An integrated model of supplier selection and inventory planning using fuzzy logic and multi-objective evolutionary algorithms

Supplier selection and inventory planning are critical and challenging tasks in supply chain management. There are many studies on both topics and many solution techniques have been proposed dealing with each problem separately. In this thesis, we present a two-stage integrated approach to the supplier selection and inventory planning. In the first stage, in order to get a risk value of each supplier, suppliers are evaluated based on various criteria derived from cost, quality, service and delivery using Interval Type-2 Fuzzy Sets (IT2FSs). In the second stage, the information of supplier rank is fed into an inventory model built to cover the effect of suppliers on the total cost of a supply chain. The proposed model is formulated as single, multi and many-objective optimisation problems, respectively. Firstly, we generated a set of new instances based on a real world problem. Twenty four problem instances are provided as a benchmark for the community. Various metaheuristics, including MOSA (Multi-objective Simulated Annealing) as a single point based search algorithm, NSGA-II (Nondominated Sorting Genetic Algorithm-II), SPEA2 (Strength Pareto Evolutionary Algorithm 2), IBEA (Indicator Based Evolutionary Algorithm) as population based multi-objective algorithms and NSGA-III (Non-dominated Sorting Genetic Algorithm-III) as a many objective algorithm are applied to those integrated supply chain management problem instances. It is a well-known fact that parameter setting is crucial for an improved performance of a metaheuristic. Hence, in order to use each algorithm at its best, we employed the experimental design methodology of Taguchi orthogonal arrays for parameter tuning detecting the best setting for each algorithm. A comparative analysis of multi-objective metaheuristics is provided to find the best performing approach in the second stage. The experimental results show that the proposed two-stage approach is indeed capable of solving the integrated supply chain management problem successfully. NSGA-III as a population based technique outperforms the single point based search approach of Simulated Annealing which aggregates multiple objectives into a single objective and other three population based techniques, NSGA-II, SPEA2 and IBEA. Within the population based approaches, NSGA-II performs the best as a multi-objective algorithm when excluding NSGA-III as a many objective algorithm

Product allocation and network configuration in global production networks – An integrated optimization approach

Driven by an increasing demand for individualized products and shorter product life-cycles, companies continuously extend their product portfolio. Simultaneously, companies expand into new markets to reach customers and to exploit varying location factors to reduce costs. Global production networks (GPNs) have to be adapted constantly to react to new circumstances and changes in the demand of products. To remain competitive, product allocation and production network configuration are essential. At the same time, companies face an increasing complexity while handling these tasks. This poses a challenge particularly for small and medium sized companies, which have limited planning capacities and management resources. Current literature describes optimization-based approaches for the integrated product allocation and network configuration of production networks. Yet, multi-objective models lack transparency of results and user friendliness. Therefore, this paper presents a multi-objective optimization model that incorporates flexibility and reconfiguration aspects to determine an optimal product allocation and network configuration of a GPN over a given planning horizon. The preemptive goal programming approach is used to identify Pareto-optimal solutions and to increase user friendliness. The subsequent verification, validation and post-optimality analysis combined in a structured process enables a wide range of companies to apply the approach. The model is successfully applied in the GPN of a special machine manufacturer, which produces high precision metrology machines. Due to its transparent approach for complex planning problems, the developed method provides a solid base for well-founded, objective decisions. Hence, the risk of costly errors in the planning phase is reduced

A novel grey multi-objective binary linear programming model for risk assessment in supply chain management

Robust and resilient agri-food supply chain management (AFSCM) is paramount to agribusinesses, given the many challenges and risks that this increased demand will bring in the coming decades. Interruptions caused by various risks to this crucial supply chain network, particularly in emerging economies, can put the lives of millions in danger, not to mention creating devastating impacts on the economy and the environment. Even so, there are only a limited number of quantitative risk management studies in the AFSCM literature. In this study, an integrated modified risk mitigation matrix (M-RMM) is developed to analyze the mitigation strategies for dealing with various risks in the context of the agri-food supply chain. The M-RMM is integrated with the grey multi-objective binary linear programming (GMOBLP) model to obtain the optimal risk mitigation strategies related to the three objective functions of risk, cost, and time minimization. The proposed model is a useful tool for formulating sustainable business policies and reducing food waste, and acquiring a context-specific (i.e., a developing economy), sector-specific (i.e., the agri-food processing sector), and multi-product (i.e., fresh and non-perishable) approach. The findings reveal that continuous training and development and vulnerability analysis of IT systems are the most effective risk mitigation strategies to lessen the impacts of lack of skilled personnel, sub-standard leadership, failure in IT systems, insufficient capacity to produce quality products, and poor customer relationships. The findings assist practitioners in managing risks in supply chains

Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems

[EN] This work presents a multi-criteria-based approach to automatically select specific non-dominated solutions from a Pareto front previously obtained using multi-objective optimization to find optimal solutions for pump control in a water supply system. Optimal operation of pumps in these utilities is paramount to enable water companies to achieve energy efficiency in their systems. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the Pareto solutions found by the non-dominated sorting genetic algorithm (NSGA-II) employed to solve the multi-objective problem. Various scenarios are evaluated under leakage uncertainty conditions, resulting in fuzzy solutions for the Pareto front. This paper shows the suitability of the approach for quasi real-world problems. In our case-study, the obtained solutions for scenarios including leakage represent the best trade-off among the optimal solutions, under some considered criteria, namely, operational cost, operational lack of service, pressure uniformity and network resilience. Potential future developments could include the use of clustering alternatives to evaluate the goodness of each solution under the considered evaluation criteria.Carpitella, S.; Brentan, BM.; Montalvo Arango, I.; Izquierdo Sebastián, J.; Certa, A. (2019). Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems. Water Science & Technology: Water Supply. 19(8):2338-2346. https://doi.org/10.2166/ws.2019.115S23382346198Ancău, M., & Caizar, C. (2010). The computation of Pareto-optimal set in multicriterial optimization of rapid prototyping processes. Computers & Industrial Engineering, 58(4), 696-708. doi:10.1016/j.cie.2010.01.015Aşchilean, I., Badea, G., Giurca, I., Naghiu, G. S., & Iloaie, F. G. (2017). Choosing the Optimal Technology to Rehabilitate the Pipes in Water Distribution Systems Using the AHP Method. Energy Procedia, 112, 19-26. doi:10.1016/j.egypro.2017.03.1109Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Joint Operation of Pressure-Reducing Valves and Pumps for Improving the Efficiency of Water Distribution Systems. Journal of Water Resources Planning and Management, 144(9), 04018055. doi:10.1061/(asce)wr.1943-5452.0000974Certa, A., Enea, M., Galante, G. M., & La Fata, C. M. (2017). ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: An alternative to the risk priority number. Computers & Industrial Engineering, 108, 100-110. doi:10.1016/j.cie.2017.04.018Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1-9. doi:10.1016/s0165-0114(97)00377-1Cruz-Reyes, L., Fernandez, E., Sanchez, P., Coello Coello, C. A., & Gomez, C. (2017). Incorporation of implicit decision-maker preferences in multi-objective evolutionary optimization using a multi-criteria classification method. Applied Soft Computing, 50, 48-57. doi:10.1016/j.asoc.2016.10.037Farmani, R., Ingeduld, P., Savic, D., Walters, G., Svitak, Z., & Berka, J. (2007). Real-time modelling of a major water supply system. Proceedings of the Institution of Civil Engineers - Water Management, 160(2), 103-108. doi:10.1680/wama.2007.160.2.103Hadas, Y., & Nahum, O. E. (2016). Urban bus network of priority lanes: A combined multi-objective, multi-criteria and group decision-making approach. Transport Policy, 52, 186-196. doi:10.1016/j.tranpol.2016.08.006Hamdan, S., & Cheaitou, A. (2017). Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach. Computers & Operations Research, 81, 282-304. doi:10.1016/j.cor.2016.11.005Ho, W. (2008). Integrated analytic hierarchy process and its applications – A literature review. European Journal of Operational Research, 186(1), 211-228. doi:10.1016/j.ejor.2007.01.004Jowitt, P. W., & Germanopoulos, G. (1992). Optimal Pump Scheduling in Water‐Supply Networks. Journal of Water Resources Planning and Management, 118(4), 406-422. doi:10.1061/(asce)0733-9496(1992)118:4(406)Jowitt, P. W., & Xu, C. (1990). Optimal Valve Control in Water‐Distribution Networks. Journal of Water Resources Planning and Management, 116(4), 455-472. doi:10.1061/(asce)0733-9496(1990)116:4(455)Kurek, W., & Ostfeld, A. (2013). Multi-objective optimization of water quality, pumps operation, and storage sizing of water distribution systems. Journal of Environmental Management, 115, 189-197. doi:10.1016/j.jenvman.2012.11.030Lima, G. M., Luvizotto, E., & Brentan, B. M. (2017). Selection and location of Pumps as Turbines substituting pressure reducing valves. Renewable Energy, 109, 392-405. doi:10.1016/j.renene.2017.03.056Mala-Jetmarova, H., Sultanova, N., & Savic, D. (2017). Lost in optimisation of water distribution systems? A literature review of system operation. Environmental Modelling & Software, 93, 209-254. doi:10.1016/j.envsoft.2017.02.009Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-448. doi:10.1111/mice.12062Odan, F. K., Ribeiro Reis, L. F., & Kapelan, Z. (2015). Real-Time Multiobjective Optimization of Operation of Water Supply Systems. Journal of Water Resources Planning and Management, 141(9), 04015011. doi:10.1061/(asce)wr.1943-5452.0000515Ostfeld, A., Uber, J. G., Salomons, E., Berry, J. W., Hart, W. E., Phillips, C. A., … Walski, T. (2008). The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms. Journal of Water Resources Planning and Management, 134(6), 556-568. doi:10.1061/(asce)0733-9496(2008)134:6(556)Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2), 115-122. doi:10.1016/s1462-0758(00)00049-2Zaidan, A. A., Zaidan, B. B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., & Abdulnabi, M. (2015). Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390-404. doi:10.1016/j.jbi.2014.11.012Żak, J., & Kruszyński, M. (2015). Application of AHP and ELECTRE III/IV Methods to Multiple Level, Multiple Criteria Evaluation of Urban Transportation Projects. Transportation Research Procedia, 10, 820-830. doi:10.1016/j.trpro.2015.09.03

