Papers by Seyedmehdi Mirmohammadsadeghi
Shamsuddin Ahmed for his support, valuable suggestions and for encouraging me to keep going. I ap... more Shamsuddin Ahmed for his support, valuable suggestions and for encouraging me to keep going. I appreciate his open mindedness and vast knowledge, which he always made available for me. He has been a very generous source of knowledge and support, and a role model to follow. I am indebted to him forever. Also, I appreciate university of Malaya (UM), Kuala Lumpur, Malaysia, for supporting me financially through UMRG (RG139-12AET) research grant. In addition, I would like to thank all members in the Department of Mechanical Engineering who supported be directly or otherwise. Thanks go to my dearest parents and my brother who deserve special gratitude for their endless support. I am deeply and forever indebted to my father and mother for their love and encouragement throughout my entire life. Appropriative words could not be found to express sincere appreciation to my wife, Shima, for her endless patience, understanding, friendliness, encouragement and absolute love in all difficulties in research and living. I dedicate this thesis to her.
Green and Low-Carbon Economy
Today, the cement industry has gone through a growing trend. Achieving the country's economic... more Today, the cement industry has gone through a growing trend. Achieving the country's economic development, social development and cultural development goals is essential. However, in line with these benefits, the environmental damage caused by cement factories is inevitable. In the present research, which was carried out to reduce environmental losses, value flow mapping and simulation by Arena software were used in two stages. It was determined in the first stage using the current simulated situation and the waste and environmental pollution created. Then by redrawing the future value flow map and using experts' opinions, the amount of reduced pollution caused by some measures was estimated. Then, in the second stage a new simulation was done to evaluate the reduced environmental pollution. The results of this research showed that by using the methods mentioned above in the primary production line process of the Cement Company, about 30% of waste and pollutions were reduced.
Decision Analytics Journal
Decision Support Systems, Jan 14, 2021
Nowadays, with the increase in business complexity of and competition, organizations are looking ... more Nowadays, with the increase in business complexity of and competition, organizations are looking to agility of business. In addition, the increasing negative effects of environmental damages on human quality of life have led organizations to pay special attention to environmental issues in addition to agility. The prerequisite for achieving organizational agility with an emphasis on achieving environmental standards is the presence of the green agile workforce that besides enjoying agility characteristics, has environmental concerns. The present study attempts to introduce the green agile workforce' behaviors.
Networks and Spatial Economics, 2015
Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have... more Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered that the deterministic truck and trailer routing problem (TTRP) cannot address the prevailing demand uncertainties and/or other complexities. The purpose of this study is to expand the deterministic TTRP model by introducing stochastic demand and time window constraints to bring the TTRP model closer to reality and solve the model in a reasonable timeframe by administering the memetic algorithm (MA). This paper presents a model that can be applied in stochastic demand conditions. To employ the MA, various crossovers, mutations and local search approaches were applied. First, two experimental tests were carried out to show the validity and consistency of the MA for solving stochastic TTRPs. The results can be compared with the VRPSDTW (vehicle routing problem with stochastic demands (VRSPD) with time windows) solution obtained using large neighbourhood search (LNS) of earlier research. The average results from Tests 1 and 2 achieved by MA are 514.927 and 516.451. However, the average result obtained by LNS is 516.877. Therefore, the MA can generate results. Thus, MA is found to be suitable for solving truck and trailer routing problem(s) under stochastic demand with a time window (TTRPSDTW). Moreover, 54 benchmark instances were modified for this case and the initial feasible solutions were generated for this purpose. The solutions were significantly improved by the MA. Also, the problems were tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results obtained by MA and sensitivity analysis. Since the differences between the results are small, the MA was found to be appropriate and better for solving TTRPSDTW. The paper also gives some suggestions for further research.
Mathematical Problems in Engineering
Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have... more Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered the deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant, first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the so...
