A particle swarm optimisation for the no-wait flow shop problem with due date constraints.

Peer ReviewedThis 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

Studying the effect of server side constraints on the makespan of the no-wait flow shop problem with sequence dependent setup times.

Peer ReviewedThis 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

Studying the impact of merged and divided storage policies on the profitability of a remanufacturing system with deteriorating revenues

Peer ReviewedMerging 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

On the exact solution of the no-wait flow shop problem with due date constraints

Peer ReviewedThis 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

Increasing Supply Chain Resiliency Through Equilibrium Pricing and Stipulating Transportation Quota Regulation

Supply chain disruption can occur for a variety of reasons, including natural disasters or market dynamics for which resilient strategies should be designed. If the disruption is profound and with dire consequences for the economy, it calls for the regulator's intervention to minimize the impact for the betterment of the society. This paper considers a shipping company with limited capacity which will ship a group of products with heterogeneous transportation and production costs and prices, and investigates the minimum quota regulation on transportation amounts stipulated by the government. An interesting example can happen in North American rail transportation market, where the rail capacity is used for a variety of products and commodities such as oil and grains. Similarly, in Europe supply chain of grains produced in Ukraine is disrupted by the Ukraine war and the blockade of sea transportation routes, which puts pressure on rail transportation capacity of Ukraine and its neighboring countries to the west that needs to be shared for shipping a variety of products including grains, military, and humanitarian supplies. Such situations require a proper execution of government intervention for effective management of the limited transportation capacity to avoid the rippling effects throughout the economy. We propose mathematical models and solutions for the market players and the government in a Canadian case study. Subsequently, the conditions that justify government intervention are identified, and an algorithm to obtain the optimum minimum quotas is presented

Studying the Reasons for Delay and Cost Overrun in Construction Projects: The Case of Iran

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 surve

An efficient tabu algorithm for the single row facility layout problem

The general goal of the facility layout problem is to arrange a given number of facilities to minimize the total cost associated with the known or projected interactions between them. One of the special classes of the facility layout problem is the Single Row Facility Layout Problem (SRFLP), which consists of finding an optimal linear placement of rectangular facilities with varying dimensions on a straight line. This paper first presents and proves a theorem to find the optimal solution of a special case of SRFLP. The results obtained by this theorem prove to be very useful in reducing the computational efforts when a new algorithm based on tabu search for the SRFLP is proposed in this paper. Computational results of the proposed algorithm on benchmark problems show the greater efficiency of the algorithm compared to the other heuristics for solving the SRFLP.Facilities planning and design Linear ordering problem Tabu search Integer programming

An efficient hybrid algorithm for the two-machine no-wait flow shop problem with separable setup times and single server

We consider the two-machine no-wait flow shop problem with separable setup times and single server side constraints, and makespan as the performance measure. This problem is strongly NP-hard. A mathematical model of the problem is developed and a number of propositions are proven for the special cases. Furthermore, a hybrid algorithm of variable neighbourhood search (VNS) and Tabu search (TS) is proposed for the generic case. For evaluation, a number of test problems with small instances are generated and solved to optimality. Computational results show that the proposed algorithm is able to reproduce the optimal solutions of all of the small-instance test problems. For larger instances, proposed solutions are compared with the results of the famous two-opt algorithm as well as a lower bound that we develop in this paper. This comparison demonstrates the efficiency of the algorithm to find good-quality solutions. [Received 25 November 2009; Revised 26 February 2010; Revised 19 March 2010; Accepted 20 March 2010]two machine flow shops; no-wait; separable setup times; makespan; single server; variable neighbourhood search; tabu search.

Simulation-based Reactive Scheduling for the Concrete Delivery Problem Using Actual Data and Delivery Site Constraints

Construction project control requires reliable delivery schedules. The delivery schedule decisions examine the information available regarding material, equipment, concrete quality controls, delivery times, construction site delays and other external factors such as congestion and traffic conditions. This paper investigates a sector-specific problem for a non-fixed construction project (highway sector) to generate a daily concrete delivery schedule. The objectives of the delivery schedule are to maximize daily throughput and minimize queues at the construction site by considering constraints from the concrete supplier and construction site — comparison of Simulation-based reactive schedule with current manual practice in industry demonstrates 13% throughput improvement by having deliveries just-in-time

A Hybrid Method to Predict Human Action Actors in Accounting Information System

Recent literature shows that adopting an accounting information system (AIS) can lead to better decision-making, planning, efficiency and on-time management control, and organisational functionality. However, the impact of AIS implementation on role creation in the organisation is unclear. With the digital transformation of AIS and daily advances in machine learning and other innovative technologies, it is also unclear how these changes interact with human roles in organisations and which AIS components are considered essential. This paper addresses the above issues by applying the actor-network theory to examine the impact of deep machine learning modules in predicting the human actor roles in accounting information systems in organisations. We targeted 120 human actors and examined the influence of deep machine learning modules in predicting 11 personnel and professional features of human actors, based on multivariate statistical analysis. Our findings show that two human factors (familiarity with accounting information and time spent on becoming familiar with it) are the most influential elements that can predict the human actor roles in accounting information systems in organisations. So, human and non-human actors are both essential parts of an integrated AIS that must be considered. The current literature has focused on the AIS structure with less on the interaction between human and non-human actors. One of the main contributions of this study is providing evidence that AIS heavily relies on its human and non-human actors to form a coherent and united AIS network to promote AIS management strategies. The practical implication of the results is that investing in either technology or human resources alone is not enough to achieve the best productivity and performance in organisations. Instead, there must be a balance between human and non-human actors