VR-PMS: a new approach for performance measurement and management of industrial systems

A new performance measurement and management framework based on value and risk is proposed. The proposed framework is applied to the modelling and evaluation of the a priori performance evaluation of manufacturing processes and to deciding on their alternatives. For this reason, it consistently integrates concepts relevant to objectives, activity, and risk in a single framework comprising a conceptual value/risk model, and it conceptualises the idea of value- and risk based performance management in a process context. In addition, a methodological framework is developed to provide guidelines for the decision-makers or performance evaluators of the processes. To facilitate the performance measurement and management process, this latter framework is organized in four phases: context establishment, performance modelling, performance assessment, and decision-making. Each phase of the framework is then instrumented with state of-the-art quantitative analysis tools and methods. For process design and evaluation, the deliverable of the value- and risk-based performance measurement and management system (VR-PMS) is a set of ranked solutions (i.e. alternative business processes) evaluated against the developed value and risk indicators. The proposed VR-PMS is illustrated with a case study from discrete parts manufacturing but is indeed applicable to a wide range of processes or systems

Water quality permitting: from end-of-pipe to operational strategies

This is the final version of the article. Available from IWA Publishing via the DOI in this record.End-of-pipe permitting is a widely practised approach to control effluent discharges from wastewater treatment plants. However, the effectiveness of the traditional regulation paradigm is being challenged by increasingly complex environmental issues, ever growing public expectations on water quality and pressures to reduce operational costs and greenhouse gas emissions. To minimise overall environmental impacts from urban wastewater treatment, an operational strategy-based permitting approach is proposed and a four-step decision framework is established: 1) define performance indicators to represent stakeholders’ interests, 2) optimise operational strategies of urban wastewater systems in accordance to the indicators, 3) screen high performance solutions, and 4) derive permits of operational strategies of the wastewater treatment plant. Results from a case study show that operational cost, variability of wastewater treatment efficiency and environmental risk can be simultaneously reduced by at least 7%, 70% and 78% respectively using an optimal integrated operational strategy compared to the baseline scenario. However, trade-offs exist between the objectives thus highlighting the need of expansion of the prevailing wastewater management paradigm beyond the narrow focus on effluent water quality of wastewater treatment plants. Rather, systems thinking should be embraced by integrated control of all forms of urban wastewater discharges and coordinated regulation of environmental risk and treatment cost effectiveness. It is also demonstrated through the case study that permitting operational strategies could yield more environmentally protective solutions without entailing more cost than the conventional end-of-pipe permitting approach. The proposed four-step permitting framework builds on the latest computational techniques (e.g. integrated modelling, multi-objective optimisation, visual analytics) to efficiently optimise and interactively identify high performance solutions. It could facilitate transparent decision making on water quality management as stakeholders are involved in the entire process and their interests are explicitly evaluated using quantitative metrics and trade-offs considered in the decision making process. We conclude that the operational strategy-based permitting shows promising for regulators and water service providers alike.The authors would like to thank the financial support from the SANITAS project (EU FP7 Marie Curie Initial Training Network – ITN – 289193) and the support of North Wyke Farm and Atkins

The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe