HEURISTIC LOT-SIZING PERFORMANCE IN A ROLLING-SCHEDULE ENVIRONMENT*

This paper examines the impact of a rolling-schedule implementation on the performance of three of the better known lot-sizing methods for single-level assembly systems——Part-Period-Cost-Balancing, Silver-Meal, and Wagner-Whitin algorithms—and a modified version of the Silver-Meal procedure. The main finding is that under certain conditions the computationally simpler Silver-Meal heuristic can provide cost performance superior to that of the Wagner-Whitin algorithm.     

HEURISTIC LOT-SIZING APPROACHES FOR DEALING WITH MRP SYSTEM NERVOUSNESS

We develop and test ways for modifying the Silver-Meal and part-period-balancing lot-sizing procedures to include the costs of schedule changes in response to changes in demand estimates. These modifications tend to reduce MRP system nervousness. We compare the performance of these approaches with the modified Wagner-Whitin algorithm, which we have shown is optimal for static conditions. We find that the modified SilverMeal approach is only slightly more costly than the modified Wagner-Whitin technique and that the modified part-period-balancing approach performs both poorly and erratically.     

A COMPARISON OF SEQUENCING RULES FOR A TWO-STATE HYBRID FLOW SHOP

A common manufacturing environment in many industries (such as the glass, steel, paper, costume jewelry, and textile industries) is a hybrid flow shop. This system has continuous-process machinery in the fist state of manufacturing and repetitive-batch equipment in the second. Little research has investigated this type system. Scheduling managers of hybrid flow shops tend either to use existing job-shop rules or to devise their own rules. These approaches often are less than adequate for efficient scheduling. In this paper we extend the rule presented by Narasimhan and Panwalker [4] to include a general class of hybrid flow shops. This extenstion, called the generalized cumulative minimum-deviation (GCMD) rule, is compared under various operation conditions to three other sequencing rules: shortest processing time, longest processing time, and minimum deviation. The operating conditions are determined by the number of machines at both stages. The results of 7200 simulation runs demonstrate that the GCMD rule is better than the other rules in minimizing each of five chosen criteria. Thus, the GCMD rule can help managers to schedule hybrid flow shops efficiently to achieve various corporate objectives.     

AN EVALUATION OF SEQUENCING RULES FOR AN ASSEMBLY SHOP

Research relating to sequencing rules in simple job shops has proliferated, but there has not been a corresponding proliferation of research evaluating similar sequencing rules in more complex assembly job shops. In a simple job shop, all operations are performed serially; but an assembly shop encompasses both serial and parallel operations. As a result of the increased complexity of assembly shops, the results associated with the performance of sequencing rules in simple job shops cannot be expected for an assembly shop. In this paper, 11 sequencing rules (some of which are common to simple job shops and some decigned specifically for assembly shops) are evaluated using a simulation analysis of a hypothetical assembly shop. The simulation results are analyzed using an ANOVA procedure that identifies significant differences in the results of several performance measures. Sensitivity analysis also is performed to determine the effect of job structure on the performance of the sequencing rules.     

IMPLICIT OPTIMAL AND HEURISTIC LABOR STAFFING IN A MULTIOBJECTIVE,MULTILOCATION ENVIRONMENT*

This paper presents a tractable set of integer programming models for the days-off scheduling of a mix of full- and part-time employees working α to β days/week (cycle) in a multiple-objective, multiple-location environment. Previous models were formulated to specifically schedule part-time employees working either two or three days per week. These models were intractable because they required complete employee schedule information. The new models are deemed implicit optimal since they are required to supply only essential information. While the number of variables in previous models is an exponential increasing function of β-α, the size of three of the new models is independent of α and β. The first three models developed here (as in [18]) deal with the trade-offs between idle time, the number of employees required to work at multiple “locations,” and the size of the total labor pool. The inherent flexibility of the implicit modeling approach is illustrated by the presentation of various modifications of the basic models. These modifications permit the use of preference weights on the number of employee work days/week (cycle) or the minimization of payroll costs where differential pay rates exist. These latter models may also be formulated such that idle time is ignored, constrained or minimized. The execution time for the implicit models (on a CDC CYBER 730 computer with commercially available software) averaged well under five seconds on 1200 trial problems for the type of application considered in [18]. A solution was obtained in less than 46 seconds of CPU time for a trial problem which would have required over 1.4 million integer variables with previous models. The availability of optimal solutions was invaluable in the development of two heuristics designed to deal with the trade-offs of [16]. In an experimental analysis a previous heuristic produced results which averaged from 74 to 508 percent above optimum across six experimental conditions. The comparable new heuristic produced results which averaged from 3 to 8 percent above optimum for the same experimental conditions. The paper concludes by developing a framework to integrate the results of this research with the tour scheduling problem and by identifying several other areas for related research.     

