European J. of Industrial Engineering (14 papers in press)
The impact of online sales in centralized and decentralized dual-channel supply chains
by Subrata Saha
Abstract: This paper studies a supply chain structure featuring two different types of distribution channels through which manufacturers sell products. The centralised and decentralised distribution channels considered in this study are affected by online sales outside the structured channels. In the centralised distribution channel, two retail stores located in geographically distinct markets are operated by a single owner. In the decentralised distribution channel, two retailers independently operate two retail stores. In the non-cooperative scenario, the manufacturer always prefers the decentralised distribution channel irrespective of whether an online channel is used. To achieve channel coordination, a revenue-sharing contract is applied, but it can be used to coordinate only the decentralised distribution system. Therefore, a modified revenue-sharing contract is proposed to coordinate the centralised distribution system. The analytical study reveals that without coordination among the channel members, the manufacturer always earns maximum profit in decentralised distribution systems. However, if the supply chain is coordinated, then the manufacturer receives more benefits from using the centralised distribution systems under certain conditions. Propositions are presented to describe the characteristics of distribution structures, and to provide meaningful management guidelines for coordinating them. Extensive numerical investigations are also presented.
Keywords: Supply chain management; Dual-channel supply chain; Revenue sharing contract; Pricing strategy; Stackelberg.
Modelling and Optimizing the Multi-item Stochastic Joint Replenishment Problem with Uncertain Lead-time and Controllable Major Ordering Cost
by Xue-Yi Ai
Abstract: In this paper, we extend the existing stochastic joint replenishment model to a more realistic condition by considering uncertainties in lead-time and effective investment to reduce the major ordering cost. The aim is to determine the optimal strict cyclic replenishment policy and the optimal major ordering cost simultaneously to minimize the total cost. The objective cost function is approximated by expressing one element of the cost function as a Taylor series expansion. A bounds-based heuristic algorithm is then developed to solve the proposed model. The performance of the algorithm and the quality of the approximation are examined by computational experiments. The results of the models without considering uncertainty and ordering cost reduction are presented to illustrate the effectiveness of the proposed model. Experimentation and analysis of results demonstrate that the standard deviation of lead-time has a significant effect on the system.
Keywords: joint replenishment problem; stochastic demand; uncertain lead-time; inventory; optimization; major ordering cost reduction; heuristics.
Investigating the performance improvement by conversion of assembly line configuration to a pure cell system in manufacturing industry
by T... VenkataDeepthi, K. Ramakotaiah, Vijaya Kumar Manupati, Chaitanya Gangal
Abstract: Seru production systems implement reconfiguration of traditional assembly lines to a flexible cell system that aim at reducing the required workforce while at the same time augmenting the productivity manifold. For evaluating the overall performance improvement, cell formatting and assigning workers to serus takes form of a complicated decision problem. In this paper, with the objective of reducing the total cost for training the worker, minimizing the processing time and the total throughput time, mathematical insights on the solution space of a multi-objective line-cell conversion model are identified, in turn proving it to be an NP-hard model. By applying the proposed heuristic algorithm on several numerical simulations, a Pareto-optimal solution of this multi-objective model is obtained. With experimental results and comparative studies, the proposed approach proves its effectiveness that may lead to further improvement in seru production systems competitive advantage to cope with fluctuating market demands by enhancing the flexibility as well as the efficiency of the system.
Keywords: Seru; manufacturing systems; production systems; flexibility; reconfiguration; cellular manufacturing.
QoS of cloud prognostic system Application to aircraft engines fleet
by Zohra Bouzidi, Labib Sadek Terrissaa, Noureddine Zerhounib, Soheyb Ayada
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 esti-mated 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.
Keywords: Prognostics and Health Management (PHM); Remaining Useful Life (RUL); Prognostic as a Service; Cloud Computing; Artificial Intelligence; Measure Performance; Quality of Service (QoS).
Performance analysis and optimisation of new strategies for the setup of a multihead weighing process
by J. Carlos García-Díaz, Alexander Pulido-Rojano
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.
Keywords: Packing; Multihead weighing process; Variability reduction; Six-Sigma process; Optimal operating conditions.
