Sustainability risk management in a cooperative environment under uncertainty

Kybernetes ◽  
2019 ◽  
Vol 48 (3) ◽  
pp. 385-406 ◽  
Author(s):  
Mojtaba Moradi ◽  
Ashkan Hafezalkotob ◽  
Vahidreza Ghezavati
Keyword(s):  
Risk Assessment ◽  
Net Present Value ◽  
Programming Model ◽  
Mathematical Programming Model ◽  
Additive Property ◽  
Content Type ◽  
Proposed Model ◽  
Project Risks ◽  
Sustainability Concept ◽  
Project Sustainability

Purpose This study considers a project scheduling model to assess the project risks and the impacts on project sustainability when subcontractors collaborate under uncertainty. Moreover, some allocation methods are applied for fair allocating utility of the project and supper-additivity, stability and satisfaction level of each coalition. Finally, sustainability concept is considered in risk assessment in all coalitions. Design/methodology/approach The proposed mathematical programming model evaluates project risks when the subcontractors cooperate with each other by sharing their limited resources. Then, some cooperative game theory methods are applied for fair allocation of net present value, of the cooperation and finally sustainability aspects (economic, social and environmental) are investigated in risk assessment for each possible coalition. Finding The results of the proposed model indicate that the subcontractors can increase their profit by 10 per cent ($14,028,450 thousand) and save the equilibrium between sustainability aspects especially in grand coalition. It means that subcontractors do not have incentive to leave the coalition and the supper-additive property is feasible. Furthermore, risk assessment shows that project risks have less impact on subcontractor profits when they cooperate with each other. Originality/value Sustainability aspects may be investigated in project management in previous studies, but the authors study sustainability indicators when subcontractors form a coalition and share their resources in response to the risks of availability to resources and delay in completing the project under uncertainty.

NSGA-II algorithm for hub location-allocation problem considering hub disruption and backup hub allocation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehnoosh Soleimani ◽  
Mohammad Khalilzadeh ◽  
Arman Bahari ◽  
Ali Heidary
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Programming Model ◽  
Mathematical Programming Model ◽  
Hub Location ◽  
Nsga Ii ◽  
Content Type ◽  
Location Allocation ◽  
Proposed Model ◽  
Location Allocation Problem

Purpose One of the practical issues in the area of location and allocation is the location of the hub. In recent years, exchange rates have fluctuated sharply for a number of reasons such as sanctions against the country. Natural disasters that have occurred in recent years caused delays in hub servicing. The purpose of this study is to develop a mathematical programming model to minimize costs, maximize social responsibility and minimize fuel consumption so that in the event of a disruption in the main hub, the flow of materials can be directed to its backup hub to prevent delays in flow between nodes and disruptions in hubs. Design/methodology/approach A multi-objective mathematical programming model is developed considering uncertainty in some parameters, especially cost as fuzzy numbers. In addition, backup hubs are selected for each primary hub to deal with disruption and natural disasters and prevent delays. Then, a robust possibilistic method is proposed to deal with uncertainty. As the hub location-allocation problem is considered as NP-Hard problems so that exact methods cannot solve them in large sizes, two metaheuristic algorithms including a non-dominated sorting genetic algorithm non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied to tackle the problem. Findings Numerical results show the proposed model is valid. Also, they demonstrate that the NSGA-II algorithm outperforms the MOPSO algorithm. Practical implications The proposed model was implemented in one of the largest food companies in Iran, which has numerous products manufactured in different cities, to seek the hub locations. Also, due to several reasons such as road traffic and route type the difference in the rate of fuel consumption between nodes, this model helps managers and decision-makers to choose the best locations to have the least fuel consumption. Moreover, as the hub set up increases the employment rate in that city and has social benefits as it requires hiring some staff. Originality/value This paper investigates the hub location problem considering backup hubs with multiple objective functions to deal with disruption and uncertainty. Also, this study examines how non-hub nodes are assigned to hub nodes.

scholarly journals A study of production and harvesting planning for the chicken industry

2018 ◽  
Vol 64 (No. 7) ◽  
pp. 316-327 ◽  
Author(s):  
You Peng-Sheng ◽  
Hsieh Yi-Chih
Keyword(s):  
Sensitivity Analysis ◽  
Production Planning ◽  
Programming Model ◽  
Computational Results ◽  
Mathematical Programming Model ◽  
Planning Period ◽  
Hybrid Heuristic ◽  
Proposed Model ◽  
Parameter Variations

To order to raise chickens for meat, chicken farmers must select an appropriate breed and determine how many broilers to raise in each henhouse. This study proposes a mathematical programming model to develop a production planning and harvesting schedule for chicken farmers. The production planning comprises the number of batches of chickens to be raised in each henhouse, the number of chicks to be raised for each batch, what breed of chicken to raise, when to start raising and the duration of the raising period. The harvesting schedule focuses on when to harvest and how many broilers to harvest each time. Our aim was to develop proper production and harvesting schedules that enable chicken farmers to maximise profits over a planning period. The problem is a highly complicated one. We developed a hybrid heuristic approach to address the issue. The computational results have shown that the proposed model can help chicken farmers to deal with the problems of chicken-henhouse assignment, chicken raising and harvesting, and may thus contribute to increasing profits. A case study of a chicken farmer in Yunlin County (Taiwan) was carried out to illustrate the application of the proposed model. Sensitivity analysis was also conducted to explore the influence of parameter variations.