THE PERFORMANCE OF A SIMPLE INCREMENTAL LOT-SIZING RULE IN A MULTILEVEL INVENTORY ENVIRONMENT

A simple incremental cost approach to lot sizing was tested in a multilevel inventory environment. The incremental approach has not previously been tested in a large-scale study involving multiple product-structure levels. Using the Wagner-Whitin (WW) algorithm as a benchmark, the simple incremental rule (IPPA) was compared to three heuristic procedures (LFL, EOQ, and POQ) frequently used in material requirements planning (MRP) lot-sizing research. The incremental rule consistently generated lower total order/setup and carrying costs than the three heuristics across the 3,200 multilevel test situations examined. In many of the test situations, the incremental rule also outperformed the WW benchmark.     

A HEURISTIC ALGORITHM FOR RESOURCE LEVELING IN MULTI-PROJECT,MULTI-RESOURCE SCHEDULING

The utility of conventional project management techniques, such as PERT and CPM, is hampered by significant and realistic network attributes and managerial considerations. There are many situations which involve more than one project and numerous resources. Of major concern in such situations is the reduction of the maximum quantity of each required resource. The algorithm presented here is capable of scheduling the individual activities in multiple projects with multiple resources and leveling the overall quantity of each resource which is required. The basis of the algorithm is a sequencing procedure for examining various combinations of activity start times. The measure of effectiveness for each combination is the sum of the squares of the required quantity of each resource in each time period.     

NEW TECHNOLOGY INVESTMENTS IN MULTISTAGE PRODUCTION SYSTEMS

The introduction of new technologies in production and manufacturing (such as robotics, flexible manufacturing systems (FMS), and computer-aided design and manufacturing (CAD/CAM)) frequently motivates capital investment decisions. Traditionally, the need for additional capacity has motivated the evaluation of investment decisions which were undertaken based on financial factors such as the net present value (NPV), internal rate of return, taxation, and depreciation. This research integrates investment decisions with operational decisions for the case of multistage production assembly systems. We show that for such systems investment decisions affect not only the financial structure but also production scheduling and material flow in the system.     

A MODEL FOR SWITCHING DISPATCHING RULES IN REAL TIME IN A FLEXIBLE MANUFACTURING CELL

We model choice of dispatching rules in real time (system state dependent) as a pattern recognition problem, using a modified version of Data Envelopment Analysis. A data base of system state and performance values is created from extensive simulation, and this data base is used to train the pattern-recognition model. Our results show that the model is very effective in choosing a mix of dispatching rules over a period of time, varying the mix with system objectives, and performing better than the strategy of using fixed rules. We show how “If-Then” decision rules can be created from the model and portrayed in a decision-tree-like diagram. Since such decision rules are based on rigorous mathematical foundations, optimization will be ensured in our approach.     

A COMPARISON OF RULES FOR ALLOCATING SETUP-REDUCTION INVESTMENTS IN A CAPACITATED ENVIRONMENT

This paper builds on a recent empirical study of the setup-reduction process that suggests setup-reduction proceeds through three major stages and that each stage is dominated by a particular type of investment function. Specifically, it examines the question of how to best prioritize investments during the stage that emphasizes standardizing setups across a work center. We compare different investment-allocation rules in a multi-item, capacity-constrained, dynamic demand environment under a variety of cost, demand, and investment assumptions. This analysis shows that significant differences in benefits can be achieved depending on the way setup-reduction investments are prioritized.     

A SIMULATION ANALYSIS OF THE JAPANESE JUST-IN-TIME TECHNIQUE (WITH KANBANS) FOR A MULTILINE,MULTISTAGE PRODUCTION SYSTEM

The Japanese “just-in-time with kanban” technique reduces in-process inventory to absolute minimal levels, in concert with the Japanese belief that inventory is an unnecessary evil. Due to the success of Japanese firms that employ this type of system, American firms would like to import this technique and emulate Japanese successes. But this Japanese success may be attributable not only to the just-in-time with kanban technique but also to the production environment in which the technique is employed. This paper simulates the just-in-time with kanban technique for a multiline, multistage production system in order to determine its adaptability to an American production environment that might include such characteristics as variable processing times, variable master production scheduling, and imbalances between production stages. The results have practical implications for those firms considering adoption of the Japanese technique.     

IMPLEMENTATION AND EVALUATION OF A FEEDBACK SYSTEM FOR EMPLOYEES IN A SALVAGE OPERATION

No abstract available for this article.     

A HEURISTIC PROCEDURE FOR DETERMINING IN-PROCESS INVENTORIES

Most prior studies that examine in-process inventory requirements associated with a manufacturing facility employ an optimizing algorithm; this article proposes a heuristic procedure for determining such requirements. A procedure is given for determining “active” and “banked” work-in-process requirements. The two are combined to give total in-process requirements. A key factor presented in the paper is the relationship between in-process inventory requirements and the “balancing” of a production facility. Most prior researchers examine these problems independently. It will be shown that both problems can be resolved concurrently. The concepts are applicable to both an existing as well as a new manufacturing facility.     