Application of memetic algorithm (MA) is considered in this work to solve the truck and trailer r... more Application of memetic algorithm (MA) is considered in this work to solve the truck and trailer routing problem (TTRP). In TTRP, some customers can be designated as vehicle customers and can be serviced either by a complete vehicle (a trailer pulling by truck) or a single truck. However, some customers are known as truck customers and have to be serviced by a single truck. To employ this algorithm, Partial-mapped crossover (PMX), various mutations and local search approaches have been applied and a truck and trailer routing problem has been solved. The results compared with the findings available in the literature. The MA used for this purpose obtained 13 best solutions out of 21, including 10 new solutions. The results obtained from the application of MA are better than other algorithms such as tabu search and simulated annealing. Therefore, application of MA is useful for solving TTRP.
Manufacturers and services providers often encounter stochastic parametric scenarios in transport... more Manufacturers and services providers often encounter stochastic parametric scenarios in transportation. Researchers have, thus far, considered deterministic truck and trailer routing problem (TTRP) that cannot address the prevailing travel time uncertainties and/or other complexities. Therefore, truck and trailer routing problem with stochastic travel times and time windows (TTRPSTTW) has practical significance. The purpose of this study is to expand the deterministic TTRP model by introducing stochastic travel times with time window constraints to bring the TTRP model closer to reality and solve the model in a reasonable timeframe by administering the simulated annealing (SA). Eighteen benchmark-instance problems have been modified for this case and solved by using these algorithms. Also the problems are tested by sensitivity analysis to understand the effects of parameters and to make a comparison between the best results obtained by SA and sensitivity analysis.
Comput. Ind. Eng., 2017
Shipper Lane Selection Problem in transportation procurement auctions.Periodicity condition in th... more Shipper Lane Selection Problem in transportation procurement auctions.Periodicity condition in the integer programming formulation.Heuristics based on the decomposition approach to solve large-scale problems.Extensive experimental analysis on benchmark test problems.Comparison with the state-of-the-art models and sensitivity analysis. In the Shipper Lane Selection Problem (SLSP) a set of lanes should be classified either to be serviced by the shippers fleet or through auction. However, it is common in real-life problems that the same lane should be served multiple times during the planning horizon. In this study, the periodicity nature of the problem is incorporated into the SLSP. Thus, a novel variant of the problem, namely the Periodic SLSP (P-SLSP) is introduced. The aim is to achieve savings on the shipper transportation costs over the extended horizon. The problem is modeled as an integer programming formulation and solved first with a general purpose software. Subsequently, th...
Journal of Human, Earth, and Future
Successful implementation of Enterprise Resource Planning (ERP) system has many benefits for orga... more Successful implementation of Enterprise Resource Planning (ERP) system has many benefits for organizations, while its failure can have irreparable damages. Identifying the critical success and failure factors in implementing these systems seems essential. Therefore, evaluating the risk of implementing the organization’s resource planning before starting the project is necessary. Failure Modes and Effects Analysis (FMEA) should be assessed and analyzed. Then, for each risk, risk management strategies are mentioned and prioritized using the TOPSIS method. After categorizing the organization’s risks in four dimensions of stakeholders, growth and learning, process and finance and ranking them based on FMEA, it was determined that the stakeholder dimension is in the first place, which shows the effectiveness of this dimension in advancing ERP implementation. The second place is given to growth and learning, which can be concluded to what extent education and knowledge management can reve...
The truck and trailer routing problem with stochastic travel and service time (TTRPSTT) is a deve... more The truck and trailer routing problem with stochastic travel and service time (TTRPSTT) is a development model of the truck and trailer routing problem (TTRP). In this case, travel and service times between customers are considered stochastic. Many researchers considered TTRP with deterministic parameters, but in real-life due to traffic congestion, different weather conditions, level of driver’s skills may be influenced by distribution technology, often travel and service times are not really deterministic between two customers and normally follow stochastic distributions. Therefore, TTRPSTT has practical significance. TTRP has been solved by different algorithms but TTRPSTT has not been addressed yet. Here multi-point simulated annealing (M-SA) is applied to solve the TTRPSTT. Forty-eight instance problems have been modified for this case and solved by using this algorithm. The purpose of the paper is to introduce and solve TTRPSTT in a reasonable time by the simulated annealing a...
Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have... more Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered the deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant, first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the so...
Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have... more Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered the deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant, first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the so...
Manufacturers and services providers often encounter stochastic parametric scenarios in
transport... more Manufacturers and services providers often encounter stochastic parametric scenarios in
transportation. Researchers have, thus far, considered deterministic truck and trailer
routing problem (TTRP) that cannot address the prevailing travel time uncertainties
and/or other complexities. Therefore, truck and trailer routing problem with stochastic
travel times and time windows (TTRPSTTW) has practical significance. The purpose of
this study is to expand the deterministic TTRP model by introducing stochastic travel
times with time window constraints to bring the TTRP model closer to reality and solve
the model in a reasonable timeframe by administering the simulated annealing (SA).
Eighteen benchmark-instance problems have been modified for this case and solved by
using these algorithms. Also the problems are tested by sensitivity analysis to
understand the effects of parameters and to make a comparison between the best results
obtained by SA and sensitivity analysis.
Manufacturers and service providers often encounter stochastic demand
scenarios. Researchers have... more Manufacturers and service providers often encounter stochastic demand
scenarios. Researchers have, thus far, considered that the deterministic truck and trailer
routing problem (TTRP) cannot address the prevailing demand uncertainties and/or
other complexities. The purpose of this study is to expand the deterministic TTRP
model by introducing stochastic demand and time window constraints to bring the
TTRP model closer to reality and solve the model in a reasonable timeframe by
administering the memetic algorithm (MA). This paper presents a model that can be
applied in stochastic demand conditions. To employ the MA, various crossovers,
mutations and local search approaches were applied. First, two experimental tests were
carried out to show the validity and consistency of the MA for solving stochastic
TTRPs. The results can be compared with the VRPSDTW (vehicle routing problem
with stochastic demands (VRSPD) with time windows) solution obtained using large
neighbourhood search (LNS) of earlier research. The average results fromTests 1 and 2
achieved by MA are 514.927 and 516.451. However, the average result obtained by
LNS is 516.877. Therefore, the MA can generate results. Thus, MA is found to be
suitable for solving truck and trailer routing problem(s) under stochastic demand with a
time window (TTRPSDTW). Moreover, 54 benchmark instances were modified for
this case and the initial feasible solutions were generated for this purpose. The
solutions were significantly improved by the MA. Also, the problems were
tested using sensitivity analysis to understand the effects of the parameters and
to make a comparison between the best results obtained by MA and sensitivity
analysis. Since the differences between the results are small, the MA was found
to be appropriate and better for solving TTRPSDTW. The paper also gives
some suggestions for further research.
Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have... more Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered the
deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other
complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP
model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the
multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose
demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant,
first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase
the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the solutions have
been tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results
obtained by three algorithms and sensitivity analysis. Since the differences between the results are insignificant, the algorithms are
found to be appropriate and relevant for solving real-world TTRPSD problem.
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Papers by Seyedmehdi Mirmohammadsadeghi
transportation. Researchers have, thus far, considered deterministic truck and trailer
routing problem (TTRP) that cannot address the prevailing travel time uncertainties
and/or other complexities. Therefore, truck and trailer routing problem with stochastic
travel times and time windows (TTRPSTTW) has practical significance. The purpose of
this study is to expand the deterministic TTRP model by introducing stochastic travel
times with time window constraints to bring the TTRP model closer to reality and solve
the model in a reasonable timeframe by administering the simulated annealing (SA).
