Activity scheduling and resource allocation with uncertainties and learning in activities

2019 ◽  
Vol 119 (6) ◽  
pp. 1289-1320 ◽  
Author(s):  
Felix T.S. Chan ◽  
Zhengxu Wang ◽  
Yashveer Singh ◽  
X.P. Wang ◽  
J.H. Ruan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Project Management ◽  
Manufacturing Industry ◽  
Learning Rate ◽  
Management Problem ◽  
Optimal Solutions ◽  
Resource Constrained ◽  
Nsga Ii ◽  
Content Type ◽  
Activity Schedule

Purpose The purpose of this paper is to develop a model which schedules activities and allocates resources in a resource constrained project management problem. This paper also considers learning rate and uncertainties in the activity durations. Design/methodology/approach An activity schedule with requirements of different resource units is used to calculate the objectives: makespan and resource efficiency. A comparisons between non-dominated sorting genetic algorithm – II (NSGA-II) and non-dominated sorting genetic algorithm – III (NSGA-III) is done to calculate near optimal solutions. Buffers are introduced in the activity schedule to take uncertainty into account and learning rate is used to incorporate the learning effect. Findings The results show that NSGA-III gives better near optimal solutions than NSGA-II for multi-objective problem with different complexities of activity schedule. Research limitations/implications The paper does not considers activity sequencing with multiple activity relations (for instance partial overlapping among different activities) and dynamic events occurring in between or during activities. Practical implications The paper helps project managers in manufacturing industry to schedule the activities and allocate resources for a near-real world environment. Originality/value This paper takes into account both the learning rate and the uncertainties in the activity duration for a resource constrained project management problem. The uncertainty in both the individual durations of activities and the whole project duration time is taken into consideration. Genetic algorithms were used to solve the problem at hand.

Modeling of the time-dependent multi-skilled RCPSP considering learning effect

2019 ◽  
Vol 14 (2) ◽  
pp. 521-558 ◽  
Author(s):  
Amir Hossein Hosseinian ◽  
Vahid Baradaran ◽  
Mahdi Bashiri
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Learning Effect ◽  
The Other ◽  
Time Dependent ◽  
Computational Time ◽  
Nsga Ii ◽  
Content Type ◽  
Proposed Model ◽  
Objective Function Values

Purpose The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously. Design/methodology/approach The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method. Findings Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values. Practical implications The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects. Originality/value Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

A multi-objective location-allocation model in mass casualty events response

2018 ◽  
Vol 13 (1) ◽  
pp. 236-274 ◽  
Author(s):  
Mahsa Pouraliakbarimamaghani ◽  
Mohammad Mohammadi ◽  
Abolfazl Mirzazadeh
Keyword(s):  
Genetic Algorithm ◽  
Health Care ◽  
Emergency Operation ◽  
Mass Casualty ◽  
Nsga Ii ◽  
Content Type ◽  
Multi Objective ◽  
Mass Casualty Event ◽  
Personnel Shortages ◽  
Injured Patients

Purpose When designing an optimization model for use in a mass casualty event response, it is common to encounter the heavy and considerable demand of injured patients and inadequate resources and personnel to provide patients with care. The purpose of this study is to create a model that is more practical in the real world. So the concept of “predicting the resource and personnel shortages” has been used in this research. Their model helps to predict the resource and personnel shortages during a mass casualty event. In this paper, to deal with the shortages, some temporary emergency operation centers near the hospitals have been created, and extra patients have been allocated to the operation center nearest to the hospitals with the purpose of improving the performance of the hospitals, reducing congestion in the hospitals and considering the welfare of the applicants. Design/methodology/approach The authors research will focus on where to locate health-care facilities and how to allocate the patients to multiple hospitals to take into view that in some cases of emergency situations, the patients may exceed the resource and personnel capacity of hospitals to provide conventional standards of care. Findings In view of the fact that the problem is high degree of complexity, two multi-objective meta-heuristic algorithms, including non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA), were proposed to solve the model where their performances were compared in terms of four multi-objective metrics including maximum spread index (MSI), spacing (S), number of Pareto solution (NPS) and CPU run-time values. For comparison purpose, paired t-test was used. The results of 15 numerical examples showed that there is no significant difference based on MSI, S and NPS metrics, and NRGA significantly works better than NSGA-II in terms of CPU time, and the technique for the order of preference by similarity to ideal solution results showed that NRGA is a better procedure than NSGA-II. Research limitations/implications The planning horizon and time variable have not been considered in the model, for example, the length of patients’ hospitalization at hospitals. Practical implications Presenting an effective strategy to respond to a mass casualty event (natural and man-made) is the main goal of the authors’ research. Social implications This paper strategy is used in all of the health-care centers, such as hospitals, clinics and emergency centers when dealing with disasters and encountering with the heavy and considerable demands of injured patients and inadequate resources and personnel to provide patients with care. Originality/value This paper attempts to shed light onto the formulation and the solution of a three-objective optimization model. The first part of the objective function attempts to maximize the covered population of injured patients, the second objective minimizes the distance between hospitals and temporary emergency operation centers and the third objective minimizes the distance between the warehouses and temporary centers.

