Real Case Problem Research Articles

Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

A multi-phase decision-making approach for supplier selection and order allocation with corporate social responsibility

Despite attention of the developed countries towards social responsibility, this issue is still not well-established in developing countries. The short-term management period in these countries, numerous management changes in organizations, and incomplete, vague, and uncertain information make the organization’s senior management pays attention only to economic issues. Accordingly, this study outlines a multi-phase methodology to address the challenges of integrating Corporate Social Responsibility (CSR) in Supplier Selection and Order Allocation Problem (SSOAP) in developing countries. A multi-objective model is developed based on traditional criteria, which takes into account the concerns of senior managers. Suppliers are then evaluated based on their CSR practices using linguistic terms and fuzzy numbers to account for the vagueness and uncertainty in these practices. The study also applies the Best Worst Method (BWM) to calculate the weight that is integrated into different stages of the methodology. Interval-valued CSR scores for each supplier are employed to estimate the interval-valued CSR score for each supply chain belonging to solutions obtained from the mathematical model. Additionally, a method based on the TOPSIS structure is developed to rank solutions. The proposed method maintains an interval-based structure throughout its steps and ranks alternatives according to the interval relative closeness index using the order relation between two intervals. The study is validated with a numerical example and a real case problem. The results are discussed with respect to the feedback provided from the case managers and a comprehensive sensitivity analysis is performed to investigate the behavior of the model under different circumstances. The model demonstrates that incorporating CSR practices alongside traditional criteria can alleviate senior managers' economic worries. Furthermore, the community stands to gain from such practices. Our methodology also highlights the impact of CSR on supplier purchases and verifies incorporating CSR practices alongside traditional criteria can enhance sourcing efficacy.

Standalone Photovoltaic Power Stabilizer Using Double Series Connected Converter in Sudden Cloud Condition

Renewable energy is clean energy that cannot harm the environment in its way, especially on standalone photovoltaic electricity generation. The only problem with renewable energy electricity generation is the intermittency and its instability in power quality and power efficiency. Power system stability in renewable energy is essential during the real environmental case problem such as the sudden cloud. The sudden cloud, known for its ability to decrease solar irradiance input, depends on how thick and big the cloud is. Several attempts had been tried to increase renewable energy power system stability, including modifying its maximum power point tracker with a new algorithm such as perturb and observe. This paper discussed an improved way of maintaining renewable energy power system stability using perturb and observe Algorithm in a double series-connected Converter as the current and power stabilizer. The results show that the current overshoot has decreased by 65,336%, and the current sudden cloud undershoot decreased by 10,529%. Power overshoot also decreased by 43,685%, and the power of sudden cloud undershoot has decreased by 7,133%.

Sensor location models with reliable optimal solution for the observation of origin–destination matrix and route flows

Origin–destination matrix (ODM) is a key element in transportation studies. The emergence of new ITS technologies like Automatic Vehicle Identification (AVI) sensors makes the ODM observation problem more interesting in recent decades. However, sensors are subject to failure in reality which highlights the sensor failure phenomenon as a significant issue in real-case problems. This study intends to include the sensor failure phenomenon in AVI Sensor Location Model (SLM) for reliable observation of ODM and route flows. While reliability and cost are usually two conflicting objectives, we try to answer the following question “Is it possible to improve reliability without increasing the cost and only by changing sensor deployment?”. In addressing this study question, first, it is shown that the solution of recent AVI SLMs are not unique. Second, we show that the reliability level of multiple optimal solutions is not the same. Third, two Mixed Integer Linear Programming (MILP) AVI SLMs for reliable observation and parital observation of ODM/route flows are developed considering the sensor failure phenomenon. The models are formulated such that their solutions are selected from the set of multiple optimal solutions. Fourth, a linear surrogate term for reliability is introduced and mathematically proven to be included in the proposed models. Finally, the applicability of the proposed models is examined on several middle-scale networks and a real-size network. Furthermore, a heuristic algorithm is customized to solve the models for the real-size network. The results suggest that there might be alternative sensor deployment strategies with the same number of sensors as in the optimal solution but with higher level of reliability for ODM/route flows observation.

HAMLET: A framework for Human-centered AutoML via Structured Argumentation

Machine Learning (ML) plays a crucial role in data analysis and data platforms (i.e., integrated sets of technologies that collectively meet end-to-end data needs). In the last decade, we have witnessed an exponential growth in both the complexity and the number of ML techniques; leveraging such techniques to solve real-case problems has become difficult for Data Scientists. Automated Machine Learning (AutoML) tools have been devised to alleviate this task, but easily became as complex as the ML techniques themselves — with Data Scientists losing again control over the process. In this paper, we design and extend HAMLET (Human-centered AutoMl via Logic and argumEnTation), a framework that helps Data Scientists to redeem their centrality. HAMLET enables Data Scientists to express ML constraints in a uniformed human- and machine-readable medium. User-defined constraints are interpreted to drive the exploration of ML pipelines (i.e., Data Pre-processing transformations shape the data so that the ML task can be performed at its best). AutoML retrieves the most performing pipeline instance, and finally, new constraints are learned and integrated through Logic and Argumentation. By doing so, HAMLET not only allows an easy exploitation of the knowledge acquired at each iteration, but also enables its continuous revision via the AutoML tool and the collaboration of both Data Scientists and domain experts.

Multi-criteria evaluation of medical waste management process under intuitionistic fuzzy environment: A case study on hospitals in Turkey

Waste management has come to the fore in the whole world with the increasing impact of the Covid-19 pandemic along with concerns about human health, environmental threats, and socio-economic factors, etc. Medical waste is one of the waste types that need special management processes including particularly collection, storage, separation, and disposal. Healthcare activities create a great amount of medical waste deriving from the hospitals. This study aims to determine the hospital that carries out medical waste management in the most effective way in Erzurum, Turkey. To handle intense uncertainty in the evaluation process, the case is analyzed by Intuitionistic Fuzzy Multi-Criteria Decision-Making (IFMCDM) methods. The present study contributes to the literature by focusing on a real case problem under IF environment in a Group Decision-Making (GDM) framework. Additionally, based on the literature review and expert judgments, the evaluation criteria relevant to the case are defined in this paper. To this end, a four-phased integrated methodology that involves Intuitionistic Fuzzy Weighted Averaging (IFWA), IF Analytical Hierarchy Process (IFAHP), IF Technique for Order Preference by Similarity to Ideal Solution (IFTOPSIS) and One-Dimensional Sensitivity Analysis, is conducted. Firstly, IFWA is aimed to express the significance levels of decision makers (DMs) based on their knowledge, qualifications and experiences. Secondly, IFAHP is used to calculate the importance weights of the decision criteria and IFTOPSIS is preferred to rank the available hospitals. Then, sensitivity analysis is employed to display robustness. According to the results, the most important criteria are Qualified personnel, Health institution infrastructure, and Control of waste, respectively and the most efficient hospital is determined.

DEVELOPMENT OF FINITE ELEMENT ANALYSIS PROGRAM FOR ROTORDYNAMIC PREDICTION OF SINGLE AND MULTI DISK ROTOR MODEL

The rotating equipment is a crucial part of system performance reliability. Vibration, as a characteristic of an oscillating body, is a fundamental parameter to determine the dynamic behaviour of the rotor system. The dynamic characteristics include natural frequency, critical speed, mode shape, and vibration response. This paper discussed the application of rotordynamics analysis as a tool to numerically predict the dynamic behaviour of a rotor system. The aim is to determine a rotor system's natural frequency and critical speed by a finite element analysis (FEA) program. The selected method is validated using various analytical methods from other references with small discrepancies in the result. The calculation of FEA using the computational program; they applied the developed program to describe the dynamic characteristic of single and multi-disk rotors. The developed works provided comprehensive results about a rotor system's natural frequency, mode shape, and critical speed and predicted the vibration response due to unbalance. However, other types of rotors, such as coaxial dual rotors, can be further investigated to make the program capable of the general purpose of the rotor. Validation with the real case problem could be an interesting investigation to satisfy the result of developed works.

Comprehensive assessment of 20 state-of-the-art multi-objective meta-heuristic algorithms for multi-reservoir system operation

Optimal operation of multi-purpose multi-reservoir dams is a challenging problem for dams’ stakeholders and decision-makers. Optimization algorithms are able to present reliable solutions for such complex problems. This study compared the capability of 20 state-of-the-art robust meta-heuristic algorithms for determining the optimal operating policy of Halilrood multi-reservoir system under the three competing operational objectives of water supply, flood control, and hydropower generation. Before that, the performance of the algorithms in solving two benchmark problems (the Schaffer problem and the MMF1 problem from the CEC suite) was evaluated. Four metrics of generational distance (GD), spacing (S), spread (Δ), and maximum spread (MS) were used to compare the algorithms’ performance in solving the benchmark problems. In addition, four reservoirs performance evaluation indicators of reliability (Rel), resiliency (Res), vulnerability (Vul), and sustainability index (SI) were used to ass the algorithms’ performance in solving the real-case Halilrood problem. The results showed that although the solving capability of each algorithm depends on the nature of the problem, some algorithms were always superior in solving all three problems (2 benchmark problems and one real case). For the Schaffer benchmark problem, the MOAHA algorithm with the performance metrics of (GD = 0.00095, S = 0.58155, Δ = 0.20375, MS = 4.0002) had the best Pareto front in terms of coverage and diversity, and so it was placed at the first rank. In this problem, the MOCryStAl algorithm with (GD = 0.00141, S = 0.48395, Δ = 1.49467, MS = 3.94799) placed at the lowest rank. For the MMF1 problem, the MOMA (GD = 0.00751, S = 0.07516, Δ = 0.29519, MS = 0.99826) followed by the MOAHA (GD = 0.00833, S = 0.07660, Δ = 0.17832, MS = 1.00) had the best performance, respectively, and the MOFA algorithms was the worst. For the Halilrood real-case problem, the MOGWO algorithm (with Rel = 83.41, Res = 67.57, Vul = 22.07 and SI = 76.01) followed by the MOAHA algorithm (Rel = 84.57, Res = 64.71, Vul = 33.60, and SI = 71.41) and the MOMA algorithm (Rel = 79.82, Res = 66.67, Vul = 21.65, and SI = 74.70) obtained the best ranks in terms of reservoir's performance evaluation indicators. They could successfully minimize all three objectives of the total deficit, the flood control and electricity generation indicating the highest performance of these algorithms in the optimization problem. The Pareto front obtained by these algorithms had better distribution and was closer to the origin of coordinates compared to the other utilized algorithms. They could favorably supply the total water demand in the Halilrood basin while increasing the hydropower energy generation up to the power plant capacity at the Jiroft dam. The results of this study can be applied to the optimal operation of any other multi-reservoir system with conflict in water demands.

Analyzing of process capability indices based on neutrosophic sets

Process capability analysis (PCA) is an important statistical approach for measuring and analyzing the ability to meet specifications. This analysis has been generally applied by obtaining process capability indices (PCIs). The indices named {C}_{rm{p}} and {C}_{mathrm{pk}} are the most commonly used for this aim. Although PCIs are completely effective statistics to analyze process’ capability, the complexity of the production processes based on uncertainty arising from human thinking, incomplete or vague information makes it difficult to analyze the process capability with precise values. When the process includes uncertain, complex, incomplete and inaccurate information, the capability of the process can be successfully analyzed by using the fuzzy set theory (FST). Neutrosophic sets (NSs), one of the fuzzy set extensions, have an ability to deliver more successful results for modeling uncertainty, since they contain the membership functions of truth, indeterminacy, and falsity definitions rather than an only membership function. This feature provides a strong advantage and important capability for modeling uncertainty. In this paper, PCA has been performed based on NSs for more effectively modeling uncertainties of the process. For this purpose, specification limits (SLs) have been reconsidered by using NSs and two of the well-known process capability indices (PCIs) named {C}_{rm {p}} and {C}_{rm {pk}} have been reformulated. Additionally, design and analysis of the indices {C}_{rm {p}} and {C}_{rm {pk}} are investigated based on NSs. Finally, the neutrosophic process capability indices (NPCIs) {C}_{rm {p}} left({widetilde{dddot C}}_{rm {p}}right) and {C}_{rm {pk}} left( {widetilde{dddot C}_{{{text{pk}}}} } right) have been derived for three cases that are created by defining SLs to model uncertainties. Additionally, the indices {widetilde{dddot C}}_{rm {p}} and {widetilde{dddot C}}_{rm {pk}} have also been applied and analyzed on some real case problems from automotive industry. The obtained results show that the NPCIs support the quality engineers to easily define SLs and obtain more flexible and realistic evaluations for PCA.

The Appropriateness of Design Cognition for Entrepreneurship Education of Engineering Students

The study argues that the needs of engineering students for managerial education are distinct and evolving over time towards entrepreneurial mindset due to new industrial and technological trends. Consequently, managerial education of engineering students is shifting towards Science and technology entrepreneurship education (STEE). Moreover, considering the relevance of design cognition in entrepreneurship and engineering, it is argued that STEE can tackle the challenge more effectively by incorporating design cognition in the education process. Propositions are derived from research literature, and a teaching model approach is proposed that elaborated on the incorporation of cognitive acts of design in various aspects at ontological, didactical, and contextual levels in STEE. The proposed framework views education as a process of co-construction, centered on students where the role of teacher is like a coach. Students work in teams and practice the cognitive acts of design that lead to the development of interpersonal, entrepreneurial, and managerial skills. For this purpose, open-ended questioning, real life case studies, problem and project based learning, and lean methodology are proposed as effective content and pedagogies to promote the entrepreneurial behaviors required in the current industrial scenario.

Design of single and double acceptance sampling plans based on interval type-2 fuzzy sets

Defectiveness of items is generally considered as a certain value in acceptance sampling plans (ASPs). It is clear that, it may not be certainly known in some real-case problems. Uncertainties of the inspection process such as measurement errors, inspectors’ hesitancies or vagueness of the process etc. should be taken into account to obtain more reliable results. The fuzzy set theory (FST) is one of the best methods to overcome these problems. There are some studies in the literature formulating the ASPs with the help of FST. Deciding the right membership functions of the fuzzy sets (FSs) has a vital importance on the quality of the uncertainty modeling. Additionally, the fuzzy set extensions have been offered to model more complicated uncertainties to achieve better modeling. As one of these extensions, type-2 fuzzy sets (T2FSs) gives an ability to model uncertainty in situations where it is not possible to determine exact membership function parameters. In this study, single and double ASPs based on interval T2FSs (IT2FSs) have been designed for binomial and Poisson distributions. Thus, it becomes possible to make more flexible, sensitive and descriptive sensitivity analyzes. The main characteristic functions of ASPs have been derived and the suggested formulations have been illustrated on a comparative application from manufacturing process. Results allowing for more comprehensive analysis as against to the traditional and T1FSs based plans have been obtained.

Design of single and double acceptance sampling plans based on neutrosophic sets

Although traditional acceptance sampling plans (ASPs) need certain mass quality characteristics, it is not easy to define them as crisp value in some real case problems. The fuzzy set theory (FST) is one of the popular techniques to model uncertainties of the process and therefore fuzzy ASPs have been offered in the literature. Fuzzy set extensions have been proposed recently for better modeling of the uncertainties having different sources and characteristics. One of these extensions named neutrosophic sets (NSs) can be used to increase the sensitiveness and flexibility of ASPs. The ASPs based on NSs can give ability to classify the items as defective, non-defective and indeterminate. Since the operator can become indecisive for slightly defective items, these plans can provide a good representation of human evaluations under uncertainty. In this study, single and double ASPs are designed based on NSs by using binomial and poisson distributions that are also re-analyzed based on NSs. For this aim, some characteristics functions of ASPs such as probability of accepting a lot (Pa), average outgoing quality (AOQ), average total inspection (ATI) and average sample number (ASN) have also been analyzed based on NSs. Numerical examples are presented to analyze the proposed plans.

Competitive scheduling in a hybrid flow shop problem using multi-leader–multi-follower game - A case study from Iran

It is logical that organizations pay more attention to their major customers which have more impacts on their abilities or benefits. In many real cases, decision makers in scheduling system must prioritize the sequence of processing orders according to the agent of orders while the classic production scheduling do not have the ability to consider the relationships between agents. These problems can be modeled by the Stackelberg problems in the field of game theory. In this study, an agent-based hybrid flow shop problem is formulated by using the multi-leader multi-follower mathematical model. Each level is evaluated by Nash equilibrium and Nash bargaining with the new co-evolutionary and genetic algorithm. The proposed model is validated in a real case problem in the tire manufacturing industry. The results show that Nash equilibrium is superior to Nash bargaining for all agents.

A novel two-phase algorithm for a centralised production planning problem by symmetric weighted DEA approach: a case study in energy efficiency

This paper proposes a novel two-phase algorithm for a centralised production planning problem that covers both long-term and mid-term planning simultaneously. Mid-term decisions (first phase) include determining the amount of new inputs and outputs of units in the next production period and long-term decisions (second phase) are related to planning to create a new efficient production unit. The first phase includes a data envelopment analysis approach with symmetric weights through the penalty function. The second phase includes combining data envelopment analysis and response surface methods. In order to evaluate the proposed method, the National Iranian Gas Company is considered as a real case problem. The results show the superiority of the algorithm in both phases over similar methods. The main advantage of the first phase is the realistic production plan. Also, the main advantage of the second phase is optimising the response surface functions and maximising the efficiency of the unit at the same time using one model. [Submitted: 18 March 2021; Accepted: 27 September 2021]

Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA

In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.

Efficient mass conservative numerical model for solving variably saturated groundwater flow

A multi-dimensional mass conservative numerical method, particularly suitable for limited computational resources, is developed for solving transient variably saturated groundwater flow problems. The Richards equation is discretized spatially with a finite element method and temporally with an implicit Euler scheme, in which mass-conservative and mass-lumping techniques are used to keep the numerical simulation stable. In addition, the stiffness and mass matrices involved are approximated in a way to guarantee less computational effort. To confirm the accuracy and the efficiency of this code, we verified it using benchmark tests using one, two and three-dimensional problems. The present model is also applied to a real field case problem, where its superiority is clearly demonstrated. The code achieved reliable results for each problem.

Application of the ADINA Software for Tasks of Educational and Technical Practice

Development of the designing process in civil engineering brings the possibility for more complex analysis of the structures. Computational software can be helpful for regular design but also for identification and solving of more specified problems. Finite Element Method (FEM) became one of the most used numerical methods. Numerous commercial programs are based on this method. However, these programs are more complex and require skilled designer to fully utilize their potential. This article brings the lead to apply the FEM method implemented in commercial software ADINA to solve the problem of structural mechanics. It is necessary to connect the theoretical approach of FEM modeling and experience from real case problems. Skilled designer can calibrate the numerical model to obtain relevant outputs. However, certain cases still require a detailed and individual approach during designing that is verified by the in-situ monitoring and observation. Lead of the student to the flexible treatment of the design is one of the important tasks of the educational process. This involves the basic knowledge about appropriate analysis of the problem for effective utilization of the computational software for particular applications even for more complex solutions.

Minimizing deviations of car sequencing in a JIT production painting line

PurposeThe first in first out (FIFO) rule is an indispensable rule in the automotive industry. However, during production, the order of coming to welding operation and exiting through the painting operation is broken. Consequently, the rule of FIFO also gets broken. The purpose of this paper is to eliminate the deviations of the automotive painting process in a just in time (JIT) environment.Design/methodology/approachThe paper has been solved using observations and expert’s opinions and recorded data in the real problem area. Bypass technique and express line have been used as a real problem for the minimization of these deviations. The current process and the bypass process have been compared with simulation optimization.FindingsThis paper provides analytical insights about how to prevent delays during the JIT production painting process. Delays have been prevented by using the bypass technique and express line together. The number of delays reduced with an improvement of 129. The ratio of effect (for four months) has been founded to be 0.12. The output rate has been increased from 80% to 89%.Research limitations/implicationsThis paper focused on the number of vehicles in the cooling area of the painting process in the study; there may occur bottleneck processes or products. It does not contain station-based bottleneck processes.Practical implicationsFrom the real-case problem, one will see that the bypass technique and the express line can be applied and generalized to many similar problems and help companies conduct their assembly lines more efficiently.Originality/valueThis paper is the only example that has been used to bypass technique and express line together on the paint/cooling process in a JIT production system.

(2011-6272) Design and analysis of process capability indices cpm and cpmk by neutrosophic sets

Process capability indices (PCIs) have been widely used to analyze capability of the process that measures how the customer expectations have been conformed. Two of the well-known PCIs, named indices $ C_{pm} $ and $ C_{pmk} $ have been developed to consider customers' ideal value that called target value ($ T $). Although, these indices have similar features of the well-known indices $ C_{p} $ and $ C_{pk} $, one of the most important differences is to consider textit{T}. In real case problems, we need to add some uncertainties related with human's evaluations into process capability analysis (PCA). One of the uncertainty modelling methods called neutrosophic sets (NSs), have an important role in modeling uncertainty based on incomplete and inconsistent information. For this aim, the PCIs have been designed by using NSs to manage the uncertainties of systems and to increase sensitiveness, flexibility and to obtain more detailed results of PCA in this paper. For this aim, the indices $ C_{pm} $ and $ C_{pmk} $ have been performed and re-designed by using single valued neutrosophic numbers for the first time in the literature. Additionally, specification limits (SLs) have been re-considered by using NSs. The neutrosophic state of the SLs provide us to have more knowledge about the process and easily applied for engineering problems that includes uncertainty. Finally, the neutrosophic process capability indices (NPCIs) $ widetilde{dddot{C}}_{pm}$ and $ widetilde{dddot{C}}_{pmk} $ have been obtained and the main formulas of them have been produced. Additionally, the proposed $ widetilde{dddot{C}}_{pm} $ and $ widetilde{dddot{C}}_{pmk} $ have been applied on real case studies from manufacturing industry. The obtained results show that the indices $ widetilde{dddot{C}}_{pm} $ and $ widetilde{dddot{C}}_{pmk} $ include more informative and flexible results to evaluate capability of process.

Minimizing Solid Waste Collection Routes Using Ant Colony Algorithm: A Case Study in Gaziantep District

This paper proposes an ant colony optimization algorithm for a capacitated vehicle routing problem to determine the shortest waste collection and transportation route covered by a waste collection truck in the Şahinbey municipality of Gaziantep/Turkey. The real-case problem concerns a capacity-restricted garbage compactor truck that collects and transports waste from 349 residential waste containers located in 148 collection points. Possible solutions obtained from the ant colony algorithm were compared with mixed-integer programming model solutions. The results of the proposed ant colony optimization algorithm showed that our model yields a 28% reduction in the total daily traveled distances and energy savings against existing consumption. The new solution also cuts the current annual waste collection and transportation expenditure per vehicle by 30%. It is shown that a considerably shorter route distance obtained in the algorithmic solution helps to reduce air pollution from the infamously inefficient garbage collection trucks. Eventually, the new route will considerably reduce labor costs, the high price of fuel, machinery and equipment maintenance as well as environmental pollution, because garbage compactor trucks are one of the least efficient vehicles on the road.