Template-Type: ReDIF-Article 1.0 Author-Name: Zohra Bouzidi Author-X-Name-First: Zohra Author-X-Name-Last: Bouzidi Author-Name: Labib Sadek Terrissa Author-X-Name-First: Labib Sadek Author-X-Name-Last: Terrissa Author-Name: Noureddine Zerhouni Author-X-Name-First: Noureddine Author-X-Name-Last: Zerhouni Author-Name: Soheyb Ayad Author-X-Name-First: Soheyb Author-X-Name-Last: Ayad Title: QoS of cloud prognostic system: application to aircraft engines fleet Abstract: Recently, prognostics and health management (PHM) solutions are increasingly implemented in order to complete maintenance activities. The prognostic process in industrial maintenance is the main step to predict failures before they occur by determining the remaining useful life (RUL) of the equipment. However, it also poses challenges such as reliability, availability, infrastructure and physics servers. To address these challenges, this paper investigates a cloud-based prognostic system of an aircraft engine based on artificial intelligence methods. We design and implement an architecture that defines an approach that is prognostic as a service (Prognostic aaS) using a data-driven approach. This approach will provide a suitable and efficient PHM solution as a service via internet, on the demand of a client, in accordance with a service level agreement (SLA) contract drawn up in advance to ensure a better quality of service and pay this service per use (pay as you go). We estimated the RUL of aircraft engines fleet by implementing three techniques. Next, we studied the performance of this system; the efficient method was concluded. In addition, we discussed the quality of service (QoS) for the cloud prognostic application according to the factors of quality. [Received: 19 May 2018; Revised: 10 August 2018; Revised: 31 August 2018; Revised: 21 March 2019; Accepted: 28 March 2019] Journal: European J. of Industrial Engineering Pages: 34-57 Issue: 1 Volume: 14 Year: 2020 Keywords: prognostics and health management; PHM; remaining useful life; RUL; prognostic as a service; Prognostic aaS; cloud computing; artificial intelligence; measure performance; quality of service; QoS. File-URL: http://www.inderscience.com/link.php?id=105080 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:1:p:34-57 Template-Type: ReDIF-Article 1.0 Author-Name: J. Carlos García-Díaz Author-X-Name-First: J. Carlos Author-X-Name-Last: García-Díaz Author-Name: Alexander Pulido-Rojano Author-X-Name-First: Alexander Author-X-Name-Last: Pulido-Rojano Title: Performance analysis and optimisation of new strategies for the setup of a multihead weighing process Abstract: This paper highlights the benefits of multihead weighing, a packaging process based on the sum of weights of several individual hoppers wherein total weight of the packed product must be close to a specified target weight while complying with applicable regulations. The paper details into performance analysis and optimisation of new strategies for setting-up the process to achieve an optimal configuration of the machine. Three strategies, designed to optimise the packaging process, are analysed and compared in terms of supplying products to the hoppers. A factorial design of the experimental model is exploited to predict the measures of performance as a function of a variety of control settings. Results of the numerical experiments are used to analyse the sources of variability and to identify the optimum operating conditions for the multihead weigher. Therefore, the findings of this paper will benefit both manufacturer and users of the multihead weigher machine. [Received: 15 September 2017; Revised: 15 November 2018; Revised: 20 March 2019; Accepted: 14 April 2019] Journal: European J. of Industrial Engineering Pages: 58-84 Issue: 1 Volume: 14 Year: 2020 Keywords: packing; multihead weighing process; Six Sigma process; optimal operating conditions; variability reduction. File-URL: http://www.inderscience.com/link.php?id=105081 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:1:p:58-84 Template-Type: ReDIF-Article 1.0 Author-Name: Rodrigo Barbosa-Correa Author-X-Name-First: Rodrigo Author-X-Name-Last: Barbosa-Correa Author-Name: Alcides Santander-Mercado Author-X-Name-First: Alcides Author-X-Name-Last: Santander-Mercado Author-Name: María Jubiz-Diaz Author-X-Name-First: María Author-X-Name-Last: Jubiz-Diaz Author-Name: Ricardo Rodríguez-Ramos Author-X-Name-First: Ricardo Author-X-Name-Last: Rodríguez-Ramos Title: Establishing call-centre staffing levels using aggregate planning and simulation approach Abstract: This paper presents an approach for determining the personnel capacity levels of a call centre for a telecommunication company. The objective is to determine the staffing levels required to meet a desired service level at a minimum cost. First, data of the number of different requests were analysed to estimate hourly workload requirements. Then, an aggregate planning model were implemented to obtain an initial solution of the staffing levels considering workforce costs, service level, personnel hiring/migration and work supplements. These results were input of a discrete-event simulation model to assess the system performance based on queuing characteristics, demand variability and resources utilisation. Alternative schedules and capacity levels were evaluated to outperform current service quality and a better match with the demand variability. Proposed options provided better results with lower waiting times and more balanced resource utilisation. The approach is useful for planning capacity levels in projects and locating new centres. [Submitted: 18 June 2018; Accepted: 15 March 2019] Journal: European J. of Industrial Engineering Pages: 1-33 Issue: 1 Volume: 14 Year: 2020 Keywords: staffing; personnel scheduling; call centres; aggregated planning; discrete-event