revenue nor the satisfaction can be missed. Therefore, hospitals should make a balance. An integrated model is developed to find out the tradeoff between the two objectives. The whole degree of mismatching that is related to patient satisfaction and other separate mismatching degree are considered. Through a computational study, it is concluded that based on the proposed model hospitals can make their decisions according to service requirement.

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

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.196-203
• /
• 2022

The resource constrained scheduling problem (RCSP) constitutes one of the most challenging problems in Project Management, as it combines multiple parameters, contradicting objectives (project completion within certain deadlines, resource allocation within resource availability margins and with reduced fluctuations), strict constraints (precedence constraints between activities), while its complexity grows with the increase in the number of activities being executed. Due to the large solution space size, this work investigates the application of Genetic Algorithms to approximate the optimal resource alolocation and obtain optimal trade-offs between different project goals. This analysis uses the cost of exceeding the daily resource availability, the cost from the day-by-day resource movement in and out of the site and the cost for using resources day-by-day, to form the objective cost function. The model is applied in different case studies: 1 project consisting of 10 activities, 4 repetitive projects consisting of 40 activities in total and 16 repetitive projects consisting of 160 activities in total, in order to evaluate the effectiveness of the algorithm in different-size solution spaces and under alternative optimization criteria by examining the quality of the solution and the required computational time. The case studies 2 & 3 have been developed by building upon the recurrence of the unit/sub-project (10 activities), meaning that the initial problem is multiplied four and sixteen times respectively. The evaluation results indicate that the proposed model can efficiently provide reliable solutions with respect to the individual goals assigned in every case study regardless of the project scale.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1999.04a
• /
• pp.271-271
• /
• 1999

• Proceedings of the IEEK Conference
• /
• 2000.11a
• /
• pp.177-180
• /
• 2000

Optical burst switching(OBS) is a new switching paradigm to supporting bursty traffic on the Internet efficiently. OBS separates burst level and control level. To handle data burst efficiently, the scheduling schemes in optical burst switching systems must keep track of future resource availability when assigning arriving data bursts to wavelength channels. In this paper, we evaluate the performance of three scheduling schemes which are called Horizon, Single-gap and Multiple-gap, as a basic study for the future research of Optical Internet. Thus, firstly, we analyze the trade-off between the performance and the processing overhead of each scheme. In addition, the performance of OBS system which uses Multiple-gap scheduling is evaluated in detail under various network size. We use simulation for performance evaluation in terms of burst loss rate(BLR), wavelength channel utilization and the number of management data.