Due Dates Of Orders Research Articles

A parallel Lagrange algorithm for order acceptance and scheduling in cluster supply chains

In a single supply chain scenario, orders are likely to be refused for lack of insufficient capacity and production time. In this paper, cluster supply chains (a kind of multiple supply chains, short for CSC) is introduced to avoid this potential operational risk via across-chain cooperation, which is not considered in any previous work. First, the framework of order selection in cluster supply chain (CSC) is presented based on four order categories (direct order, reserve order, across-chain order and rejected order), followed by that the model without and with across-chain cooperation in cluster supply chains are proposed to aid operational managers to make joint decision regarding order acceptance and scheduling under maximizing the overall profit. Considering the complexity of cluster supply chains structure and a mass of data from actual operations, a parallel Lagrange heuristic algorithm is devised to solve the Mixed-Integer Non-Linear Program (MINLP) problem. Meanwhile, Benders algorithm is utilized to compare with it for evaluating performance. The result proves the parallel Lagrange heuristic algorithm outperforms Benders approach, the former can efficiently solve large-scale-data problem instances at relatively short time. The outcomes also reveal that, by designing the different combination of the factor of rejected order and that of across-chain order, it can be better trade-off between order due-date and cost while better aligning with the long-term business strategy in cluster supply chains.

Lot-Order Assignment Applying Priority Rules for the Single-Machine Total Tardiness Scheduling with Nonnegative Time-Dependent Processing Times

Lot-order assignment is to assign items in lots being processed to orders to fulfill the orders. It is usually performed periodically for meeting the due dates of orders especially in a manufacturing industry with a long production cycle time such as the semiconductor manufacturing industry. In this paper, we consider the lot-order assignment problem (LOAP) with the objective of minimizing the total tardiness of the orders with distinct due dates. We show that we can solve the LOAP optimally by finding an optimal sequence for the single-machine total tardiness scheduling problem with nonnegative time-dependent processing times (SMTTSP-NNTDPT). Also, we address how the priority rules for the SMTTSP can be modified to those for the SMTTSP-NNTDPT to solve the LOAP. In computational experiments, we discuss the performances of the suggested priority rules and show the result of the proposed approach outperforms that of the commercial optimization software package.

Simultaneous order-lot pegging and wafer release planning for semiconductor wafer fabrication facilities

In semiconductor wafer fabrication facilities, order-lot pegging is the process of assigning wafer lots to orders and meeting the due dates of orders is considered one of the most important operational issues. In many cases of order-lot pegging, some orders cannot be fulfilled with the current wafers in the lots being processed, necessitating the release of additional new wafer lots into the wafer fabrication facility. In this paper, we propose a simultaneous decision model for order-lot pegging and wafer release planning in semiconductor wafer fabrication facilities, and develop a Lagrangian heuristic for solving the model. The results of computational experiments conducted using randomly generated problem instances that mimic actual field data from a Korea semiconductor wafer fabrication facility indicate that the performance of the Lagrangian heuristic is superior to that of a practical greedy algorithm for practical-sized problem instances. The results also point to how sensitivity analysis can be used to answer important managerial questions for effective management of the semiconductor wafer fabrication process.

Workload based order acceptance in job shop environments

In practice, order acceptance and production planning are often functionally separated. As a result, order acceptance decisions are made without considering the actual workload in the production system, or by only regarding the aggregate workload. We investigate the importance of a good workload based order acceptance method in over-demanded job shop environments, and study approaches that integrate order acceptance and resource capacity loading. We present sophisticated methods that consider technological restrictions, such as precedence relations, and release and due dates of orders. We use a simulation model of a generic job shop to compare these methods with straightforward methods, which consider capacity restrictions at an aggregate level and ignore precedence relations. We compare the performance of the approaches based on criteria such as capacity utilisation. The simulation results show that the sophisticated approaches significantly outperform the straightforward approaches in case of tight due dates (little slack). In that case, improvements of up to 30% in utilisation rate can be achieved. In case of much slack, a sophisticated order acceptance method is less important.

A mathematical model for selecting mixed models with due dates

The performance of a sequencing procedure to smooth out the fluctuating workload (and part utilization) on a paced assembly line relies heavily on the average load of the model mixes chosen from the order-bank. Meanwhile, the due-dates of orders may conflict with this production-centred goal. This study proposes a mathematical model to select a fixed number of jobs while minimizing the total cost of producing them at the next period and satisfying capacity (RHS) limits at stations. A branch-and-bound procedure, which employs some dominance criteria, is proposed to provide optimal solutions. Pairwise interchange heuristics are developed to improve the initial solution, which is optimum but not feasible. Computational results show that optimal solutions can be obtained very efficiently for 100-job and 10-station problems. A three-factor experiment indicates that the RHS limit is the only significant parameter on the performance of the procedures. For over 1000-job problems, the best heuristic finds the optimal solution most of the time and, in the worst case, yields a solution that is 7.38% from optimality.

Optimal Short-Term Scheduling of Multiproduct Single-Stage Batch Plants with Parallel Lines

The short-term scheduling of multiproduct single-stage batch plants with parallel lines involves the constraints concerning the release times and due dates of orders, as well as the sequence-dependent setup times and forbidden subsequences of production orders and the ready times of units. Taking the above constraints into consideration, this paper develops a short-term scheduling model for the multiproduct single-stage batch plants with parallel lines using the continuous-time domain representation and the notation of time slot. When the model is developed, the allocation of orders and units to time slots is represented by two sets of binary variables. The model is formulated as a mixed-integer linear programming (MILP) problem. It not only involves fewer binary variables than any other model based on the notation of time slot (Pinto, J. M.; Grossman, I. E. Ann. Oper. Res. 1998, 81, 433) but also can be used to optimize several types of objective functions. Based on the characteristics of the model and multiproduct single-stage batch plants, some heuristic rules are introduced. The rational employment of these heuristic rules can cut down the size of the model and has no effect on the optimality of the scheduling problem. A number of computational examples show that the optimal scheduling can be obtained by solving the MILP model in reasonable CPU time, especially when the scheduling problem involving several identical orders is considered.

A bottleneck-based due-date assignment methodology

On-time delivery is one of the most important performance criteria in today's manufacturing systems. To enhance delivery performance, the appropriate assignment of an order's due-date is necessary. In recent years, many due-date setting methods based on regression analysis models have been proposed. Although some of them emphasise the use of shop floor information in due-date setting, the bottleneck scheduling information has not yet been taken into consideration by those methods. It is the objective of this paper to propose a new due-date assignment method based on bottleneck scheduling information. To assign an order's due-date, this proposed method first optimises the job sequence at the bottleneck using a Tabu search algorithm and then predicts the order's flow time after its completion of its bottleneck operation. Experimental results show that the proposed method can greatly enhance the delivery performance.