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In this research, we study a supply chain which consists of a firm and a set of customer groups, where the demand (order quantity) from each customer group depends on the quoted delivery time. The prices of the product are determined and different to each customer group. The firm's objective is to maximize the profit by quoting different delivery times for all customer groups. A mathematical model is developed, which is proved to be NP hard. Both a mixed integer programming (MIP) formulation and the branch-and-bound algorithm are proposed to study the moderate size of the problem. Also, a heuristic algorithm is developed which is able to obtain the optimal solution quickly in most of cases when customer size is less than 20.

Nowadays, it is popular to outsource transportation and distribution of finished products to third-party logistics (3PL) providers in many industries. In order to shorten the response time from order receipt to delivery, and also to improve on-time delivery accuracy, the decision of manufacturing scheduling and transportation scheduling should consider the constraints between manufacturing and transportation. In this paper, we study a coordinated scheduling problem of parallel machine assembly manufacturing and multi-destination transportation in the make-to-order (MTO) consumer electronics supply chain (CESC). By considering the constraints between parallel machine assembly and 3PL transportation, the overall problem is decomposed into a parallel machine scheduling sub-problem and a 3PL transportation sub-problem. The 3PL transportation problem is proved to be NP-complete. Heuristic algorithms are proposed to solve the parallel machine assembly scheduling problem.

With the advent of the Just-In-Time (JIT) manufacturing philosophy, scheduling problems with delivery performance have received significant attention in recent years. The JIT concept requires not only a penalty for lateness but also for earliness and therefore both early and tardy deliveries are highly discouraged. In this paper, the dynamic scheduling of a single batch processing machine has been addressed for controlling the delivery performance between earliness and tardiness measures. A Look Ahead Batching (LAB) strategy has been proposed. In LAB, the scheduling decisions are made considering the arrival epochs and due dates of the incoming lots, which are easily predictable in a computer-integrated manufacturing environment, especially in the semiconductor industry. LAB strategy is used to control the delivery performance, which involves a trade-off between the conflicting objectives of minimizing earliness- and tardiness-related measures simultaneously. The potential of LAB strategy in controlling d...

This paper considers how to schedule appointments for outpatients, for a clinic that is subject to appointment lead-time targets for both new and returning patients. We develop heuristic rules, which are the exact and relaxed appointment scheduling rules, to schedule each new patient appointment (only) in light of uncertainty about future arrivals. The scheduling rules entail two decisions. First, the rules need to determine whether or not a patient's request can be accepted; then, if the request is not rejected, the rules prescribe how to assign the patient to an available slot. The intent of the scheduling rules is to maximize the utilization of the planned resource (i.e., the physician staff), or equivalently to maximize the number of patients that are admitted, while maintaining the service targets on the median, the 95th percentile, and the maximum appointment lead-times. We test the proposed scheduling rules with numerical experiments using real data from the chosen clinic of Tan Tock Seng hospital in Singapore. The results show the efficiency and the efficacy of the scheduling rules, in terms of the service-target satisfaction and the resource utilization. From the sensitivity analysis, we find that the performance of the proposed scheduling rules is fairly robust to the specification of the established lead-time targets.

This paper presents look-ahead batch scheduling for the real-time control of due-date objectives, such as average tardiness, maximum tardiness, and number of tardy jobs in batch processes. These due-date objectives play an important role in several production industries such as semiconductor manufacturing, aircraft industry, metalworking industry, etc. In look-ahead batching, the scheduling decisions are based on the arrival epochs and due-dates of incoming lots at each work station which are easily predictable in a computer integrated manufacturing environment. The steady state performance of the developed control strategy is compared to the steady state performance of the theoretically optimal control strategy (i.e. a Minimum Batch Size strategy). The experimental results indicate that the developed look-ahead batch scheduling strategy improves the performance of due-date objectives under wide conditions of arrival pattern, due-date distribution and traffic intensity.

A discrete event simulation based "online near-real-time" dynamic multiobjective scheduling system has been conceptualized, designed, and developed to achieve Pareto optimal solutions in a complex manufacturing environment of semiconductor back-end. Our approach includes the use of linear weighted aggregation optimization approach for multiple objectives and auto simulation model generation for online simulation. Developed concepts are implemented at a semiconductor back-end site and are in use. The impact of the system includes a better customer delivery achievement, consistent cycle time with narrower distribution, improved machine utilization, reduction in the time that planners and manufacturing personnel spend on scheduling, and more predictable and repeatable manufacturing performance. In addition, it enables managers and senior planners to carry out "what now" analysis to make effective current decisions and "what if" analysis to plan for the future.