Prioritization and Assessment of leagile manufacturing enablers using Interpretive Structural Modeling (ISM) approach
by NAVEEN VIRMANI, Vikram Sharma
Abstract: Nowadays, market is very competitive. Industries are required to make changes in the production system quickly so as to meet the fluctuating needs of customers. Also, the components or product made should be of good quality at reasonable cost. To accomplish this task, industries are required to have state of art facilities like flexible manufacturing system (FMS), robotics, programmable logic controller (PLC), electric discharge machining (EDM), etc. In this paper, leagile system, i.e., combination of lean and agile system has been discussed. The concept of leagile manufacturing tries to reduce the cost of the product to minimum possible level and reduces the cycle time so that the finished products can be delivered to the customer as quickly as possible. In this paper, leagile enablers have been found out through literature review and in discussion with experts. Interpretive structural modelling technique (ISM) has been applied to find the relationship between these enablers and for constructing digraph. [Received 28 March 2018; Accepted 1 January 2019]
Keywords: Agile manufacturing; Lean manufacturing; Leagile manufacturing.
Evaluation of the effectiveness of methods and criteria for product classification in the warehouse
by Augustyn Lorenc, Maciej Szkoda, Andrzej Szarata, Ilona Jacyna-Gołda
Abstract: The efficiency of a warehouse is determined by the effectiveness of its internal processes. Evaluation of the effectiveness of the picking process requires the use of a high-precision method instead of typical factors. 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. This software is useful for making decisions about the product storage policy.
In the research, different product classification methods were investigated and the paper uses the rack storage warehouse. 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%.
Keywords: product classification; ABC analysis; XYZ analysis; COI Index; sensitivity analysis; order picking efficiency.
Multi-Objective Invasive Weeds Optimization Algorithm for Solving Simultaneous Scheduling of Machines and Multi-Mode Automated Guided Vehicles
by Hojat Nabovati, Hassan Haleh, Behnam Vahdani
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 optimization 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 optimization algorithm are applied to validate the solutions obtained by the developed multi-objective invasive weeds optimization 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 optimization algorithm had the best performance in terms of solving the mentioned problems.
Keywords: MOIWO; AGV; Scheduling; Machines Scheduling.
Optimization of cost efficient robotic assembly line using metaheuristic algorithms
by Mukund Nilakantan Janardhanan, Peter Nielsen
Abstract: Robotic assembly lines (RALs) are utilized 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 optimization in RALs. This paper focuses on proposing a new model in RAL with the main objective of maximizing line efficiency by minimizing total assembly line cost. The proposed model can be used production managers to balance a robotic assembly line 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 optimization (PSO) and Differential evolution (DE) are applied as the optimization 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.
Keywords: Robotic Assembly Line Balancing; Cost efficient assembly line; Line efficiency; Metaheuristics.
A bi-objective integrated model for the uncertain blood network design with raising products quality
by Mohammad Reza Ghatreh Samani, Seyyed-Mahdi Hosseini-Motlagh, Maryam Izadidoost Sheshkol, Seyyed-Nader Shetab-Boushehri
Abstract: Blood transfusion is a multi-step process with risks in each process from selecting donors to transfusing the patient. Meanwhile, the quality plays an essential part throughout the blood supply chain. In this regard, this study proposes a bi-objective model for an integrated blood supply chain network design. The first objective function tries to minimise the total network cost, whereas the second objective seeks to maximise the quality factor. Due to the epistemic uncertainty of critical parameters, a fuzzy method, as well as some robust approaches, are tailored. The applicability and performance of these proposed methods and their validation are studied in a real case of Tehran's blood network. The results illustrate the preference of realistic robust approach to the other methods due to reduction in cost as well as preserving the quality. Finally, the paper comes to an end with the sensitivity analysis, conclusion and some suggestions for future directions. [Received: 22 June 2018; Accepted: 9 January 2019]
Keywords: blood supply chain; healthcare management; multi-objective optimisation; robust programming; raising quality.