Eighteen benchmark-instance problems have been modified for this case and solved by
using these algorithms. Also the problems are tested by sensitivity analysis to
understand the effects of parameters and to make a comparison between the best results
obtained by SA and sensitivity analysis.
scenarios. Researchers have, thus far, considered that the deterministic truck and trailer
routing problem (TTRP) cannot address the prevailing demand uncertainties and/or
other complexities. The purpose of this study is to expand the deterministic TTRP
model by introducing stochastic demand and time window constraints to bring the
TTRP model closer to reality and solve the model in a reasonable timeframe by
administering the memetic algorithm (MA). This paper presents a model that can be
applied in stochastic demand conditions. To employ the MA, various crossovers,
mutations and local search approaches were applied. First, two experimental tests were
carried out to show the validity and consistency of the MA for solving stochastic
TTRPs. The results can be compared with the VRPSDTW (vehicle routing problem
with stochastic demands (VRSPD) with time windows) solution obtained using large
neighbourhood search (LNS) of earlier research. The average results fromTests 1 and 2
achieved by MA are 514.927 and 516.451. However, the average result obtained by
LNS is 516.877. Therefore, the MA can generate results. Thus, MA is found to be
suitable for solving truck and trailer routing problem(s) under stochastic demand with a
time window (TTRPSDTW). Moreover, 54 benchmark instances were modified for
this case and the initial feasible solutions were generated for this purpose. The
solutions were significantly improved by the MA. Also, the problems were
tested using sensitivity analysis to understand the effects of the parameters and
to make a comparison between the best results obtained by MA and sensitivity
analysis. Since the differences between the results are small, the MA was found
to be appropriate and better for solving TTRPSDTW. The paper also gives
some suggestions for further research.
deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other
complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP
model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the
multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose
demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant,
first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase
the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the solutions have
been tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results
obtained by three algorithms and sensitivity analysis. Since the differences between the results are insignificant, the algorithms are
found to be appropriate and relevant for solving real-world TTRPSD problem.
transportation. Researchers have, thus far, considered deterministic truck and trailer
routing problem (TTRP) that cannot address the prevailing travel time uncertainties
and/or other complexities. Therefore, truck and trailer routing problem with stochastic
travel times and time windows (TTRPSTTW) has practical significance. The purpose of
this study is to expand the deterministic TTRP model by introducing stochastic travel
times with time window constraints to bring the TTRP model closer to reality and solve
the model in a reasonable timeframe by administering the simulated annealing (SA).
Eighteen benchmark-instance problems have been modified for this case and solved by
using these algorithms. Also the problems are tested by sensitivity analysis to
understand the effects of parameters and to make a comparison between the best results
obtained by SA and sensitivity analysis.
scenarios. Researchers have, thus far, considered that the deterministic truck and trailer
routing problem (TTRP) cannot address the prevailing demand uncertainties and/or
other complexities. The purpose of this study is to expand the deterministic TTRP
model by introducing stochastic demand and time window constraints to bring the
TTRP model closer to reality and solve the model in a reasonable timeframe by
administering the memetic algorithm (MA). This paper presents a model that can be
applied in stochastic demand conditions. To employ the MA, various crossovers,
mutations and local search approaches were applied. First, two experimental tests were
carried out to show the validity and consistency of the MA for solving stochastic
TTRPs. The results can be compared with the VRPSDTW (vehicle routing problem
with stochastic demands (VRSPD) with time windows) solution obtained using large
neighbourhood search (LNS) of earlier research. The average results fromTests 1 and 2
achieved by MA are 514.927 and 516.451. However, the average result obtained by
LNS is 516.877. Therefore, the MA can generate results. Thus, MA is found to be
suitable for solving truck and trailer routing problem(s) under stochastic demand with a
time window (TTRPSDTW). Moreover, 54 benchmark instances were modified for
this case and the initial feasible solutions were generated for this purpose. The
solutions were significantly improved by the MA. Also, the problems were
tested using sensitivity analysis to understand the effects of the parameters and
to make a comparison between the best results obtained by MA and sensitivity
analysis. Since the differences between the results are small, the MA was found
to be appropriate and better for solving TTRPSDTW. The paper also gives
some suggestions for further research.
deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other
complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP
model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the
multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose
demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant,
first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase
the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the solutions have
been tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results
obtained by three algorithms and sensitivity analysis. Since the differences between the results are insignificant, the algorithms are
found to be appropriate and relevant for solving real-world TTRPSD problem.