This paper presents a preliminary analysis of the typical scheduling environment in semiconductor manufacturing involving multiple job families, and where more than one objective such as cycle time, machine utilization and the due-date accuracy needs to be simultaneously considered. In this study, the NP-hard problem of scheduling N independent jobs on a single testing machine with due dates and sequence-dependent setup times is addressed, where the multiple objectives are to minimize average cycle time, to minimize average tardiness, and to maximize machine utilization. A Pareto optimal solution, which is not inferior to any other feasible solutions in terms of all objectives, is generated combining the analytically optimal and conjunctive simulated scheduling approach. First, the machine-scheduling problem is modeled using the discrete event simulation approach and the problem is divided into simulation clock based lot selection sub-problems. Then, a Pareto optimal lot is selected using the compromise programming technique for multiobjective optimization at each decision instant in simulated time. With the help of a broad experimental design, this developed solution is then compared with common heuristic-dispatching rules such as SPT and EDD, which show better results for all the objectives over a wide range of problems. The developed scheduling method shows approximately 16.7% reduction in average cycle time, 25.6% reduction in average tardiness, and 21.6% improvement in machine utilization over the common dispatching rules, SPT and EDD.

The paper studies the problem of synchronizing air transportation and assembly manufacturing to achieve accurate delivery with minimized cost in a consumer electronics supply chain. The motivation for this study came from a major computer manufacturer in a consumer electronics industry that faces challenges in the final stages of delivery of a supply chain. The synchronization problem is decomposed and formulated as two sub-problems, i.e. a multi-destination air transportation allocation problem and an assembly-scheduling problem. The first sub-problem is formulated and solved as an integer linear programming problem. For the assembly-scheduling problem, two solution methodologies using the concepts of forward and backward scheduling are presented. Computational results indicate that the proposed methodologies can achieve considerable cost reduction compared with the existing methodology applied in industry.

This paper presents a brief review on job shop scheduling techniques in semiconductor manufacturing. The manufacturing environment in a semiconductor industry is considered a highly complex job shop, involving multiple types of work centers, large and changing varieties of products, sequence-dependent setup times, reentrant process flow, etc., in a dynamic scheduling environment. Due to the stubborn nature of the deterministic job shop scheduling problem itself, many of the solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem-specific strategies. In this paper, we seek to provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.

To deal with today’s stiff competition in the logistics of semiconductor manufacturing, the development of an efficient scheduling approach for the complex semiconductor back-end testing operations is very essential, where more than one objective, such as cycle time, machine utilization and due date accuracy are kept in the focus to various degrees simultaneously. In this paper, the problem of scheduling N independent jobs on a single testing machine with due dates and sequence-dependent setup times is addressed, where the multiple objectives are to minimize average cycle time and average tardiness and to maximize machine utilization. A near optimal solution, which is not inferior to any other feasible solution in terms of all objectives, is generated combining the analytically optimal and conjunctive simulated scheduling approach. First, the machine-scheduling problem is modeled using the discrete event simulation approach and the problem is divided into simulation clock-based lot selection sub-problems. Then, at each decision instance in simulated time, a Pareto optimal lot is selected using the compromise programming technique for multi-objective optimization. With the help of a broad experimental design, this developed solution is then compared with common heuristic-dispatching rules used in industry such as the shortest processing time (SPT) and earliest due date (EDD). The developed scheduling method shows better results for all the objectives over a wide range of problems. It shows approximately 16.7% reduction in average cycle time, 25.6% reduction in average tardiness, and 21.6% improvement in machine utilization over the common dispatching rules, SPT and EDD.

The scheduling of highly complex job shops, such as those found in semiconductor manufacturing, involves multiple types of work centers, large and changing varieties of the products, sequence dependent set up times, reentrant process flow, dynamically changing environment, etc. These complexities make the job shop scheduling problem very strongly nondeterministic polynominally (NP)-hard and intractable to solve by the traditional scheduling approaches. This paper presents the concept and applications of the conjunctive simulated scheduling (CSS) approach, which incorporates the benefits of discrete event simulation in scheduling the shop and avoids the NP-hard formulation of these complex scheduling problems. The differences between CSS and typical simulation studies and its application to online simulation are also presented.

