Scheduling jobs with ready times and precedence constraints on parallel batch machines using metaheuristics

In this paper, we discuss a scheduling problem for parallel batch machines where the jobs have ready times. Problems of this type can be found in semiconductor wafer fabrication facilities (wafer fabs). In addition, we consider precedence constraints among the jobs. Such constraints arise, for example, in scheduling subproblems of the shifting bottleneck heuristic when complex job shop scheduling problems are tackled. We use the total weighted tardiness as the performance measure to be optimized. Hence, the problem is NP-hard and we have to rely on heuristic solution approaches. We consider a variable neighborhood search (VNS) scheme and a greedy randomized adaptive search procedure (GRASP) to compute efficient solutions. We assess the performance of the two metaheuristics based on a large set of randomly generated problem instances and based on instances from the literature. The obtained computational results demonstrate that VNS is a very fast heuristic that quickly leads to high-quality solutions, whereas the GRASP is slightly outperformed by the VNS approach. However, the GRASP approach has the advantage that it can be parallelized in a more natural manner compared to VNS.     

Lot scheduling on a single machine

In a practical situation, a manufacturer receives different orders from its customers. Different orders may contain different quantities of the product. Therefore, the manufacturer has to decide how to group these orders into different lots based on the capacity of the lot processing machine (such as integrated circuit tester, heated container, etc.) and then decides the sequence of these lots. In this paper, we study a lot scheduling problem with orders which can be split. The objective is to minimize the total completion time of all orders. We show that this problem can be solved in polynomial time.     

Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan

The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.     

Unit Maintenance Scheduling: A robust model,based on fuzzy cost factors and peak loads

As a mid-term planning scheduling, Unit Maintenance Scheduling (UMS) has a significant effect on Generation companies (Gencos) profit. System Operator (SO) is a central entity that provides the Gencos with related maintenance schedule. This schedule would be finalized through repetitive iterations between the SO and different Gencos in the market, to come up with a generally accepted schedule. Bidding strategy, on the other side, does affect the Gencos profit. Being considered as a short-term planning, bidding process is done on the hourly basis to determine the allocated demand to each Genco for each hour of the day. These short-term and mid-term schedules planning received enough attention in the literature, but to consider both simultaneously has not been well studied. In addition, one may not have all the information for the future periods. For production cost factors and load, an estimation can be obtained from the historical data, but that would not be the exact value and there remains an uncertainty on the value of these parameters. This would highlight the importance of developing a robust model that protect the schedule against changes in the values of these parameters, and ensure an acceptable, near optimal solution as well. In this paper, we would model the uncertainties in load and production cost factors, based on a fuzzy framework. The UMS problem is modeled and solved as a dynamic non-cooperative fuzzy game. The results show the effectiveness of the proposed approach.     

Fast and Accurate Risk Evaluation for Scheduling Large-Scale Construction Projects

This paper presents the development of a novel probabilistic scheduling model that enables fast and accurate risk evaluation for large-scale construction projects. The model is designed to overcome the limitations of existing probabilistic scheduling methods, including the inaccuracy of the program evaluation and review technique (PERT) and the long computational time of the Monte Carlo simulation method. The model consists of three main modules: PERT model; fast and accurate multivariate normal integral method; and a newly developed approximation method. The new approximation method is designed to focus the risk analysis on the most significant paths in the project network by identifying and removing insignificant paths that are either highly correlated or have high probability of completion time. The performance of the new model is analyzed using an application example. The results of this analysis illustrate that the new model was able to reduce the computational time for a large-scale construction project by more than 94% while keeping the error of its probability estimates to less than 3%, compared with Monte Carlo Simulation methods.     

Efficient Hybrid Genetic Algorithm for Resource Leveling via Activity Splitting

Resource leveling problem is an attractive field of research in project management. Traditionally, a basic assumption of this problem is that network activities could not be split. However, in real-world projects, some activities can be interrupted and resumed in different time intervals but activity splitting involves some cost. The main contribution of this paper lies in developing a practical algorithm for resource leveling in large-scale projects. A novel hybrid genetic algorithm is proposed to tackle multiple resource-leveling problems allowing activity splitting. The proposed genetic algorithm is equipped with a novel local search heuristic and a repair mechanism. To evaluate the performance of the algorithm, we have generated and solved a new set of network instances containing up to 5,000 activities with multiple resources. For small instances, we have extended and solved an existing mixed integer programming model to provide a basis for comparison. Computational results demonstrate that, for large networks, the proposed algorithm improves the leveling criterion at least by 76% over the early schedule solutions. A case study on a tunnel construction project has also been examined.     

