Finance and Management Science
http://hdl.handle.net/10388/8654
Sun, 16 Jun 2019 17:07:35 GMT2019-06-16T17:07:35ZContinuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation
http://hdl.handle.net/10388/11570
Continuous process improvement implementation framework using multi-objective genetic algorithms and discrete event simulation
Kang, Parminder; Bhatti, Rajbir
Purpose
Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems.
Design/methodology/approach
This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources.
Findings
Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework.
Originality/value
Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10388/115702018-01-01T00:00:00ZStudying the impact of merged and divided storage policies on the profitability of a remanufacturing system with deteriorating revenues
http://hdl.handle.net/10388/11497
Studying the impact of merged and divided storage policies on the profitability of a remanufacturing system with deteriorating revenues
Samarghandi, Hamed
Merging capacity for a remanufacturing system is studied in this paper. In the system under study, there are two streams for returns and each stream has its dedicated processing line. However, the storage space is merged between the streams. Two strategies are investigated and compared in this paper. The first strategy is to divide the storage space between the two streams in the way that each type of return has its predetermined space in the storage area (divided capacity). In the second strategy, storage space is not split between the two streams and each unit of return, independent of its type, is admitted if there is vacant space (merged capacity). In both strategies, the value of remanufactured products decreases over time by a known factor called the decay rate. Mathematical models to maximize the total profit in each strategy is presented and also verified by a simulation model. From a practical point of view, selecting the correct strategy is an important decision for the remanufacturers because choosing the wrong policy leads to lost profits. Numerical experiments reveal that neither of the scenarios is always preferred to the other one and the choice of the optimal strategy depends on the parameters' values and product types. For instance, increasing the remanufacturing cost of the superior product, or increasing the sale price of the inferior product make the merged storage strategy more desirable. On the contrary, increasing the remanufacturing cost of the inferior product, or increasing the sale price of the superior product make the divided storage policy more appealing.
Wed, 01 Nov 2017 00:00:00 GMThttp://hdl.handle.net/10388/114972017-11-01T00:00:00ZA particle swarm optimisation for the no-wait flow shop problem with due date constraints.
http://hdl.handle.net/10388/11496
A particle swarm optimisation for the no-wait flow shop problem with due date constraints.
Samarghandi, Hamed
This paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10388/114962016-01-01T00:00:00ZOn the exact solution of the no-wait flow shop problem with due date constraints
http://hdl.handle.net/10388/11495
On the exact solution of the no-wait flow shop problem with due date constraints
Samarghandi, Hamed; Behroozi, Mehdi
This paper deals with the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, the jobs should be completed before their respective due dates; due date constraints are dealt with as hard constraints. The considered performance criterion is makespan. The problem is strongly NP-hard. This paper develops a number of distinct mathematical models for the problem based on different decision variables. Namely, a mixed integer programming model, two quadratic mixed integer programming models, and two constraint programming models are developed. Moreover, a novel graph representation is developed for the problem. This new modeling technique facilitates the investigation of some of the important characteristics of the problem; this results in a number of propositions to rule out a large number of infeasible solutions from the set of all possible permutations. Afterward, the new graph representation and the resulting propositions are incorporated into a new exact algorithm to solve the problem to optimality. To investigate the performance of the mathematical models and to compare them with the developed exact algorithm, a number of test problems are solved and the results are reported. Computational results demonstrate that the developed algorithm is significantly faster than the mathematical models.
Mon, 01 May 2017 00:00:00 GMThttp://hdl.handle.net/10388/114952017-05-01T00:00:00ZStudying the effect of server side constraints on the makespan of the no-wait flow shop problem with sequence dependent setup times.
http://hdl.handle.net/10388/11494
Studying the effect of server side constraints on the makespan of the no-wait flow shop problem with sequence dependent setup times.
Samarghandi, Hamed
This paper deals with the problem of scheduling the no-wait flow-shop system with sequence-dependent set-up times and server side-constraints. No-wait constraints state that there should be no waiting time between consecutive operations of jobs. In addition, sequence-dependent set-up times are considered for each operation. This means that the set-up time of an operation on its respective machine is dependent on the previous operation on the same machine. Moreover, the problem consists of server side-constraints i.e. not all machines have a dedicated server to prepare them for an operation. In other words, several machines share a common server. The considered performance measure is makespan. This problem is proved to be strongly NP-Hard. To deal with the problem, two genetic algorithms are developed. In order to evaluate the performance of the developed frameworks, a large number of benchmark problems are selected and solved with different server limitation scenarios. Computational results confirm that both of the proposed algorithms are efficient and competitive. The developed algorithms are able to improve many of the best-known solutions of the test problems from the literature. Moreover, the effect of the server side-constraints on the makespan of the test problems is explained using the computational results.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10388/114942015-01-01T00:00:00ZStudying the Reasons for Delay and Cost Overrun in Construction Projects: The Case of Iran.
http://hdl.handle.net/10388/11493
Studying the Reasons for Delay and Cost Overrun in Construction Projects: The Case of Iran.
Samarghandi, Hamed; Mousavi, Seyed; Taabayan, Pouria; Mir Hashemi, Ahmad; Willoughby, Keith
Undesirable delays in construction projects impose excessive costs and precipitate exacerbated durations. Investigating Iran, a developing Middle Eastern country, this paper focuses on the reasons for construction project delays. We conducted several interviews with owners, contractors, consultants, industry experts and regulatory bodies to accurately ascertain specific delay factors. Based on the results of our industry surveys, a statistical model was developed to quantitatively determine each delay factor's importance in construction project management. The statistical model categorises the delay factors under four major classes and determines the most significant delay factors in each class: owner defects, contractor defects, consultant defects and law, regulation and other general defects. The most significant delay factors in the owner defects category are lack of attention to inflation and inefficient budgeting schedule. In the contractor defects category, the most significant delay factors are inaccurate budgeting and resource planning, weak cash flow and inaccurate pricing and bidding. As for the consultant defects delay factors such as inaccurate first draft and inaccuracies in technical documents have the most contribution to the defects. On the other hand, outdated standard mandatory items in cost lists, outdated mandatory terms in contracts and weak governmental budgeting are the most important delay factors in the law, regulation and other general defects. Moreover, regression models demonstrate that a significant difference exists between the initial and final project duration and cost. According to the models, the average delay per year is 5.9 months and the overall cost overrun is 15.4%. Our findings can be useful in at least two ways: first, resolving the root causes of particularly important delay factors would significantly streamline project performance and second, the regression models could assist project managers and companies with revising initial timelines and estimated costs. This study does not consider all types of construction projects in Iran: the scope is limited to certain types of private and publicly funded projects as will be described. The data for this study has been gathered through a detailed questionnaire survey.
Sun, 31 Jul 2016 00:00:00 GMThttp://hdl.handle.net/10388/114932016-07-31T00:00:00Z