Wooseung Jang

Unrelated parallel machine scheduling with setup times using simulated annealing

This paper presents a scheduling problem for unrelated parallel machines with sequence-dependent setup times, using simulated annealing (SA). The problem accounts for allotting work parts of L jobs into M parallel unrelated machines, where a job refers to a lot composed of N items. Some jobs may have different items while every item within each job has an identical processing time with a common due date. Each machine has its own processing times according to the characteristics of the machine as well as job types. Setup times are machine independent but job sequence dependent. SA, a meta-heuristic, is employed in this study to determine a scheduling policy so as to minimize total tardiness. The suggested SA method utilizes six job or item rearranging techniques to generate neighborhood solutions. The experimental analysis shows that the proposed SA method significantly outperforms a neighborhood search method in terms of total tardiness.

Single machine stochastic scheduling to minimize the expected number of tardy jobs using mathematical programming models

This paper studies the single machine scheduling problem for the objective of minimizing the expected number of tardy jobs. Jobs have normally distributed processing times and a common deterministic due date. We develop new approaches for this problem that generate near optimal solutions. The original stochastic problem is transformed into a non-linear integer programming model and its relaxations. Computational study validates their effectiveness by comparison with optimal solutions.

Dynamic scheduling of stochastic jobs on a single machine

Abstract In this paper, the problem of minimizing the expected number of tardy jobs on a single machine is investigated. Jobs having stochastic processing times and deterministic due dates arrive randomly. The objectives of this research are to evaluate the role of the variance of processing time and to develop a dynamic scheduling policy, which is simple and robust, so that it can be used in rapidly changing and uncertain manufacturing environments. A heuristic based on a myopically optimal solution is developed. The sufficient conditions for the optimality and the worst case analysis are also provided. Computational study validates its effectiveness by comparison with optimal solutions.

A new rule for minimizing the number of tardy jobs in dynamic flow shops

In this paper we present a new scheduling rule for minimizing the number of tardy jobs in a dynamic flow shop consisting of m machines. Jobs with processing times and due dates randomly arrive to the system. We assume that job arrival or release dates are not known in advance. The new rule is derived by dividing the m machine problem into several one-machine sub-problems, and optimally solving each one-machine sub-problem by applying a variation of the Moore–Hodgson algorithm. Computational results indicate that the proposed rule performs 15–20% better than SPT, which is currently one of the most effective methods for minimizing the number of tardy jobs in dynamic flow shops. Results are given for 10, 20, and 50 jobs and 2, 5, and 10 machines under various shop conditions.

Minimizing the expected number of tardy jobs when processing times are normally distributed

This paper studies the single machine scheduling problem for the purpose of minimizing the expected number of tardy jobs. The effect of variance of processing time is evaluated, and a dynamic policy based on a myopic approach is developed. The conditions for the optimality and the worst case analysis are provided.

Time and quantity dependent waiting costs in a newsvendor problem with backlogged shortages

Upon demand realization in the newsvendor problem, it is often assumed that shortages result in lost sales penalties. However, in some practical situations, shortages are backlogged and the inventory manager is penalized based on the magnitude and duration of the shortage. In this paper, we investigate a variation of the newsvendor problem in which all shortages are backlogged and replenished through an emergency procurement process. Costs incurred during emergency procurement include a variable emergency ordering cost and a non-linear customer waiting cost. We derive closed form expressions for the optimal order quantity and emergency procurement rate for two special cases, and we propose a heuristic approximation for the general case. The effectiveness of the approximate solution is assessed through numerical experiments and sensitivity analysis.

Supply chain models for small agricultural enterprises

Small agricultural production enterprises have been under immense economic pressures for many years. It is our belief that a favorable impact can be made on small farm enterprises through the development and implementation of models that address their basic needs and open new markets and production initiatives. In this paper, we develop models for supply chain issues facing small farmers, solve them, and suggest their uses and future considerations. In particular, we consider B2C and B2B aspects of an agricultural supply chain model. The B2C aspect is considered in direct farmer to consumer sales in retail markets, while the B2B aspect is represented by transactions through agricultural cooperatives. We evaluate key strategic decisions such as whether to form cooperative agreements with other farmers and if so, how large to make the cooperative, and the production quantities at which farmers would like to sell directly to customers with or without using the cooperative.

Minimizing response time in a two-stage supply chain system with variable lead time and stochastic demand

We consider customer response time minimization in a two-stage system facing stochastic demand. Traditionally, the objective of representative mathematical models is to minimize costs related to production, inventory holding, and shortage. However, the highly competitive market characterized by impatient customers warrants the inclusion of costs related to customer waiting. Therefore we investigate a supply chain system in an uncertain demand setting that encompasses customer waiting costs as well as traditional plant costs (i.e. production and inventory costs). A representative expected cost function is derived and the closed form optimal solution is determined for a general demand distribution. We also provide examples to illustrate results for some common probability distributions. Our results indicate significant cost savings under certain assumptions when comparing solutions from the proposed model to the traditional newsvendor order/production quantity.

Joint Demand and Capacity Management in a Restaurant System

This paper studies the demand and capacity management problem in a restaurant system. A queueing-based optimization model with underlying quasi birth-and-death process and state-dependent functions is developed to address the dynamic and nonlinearity difficulties. In particular, our model explicitly captures the demand changes with respect to the system congestion state on a near real-time dynamic basis. With this framework, we empirically examine the relative performance of commonly used strategies for the case of a local restaurant. The study shows that a strategy that balances service quality and cost yields maximum profit. The result indicates that the traditional view of the conflict between service quality and cost can be overcome by using an interdisciplinary perspective of marketing and operations. Both perspectives should be embraced in academic research and industrial practice in capacity planning decisions for services.

Production and allocation policies in a two-class inventory system with time and quantity dependent waiting costs

This paper presents an integrated production and inventory allocation model in a two-echelon supply chain system. The higher echelon is a manufacturer, who produces a single commodity. The lower echelon consists of two types of major commodity distributors who might face stochastic or deterministic demands from multiple retailers. Our analytical model provides optimal decision policies that minimize total production and customer waiting costs from the manufacturers perspective when there are time and quantity dependent customer waiting costs. We identify the value of the integrated policy and compare it with typical approximations such as aggregating the multiple demands or applying the single demand multiple times.