Many companies are enhancing their competitiveness by offering Just-in-Time (JIT) delivery. Costs or penalties are incurred by delivering an order either earlier or later than the customer’s due-dates. Besides, maintaining short response time from order acceptance to final delivery is one of the key competitive advantages. Thus, many companies deliver products to customers directly after production without holding finished product inventory. This is particularly true for the industries with short product life cycle, such as consumer electronics manufacturing, ready-mix concrete supplying and food catering industry (Garcia et al. 2004; Chen and Vairaktarakis, 2005; Li et al., 2005). In order to improve customer service and reduce production and transportation costs, scheduling of assembly manufacturing and transportation should be synchronized. Nowadays, due to the professional services provided by third party logistics (3PL) provider, it is more efficient to outsource the transportation or distribution to 3PL. There are two types of operations. If 3PL only serves one customer, the schedule of 3PL’s vehicle is determined by the order completion time in manufacturing. This type of operation is particularly true for 3PLs that providing road transportation services. On the hand, if 3PL provides services to more than one manufacturer, the departure and arrival time of the vehicle is determined by 3PL rather than by the manufacturer. In addition, the unit transportation cost of each vehicle varies. The manufacturer can book capacity on the available vehicles accordingly. Then, decision on allocation of orders to the vehicles is made to utilize the booked capacity efficiently. The typical case is the air-cargo transportation service provided by cargo airlines. Motivated by above application, this chapter studies the problem of synchronized scheduling of assembly manufacturing and transportation in the make-to-order (MTO) consumer electronics supply chain (CESC). In this context, materials or components are kept in inventory before assembly. Upon reception of customer orders, the materials are transferred to manufacturing job shop. Through several processes such as assembly, testing, packing, the assembly manufacturing is completed. Then, the order is transported to customers directly by the vehicle of 3PL. Chen and Vairaktarakis (2005) addressed the

In semiconductor manufacturing, hot lots are often introduced into the system out of marketing and business considerations for extra flexibility regarding delivery lead times. The objective of this paper is to study the scheduling policies to minimize the cycle time of hot lots in batch processes. We present three scheduling policies based on look ahead batching (LAB) and study their effect on the performance of cycle time of hot lots and other priority lots in the system. In LAB, the scheduling decisions are based on the arrival epochs of incoming lots, which are easily predictable in a computer integrated manufacturing environment. The steady state simulation results show that exploiting the knowledge of future arrivals leads to 10% reduction in the cycle time of hot lots without affecting their standard deviation and also without affecting the cycle time performance of other priority lots in batch processes at lower values of utilization level.

This work introduces the concept of Pareto-controllability of objectives in dynamic scheduling of single machine systems in a production supply chain. A convex combination approach based on compromise programming is presented in this paper to achieve the desired level of control in dynamic scheduling considering two contradicting objectives, viz., the mean cycle time and the maximum tardiness. At each decision instance during simulation, a Pareto-job is selected using the convex combination approach and loaded on the machine for processing. This approach uses the combination of the concepts of simulation and scheduling together, called as conjunctive simulated scheduling (CSS), for dynamic scheduling of jobs in production environment. We demonstrate using simulation experiments the concept of Pareto-controllability, and show how a single machine system can be controlled at specified operating conditions (scheduling objectives) dictated by the customer demands, which vary with time based on dynamic market forces.

A simulation-based real-time scheduling mechanism for dynamic discrete manufacturing is presented. Modified mean flow time performance for different scheduling approaches is compared through off-line simulation experiments, under dynamic manufacturing environments that are subject to disturbances such as machine breakdowns. These experimental results are used as reference indices for the real-time scheduling mechanism to select the better scheduling approaches for further evaluation based on the actual manufacturing conditions. Discrete-event simulation is used online to evaluate the selected approaches and the corresponding schedules to determine the best solution. The selected schedule is used until the deviation of actual performance from the estimated one exceeds a given limit, or when a major event occurs. A new simulation is then performed with the remaining operations to select a new schedule.

In semiconductor manufacturing, it requires more than one objective such as cycle time, machine utilization and due date accuracy to be kept in focus simultaneously, while developing an effective scheduling. In this paper, a near optimal solution, which is not inferior to any other feasible solutions in terms of all objectives, is generated with a combination of the analytically optimal and simulation based scheduling approach. First, the job shop scheduling problem is modeled using the discrete event simulation approach and the problem is divided in to simulation clock based lot selection sub-problems. Then, at each decision instant in simulated time, a Pareto optimal lot is selected using the various techniques to deal with multiobjective optimization such as weighted aggregation approach, global criterion method, minimum deviation method, and compromise programming. An illustration shows how these techniques work effectively in solving the multiobjective scheduling problem using discrete event simulation.

