Sadeque Hamdan

Supplier selection and order allocation with green criteria

Green and traditional criteria for supplier selection were split into two sets.Two bi-objective and one multi-objective optimization models were proposed.The models use a combination of three tools: AHP, Fuzzy TOPSIS and optimization.The results show that the proposed approach is more flexible than the existing ones. This research provides a decision-making tool to solve a multi-period green supplier selection and order allocation problem. The tool contains three integrated components. First, fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) is used to assign two preference weights to each potential supplier according to two sets of criteria taken separately: traditional and green. Second, top management uses an analytic hierarchy process (AHP) to assign a global importance weight to each of the two sets of criteria based on the strategy of the company and independently of the potential suppliers. Third, for each supplier, the preference weight obtained from fuzzy TOPSIS regarding the traditional criteria is then multiplied by the global importance weight of the set of traditional criteria. The same is done for the green criteria. The two combined preference weights obtained for each supplier are then used in addition to total cost to select the best suppliers and to allocate orders using multi-period bi-objective and multi-objective optimization. The mathematical models are solved using the weighted comprehensive criterion method and the branch-and-cut algorithm. The approach of this research has a major advantage: it provides top management with flexibility in giving more or less importance weight to green or traditional criteria regardless of the number of criteria in each category through the use of AHP, which reduces the effect of the number of criteria on the preference weight of the suppliers. Contrary to the case in which each supplier is evaluated on the basis of all criteria at the same time, the proposed approach would not necessarily result in a supplier with poor green performance being ranked among the best for a situation in which the number of green criteria is smaller than the number of traditional criteria. In this case, the final ranking would mainly depend on the global weight of the green criteria set given by the top management using AHP as well as on the ranking of the supplier in terms of green criteria obtained from fuzzy TOPSIS. Extensive numerical experiments are conducted in which the bi-objective and multi-objective models are compared and the effect of the separation between green and traditional criteria is investigated. The results show that the two optimization approaches provide very close solutions, which leads to a preference for the bi-objective approach because of its lower computation time. Moreover, the results confirm the flexibility of the proposed approach and show that combining all criteria in one set is a special case. Finally, a time study is performed, which shows that the bi-objective integer linear programming model has a polynomial computation time.

Green supplier selection and order allocation using an integrated fuzzy TOPSIS, AHP and IP approach

This paper proposes a model to solve an integrated green supplier selection and order allocation multi-period problem. The model consists of three stages; first stage uses fuzzy TOPSIS to rank and assign preference weights to a set of traditional and green criteria. Moreover, in the second stage, the criteria are grouped into two subsets, traditional and green, and then AHP is used to assign importance weights to each subset. The outputs of the first and second stage are used as an input for a bi-objective optimization model. The model assumes a deterministic demand. It also allows for shortage while ensuring that total demand will be satisfied at the end of the planning horizon even if with some delay. Comprehensive Criterion Method (CCM) is adopted to solve the bi-objective optimization and LINGO software is used to find the optimal solution.

Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability

Abstract This paper aims to solve a multi-period green supplier selection and order allocation problem with all-unit quantity discounts, in which the availability of suppliers differs from one period to another. The proposed approach involves three stages. In the first stage, decision makers use fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to assign two preference weights to every potential supplier based on the suppliers performance in two sets of criteria considered separately: traditional and green. In the second stage, top management uses the analytic hierarchy process to assign an importance weight to each of the two sets of criteria based on the organizations strategy. The outputs of the first and second stages serve as inputs for a single-product bi-objective integer linear programming model with deterministic demand that takes into account all-unit quantity discounts and a varying number of suppliers in each period of the planning horizon. We implement the proposed mathematical model in MATLAB R2014a software using the weighted comprehensive criterion method and the branch-and-cut algorithm. Statistical analysis helps determine the most suitable ranking approach for suppliers when their availability changes in each period. This paper presents a numerical comparison between two settings: the first considers all-unit quantity discounts, and the second does not. Moreover, a time study shows that the proposed bi-objective integer linear programming model has an exponential computation time.

A two stage green supplier selection and order allocation using AHP and multi-objective genetic algorithm optimization

Green supplier selection and order allocation problem involves multi-criteria decisions. In this paper, the available suppliers are ranked based on selected green criteria by decision makers in the purchasing department using analytic hierarchy process (AHP). Then, genetic algorithm (GA), that uses real-coded representation chromosomes, is used to find the optimal solution for the multi-objective integer linear programming model. The model deals with three conflicting objectives which are: total purchasing cost (TCP), total green value of purchasing (TGVP) and total rejected item due to quality (TR). The model is illustrated by a numerical example.