simulation. File-URL: http://www.inderscience.com/link.php?id=105083 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:1:p:1-33 Template-Type: ReDIF-Article 1.0 Author-Name: Armagan Bayram Author-X-Name-First: Armagan Author-X-Name-Last: Bayram Author-Name: Xi Chen Author-X-Name-First: Xi Author-X-Name-Last: Chen Title: Optimising teams and the outcomes of surgery Abstract: The outcomes of robotic surgery involve nonlinear interactions of many factors, including patient-related and surgical team-related elements. In robotic surgery, not only the surgeon but also all team members play an important role in determining surgery outcomes. Therefore, it is important to study optimal surgical team configuration decisions. In this study, we investigate regression models for accurate predictions of surgical outcomes by analysing robotic surgery data. We further develop an optimisation model to investigate the optimal team configuration decisions by considering two separate objectives: 1) to minimise the maximum operating room occupation time; 2) to minimise the average operating room occupation time. In our numerical analyses, we compare the optimal team configuration decisions with the current configuration decisions and show that the optimal team allocation decision can result in a 17% decrease in operating room occupation time. Our results suggest that efforts for reducing operating room occupation time should focus on increasing the experience of surgery team members, e.g., via running training programs. [Submitted: 10 September 2018; Accepted: 31 May 2019] Journal: European J. of Industrial Engineering Pages: 127-145 Issue: 1 Volume: 14 Year: 2020 Keywords: decision making; team experience; robotic surgery; surgical team optimisation. File-URL: http://www.inderscience.com/link.php?id=105084 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:1:p:127-145 Template-Type: ReDIF-Article 1.0 Author-Name: Harun Öztürk Author-X-Name-First: Harun Author-X-Name-Last: Öztürk Title: Economic order quantity models for the shipment containing defective items with inspection errors and a sub-lot inspection policy Abstract: This paper assumes that there may be some defective items in the various lots of an ordered shipment and chooses a sub-lot inspection policy. Another assumption that this paper makes is that a shipment is sent by a distant supplier and, therefore, the replacement of the defective items is not economical if an additional order is given to the same supplier. This paper incorporates misclassification errors, which are of two types: type 1 and type 2. In order to deal with the received defective items in the shipment, two cases are discussed in this study. The first case is to send them back to the repair shop to be reworked, whereas in the second case, those defective items are sold and replaced with perfect items by buying at a higher cost from a local supplier. For each case, a mathematical model is developed, and an example is solved. The results show a close connection between the optimal order size and sample size, which can be adjusted to maximise the total profit. The results also indicate that the local purchase of replacements for defective items tends to produce greater total profit than reworking them. [Received: 26 June 2018; Accepted: 17 May 2019] Journal: European J. of Industrial Engineering Pages: 85-126 Issue: 1 Volume: 14 Year: 2020 Keywords: economic order quantity; EOQ; defective items; sub-lot inspection; inspection errors; rework; emergency purchase. File-URL: http://www.inderscience.com/link.php?id=105085 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:1:p:85-126 Template-Type: ReDIF-Article 1.0 Author-Name: Augustyn Lorenc Author-X-Name-First: Augustyn Author-X-Name-Last: Lorenc Author-Name: Maciej Szkoda Author-X-Name-First: Maciej Author-X-Name-Last: Szkoda Author-Name: Andrzej Szarata Author-X-Name-First: Andrzej Author-X-Name-Last: Szarata Author-Name: Ilona Jacyna-Gołda Author-X-Name-First: Ilona Author-X-Name-Last: Jacyna-Gołda Title: Evaluation of the effectiveness of methods and criteria for product classification in the warehouse Abstract: The paper presents an algorithm for calculating the approximate picking time. In order to perform a simulation, the PickupSimulo software was developed. For this purpose, the PHP language and MySQL relational databases were used. The PickupSimulo makes it possible to define the warehouse topology, solve the product allocation problem (PAP) based on pre-defined criteria and calculate an approximate time of the picking process for that product layout. The warehouse under analysis enables the stocking of over 22,000 pallets. Two variants were analysed. In the first one, the product weight does not matter, whilst in the other the picker must make sure the lightest products are placed at the top of the logistic unit. Such an approach reduces the risk of damaging light products by heavy ones. The research results show that the presented method enables a reduction of the total warehouse costs by 10–16%. [Received: 18 August 2017; Revised: 20 May 2018; Revised: 18 December 2018; Accepted: 20 May 2019]. Journal: European J. of Industrial Engineering Pages: 147-164 Issue: 2 Volume: 14 Year: 2020 Keywords: product classification; ABC analysis; XYZ analysis; COI index; sensitivity analysis; order picking efficiency. File-URL: http://www.inderscience.com/link.php?id=105692 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:2:p:147-164 Template-Type: ReDIF-Article 1.0 Author-Name: Hojat Nabovati Author-X-Name-First: Hojat Author-X-Name-Last: Nabovati Author-Name: Hassan Haleh Author-X-Name-First: Hassan Author-X-Name-Last: Haleh Author-Name: Behnam Vahdani Author-X-Name-First: Behnam Author-X-Name-Last: Vahdani Title: Multi-objective invasive weeds optimisation algorithm for solving