IT project risk assessment of learning organizations by fuzzy set and systems

2014 ◽  
Vol 22 (2) ◽  
pp. 161-180 ◽  
Author(s):  
Osman Taylan
Keyword(s):  
Risk Assessment ◽  
Organizational Learning ◽  
High Risk ◽  
Fuzzy Sets ◽  
Learning Organizations ◽  
Continuous Process ◽  
It Projects ◽  
Content Type ◽  
It Project ◽  
Project Risks

Purpose – IT projects carry high risk of failure due to the existence of great obstacles during the planning, application and development phase. The projects' risks are multi-dimensional, and they must be assessed by multi-attribute decision-making methods. The purpose of this article is to provide analytic tools to evaluate the learning organization's IT project risks under incomplete and vague information. It was also aimed to place the risk in a proper category and predict the level of it in advance to develop strategies to counteract the high-risk factors. Design/methodology/approach – In this study, three mutual approaches were used to analyze the organizations IT applications. These are enterprise analysis for determination of information requirements of organization, payback method for IT project financial analysis and risk assessment using fuzzy sets and systems. The developed fuzzy model is an expert system which can predict the category of risk for IT projects in learning organizations. Findings – This study revealed that the greatest obstacles to IT project success were the lack of organizational learning, resistance to change, etc. User involvement limitation was found to be also one of the common reasons of IT project failure. The information sharing policy was determined to increase productivity of employees in offices and to decide the creators and the users of knowledge. Learning is a continuous process for organizational transformation. Individual and organizational learning were searched to minimize the level of risks factors and learning culture. IT project risks were categorized properly using fuzzy sets and systems to reduce or even eliminate high risks. Originality/value – The paper is original and gives the first such work for industry.

scholarly journals The unit train make-up scheme for loaded direction in the heavy haul railway

2019 ◽  
Vol 1 (1) ◽  
pp. 30-44 ◽  
Author(s):  
Yuqiang Wang ◽  
Yuguang Wei ◽  
Hua Shi ◽  
Xinyu Liu ◽  
Liyuan Feng ◽  
...  
Keyword(s):  
Integer Programming ◽  
Design Methodology ◽  
Arrival Time ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Content Type ◽  
Proposed Model ◽  
Heavy Haul ◽  
Heavy Haul Railway

Purpose The purpose of this paper is to study the unit train make-up scheme for loaded direction in the heavy haul railway. Design/methodology/approach A 0-1 nonlinear integer programming model with the aim of minimizing the idling period between actual train arrival time and expected train arrival time for all loaded unit trains are proposed. Findings The proposed model is applied into a case study based on Daqin heavy haul railway. Results show that the proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway. Originality/value The proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway.

scholarly journals Crew Scheduling Considering both Crew Duty Time Difference and Cost on Urban Rail System

2016 ◽  
Vol 28 (5) ◽  
pp. 449-460 ◽  
Author(s):  
Wenliang Zhou ◽  
Xia Yang ◽  
Lianbo Deng ◽  
Jin Qin
Keyword(s):  
Ant Colony Algorithm ◽  
Programming Model ◽  
Crew Scheduling ◽  
Computational Time ◽  
Mathematical Programming Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Train Services ◽  
Duty Time

Urban rail crew scheduling problem is to allocate train services to crews based on a given train timetable while satisfying all the operational and contractual requirements. In this paper, we present a new mathematical programming model with the aim of minimizing both the related costs of crew duty and the variance of duty time spreads. In addition to iincorporating the commonly encountered crew scheduling constraints, it also takes into consideration the constraint of arranging crews having a meal in the specific meal period of one day rather than after a minimum continual service time. The proposed model is solved by an ant colony algorithm which is built based on the construction of ant travel network and the design of ant travel path choosing strategy. The performances of the model and the algorithm are evaluated by conducting case study on Changsha urban rail. The results indicate that the proposed method can obtain a satisfactory crew schedule for urban rails with a relatively small computational time.