Green Traveling Purchaser Problem model: A bi-objective optimization approach

The green traveling purchaser problem (GTPP) is a generalization of the Traveling Purchaser Problem which consists of selecting suppliers, allocating orders and choosing the best routes, while minimizing the purchasing and traveling costs and CO2 emissions. The two objective functions pertaining to minimization of CO2 emissions and purchasing costs are in some cases conflicting and are thus considered separately. This paper presents an exact method to solve the proposed bi-objective optimization model where the bi-objective mathematical model is transformed into a single objective function model using the weighted comprehensive criterion method. The model is solved using branch and cut algorithm in MATLAB software. Computational experiments were carried out using two random instances, and the results show that the algorithm gives the bi-objective Pareto optimal solutions with significant difference in computation times when the speed is constant or varies between the routes. Using different weighting factors, this model can be considered as a decision making tool that allows decision makers to use the solution that fits the most with their organizations strategy.

A multi-objective optimization of maintenance policies using weighted comprehensive criterion method (WCCM)

Maintenance is playing a crucial role in keeping and improving availability, performance efficiency, quality products, on-time deliveries, and environment and safety requirements at high levels. Usually decision maker has to consider conflicting criteria such as cost, reliability, safety, risk, and availability when selecting among various maintenance policies. The purpose of this paper is to develop a multi-objective optimization model that will be used to compare the performance of maintenance policies (age, block good as new, block bad as old) based on four performance criteria which are cost, availability, life-time and reliability by using weighted comprehensive criterion method to convert all the objectives into a single objective. The model is illustrated with a typical numerical example. The model presented in this paper works as a tool for decision maker to select the most effective maintenance policy under different scenarios.

Datasets for supplier selection and order allocation with green criteria, all-unit quantity discounts and varying number of suppliers

This data article provides detailed optimization input and output datasets and optimization code for the published research work titled “Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability” (Hamdan and Cheaitou, 2017, In press) [1]. Researchers may use these datasets as a baseline for future comparison and extensive analysis of the green supplier selection and order allocation problem with all-unit quantity discount and varying number of suppliers. More particularly, the datasets presented in this article allow researchers to generate the exact optimization outputs obtained by the authors of Hamdan and Cheaitou (2017, In press) [1] using the provided optimization code and then to use them for comparison with the outputs of other techniques or methodologies such as heuristic approaches. Moreover, this article includes the randomly generated optimization input data and the related outputs that are used as input data for the statistical analysis presented in Hamdan and Cheaitou (2017 In press) [1] in which two different approaches for ranking potential suppliers are compared. This article also provides the time analysis data used in (Hamdan and Cheaitou (2017, In press) [1] to study the effect of the problem size on the computation time as well as an additional time analysis dataset. The input data for the time study are generated randomly, in which the problem size is changed, and then are used by the optimization problem to obtain the corresponding optimal outputs as well as the corresponding computation time.

Optimizing the parameters of a biodynamic responses to vibration model using Particle Swarm and Genetic Algorithms

Various local optimization techniques such as Interior Point Algorithm have been widely used to optimize the parameters of models representing biodynamic responses to vibration. The quality of the obtained solutions depends on the initial guesses. This paper presents a comparison between the performance of Particle Swarm Optimization and Genetic Algorithm in optimizing the parameters of a human body model, where these techniques do not require initial guesses. The model represents the vertical apparent mass and the fore-and-aft cross-axis apparent mass of the seated human body during vertical excitation. With both optimization methods, the model provided close fits to the moduli and phases for both median data and the responses of 12 individual subjects. However, it was noted that using PSO provided a better solution with less mean error than GA and a faster solution in most of the cases.

Green supplier selection and order allocation with incremental quantity discounts

Pricing strategies are critical to attract buyers and motivate them to purchase more. Quantity discounts are pricing incentives used by suppliers and benefit the buyers in the sense that the more the purchased quantity the lower the unit price. This paper uses an incremental quantity discount scheme in a green supplier selection and order allocation context and proposes a multi-period single-product bi-objective optimization model. The developed model attempts to maximize the green value and minimize the total cost of the purchased items from the buyer perspective. The model is converted into a single objective one by adopting a weighted comprehensive criterion method and solved using the branch-and-cut algorithm. The results show the effect of the use of incremental quantity discounts on the optimal ordered quantities.

Forecasting of peak electricity demand using ANNGA and ANN-PSO approaches

Electrical load forecasting is essential in the field of power systems to enhance the operation and economical utilization In this paper, a combined approaches of artificial neural networks (ANN) with particle-swarm-optimization (PSO) and genetic algorithm optimization (GA) for short and mid-term load forecasting is developed. The model identifies the relationship among load, temperature and humidity using a case study of Sharjah City in United Arab Emirates. The ANN model trains the hourly peak load data for a set of days and then forecasts the load for next day. Actual data obtained from Sharjah Electricity and Water Authority (SEWA) is used to validate the results.