simultaneous scheduling of machines and multi-mode automated guided vehicles Abstract: In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem, considering the transferring of jobs between different machines using a limited number of multi-mode automated guided vehicles. Since the model is strictly NP-hard, and because objectives contradict each other, a meta-heuristic algorithm called 'multi-objective invasive weeds optimisation algorithm' with a new chromosome structure which guarantees the feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms namely 'non-dominated sorting genetic algorithm' and 'multi-objective particle swarm optimisation algorithm' are applied to validate the solutions obtained by the developed multi-objective invasive weeds optimisation algorithm. A certain method was applied to select the algorithm with the best performance. The result of ranking the algorithms indicated that the developed multi-objective invasive weeds optimisation algorithm had the best performance in terms of solving the mentioned problems. [Received: 7 January 2017; Revised: 30 December 2017; Revised: 17 August 2018; Revised: 22 January 2019; Accepted: 26 July 2019] Journal: European J. of Industrial Engineering Pages: 165-188 Issue: 2 Volume: 14 Year: 2020 Keywords: MOIWO; AGV; scheduling; machines scheduling; job shop scheduling; simultaneous scheduling; invasive weeds optimisation; industrial engineering. File-URL: http://www.inderscience.com/link.php?id=105696 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:2:p:165-188 Template-Type: ReDIF-Article 1.0 Author-Name: Mukund Nilakantan Janardhanan Author-X-Name-First: Mukund Nilakantan Author-X-Name-Last: Janardhanan Author-Name: Peter Nielsen Author-X-Name-First: Peter Author-X-Name-Last: Nielsen Title: Optimisation of cost efficient robotic assembly line using metaheuristic algorithms Abstract: Robotic assembly lines (RALs) are utilised due to the flexibility it provides to the overall production system. Industries mainly focus on reducing the operation costs involved. From the literature survey it can be seen that only few research has been reported in the area of cost related optimisation in RALs. This paper focuses on proposing a new model in RALs with the main objective of maximising line efficiency by minimising total assembly line cost. The proposed model can be used production managers to balance a RAL in an efficient manner. Since simple assembly line balancing problem is classified as NP-hard, proposed problem due to additional constraints also falls under the same category. Particle swarm optimisation (PSO) and differential evolution (DE) are applied as the optimisation tool to solve this problem. The performances of this proposed algorithm are tested on a set of reported benchmark problems. From the comparative study, it is found that the proposed DE algorithm obtain better solutions for the majority of the problems tested. [Received: 2 July 2018; Revised: 16 December 2018; Revised: 8 May 2019; Accepted: 2 August 2019] Journal: European J. of Industrial Engineering Pages: 247-264 Issue: 2 Volume: 14 Year: 2020 Keywords: robotic assembly line balancing; RALB; cost efficient assembly line; line efficiency; metaheuristics. File-URL: http://www.inderscience.com/link.php?id=105698 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:2:p:247-264 Template-Type: ReDIF-Article 1.0 Author-Name: Nabil Nahas Author-X-Name-First: Nabil Author-X-Name-Last: Nahas Title: Buffer allocation, equipment selection and line balancing optimisation in unreliable production lines Abstract: This paper presents an integrated optimisation model to simultaneously solve buffer allocation, equipment selection and line balancing problems in unreliable production line systems. The considered unreliable serial production line consists of <i>m</i> workstations and <i>m</i> − 1 intermediate buffers. The objective is to maximise the system throughput level. A decomposition method is used to estimate the production line throughput. The decision variables in the formulated optimal design problem are buffer levels, types of equipment and the sets of tasks assigned to the workstations. An efficient algorithm, based on the nonlinear threshold accepting algorithm (NLTA) is proposed to solve this problem. The efficiency of the proposed approach is compared to existing algorithms and first tested on a simple assembly line balancing type-2 problem (SALB-2). Here the objective is to minimise the cycle time with a fixed number of workstations. In the second numerical experiment, the integrated model is solved using the NLTA, and its performance is compared to that of the great deluge algorithm (GDA) through several numerical examples. [Received: 9 June 2018; Revised: 15 September 2018; Revised: 18 April 2019; Accepted: 2 August 2019] Journal: European J. of Industrial Engineering Pages: 217-246 Issue: 2 Volume: 14 Year: 2020 Keywords: line balancing; buffer allocation problem; equipment selection; nonlinear threshold algorithm; great deluge algorithm. File-URL: http://www.inderscience.com/link.php?id=105703 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:2:p:217-246 Template-Type: ReDIF-Article 1.0 Author-Name: Kamal Deep Author-X-Name-First: Kamal Author-X-Name-Last: Deep Title: Machine cell formation for dynamic part population considering part operation trade-off and worker assignment using simulated annealing-based genetic algorithm Abstract: In this study, an integrated mathematical model for the cell formation problem is proposed considering the dynamic production environment. The proposed model yields, manufacturing cells, part families and worker's assignment simultaneously by allowing a cubic search space of 'machine-part-worker' in the CMS. The resources are aggregated into manufacturing cells based on the optimal process