A SIMPLE HEURISTIC FOR DYNAMIC ORDER SIZING AND SUPPLIER SELECTION WITH TIME‐VARYING DATA

In this paper we consider the problem of supplier selection and purchase order sizing for a single item under dynamic demand conditions. Suppliers offer all‐units and/or incremental quantity discounts, which may vary over time. Although the problem refers to a typical planning task of a purchasing agent, which is often solved without algorithmic assistance, in an eBusiness (b2b) environment, the need for the integration of an automatic performance of this planning task into a web‐based procurement process becomes evident. A new model formulation for this problem is developed, and a simple but easily extendible heuristic procedure is presented and tested. The heuristic is implemented as part of the Advanced Planner and Optimizer (apo) software of SAP AG, Walldorf, Germany.     

SEQUENCING AND BATCHING FOR TWO CLASSES OF JOBS WITH DEADLINES AND SETUP TIMES

We formulate a general sequencing problem that includes two classes of jobs with setup times, setup costs, holding costs, and deadlines. The formulation is unique in its explicit recognition of the opportunities to exploit productive capacity increases due to batching. An algorithm based on tabu search is then used as a solution method. Computational results are presented that suggest that the algorithm is effective.     

OPTIMAL AND HEURISTIC SOLUTIONS FOR THE MULTI‐ITEM NEWSVENDOR PROBLEM WITH A SINGLE CAPACITY CONSTRAINT

This research considers a multi‐item newsvendor problem with a single capacity constraint. While this problem has been addressed in the literature, the focus here is on developing simple, closed‐form expressions for the order quantities. The benefit of such an approach is that the solutions are straightforward to calculate and have managerial appeal. Additionally, we show these expressions to be optimal under a variety of conditions. For more general cases when these optimality conditions do not hold, we use these expressions as heuristic solutions. Via computational studies, we demonstrate that these heuristics are extremely effective when the optimality conditions are not satisfied.     

MULTISTAGE SAFETY STOCK PLANNING WITH ITEM DEMANDS CORRELATED ACROSS PRODUCTS AND THROUGH TIME

Determining safety stocks in multistage manufacturing systems with serial or divergent structures, where end-item demands are allowed to be correlated both between products as well as in time, is my focus. I show that these types of correlation have contrary effects on the distribution of safety stocks over the manufacturing stages and that neglecting the correlation of demand can lead to significant deviation from the optimal buffer policy. Using base-stock control and assuming total reliability for internal supplies, I present a procedure for integrated multilevel safety stock optimization that can be applied to arbitrary serial and divergent systems even when demand is jointly cross-product and cross-time correlated. As I demonstrate in an example for autocorrelated demands of a moving average type, there are specific solution properties that drastically reduce the computational effort for safety stock planning. Safety stocks determined in that way can be used as an appropriate protection against demand uncertainties in material requirements planning systems.     

A COMPARISON OF ORDER-RELEASE AND DISPATCH RULES FOR THE DYNAMIC WEIGHTED EARLY/TARDY PROBLEM

The early/tardy problem is one of the most vexing pieces of the complex production scheduling decision process. So far most of the research has been on single-machine environments. Hence, we considered the weighted early/tardy scheduling problem in a simulated dynamic multimachine job shop. We analyzed controlled job-release and dispatch rules using time and cost information at a variety of stationary and nonstationary utilization rates, due-date allowances, and early/tardy cost levels. We found a newly developed method for controlling the release for all job operations using early/tardy cost information, superior to other release mechanisms overall in both our stationary and nonstationary analyses. We found immediate release useful at many high utilization conditions and a gateway-only release method best in many low utilization conditions. A modified version of a single-machine early/tardy dispatch method was clearly superior to the dispatch rules for almost all the simulated shop conditions.     

ESTIMATING WAITING TIME IN A QUEUING SYSTEM*

This study examines how customers joining a queuing system assess their waiting time in the system. Data obtained from an actual queuing system support the paradigm of Parkan and Warren [5] on the use of the observed number in the system in this assessment. It is concluded that customers overestimate mean service time but that this overestimation is not dependent on the number in the system.     

DYNAMIC FEEDING IN A STOCHASTIC PARALLEL PROCESSING SYSTEM

I consider a dynamic input scheduling problem of a stochastic parallel processing system consisting of n identical flexible machining cells. The processing times at each cell are independent random variables. Previous study has indicated the NP complexity of the problem. In this paper, I prove the separability under an ideal just-in-time input condition. Using the separability, I then construct an approximation procedure for most realistic applications where the separability condition is violated. The approximation procedure requires only linear time and performed quite well on an extensive test with numerical examples.