Arslan M. Örnek

Simulation analysis of lot streaming in job shops with transportation queue disciplines

Abstract In this paper we study a multi-product lot streaming problem in a stochastic job shop with equal and discrete sublots. The problem involves splitting order quantities of different products into different number of equal sublots (NES) and analyzing the effects of sublot-related transportation queue disciplines (TRQD) for different performance measures. Our simulation results show that some combination of TRQD and NES alternatives are appropriate for some performance measures. We also propose a simple heuristic to determine the NES of each product type to further improve the values of performance measures.

Simultaneous Balancing and Scheduling of Flexible Mixed Model Assembly Lines with Sequence-Dependent Setup Times

We consider Simultaneous Balancing and Scheduling of Flexible Mixed Model Assembly Lines with Sequence-Dependent Setup Times (SBSFMMAL-SDST). We propose alternate Mixed Integer Programming (MIP) and Constraint Programming (CP) formulations. Our experiments show that while the MIP models could not solve relatively small instances, the CP approach seems more promising.

A simulation based experimental design to analyze factors affecting production flow time

Abstract In this paper we analyze and evaluate the effects of some pre-defined process parameters on the performance of a manufacturing system. These parameters include two different plant layout types, namely functional layout (FL) and cellular manufacturing layout (CL), as well as scheduling rule, machine downtimes, batch sizes, and transporter (interstage transporters) capacities. First we employ simulation to evaluate the effects of these factors on the performance of the system and then conduct designed experiments to set the best levels for these factors. The performance evaluation function is defined in terms of the average flow time of all the part types through the manufacturing system. Arena 10.0 simulation software and SPSS 9.0 statistical package are used to measure the main effects and interactions between these factors. This work demonstrates that various manufacturing parameters should be considered jointly when designing or redesigning a facility because setting different levels for parameters can considerably affect the performance of a facility.

Capacitated lot sizing with alternative routings and overtime decisions

Material requirements planning (MRP) is a basic tool for performing detailed material planning function in the manufacture of component parts and their assembly into finished items. MRPs managerial objective is to provide ‘the right part at the right time’ to meet the schedules for completed products. However satisfying end customer demands faster with lower inventories implies smarter scheduling which must simultaneously reflect actual capacity conditions. Therefore, the need is to schedule both capacity and materials simultaneously. Since MRP does not consider the availability of capacity resources to schedule production, consequently the schedules so developed are usually capacity infeasible. This paper proposes a three-step procedure to develop capacity feasible material and production schedules in a finite capacity environment. In the first step, an LP model produces capacity feasible but lot size relaxed planned order releases for all end products and assembly components which are then fed into a MRP processor, where a bill of material (BOM) explosion process generates material plans. Finally, these material plans are introduced to another LP model which assures that capacity feasibility is again restored. The mathematical models developed consider restrictions on lot sizes as well as alternative production routings and overtime decisions. A numerical example also is provided and some future research directions are outlined.

An improved metric for measuring multi-item multi-level schedule instability under rolling schedules

Disruptions in material plans due to unrealistic schedules and frequent plan revisions are common symptoms of a phenomenon generally referred to as nervousness or schedule instability in literature. A number of instability measures had been proposed so far. However, none of them deals with instability measurement comprehensively. An appropriate measurement should be able to reflect the degree of changes under rolling schedules, as well as a tool for analyzing the performance of planning systems and nervousness dampening procedures. The intent of this paper is to present a new metric for measuring multi-item multi-level schedule instability under rolling schedules and to compare it with the previous measures in literature. The new metric composed of linear combination of four sub-instability measures separates quantity and timing changes for both scheduled receipts (SRs) and planned orders (PORs). The new metric is tested by a detailed numerical example taken from literature, and results of simulation studies under various experimental factors are presented at the end of the study.

A note on “A mixed integer programming model for advanced planning and scheduling (APS)”

In a recent paper, Chen and Ji [Chen, K., Ji, P., 2007. A mixed integer programming model for advanced planning and scheduling (APS). European Journal of Operational Research 181, 515-522] develop a mixed integer programming model for advanced planning and scheduling problem that considers capacity constraints and precedence relations between the operations. The orders require processing of several operations on eligible machines. The model presented in the above paper works for the case where each operation can be processed on only one machine. However, machine eligibility means that only a subset of machines are capable of processing a job and this subset may include more than one machine. We provide a general model for advanced planning and scheduling problems with machine eligibility. Our model can be used for problems where there are alternative machines that an operation can be assigned to.

Determining optimum (s, S) levels of floor stock items in a paint production environment

Abstract In this paper, we develop an ( s , S ) inventory model to determine inventory levels of floor stock items (FSIs) in a manufacturing company producing paint products. FSIs are raw materials that are transferred from the main warehouse to be stored at the periphery of production area for a temporary period of time to be used in the manufacturing process. These items are commonly and frequently used materials during production; hence the optimum inventory levels of these materials to be carried in the floor stock storage areas (FSSAs) to prevent costly interruptions in the manufacturing processes should be determined. We model the problem as a multi-item, single-echelon ( s , S ) inventory policy where items can be stored in stocking locations that are supplied by an indoor vendor (i.e., the main warehouse) with infinite capacity. The objective is to minimize the average number of daily replenishments dispatched from the main warehouse subject to the capacity of FSSAs and satisfying a pre-defined fill rate. We consider stochastic demand and lead time, and use simulation for the optimization purpose. Finally, we present and discuss the numerical results of the study along with our conclusions.

A Heuristic Approach for Determining Lot Sizes and Schedules Using Power-of-Two Policy

We consider the problem of determining realistic and easy-to-schedule lot sizes in a multiproduct, multistage manufacturing environment. We concentrate on a specific type of production, namely, flow shop type production. The model developed consists of two parts, lot sizing problem and scheduling problem. In lot sizing problem, we employ binary integer programming and determine reorder intervals for each product using power-of-two policy. In the second part, using the results obtained of the lot sizing problem, we employ mixed integer programming to determine schedules for a multiproduct, multistage case with multiple machines in each stage. Finally, we provide a numerical example and compare the results with similar methods found in practice.