route among the user specified multiple routes. The model interprets flexibility in the processing of subsets of a part operation sequence in the different production mode (internal production/subcontracting part operation). It is a tangible advantage during unavailability of worker and unexpected machine break down occurring in the real world. The proposed cell formation problem has been solved by using a simulated annealing-based genetic algorithm (SAGA). The algorithm imparts synergy effect to improve intensification, diversification in the cubic search space and increases the possibility of achieving near-optimum solutions. To evaluate the computational performance of the proposed approach the algorithm is tested on a number of randomly generated instances. The results substantiate the efficiency of the proposed approach by minimising overall cost. [Received: 17 August 2018; Accepted: 28 July 2019] Journal: European J. of Industrial Engineering Pages: 189-216 Issue: 2 Volume: 14 Year: 2020 Keywords: dynamic cellular manufacturing systems; worker assignment; multiple process route; system reconfiguration; part operation trade-off; subcontracting part operation; simulated annealing-based genetic algorithm. File-URL: http://www.inderscience.com/link.php?id=105720 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:2:p:189-216 Template-Type: ReDIF-Article 1.0 Author-Name: Shima Pashapour Author-X-Name-First: Shima Author-X-Name-Last: Pashapour Author-Name: Ali Azadeh Author-X-Name-First: Ali Author-X-Name-Last: Azadeh Author-Name: Ali Bozorgi-Amiri Author-X-Name-First: Ali Author-X-Name-Last: Bozorgi-Amiri Author-Name: Abbas Keramati Author-X-Name-First: Abbas Author-X-Name-Last: Keramati Author-Name: Seyed Farid Ghaderi Author-X-Name-First: Seyed Farid Author-X-Name-Last: Ghaderi Title: A resource allocation model to choose the best portfolio of economic resilience plans: a possibilistic stochastic programming model Abstract: Economic resilience is defined as a tool capable of reducing the losses caused by disasters. It can be defined in two major concepts. Static economic resilience is the effective allocation of available resources and dynamic economic resilience refers to accelerating the recovery process through the repair and rebuilding of the capital stock. In this research, the performance of a petrochemical plant in the face of crisis is investigated. For this, a bi-objective mathematical model that considers cost and resilience capability as objective functions is developed to choose the best portfolio of static and dynamic plans. To solve the mathematical model, a weighted augmented <i>ε</i>-constraint method and a multi-stage possibilistic stochastic programming (MSPSP) approach are employed. The numerical results showed that the proposed approach is effective in optimising the performance of a petrochemical plant in facing crisis situations and in choosing the best portfolio of economic resilience plans. [Received: 11 January 2019; Revised: 9 July 2019; Revised: 13 August 2019; Accepted: 13 August 2019] Journal: European J. of Industrial Engineering Pages: 301-334 Issue: 3 Volume: 14 Year: 2020 Keywords: economic resilience; resilience capability; multi-stage possibilistic stochastic programming; MSPSP; petrochemical plant; resource allocation. File-URL: http://www.inderscience.com/link.php?id=107669 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:3:p:301-334 Template-Type: ReDIF-Article 1.0 Author-Name: Mehmet Kabak Author-X-Name-First: Mehmet Author-X-Name-Last: Kabak Author-Name: Eren Özceylan Author-X-Name-First: Eren Author-X-Name-Last: Özceylan Author-Name: Mehmet Erbaş Author-X-Name-First: Mehmet Author-X-Name-Last: Erbaş Author-Name: Cihan Çetinkaya Author-X-Name-First: Cihan Author-X-Name-Last: Çetinkaya Title: A multi-criteria spatial analysis using GIS to evaluate potential sites for a new border gate on Turkey's Syria frontier Abstract: After the internal disturbance in Syria in 2011, many Syrian refugees migrated to Turkey progressively, and the Turkish Government provided humanitarian aid to people in Syria. These incidents caused a huge amount of density on current border gates. Also, increasing potential terrorist attacks and growing frontier infringements also create a need for a new border gate on Turkey's Syria frontier. Thus, a four-step hybrid solution approach is developed for this problem. This approach starts with determination of selection criteria; then, the spatial database of these criteria is created by using a geographical information system. In the third step, the DEMATEL technique is applied to assign importance levels to the criteria. Lastly, MULTIMOORA technique is used to rank the potential sites. The results indicate that, recommended potential sites are more suitable than current border gates. This paper can serve as a scientific-base while selecting the optimal site for border gates. [Received: 8 February 2019; Revised: 1 July 2019; Accepted: 7 August 2019] Journal: European J. of Industrial Engineering Pages: 265-300 Issue: 3 Volume: 14 Year: 2020 Keywords: border check-point; GIS; multi-criteria decision making; MCDM; DEMATEL; MULTIMOORA; site location; Turkey; Syria. File-URL: http://www.inderscience.com/link.php?id=107670 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:3:p:265-300 Template-Type: ReDIF-Article 1.0 Author-Name: Hadis Derikvand Author-X-Name-First: Hadis Author-X-Name-Last: Derikvand Author-Name: Seyed Mohammad Hajimolana Author-X-Name-First: Seyed Mohammad Author-X-Name-Last: Hajimolana Author-Name: Armin Jabbarzadeh Author-X-Name-First: Armin Author-X-Name-Last: Jabbarzadeh Author-Name: Seyed Esmaeil Najafi Author-X-Name-First: Seyed Esmaeil Author-X-Name-Last: Najafi Title: A robust stochastic bi-objective model for blood inventory-distribution management in a blood supply chain