A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

Coordinated budget allocation in multi-district highway agencies

Ph.DDOCTOR OF PHILOSOPH

MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

Fuzzy linear programming problems : models and solutions

We investigate various types of fuzzy linear programming problems based on models and solution methods. First, we review fuzzy linear programming problems with fuzzy decision variables and fuzzy linear programming problems with fuzzy parameters (fuzzy numbers in the definition of the objective function or constraints) along with the associated duality results. Then, we review the fully fuzzy linear programming problems with all variables and parameters being allowed to be fuzzy. Most methods used for solving such problems are based on ranking functions, alpha-cuts, using duality results or penalty functions. In these methods, authors deal with crisp formulations of the fuzzy problems. Recently, some heuristic algorithms have also been proposed. In these methods, some authors solve the fuzzy problem directly, while others solve the crisp problems approximately

Heuristic scheduling for clinical physicians.

Personnel scheduling is a problem faced by many organizations in the healthcare industry, particularly in rapidly developing outpatient centers. The task of creating a schedule that adequately covers patient demand while satisfying the preferences of employees, observing work regulations, and ensuring a fair distribution of work is highly complex. Even though this highly complex task directly affects measures such as patient waiting time and employee satisfaction, many organizations still resort to the traditional and cumbersome manual solution methods. A large segment of prior research on personnel scheduling in healthcare focuses on nurse rostering and the development of automated tools to aid in scheduling. The drawbacks to these methods include the lack of generality and the need for specialized software packages and training. The aim of this study is the development of an effective, low cost, and uncomplicated heuristic tool to aid schedulers in outpatient centers. Solution methodologies used by previous researchers in problems such as nurse rostering and aircrew rostering are adapted to the particular problem of physician scheduling in mixed specialty outpatient clinics. The developed heuristic tool obtains an initial feasible solution using a greedy algorithm and then uses the simulated annealing metaheuristic to improve the solution, which is a measure of physician satisfaction. The heuristic tool developed in this study was tested using eight randomly generated data sets to model 45 unique cases. The heuristic found the optimal solution in 19 of the 45 tested cases. The average difference from the optimal physician satisfaction rating in the other 26 cases was 0.35%

Simulation and optimization model for the construction of electrical substations

One of the most complex construction projects is electrical substations. An electrical substation is an auxiliary station of an electricity generation, transmission and distribution system where voltage is transformed from high to low or the reverse using transformers. Construction of electrical substation includes civil works and electromechanical works. The scope of civil works includes construction of several buildings/components divided into parallel and overlapped working phases that require variety of resources and are generally quite costly and consume a considerable amount of time. Therefore, construction of substations faces complicated time-cost-resource optimization problems. On another hand, the construction industry is turning out to be progressively competitive throughout the years, whereby the need to persistently discover approaches to enhance construction performance. To address the previously stated afflictions, this dissertation makes the underlying strides and introduces a simulation and optimization model for the execution processes of civil works for an electrical substation based on database excel file for input data entry. The input data include bill of quantities, maximum available resources, production rates, unit cost of resources and indirect cost. The model is built on Anylogic software using discrete event simulation method. The model is divided into three zones working in parallel to each other. Each zone includes a group of buildings related to the same construction area. Each zone-model describes the execution process schedule for each building in the zone, the time consumed, percentage of utilization of equipment and manpower crews, amount of materials consumed and total direct and indirect cost. The model is then optimized to mainly minimize the project duration using parameter variation experiment and genetic algorithm java code implemented using Anylogic platform. The model used allocated resource parameters as decision variables and available resources as constraints. The model is verified on real case studies in Egypt and sensitivity analysis studies are incorporated. The model is also validated using a real case study and proves its efficiency by attaining a reduction in model time units between simulation and optimization experiments of 10.25% and reduction in total cost of 4.7%. Also, by comparing the optimization results by the actual data of the case study, the model attains a reduction in time and cost by 13.6% and 6.3% respectively. An analysis to determine the effect of each resource on reduction in cost is also presented

Nurse Rostering Problems: A Bibliographic Survey

10.1016/S0377-2217(03)00021-3European Journal of Operational Research1513447-460EJOR

Multi-skilled Labor Optimization with Partial Allocation of Resources

The current practice of labor allocation in construction schedules assumes single-skilled workforce; meaning that each worker is assumed to be skilled in only one trade. In such practice, at any instance in the project lifecycle, some of the workforce become idle waiting for other labor types to complete their work. Traditionally, companies may relocate idle workers to other projects and return them back to their original project when needed again. This complicates the resource management process and is not often performed successfully, leading to schedule and cost overruns. Alternatively, project managers may keep the idle workforce at their projects because they will be needed at a later stage and pay them in their idle days, which adds unnecessary costs to the project. Another solution would be continuously hiring and laying off labor at need, which has severe negative impacts on projects and firms. Due to the inefficiencies of these solutions, some research discussed the idea of “multi-skilled” labor, where some of the workers may have enough training to carry out different activity types. Multi-skilling decreases inefficiencies and ensures a smooth and continuous progress of works whilst maintaining the workforce and keeping their idle time to a minimum. Multi-skilling could be also used to speed up progress in construction schedules. Previous research efforts have been made to encourage contractors in pursuing multiskilling as a solution to the non-smooth resource histograms. Yet, the literature falls short in providing a robust multi-skilling framework; specifically, one that considers the cost of training labor and solves the partial allocation problem. The objective of this research is to improve project duration and minimize unnecessary costs through the utilization of multi-skilled labor. Through a multi-step methodology, a model that optimizes the allocation of multi-skilled labor resources was developed. The novelty of the presented model is that it further minimizes the idle times of labor when compared to previous multi-skilled labor models, due to its capability in allocating resources “partially” to segments of activities rather than to full activities. In other words, unlike previous models, the developed model recognizes the fact that a crew can work for a period of time in an activity, then some workers in that crew can be allocated to another activity, leaving the rest of the crew to complete the first activity. The model allows the user to enter any number of activities and up to ten different resource types. With the use of genetic algorithms idle resources are assigned to activities that require additional manpower in order to reduce their durations, and in turn reduce the project’s indirect costs. When applied to a case study, the model generated promising results, where the reduction in duration between the single skilled allocation and multi-skilled labor allocation was 31% and this reduction jumped to 44% when partial allocation was applied. Multiskilling did not only reduce the idle labor days, but it will also shift the resource usage histogram’s end point to the left, reducing the total project duration. This did not only reduce the unnecessary costs being paid to workers on days where they have no work, but it also reduced the total indirect costs which are directly proportional to the overall project duration