Simulation-Based Schedule Estimation Model for ACS-Based Core Wall Construction of High-Rise Building

Many existing studies about construction schedule management focus on the planning phase of a project, particularly on schedule estimation based on the labor resources involved in the project. However, equipment resources, which are another crucial factor in the productivity of a construction project, have not been considered in existing research. Therefore, this study aimed to develop a schedule estimation model considering both labor and equipment resources. For the purpose of this study, core wall construction was selected because it is a very important construction activity in terms of schedule estimation for high-rise building construction. To develop a schedule estimation model for core wall construction, an in-depth case study was conducted. On the basis of the results of the case study, a simulation model was developed using the CYCLONE method. Finally, by using the results of the simulation, a schedule estimation model for core wall construction was developed by conducting multiple-regression analysis. By using the developed model, a project manager can easily, quickly, and accurately perform schedule estimation when there are problems that may cause construction schedule delays during the construction phase.     

PDM++: Planning Framework from a Construction Requirements Perspective

Construction requirements represent the key preconditions for construction. These include topological precedence, key resources, space requirements, etc. Consequently, identifying them is necessary for feasible construction planning to be achieved. Despite this, little attention has been given to the impact of construction requirements on a project schedule, possibly because of the lack of a good tool for representing these requirements. This paper distinguishes construction requirements into static and dynamic types, according to changes in the need of the requirement during its life cycle. A modeling framework, PDM++, is then proposed. The framework deals with schedule constraints arising from both static and dynamic construction requirements, provides greater semantic expression to capture schedule constraints unambiguously, and facilitates the representation of interdependent conditional relationships. The concept of meta-intervals is also devised to represent complex requirements involving several activities and schedule constraints, and it facilitates modeling at higher levels of plan abstractions. Finally, an illustrative case study is presented to show the applicability of PDM++ in representing schedule constraints and alternative scheduling from a construction requirements perspective.     

Irrigation Scheduling for Green Bell Peppers Using Capacitance Soil Moisture Sensors

Vegetable production areas are intensively managed with high inputs of fertilizer and irrigation. The objectives of this study were to evaluate the interaction between N-fertilizer rates and irrigation scheduling using soil moisture sensor irrigation controllers (SMS) on yield, irrigation water use efficiency (IWUE) of bell pepper cultivated under plastic mulch and drip irrigation. Treatments included three irrigation scheduling and three N-rates (176, 220, and 330 kg/ha). Irrigation treatments were: SS10, water application controlled by SMS-based irrigation set at 10% volumetric water content (VWC) which was allotted five irrigation windows daily and bypassed events if the soil VWC exceeded the established threshold; SS12, threshold set at 12% VWC; and TIME, control with irrigation being applied once a day similar to grower irrigation management. Marketable yields ranged between 16 and 29 Mg/ha. The SMS treatments reduced the applied irrigation in 7 to 62% compared to TIME treatment without reducing yield. The treatments SS10 and SS12 reduced nitrate leaching by 25 to 73% compared to TIME treatment.     

Computerizing ICBF Method for Schedule Delay Analysis

Schedule delay is a common construction dispute. For analyzing schedule delays, a previous study had proposed an academic analysis method, the “isolated collapsed but-for” (ICBF) method. However, because many construction projects involve numerous complex activities, the procedure of using the ICBF method for schedule delay analysis is time-consuming. Therefore, this study used Microsoft Visual Basic for Applications (VBA) language and spreadsheet techniques to develop an Excel-based program for rapid delay analysis rather than manual calculation. A case study confirmed that the program automatically produces analytical results with only common delay documents (as-planned and as-built schedules and identified delay events). Compared with previous studies, schedule analysts can obtain analysis results quickly and correctly. Research results provide not only a convenient tool for schedule delay analysis but also a guide to computerize various academic delay analysis methods in the future.