Abstract: Providing blood units in a blood supply chain should be effective, appropriate and well-organised since it directly affects the health of individuals, and if not provided promptly, can even lead to the death of patients. This study presents a robust stochastic bi-objective programming model for an inventory-distribution problem in a blood supply chain, the first objective of which attempts to minimise the total number of shortages and wastages and the second objective maximises the connection between two different types of hospitals. The blood supply chain under investigation includes one blood centre, type-1 and type-2 hospitals and patients. Mathematical approximations are employed to remove the nonlinear terms, and a hybrid solution approach, combining the <i>ε</i>-constraint and the Lagrangian relaxation method, is applied to solve the proposed bi-objective model. Finally, the model is implemented and analysed using the data inspired by a real case study in Iran to show its potential applicability [Received: 24 September 2018; Revised: 15 June 2019; Revised: 1 September 2019; Accepted: 1 September 2019] Journal: European J. of Industrial Engineering Pages: 369-403 Issue: 3 Volume: 14 Year: 2020 Keywords: blood supply chain; robust programming approach; ε-constraint; Lagrangian relaxation approach; blood inventory-distribution management. File-URL: http://www.inderscience.com/link.php?id=107676 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:3:p:369-403 Template-Type: ReDIF-Article 1.0 Author-Name: Abdollah Abdi Author-X-Name-First: Abdollah Author-X-Name-Last: Abdi Author-Name: Sharareh Taghipour Author-X-Name-First: Sharareh Author-X-Name-Last: Taghipour Title: A Bayesian networks approach to fleet availability analysis considering managerial and complex causal factors Abstract: Availability analysis of a fleet of assets requires modelling uncertainty sources that affect equipment reliability and maintainability. These uncertainties include complex, managerial causalities and risks which have been seldom examined in the asset management literature. The objective of this study is to measure the reliability, maintainability and availability of a fleet, considering the effect of common causal factors and extremely rare or previously unobserved events. We develop a fully probabilistic availability analysis model using hybrid Bayesian networks (BNs), to capture managerial, organisational and environmental causal factors that influence failure or repair rate, as well as those that affect both failure and repair rates simultaneously. The proposed methodology has been found more accurate in forecasting failure rate, repair rate, and average availability level of a fleet of assets, providing asset managers with an inference mechanism to not only measure the performance of the assets based on common causal factors, but also learn the actual level of such factors and thereby identify improvement areas. We have demonstrated the application of the model using a fleet of excavators located in Toronto, Ontario. The prediction accuracy of the proposed model is evaluated by use of a measure of prediction error. [Received: 19 March 2019; Accepted: 3 September 2019] Journal: European J. of Industrial Engineering Pages: 404-442 Issue: 3 Volume: 14 Year: 2020 Keywords: fleet; availability; failure rate; repair rate; causal factors; Bayesian networks. File-URL: http://www.inderscience.com/link.php?id=107696 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:3:p:404-442 Template-Type: ReDIF-Article 1.0 Author-Name: Ivan Kristianto Singgih Author-X-Name-First: Ivan Kristianto Author-X-Name-Last: Singgih Author-Name: Byung-In Kim Author-X-Name-First: Byung-In Author-X-Name-Last: Kim Title: Multi-type electric vehicle relocation problem considering required battery-charging time Abstract: This research discusses an electric vehicle (EV) relocation problem, wherein multiple types of EVs are transported using heterogeneous trucks. The initial position, battery level of the EVs, and the required number of EVs and empty parking slots at each station are provided as inputs. Relocations are performed during the night, while no EVs are used. Before the end of the relocation planning horizon, each EV must be charged to a certain battery level. The charging process can only be performed when the EV is not being transported. The objectives are to minimise the total transportation costs, the total truck fixed costs, and the total unsatisfied empty parking slot requirements while ensuring that all EV demands are satisfied. A mixed-integer linear programming (MILP) model and construction and improvement heuristic approaches are proposed. The results of the computational experiments indicate that the proposed approaches perform well. [Received: 25 February 2019; Accepted: 26 August 2019] Journal: European J. of Industrial Engineering Pages: 335-368 Issue: 3 Volume: 14 Year: 2020 Keywords: electric vehicle relocation; battery-charging; heterogeneous truck; heuristic; adaptive large-neighbourhood search; mixed-integer linear programming; MILP. File-URL: http://www.inderscience.com/link.php?id=107697 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:3:p:335-368 Template-Type: ReDIF-Article 1.0 Author-Name: Karim Amrouche Author-X-Name-First: Karim Author-X-Name-Last: Amrouche Author-Name: Mourad Boudhar Author-X-Name-First: Mourad Author-X-Name-Last: Boudhar Author-Name: Nazim Sami Author-X-Name-First: Nazim Author-X-Name-Last: Sami Title: Two-machine chain-reentrant flow shop with the no-wait constraint Abstract: This paper addresses the chain-reentrant flow shop scheduling problem with two machines and <i>n</i> non-preemptive jobs in the presence of the no-wait constraint; we assume that each job passes from the first machine to the second and returns back to the first