European J. of Industrial Engineering (13 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 Resource Allocation Model to Choose the Best Portfolio of Economic Resilience Plans: A PossibilisticStochastic Programming Model
by Shima Pashapour, Ali Azadeh, Ali Bozorgi-Amiri, Abbas Keramati, Seyed Farid Ghaderi
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 -constraint method and a Multi-Stage Possibilistic Stochastic Programming approach (MSPSP) are employed. The numerical results showed that the proposed approach is effective in optimizing the performance of a petrochemical plant in facing crisis situations and in choosing the best portfolio of economic resilience plans.
Keywords: Petrochemical Plant; Economic Resilience; Resilience Capability; Multi-Stage Possibilistic Stochastic Programming (MSPSP); Resource Allocation.
A multi-criteria spatial analysis using GIS to evaluate potential sites for a new border gate on Turkey's Syria frontier
by Mehmet Kabak, Eren Özceylan, Mehmet Erbaş, Cihan Çetinkaya
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 Turkeys 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.
Keywords: DEMATEL; border check-point; GIS; Multi-criteria decision making; MULTIMOORA; Site location.
Buffer Allocation, Equipment Selection and Line
Balancing Optimization in Unreliable Production Lines
by Nabil Nahas
Abstract: This paper presents an integrated optimization 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 m workstations and m 1 intermediate buffers. The objective is to maximize 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 minimize 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.
Keywords: Line balancing; Buffer allocation problem; Equipment selection;
Non-linear Threshold algorithm; Great deluge algorithm.
A robust stochastic bi-objective model for blood inventory-distribution management in a blood supply chain
by Hadis Derikvand, Seyed Mohammad Hajimolana, Armin Jabbarzadeh, Seyed Esmaeil Najafi
Abstract: Providing blood units in a blood supply chain should be effective, appropriate and well-organized 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 minimize the total number of shortages and wastages and the second objective maximizes the connection between two different types of hospitals. The blood supply chain under investigation includes one blood center, type-1 and type-2 hospitals, and patients. In doing so, the robust programming approach has been applied in order to minimize the expected value as well as the variance of the number of shortages and wastages in the whole blood supply chain and while simultaneously penalizing the solutions infeasibility due to the uncertain parameter(s) and maximizing the expected value and the variance of connection between two types of hospitals. Mathematical approximations are employed to remove the non-linear terms, and a hybrid solution approach, combining the -constraint and the Lagrangian relaxation method, is applied to solve the proposed bi-objective model. Finally, the proposed robust stochastic optimization model is implemented and analyzed using the data inspired by a real case study in Iran to show its potential applicability as to decisions concerning the blood inventory-distribution problem.
Keywords: blood supply chain; blood inventory-distribution management; robust programming approach; ɛ-constraint; lagrangian relaxation approach.