A dominance-based heuristic to minimise completion time variance in a single machine system
by H.M. Soroush, Fatmah Almathkour
Abstract: This paper addresses the problem of minimising the variance of job completion times in a deterministic single-machine scheduling system. Minimising completion time variance is an appropriate objective in scheduling environments where service uniformity is essential. Due to the NP-hard nature of the problem, various heuristics have been presented to obtain a near-optimal solution. In this paper, we introduce a new heuristic based on some powerful precedence (dominance) relation structures, including the concepts of permanent and temporary precedence (dominance) relationships, to determine the positions of adjacent and non-adjacent jobs in a sequence. Our computational experiments demonstrate that the proposed heuristic significantly outperforms the existing ones in deriving the optimal or near-optimal solutions for three well-known sets of benchmark problems and some large randomly generated instances. [Received: 5 August 2018; Revised: 24 December 2018; Accepted: 23 January 2019]
Keywords: scheduling; single machine; completion time variance; heuristic; precedence; dominance.
A novel hierarchical approach for a heterogeneous 3D pallet loading problem subject to factual loading and delivery constraints
by Sena Kır, Harun Resit Yazgan
Abstract: This paper presents a hierarchical approach, which consists of a two-stage genetic algorithm and a mixed integer linear programming, for a heterogeneous three-dimensional pallet loading problem in consideration of the rotation, the relative positioning, the load-bearing strength and the fragility constraints. Stage #1 of the proposed two-stage genetic algorithm provides to reduce the number of items to be packed by combining similar items based on a stack-building approach. And, stage #2 provides to estimate the number of required free pallets. After that, the proposed mixed integer linear programming solves the problem considering the findings of the proposed two-stage genetic algorithm. The proposed hierarchical approach was tested on well-known instances leading to favourable results and compared with a decent solution approach. In addition, a case study was presented. [Received: 7 July 2018; Revised: 20 November 2018; Accepted: 25 January 2019]
Keywords: logistics; 3D pallet loading problem; MILP; genetic algorithm; intelligent dynamic crossover.
Humanitarian supply chain network design using data envelopment analysis and multi-objective programming models
by Jae-Dong Hong, Ki-Young Jeong
Abstract: Emergency events such as natural disasters or terrorist attacks seem to occur anywhere and tend to increase. This paper studies a humanitarian supply chain network (HTSCN) design problem in a pre-disaster scenario, which consists of finding the optimal emergency response facility (ERF) locations and allocation scheme of humanitarian supplies through ERFs, where all ERFs are under the risk of disruptions. Naturally, this type of design problem should deal with multiple goals. An innovative two-step framework of designing efficient HTSCN by combining multi-objective programming (MOP) models with data envelopment analysis (DEA) is proposed. A case study using the historical data on the disasters in South Carolina, USA is presented to illustrate the effectiveness and efficiency of the proposed combining framework. The case study demonstrates that the proposed procedure would help practitioners and researchers generate a finer evaluation of efficiency and would provide a benchmarking methodology for designing HTSCN system. [Received: 22 October 2017; Revised: 9 April 2018; Revised: 15 October 2018; Revised: 22 December 2018; Accepted: 26 January 2019]
Keywords: humanitarian supply chain network; HTSCN; emergency response facility; ERF; data envelopment analysis; DEA; multi-objective programming approach.
Bi-objective scheduling on two dedicated processors
by Adel Kacem, Abdelaziz Dammak
Abstract: In this work, we study a bi-objective scheduling problem on two dedicated processors. The aim is to minimise the makespan and the total tardiness. This NPhard problem in the strong sense requires the use of well-adapted methods. Our contribution includes lower bounds for each studied criterion and a genetic algorithm adapted for the multi-criteria context. The lower bounds allows us to evaluate the quality of feasible solutions and the genetic algorithm incorporates the optimisation part. We implemented our approach by considering the aggregative, NSGA-II and the Pareto scenarios on a large set of instances. The obtained results show the effectiveness of the proposed algorithms. [Received: 27 August 2018; Revised: 25 January 2019; Accepted: 12 March 2019]
Keywords: scheduling; total of tardiness; tasks; makespan; dedicated processors; genetic algorithms; lower bound; bi-objective; Pareto front; NSGA-II.