machine. The objective is to minimise the makespan. The general problem is NP-hard in the strong sense. Based on a dynamic programming algorithm, we prove that the problem is polynomially solvable when the execution order of the jobs through the machines is a fixed permutation. For the resolution of the general problem, we propose a linear mathematical model, local search heuristics, a simulated annealing metaheuristic and lower bounds with numerical experiments. [Received: 8 February 2019; Revised: 19 June 2019; Revised: 26 August 2019; Revised: 8 October 2019; Revised: 9 November 2019; Accepted: 11 November 2019] Journal: European J. of Industrial Engineering Pages: 573-597 Issue: 4 Volume: 14 Year: 2020 Keywords: chain-reentrant flow shop; no-wait constraint; dynamic programming; mathematical formulation; heuristics; simulated annealing. File-URL: http://www.inderscience.com/link.php?id=108577 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:4:p:573-597 Template-Type: ReDIF-Article 1.0 Author-Name: Javier Panadero Author-X-Name-First: Javier Author-X-Name-Last: Panadero Author-Name: Angel A. Juan Author-X-Name-First: Angel A. Author-X-Name-Last: Juan Author-Name: Christopher Bayliss Author-X-Name-First: Christopher Author-X-Name-Last: Bayliss Author-Name: Christine Currie Author-X-Name-First: Christine Author-X-Name-Last: Currie Title: Maximising reward from a team of surveillance drones: a simheuristic approach to the stochastic team orienteering problem Abstract: We consider the problem of routing a team of unmanned aerial vehicles (drones) being used to take surveillance observations of target locations, where the value of information at each location is different and not all locations need be visited. As a result, this problem can be described as a stochastic team orienteering problem (STOP), in which travel times are modelled as random variables following generic probability distributions. The orienteering problem is a vehicle-routing problem in which each of a set of customers can be visited either just once or not at all within a limited time period. In order to solve this STOP, a simheuristic algorithm based on an original and fast heuristic is developed. This heuristic is then extended into a variable neighbourhood search (VNS) metaheuristic. Finally, simulation is incorporated into the VNS framework to transform it into a simheuristic algorithm, which is then employed to solve the STOP. [Received 5 January 2019; Revised 15 June 2019; Accepted 13 October 2019] Journal: European J. of Industrial Engineering Pages: 485-516 Issue: 4 Volume: 14 Year: 2020 Keywords: simulation-optimisation; unmanned aerial vehicles; UAVs; team orienteering problem; TOP; simheuristics. File-URL: http://www.inderscience.com/link.php?id=108581 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:4:p:485-516 Template-Type: ReDIF-Article 1.0 Author-Name: Elif Elçin Günay Author-X-Name-First: Elif Elçin Author-X-Name-Last: Günay Author-Name: Ufuk Kula Author-X-Name-First: Ufuk Author-X-Name-Last: Kula Title: A hybrid model for mix-bank buffer content determination in automobile industry Abstract: In mixed-model automobile assembly lines, paint defective vehicles are the main reason for unintentional sequence alteration that stirs up the production sequence so that resequencing is required. This study aims to decide optimal spare vehicles that are held in the mix-bank buffer to be replaced instead of defective vehicles in order to regain the production sequence. We develop a hybrid solution methodology in which optimal spare vehicle content is determined by genetic algorithm (GA) and the releasing order of the vehicles to final assembly (FA) are decided by stochastic mixed integer programming (MIP) model. In addition to discussing the efficiency of the hybrid model, the following insights were gained: 1) not only FA but also paint shop constraints should be considered in production sequence determination when mix-bank buffer is efficiently used; 2) the decrease in defect rate improves sequence restoration linearly; 3) effect of an additional lane on SSAR increase is diminishing. [Received: 22 December 2018; Accepted: 28 October 2019] Journal: European J. of Industrial Engineering Pages: 544-572 Issue: 4 Volume: 14 Year: 2020 Keywords: mixed-model assembly lines; genetic algorithm; sample average approximation; SAA; mixed integer programming; MIP; mix-bank buffer. File-URL: http://www.inderscience.com/link.php?id=108599 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:4:p:544-572 Template-Type: ReDIF-Article 1.0 Author-Name: M. Keerthana Author-X-Name-First: M. Author-X-Name-Last: Keerthana Author-Name: N. Saranya Author-X-Name-First: N. Author-X-Name-Last: Saranya Author-Name: B. Sivakumar Author-X-Name-First: B. Author-X-Name-Last: Sivakumar Title: A stochastic queueing - inventory system with renewal demands and positive lead time Abstract: This article analyses a stochastic inventory system with a service facility. This is an extended work of Yadavalli et al. (2008) by including the positive lead time. The customer arrives according to a renewal process and demanded item is delivered to the customer after performing an exponentially distributed service time. An (<i>s</i>, <i>S</i>) type ordering policy is adopted with exponentially distributed lead times. The stationary probability distribution for number of customers in the system and inventory level at arrival epoch and at arbitrary time point are derived. Some system performance measures in the steady state are computed and using these system performance measures the long-run expected cost rate is calculated. Since the long run expected cost rate is highly complex, the mixed integer distributed ant colony optimisation is used to obtain the optimal values. A sensitivity analysis to illustrate the effects of