Integrated post-disaster medical assistance team scheduling and relief supply distribution

2018 ◽  
Vol 29 (4) ◽  
pp. 1279-1305 ◽  
Author(s):  
Shengbin Wang ◽  
Feng Liu ◽  
Lian Lian ◽  
Yuan Hong ◽  
Haozhe Chen
Keyword(s):  
Programming Model ◽  
Real Data ◽  
Humanitarian Logistics ◽  
Mixed Integer ◽  
Medical Assistance ◽  
Content Type ◽  
Support Tool ◽  
Numerical Example ◽  
Proposed Model ◽  
Post Disaster

Purpose The purpose of this paper is to solve a post-disaster humanitarian logistics problem in which medical assistance teams are dispatched and the relief supplies are distributed among demand points. Design/methodology/approach A mixed integer-programming model and a two-stage hybrid metaheuristic method are developed to solve the problem. Problem instances of various sizes as well as a numerical example based on the 2016 Kyushu Earthquake in Japan are used to test the proposed model and algorithm. Findings Computational results based on comparisons with the state-of-the-art commercial software show that the proposed approach can quickly find near-optimal solutions, which is highly desirable in emergency situations. Research limitations/implications Real data of the parameters of the model are difficult to obtain. Future collaborations with organizations such as Red Cross and Federal Emergency Management Agency can be extremely helpful in collecting data in humanitarian logistics research. Practical implications The proposed model and algorithm can help governments and non-governmental organizations (NGOs) to effectively and efficiently allocate and coordinate different types of humanitarian relief resources, especially when these resources are limited. Originality/value This paper is among the first ones to consider both medical team scheduling (routing) and relief aid distribution as decision variables in the humanitarian logistics field. The contributions include developing a mathematical model and a heuristic algorithm, illustrating the model and algorithm using a numerical example, and providing a decision support tool for governments and NGOs to manage the relief resources in disasters.

Optimal project portfolio selection with reinvestment strategy considering sustainability in an uncertain environment: a multi-objective optimization approach

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammadali Zarjou ◽  
Mohammad Khalilzadeh
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Sustainability Indicators ◽  
Selection Problem ◽  
Project Portfolio ◽  
Mathematical Programming Model ◽  
Content Type ◽  
Multi Objective ◽  
Project Portfolio Selection

PurposeThis study aims to develop a model for project portfolio selection considering organizational goals such as budgets, sustainability cash flow and reinvestment strategy under an uncertain environment.Design/methodology/approachA multi-objective mathematical programming model is proposed for project selection, which takes the social, environmental and financial aspects into account as the objectives of the project portfolio selection problem. The project evaluation and selection process in one of the large capitals in the Middle East with numerous urban construction projects was considered as a real case study, in which the subjects of environmental and social sustainability are of great importance. Then, the most significant criteria for project evaluation and selection based on sustainability were identified and ranked using the fuzzy best-worst method (BWM).FindingsThe criterion of “defining clear and real objectives” was ranked first, “project investment return period” was ranked second, “minimum changes in the predicted range” was ranked third, and the other ten sustainability indicators were ranked as well. Next, the presented mathematical programming model was solved using the augmented e-constraint method. The sensitivity analysis indicated that increasing the amount of investments in projects would increase their net present value. Also, increased investment had no effect on sustainability, while decreased investment caused sustainability to not being optimal.Originality/valueThis study focuses on the impact of the amount of investments on projects, and the associated costs of sustainable projects. Further to the authors' knowledge, there has been no relevant study taking uncertainty into account. Also, very few studies proposed a mathematical programming model for the project portfolio selection problem. Moreover, this research uses the brainstorming and Delphi method to identify the sustainability indicators influencing the organization and screens the evaluation indicators. Furthermore, the weights of the evaluation indicators are determined using the fuzzy BWM based on the consistency of opinions.

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.

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bahareh Shafipour-Omrani ◽  
Alireza Rashidi Komijan ◽  
Seyed Jafar Sadjadi ◽  
Kaveh Khalili-Damghani ◽  
Vahidreza Ghezavati
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Cost Minimization ◽  
Crew Scheduling ◽  
Mixed Integer ◽  
Medium Size ◽  
Content Type ◽  
Solution Approach ◽  
Proposed Model ◽  
Crew Pairing

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

A multi-level programming model for green supplier selection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Srikant Gupta ◽  
Prasenjit Chatterjee ◽  
Morteza Yazdani ◽  
Ernesto D.R. Santibanez Gonzalez
Keyword(s):  
Fuzzy Sets ◽  
Large Scale ◽  
Social Issues ◽  
Programming Model ◽  
Global Market ◽  
Content Type ◽  
Unit Energy ◽  
Proposed Model ◽  
Interval Type

PurposeIndustrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.Design/methodology/approachIn this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.FindingsThis research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.Research limitations/implicationsThe proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.Practical implicationsThe proposed model is generic and can be applied for large-scale GSC environments with little modifications.Originality/valueNo prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.

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