This paper involves the study of a simulation based proactive decision support module for the shop-floor scheduling of the plastic processing section (PPS) at Bharti Telecom Limited, Gurgaon, India. The flow of material and information in this shop is highly complex as it involves multiple product parts, sequence dependent setup, molding machine specifications, mould restrictions etc. with a variety of scheduling and operational choices. The shop floor planning and scheduling decisions are being exercised manually and there is enough scope of using this simulation based tool to improve the shop-performance. In this work, efforts have been made to simulate the scheduling environment of this section. The performance of the shop depends on various parameters such as initial conditions of the machines, the load on the system, sequencing rule etc. Also the user requirements are so varied and situation dependent that it requires the use of simulation techniques to get a better schedule. Further, there are some findings based on the simulation experiments, which are in the process of implementation in the shop.

A discrete event simulation based "on-line near-real-time" dynamic scheduling and optimization system has been conceptualized, designed, and developed to optimize cycle time and asset utilization in the complex manufacturing environment of semiconductor test manufacturing. Our approach includes the application of rules and optimization algorithm, using multiple variables as an integral part of discrete event simulation of the manufacturing operation and auto simulation model generation at a desired frequency. The system has been implemented at a semiconductor back-end site. The impact of the system includes the achievement of world class cycle time, improved machine utilization, reduction in the time that planners and manufacturing personnel spend on scheduling, and more predictable and highly repeatable manufacturing performance. In addition it enables managers and senior planners to carry out "what if" analysis to plan for future.

Motivated by a major PC manufacturer in the consumer electronics industry, we study the problem of synchronisation of assembly manufacturing and final deliveries using air transportation to achieve accurate delivery with minimised delivery cost in consumer electronics supply chain (CESC). The customer orders are shipped to the customers by cargo flights. Orders are allocated to the available flight capacities based on customer delivery dates. Synchronisation is considered to match the assembly completion of each order with the departure time of its predetermined allocated flights. We break the synchronisation problem down into two sub-problems consisting of an air transportation allocation problem and an assembly scheduling problem. The first sub-problem is formulated as an Integer Linear Programming (ILP) model. Two solution methodologies are proposed for the second sub-problem. Computational results indicate that the proposed methodologies can achieve considerable cost reduction compared to the existing industry practice.

Customers have varying requirements on price and delivery time in make-to-order manufacturing environment. Due to limited availability of the production capacity, the firm can increase revenue if it allocates priority to time-sensitive customers, charging them a higher price for the same product. The remaining capacity can then be allocated amongst the price-sensitive customers with a relatively lower price. This strategy is aimed at maximizing the overall revenue for the firm. Moreover, the quoted price and delivery time have effects on the demand in the market. In literature, the demand function is considered as a linear decreasing function with respect to price and delivery time, respectively. The challenge is to coordinate the price quotation and the delivery time quotation for customers so that it results in maximizing the net revenue for a manufacturer. There are two types of capacity settings to differentiate products by price and delivery time. These are dedicated capacity and shared capacity. In the dedicated capacity, customers are categorized into several groups. Different customer groups are served in different production facilities. Customers from one customer group share a common price quotation and a common delivery time quotation. The common price and delivery time quotation problem is formulated for a single customer group. The optimality of this problem is studied and applied to develop an algorithm to find the optimal solution in polynomial time. Numerical examples show that the common price and delivery time quotation strategy is applicable when customers are more price-sensitive rather than time-sensitive. In the situation of shared capacity, all customers are served using the same production facility. The production priority is given to the customers who are more time-sensitive. Orders from the customers who are more price-sensitive are scheduled to the rear of the production sequence. Each customer will have a unique quotation of price and a unique quotation of delivery time. The problem is formulated and proven to be NP-complete even when prices are pre-determined parameters. A branch-and-bound algorithm is developed to obtain optimal solutions for the moderate-sized problem. Moreover, a heuristic algorithm is proposed to obtain the near-optimal solution in a short time. Numerical experiments are conducted to test the performance of these two methods. DOCTOR OF PHILOSOPHY (MAE)