scholarly journals An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II

2019 ◽  
Vol 4 (3) ◽  
pp. 291
Author(s):  
Farid Jauhari ◽  
Wayan Firdaus Mahmudy ◽  
Achmad Basuki
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Genetic Parameters ◽  
The Other ◽  
Optimal Solutions ◽  
Tuition Fees ◽  
Nsga Ii ◽  
Chromosome Representation ◽  
Time Required

Proportional tuition fees assessment is an optimization process to find a compromise point between student willingness to pay and institution income. Using a genetic algorithm to find optimal solutions requires effective chromosome representations, parameters, and operator genetic to obtain efficient search. This paper proposes a new chromosome representation and also finding efficient genetic parameters to solve the proportional tuition fees assessment problem. The results of applying the new chromosome representation are compared with another chromosome representation in the previous study. The evaluations show that the proposed chromosome representation obtains better results than the other in both execution time required and the quality of the solutions.

Manufacturing excellence using lean systems – a case of an automotive aggregate manufacturing plant in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ravindra Ojha ◽  
Umashankar Venkatesh
Keyword(s):  
Project Management ◽  
Business School ◽  
Manufacturing Industry ◽  
Success Factors ◽  
Systems Approach ◽  
Real Life ◽  
Shop Floor ◽  
Content Type ◽  
Production Shop

PurposeThe paper aims at fulfilling two purposes: (1) to enrich young production shop floor managers to understand and appreciate the different dimensions of manufacturing excellence and (2) to provide a comprehensive industry-based case study to a faculty involved in the teaching-learning process of Lean systems to the Business school management students.Design/methodology/approachImparting learnings through a real-life case study from a manufacturing industry, which successfully doubled its delivery capacity using the project management and Lean systems approach. Value flow techniques have been utilised in the production shop floor.FindingsEffective implementation of lean thinking can significantly facilitate enhancing plant capacity within the original shop floor area and without hindering the delivery to the customers with growing demand. Outcomes of the plant transformation re-emphasised that effective leadership, a well-constituted project team, project management tools, applied knowledge of lean enablers and its metrics and management's engagement are the critical success factors.Research limitations/implicationsThe case has been automotive industry driven.Practical implicationsThis real life industry case study is expected to enrich not only the management graduates who would be industry leaders tomorrow but also the practising young shop floor managers who aspire to achieve manufacturing excellence through lean enablers and metrics.Originality/valueUseful real-life industry-based Lean manufacturing case study to be utilised by the business school faculty members in their class to enrich students/young practising managers.

Resource levelling in projects considering different activity execution modes and splitting

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Khalilzadeh
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Programming Model ◽  
Exact Method ◽  
Computational Time ◽  
Resource Usage ◽  
Optimal Solutions ◽  
Content Type ◽  
Proposed Model ◽  
Multi Mode

Purpose This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources. Design/methodology/approach The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time. Findings The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon. Originality/value There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

IDENTIFYING OCCUPANTS' APPROPRIATE SEATING POSITION AND VIEW DIRECTION IN OFFICE BUILDINGS: A STOCHASTIC SHADE CONTROL BASED MULTIOBJECTIVE VISUAL COMFORT OPTIMIZATION

2020 ◽  
Vol 15 (1) ◽  
pp. 15-36
Author(s):  
Jian Yao
Keyword(s):  
Genetic Algorithm ◽  
Statistical Analysis ◽  
Behavior Model ◽  
Visual Comfort ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Office Rooms ◽  
Seating Position

ABSTRACT Manually operated solar shades have a significant impact on indoor visual comfort. This research investigates occupants' appropriate seating position and view direction in a west-facing office cell using a previously developed shade behavior model. The non-dominant sorting genetic algorithm (NSGA-II) based Multi-objective optimization was adopted to identify the optimal and near optimal solutions. Daylight and glare index were used as two visual comfort objectives for optimization and robustness of optimization results against shade behavior uncertainty that was analyzed using statistical analysis. Results show that near optimal solutions can be used instead of the optimal one since they provide more flexibility in seating positions while maintaining almost the same visual comfort performance. And thus, the appropriate seating position considering occupants' preference is 1.5m away from the external window with two view directions near parallel to the window for west-facing office rooms.

scholarly journals Optimal Solutions of Multiproduct Batch Chemical Process Using Multiobjective Genetic Algorithm with Expert Decision System

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Diab Mokeddem ◽  
Abdelhafid Khellaf
Keyword(s):  
Genetic Algorithm ◽  
Decision Maker ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Solution Space ◽  
Fine Tuning ◽  
Plant Design ◽  
Optimal Solutions ◽  
Nsga Ii ◽  
Promethee Ii

Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples.

