A multiple criteria decision making technique for supplier selection and inventory management strategy: A case of multi-product and multi-supplier problem

Selection of an appropriate supplier along with planning a good inventory system has become an area of open research for the past few years. In this paper, we present a multi objective decision making supplier and inventory management model where two objectives including the quality and offering price of supplier are minimized, simultaneously. The proposed model is formulated as mixed integer programming and it is converted into an ordinary single objective function using Lp-Norm. In order to find efficient solution, we use NSGA-II as meta-heuristic technique and the performance of the proposed model is examined using some instances. The preliminary results indicate that both Lp-Norm and NSGA-II methods can be used to handle problems in various sizes

Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

An efficient controlled elitism non-dominated sorting genetic algorithm for multi-objective supplier selection under fuzziness

Supplier selection and order allocation constitute vital strategic decisions that must be made by managers within supply chain management environments. In this paper, we propose a multi-objective fuzzy model for supplier selection and order allocation in a two-level supply chain with multi-period, multi-source, and multi-product characteristics. The supplier evaluation objectives considered in this model include cost, delay, and electronic-waste (e-waste) minimization, as well as coverage and weight maximization. A signal function is used to model the price discount offered by the suppliers. Triangular fuzzy numbers are used to deal with the uncertainty of delay and e-waste parameters while the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain the weights of the suppliers. The resulting NP-hard problem, a Pareto-based meta-heuristic algorithm called controlled elitism non-dominated sorting genetic algorithm (CENSGA), is developed. The Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to validate the applicability of the CENSGA algorithm and the Taguchi technique to tune the parameters of the algorithms. The results are analysed using graphical and statistical comparisons illustrating how the proposed CENSGA dominates NSGA-II and MOPSO in terms of mean ideal solution distance (MID) and spacing metrics

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

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

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

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Optimization of a dynamic supply portfolio considering risks and discount’s constraints

Purpose: Nowadays finding reliable suppliers in the global supply chains has become so important for success, because reliable suppliers would lead to a reliable supply and besides that orders of customer are met effectively . Yet, there is little empirical evidence to support this view, hence the purpose of this paper is to fill this need by considering risk in order to find the optimum supply portfolio. Design/methodology/approach: This paper proposes a multi objective model for the supplier selection portfolio problem that uses conditional value at risk (CVaR) criteria to control the risks of delayed, disrupted and defected supplies via scenario analysis. Also we consider discount’s constraints which are common assumptions in supplier selection problems. The proposed approach is capable of determining the optimal supply portfolio by calculating value-at-risk and minimizing conditional value-at-risk. In this study the Reservation Level driven Tchebycheff Procedure (RLTP) which is one of the reference point methods, is used to solve small size of our model through coding in GAMS. As our model is NP-hard; a meta-heuristic approach, Non-dominated Sorting Genetic Algorithm (NSGA) which is one of the most efficient methods for optimizing multi objective models, is applied to solve large scales of our model. Findings and Originality/value: In order to find a dynamic supply portfolio, we developed a Mixed Integer Linear Programming (MILP) model which contains two objectives. One objective minimizes the cost and the other minimizes the risks of delayed, disrupted and defected supplies. CVaR is used as the risk controlling method which emphases on low-probability, high-consequence events. Discount option as a common offer from suppliers is also implanted in the proposed model. Our findings show that the proposed model can help in optimization of a dynamic supplier selection portfolio with controlling the corresponding risks for large scales of real word problems. Practical implications: To approve the capability of our model various numerical examples are made and non-dominated solutions are generated. Sensitive analysis is made for determination of the most important factors. The results shows that how a dynamic supply portfolio would disperse the allocation of orders among the suppliers combined with the allocation of orders among the planning periods, in order to hedge against the risks of delayed, disrupted and defected supplies. Originality/value: This paper provides a novel multi objective model for supplier selection portfolio problem that is capable of controlling delayed, disrupted and defected supplies via scenario analysis. Also discounts, as an option offered from suppliers, are embedded in the model. Due to the large size of the real problems in the field of supplier selection portfolio a meta-heuristic method, NSGA II, is presented for solving the multi objective model. The chromosome represented for the proposed solving methodology is unique and is another contribution of this paper which showed to be adaptive with the essence of supplier selection portfolio problemPeer Reviewe

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems

Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.The project is funded by Department of Science and Technology, Science and Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT–Portuguese Foundation for Science and Technology within the R&D Units Projects Scopes: UIDB/00319/2020, UIDP/04077/2020, and UIDB/04077/2020