parameters and cost on the optimal values is also carried out in this work. [Received: 13 December 2018; Accepted: 10 October 2019] Journal: European J. of Industrial Engineering Pages: 443-484 Issue: 4 Volume: 14 Year: 2020 Keywords: queueing-inventory system; (s, S) policy; MIDACO algorithm; infinite waiting hall. File-URL: http://www.inderscience.com/link.php?id=108600 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:4:p:443-484 Template-Type: ReDIF-Article 1.0 Author-Name: Xiyang Hou Author-X-Name-First: Xiyang Author-X-Name-Last: Hou Author-Name: Yongjiang Guo Author-X-Name-First: Yongjiang Author-X-Name-Last: Guo Author-Name: Ping Cao Author-X-Name-First: Ping Author-X-Name-Last: Cao Title: Commission production contracts with revenue sharing for a capacitated manufacturer and multiple retailers Abstract: In this paper, we consider a manufacturer with limited production capacity producing multiple kinds of independent products, such that each kind of product is sold by a distinct retailer who is offered a commission production contract with revenue sharing by the manufacturer. We study the contract in the centralised and decentralised systems respectively. Under certain conditions of price elasticities and cost fractions, we show the uniqueness of optimal revenue share for all products. Moreover, by comparing both systems with same capacity constraint, we find that at least one retailer's price in the centralised system is higher than that of the decentralised system, and the order quantity for that retailer is lower under some conditions. As a consequence, the decentralised system's profit is always higher than the centralised system's profit under that condition. Also, the retailers' optimal prices (resp. order quantities) are increasing (resp. decreasing) in production capacity of the manufacturer, whereas the manufacturer's expected profit is increasing in its production capacity in both systems. Finally, we conduct numerical study to justify our theoretical results, and examine the effect of processing cost on both systems' profits, and the effect of demand uncertainty on the optimal prices and order quantities. [Received 22 May 2018; Accepted 28 October 2019] Journal: European J. of Industrial Engineering Pages: 517-543 Issue: 4 Volume: 14 Year: 2020 Keywords: production capacity; order quantity; retail price; revenue sharing contract. File-URL: http://www.inderscience.com/link.php?id=108603 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:4:p:517-543 Template-Type: ReDIF-Article 1.0 Author-Name: João Faria Author-X-Name-First: João Author-X-Name-Last: Faria Author-Name: Madalena Araújo Author-X-Name-First: Madalena Author-X-Name-Last: Araújo Author-Name: Erik Demeulemeester Author-X-Name-First: Erik Author-X-Name-Last: Demeulemeester Author-Name: Anabela Tereso Author-X-Name-First: Anabela Author-X-Name-Last: Tereso Title: Project management under uncertainty: using flexible resource management to exploit schedule flexibility Abstract: Project management still faces a wide gap separating theory from practice, especially regarding the robustness of the generated project schedules facing the omnipresence of uncertainty. A new approach to deal with uncertainty is presented to explore slack that might exist in a given project schedule. We propose that renewable resources' capacity to perform work can be increased so that they can perform additional work in a time unit or can be decreased with the consequent reduction on the performed work. This possibility combined with the slack that some activities have in a specific schedule can be used to absorb deviations that might occur during a project's execution. When a critical activity is about to have its duration increased, slowing down other non-critical activities by putting their resources in a decreased work mode enables the activity to still be executed within time by using resources in an increased working mode. [Received: 14 February 2018; Revised: 2 January 2019; Revised: 8 June 2019; Accepted: 17 November 2019] Journal: European J. of Industrial Engineering Pages: 599-631 Issue: 5 Volume: 14 Year: 2020 Keywords: project management; scheduling; resource allocation; RCPSP; uncertainty. File-URL: http://www.inderscience.com/link.php?id=109899 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:5:p:599-631 Template-Type: ReDIF-Article 1.0 Author-Name: Yassine Adouani Author-X-Name-First: Yassine Author-X-Name-Last: Adouani Author-Name: Bassem Jarboui Author-X-Name-First: Bassem Author-X-Name-Last: Jarboui Author-Name: Malek Masmoudi Author-X-Name-First: Malek Author-X-Name-Last: Masmoudi Title: Efficient matheuristic for the generalised multiple knapsack problem with setup Abstract: This paper introduces a new variant of the knapsack problem with setup (KPS). We refer to it as the generalised multiple knapsack problem with setup (GMKPS). GMKPS originates from industrial production problems where the items are divided into classes and processed in multiple periods. We refer to the particular case where items from the same class cannot be processed in more than one period as the multiple knapsack problem with setup (MKPS). First, we provide mathematical formulations of GMKPS and MKPS and provide an upper bound expression for the knapsack problem. We then propose a matheuristic that combines variable neighbourhood descent (VND) with integer programming (IP). We consider local search techniques to assign classes to knapsacks and apply the IP to select the items in each knapsack. Computational experiments on randomly generated instances show the efficiency of our matheuristic in comparison to the direct use of a commercial solver. [Received: 4 March 2018; Revised: 1 June 2019; Revised: 12 July 2019; Revised: 22 November 2019; Accepted: 6 January 2020] Journal: European J. of Industrial Engineering Pages: 715-741 Issue: 5 Volume: 14 Year: 2020 Keywords: knapsack problems; setup; matheuristic; variable neighbourhood descent; VND; integer programming. File-URL: http://www.inderscience.com/link.php?id=109906 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:5:p:715-741 Template-Type: ReDIF-Article 1.0 Author-Name: Tahir Nawaz Author-X-Name-First: Tahir Author-X-Name-Last: Nawaz Author-Name: Dong Han Author-X-Name-First: Dong Author-X-Name-Last: Han Title: Neoteric ranked set sampling based combined Shewhart-CUSUM and Shewhart-EWMA control charts for monitoring the process location Abstract: The use of efficient sampling designs can play a vital role to enhance the efficiency of the control charts for improved process monitoring. In this paper, the neoteric ranked set sampling scheme (NRSS) is used to design combined Shewhart-CUSUM (CSCUSUM) and Shewhart-EWMA (CSEWMA) control charts for monitoring of the process location with an aim to enhance the shift detection ability of these charts. Monte Carlo simulations are used to obtain the run lengths profiles of the proposed charts. The performance of the proposed charts is compared with their competing control charts using average run length, the standard deviation of run length, median run length and extra quadratic loss (EQL) as performance metrics. The proposed control charts emerged as more sensitive and in-control robust as compare to the existing charts. A real industrial dataset is used to demonstrate the implementation of the proposed charts for the practitioners. [Received: 11 May 2019; Accepted: 17 December 2019] Journal: European J. of Industrial Engineering Pages: 649-683 Issue: 5 Volume: 14 Year: 2020 Keywords: neoteric ranked set sampling; Shewhart-CUSUM; statistical process monitoring; Shewhart-EWMA; in-control robust. File-URL: http://www.inderscience.com/link.php?id=109913 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:5:p:649-683 Template-Type: ReDIF-Article 1.0 Author-Name: Merve Uzuner Sahin Author-X-Name-First: Merve Uzuner Author-X-Name-Last: Sahin Author-Name: Berna Dengiz Author-X-Name-First: Berna Author-X-Name-Last: Dengiz Author-Name: Kumru Didem Atalay Author-X-Name-First: Kumru Didem Author-X-Name-Last: Atalay Title: Performance enhancement of production systems using fuzzy-based availability analysis and simulation method Abstract: Due to the complexity of today's engineering systems, accurate system performance analysis is important to obtain a more productive system design. This study introduces a new approach to obtain a new system design considering system availability to increase its productivity in a more consistent and logical manner. To obtain more productive system design, both fuzzy availability analysis and simulation are used together. The conventional simulation modelling can be used to analyse the system behaviour considering failure and repair data to predict system throughput. This approach solves the problems of scarce data for failures and repairs in the system. The fuzzy availability analysis is used to consider system failures and repairs based on fuzzy set theory. In other words, the integration of simulation results and fuzzy system availability allows for analysing system performance to improve system productivity with new system design even though system failure and repair data are scarce. This new approach has been applied to improve system productivity in a battery production company in Turkey. The analysis results show that this new approach is able to analyse system performance accurately and improve system productivity with a new system design. [Received: 26 November 2018; Accepted: 17 December 2019] Journal: European J. of Industrial Engineering Pages: 632-648 Issue: 5 Volume: 14 Year: 2020 Keywords: availability analysis; battery production system; triangular fuzzy numbers; TFNs; simulation modelling. File-URL: http://www.inderscience.com/link.php?id=109914 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:5:p:632-648 Template-Type: ReDIF-Article 1.0 Author-Name: Binghai Zhou Author-X-Name-First: Binghai Author-X-Name-Last: Zhou Author-Name: Xiumei Liao Author-X-Name-First: Xiumei Author-X-Name-Last: Liao Title: An efficient generalised opposition-based multi-objective optimisation method for factory cranes with time-space constraints Abstract: In order to improve the performance of large manufacturing enterprises, besides the adoption of new technologies, it is also feasible to efficiently schedule logistics equipment such as cranes, which costs much less since only software changes are involved. In this research, the objectives of minimising total waiting cost and total delay cost are optimised simultaneously when executing crane-delivery tasks in factories. Given the time-space constraints and NP-hard nature of the problem, a generalised opposition-based learning (GOBL) mechanism and two problem-based searching strategies are developed and fused into the multi-objective differential evolution approach, namely GOMODE. The introduction of GOBL mechanism enables the proposed algorithm to search in a more extensive solution space, which improves the population diversity and avoids the premature problem. The performance of the GOMODE has been compared with classical multi-objective optimisation algorithms. The experimental results indicate that the GOMODE achieves a better performance both on solutions' quality and diversity. [Received: 11 December 2018; Accepted: 23 December 2019] Journal: European J. of Industrial Engineering Pages: 684-714 Issue: 5 Volume: 14 Year: 2020 Keywords: generalised opposition-based learning; GOBL; factory crane scheduling; multi-objective optimisation; time-space constraints. File-URL: http://www.inderscience.com/link.php?id=109923 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:14:y:2020:i:5:p:684-714