Improved genetic algorithm for resource-constrained scheduling of large projects

2009 ◽  
Vol 36 (6) ◽  
pp. 1016-1027 ◽  
Author(s):  
Jin-Lee Kim
Keyword(s):  
Genetic Algorithm ◽  
Project Management ◽  
Project Scheduling ◽  
Selection Process ◽  
Transformation Method ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm ◽  
Resource Constrained ◽  
Practical Applications ◽  
Resource Constrained Project Scheduling

The generalized model of the resource-constrained project scheduling problem (RCPSP) is valuable because it can be incorporated into the advanced computational methods of commercial project management software for practical applications. A construction schedule generated by most commercial project management programs does not guarantee its optimality when the resources are limited. This paper presents an improved elitist genetic algorithm (GA) for resource-constrained scheduling of large projects. The proposed algorithm allocates multiple renewable resources to activities of a single large-sized project to achieve the objective of minimizing the project duration. A permutation-based decoding procedure is developed using the improved parallel schedule generation scheme. A new parameter, named transformation power, is created in the transformation method of the improved algorithm to ensure that the individual selection process performs correctly. Extensive computational results using a standard set of large-sized multiple resource-constrained project scheduling problems are presented to demonstrate the performance and accuracy of the algorithm.

Multi-objective multi-facility green manufacturing closed-loop supply chain under uncertain environment

2019 ◽  
Vol 39 (1) ◽  
pp. 58-76 ◽  
Author(s):  
Behzad Karimi ◽  
Amir Hossein Niknamfar ◽  
Babak Hassan Gavyar ◽  
Majid Barzegar ◽  
Ali Mohtashami
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Closed Loop ◽  
Distribution Centers ◽  
Nsga Ii ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Uncertain Environment ◽  
Multi Objective ◽  
Hierarchy Process

Purpose Today’s, supply chain production and distribution of products to improve the customer satisfaction in the shortest possible time by paying the minimum cost, has become the most important challenge in global market. On the other hand, minimizing the total cost of the transportation and distribution is one of the critical items for companies. To handle this challenge, this paper aims to present a multi-objective multi-facility model of green closed-loop supply chain (GCLSC) under uncertain environment. In this model, the proposed GCLSC considers three classes in case of the leading chain and three classes in terms of the recursive chain. The objectives are to maximize the total profit of the GCLSC, satisfaction of demand, the satisfactions of the customers and getting to the proper cost of the consumers, distribution centers and recursive centers. Design/methodology/approach Then, this model is designed by considering several products under several periods regarding the recovery possibility of products. Finally, to evaluate the proposed model, several numerical examples are randomly designed and then solved using non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. Then, they are ranked by TOPSIS along with analytical hierarchy process so-called analytic hierarchy process-technique for order of preference by similarity to ideal solution (AHP-TOPSIS). Findings The results indicated that non-dominated ranked genetic algorithm (NRGA) algorithm outperforms non-dominated sorting genetic algorithm (NSGA-II) algorithm in terms of computation times. However, in other metrics, any significant difference was not seen. At the end, to rank the algorithms, a multi-criterion decision technique was used. The obtained results of this method indicated that NSGA-II had better performance than ones obtained by NRGA. Originality/value This study is motivated by the need of integrating the leading supply chain and retrogressive supply chain. In short, the highlights of the differences of this research with the mentioned studies are as follows: developing multi-objective multi-facility model of fuzzy GCLSC under uncertain environment and integrating the leading supply chain and retrogressive supply chain.

A hybrid method for optimizing selective disassembly sequence planning in adaptive reuse of buildings

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehran Mahmoudi Motahar ◽  
Seyed Hossein Hosseini Nourzad
Keyword(s):  
Hybrid Method ◽  
Manufacturing Industry ◽  
Adaptive Reuse ◽  
Nsga Ii ◽  
Content Type ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Disassembly Sequence ◽  
Selective Disassembly ◽  
Disassembly Planning

PurposeA successful adaptive reuse process relies heavily on the strong performance of disassembly sequence planning (DSP), yet the studies in the field are limited to sequential disassembly planning (SDP). Since in sequential disassembly, one component or subassembly is removed with only one manipulator at a time, it can be a relatively inefficient and lengthy process for large or complex assemblies and cannot fully utilize the DSP benefits for adaptive reuse of buildings. This study aims to present a new hybrid method for the single-target selective DSP that supports both sequential and parallel approaches.Design/methodology/approachThis study uses asynchronous parallel selective disassembly planning (aPDP) method, one of the newest and most effective parallel approaches in the manufacturing industry, to develop a parallel approach toward DSP in adaptive reuse of buildings. In the proposed method, three objectives (i.e. disassembly sequence time, cost and environmental impacts) are optimized using the Non-dominated Sorting Genetic Algorithm (NSGA-II).FindingsThe proposed method can generate feasible sequential solutions for multi-objective DSP problems as the sequence disassembly planning for buildings (SDPB) method, and parallel solutions lead to 17.6–23.4% time reduction for understudy examples. Moreover, in disassembly planning problems with more complex relations, the parallel approach generates more effective and time-efficient sequences.Originality/valueThis study introduces the parallel approach for the first time in this field. In addition, it supports both sequential and parallel approaches as a novel strategy that enables the decision-makers to select the optimum approach (i.e. either the parallel or the sequential approach) for DSP. Moreover, a metaheuristic method (i.e. NSGA-II) is adopted as the optimization tool with robust results in the field in which those heuristic methods have only been employed in the past.

Sign in / Sign up

Export Citation Format

Share Document