Showing papers on "Goal programming published in 2019"
TL;DR: The significance of the CLSC model is empirically established as a decision support tool for improving the TBL performance of a particular Indian laptop manufacturer and a generalised quantitative closed-loop model can be effectively adapted by other electronic manufacturers to increase their competitiveness, profitability, and to improve their TBL.
Abstract: Immense concern for sustainability and increasing stakeholders’ involvement has sparked tremendous interest towards designing optimal supply chain networks with significant economic, environmental, and social influence. Central to the idea, this study aims to design a closed loop supply chain (CLSC) network for an Indian laptop manufacturer. The network configuration, which involves a manufacturer, suppliers, third party logistics providers (forward and reverse), retailers, customers and a non-government organisation (NGO), is modelled as a mixed integer linear programming problem with fuzzy goals of minimising environmental impact and maximising net profit and social impact, subject to fuzzy demand and capacity constraints. Profit is generated from the sale of primary and secondary laptops, earned tax credits, and revenue sharing with reverse logistics providers. The environmental implications are investigated by measuring the carbon emitted due to activities of manufacturing, assembling, dismantling, fabrication, and transportation. The social dimension is quantified in terms of the number of jobs created, training hours, community service hours, and donations to NGO. The novelty of the model rests on its quantification of the three triple bottom line (TBL) indicators and on its use of AHP–TOPSIS for modelling the multi-criteria perspectives of the stakeholders. Numerical weights for the triple lines of sustainability are utilized. Further, a fuzzy multi-objective programming approach that integrates fuzzy set theory with goal programming techniques is utilised to yield properly efficient solutions to the multi-objective problem and to provide a trade-off set for conflicting objectives. The significance of the CLSC model is empirically established as a decision support tool for improving the TBL performance of a particular Indian laptop manufacturer. Practical and theoretical implications are derived from the result analysis, and a generalised quantitative closed-loop model can be effectively adapted by other electronic manufacturers to increase their competitiveness, profitability, and to improve their TBL.
81 citations
TL;DR: Two solution methods of non-dominated sorting genetic algorithm II and multi-objective invasive weed optimization algorithm (MOIWO) are designed to solve theAPP problem and the results obtained from different comparison criteria demonstrate the high quality of the proposed solution methods in terms of speed and accuracy in finding optimal solutions.
Abstract: This paper addresses a robust multi-objective multi-period aggregate production planning (APP) problem based on different scenarios under uncertain seasonal demand. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/unemployment costs, and maximize the customers’ satisfaction level. To deal with demand uncertainty, robust optimization approach is applied to the proposed mixed integer linear programming model. A goal programming method is then implemented to cope with the multi-objectiveness and validate the suggested robust model. Since APP problems are classified as NP-hard, two solution methods of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization algorithm (MOIWO) are designed to solve the problem. Moreover, Taguchi design method is implemented to increase the efficiency of the algorithms by adjusting the algorithms’ parameters optimally. Finally, several numerical test problems are generated in different sizes to evaluate the performance of the algorithms. The results obtained from different comparison criteria demonstrate the high quality of the proposed solution methods in terms of speed and accuracy in finding optimal solutions.
81 citations
TL;DR: The results indicate that integrating the strategic decisions of selection of logistics partners with the operational flow planning decisions can immensely improve the sustainable performance value of the SC network and secure reasonable profits.
Abstract: Due to rising concerns for environmental sustainability, the Indian electronic industry faces immense pressure to incorporate effective sustainable practices into the supply chain (SC) planning. Consequently, manufacturing enterprises (ME) are exploring the option of re-examining their SC strategies and taking a formalized approach towards a sustainable partnership with logistics providers. To begin with, it is imperative to associate with sustainable forward and reverse logistics providers to manage effectively the upward and downstream flows simultaneously. In this context, this paper proposes an integrated SC network for the evaluation and selection of forward distribution partners (FDP) and third party reverse logistic providers (3PRLP) from a sustainable perspective of an Indian electronic ME. The sustainable evaluation of the logistic partners is performed using fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution. The integrated logistics network is modeled as a bi-objective mixed-integer programming problem with the objective of maximizing the profit of the manufacturer and maximizing the sustainable score of the selected forward and reverse logistics providers. The novelty of the study is its ranking of the FDPs and 3PRLPs on the economic, environmental, and social dimensions of sustainability and the simultaneous integration of logistics outsourcing decisions for the forward and reverse flow of products. Goal programming approach is utilized to capture the trade-off between the conflicting objectives and to attain a satisfying solution to the bi-objective problem. The results indicate that integrating the strategic decisions of selection of logistics partners with the operational flow planning decisions can immensely improve the sustainable performance value of the SC network and secure reasonable profits. The managerial implications drawn from the result analysis provide a sustainable framework to the ME for enhancing its corporate image.
72 citations
TL;DR: A multi-attribute closed-loop supply chain model for self-healing polymers based returnable transport packaging with single supplier, single manufacturer, and multi-retailers under budget and storage constraints is developed.
68 citations
TL;DR: It is observed from the extracted results that TOPSIS provides a better optimal solution than WGP and fuzzy programming, and overcomes some difficulties which arise in WGP.
Abstract: In this paper, we investigate a multi-objective multi-item fixed-charge solid transportation problem (MOMIFCSTP) with fuzzy-rough variables as coefficients of the objective functions and of the constraints. The main focus of the paper is to analyze MOMIFCSTP under a fuzzy-rough environment for a transporting system. In practical situations, the parameters of a MOMIFCSTP are imprecise in nature, due to several uncontrollable factors. For these reasons, we introduce the fuzzy-rough variables in MOMIFCSTP to tackle vague data which are different from fuzziness and roughness.
Fuzzy-rough expected-value operator is employed to convert fuzzy-rough MOMIFCSTP into deterministic MOMIFCSTP.
Thereafter, we develop a methodology to solve the deterministic MOMIFCSTP by technique for order preference by similarity to ideal solution (TOPSIS). Three distinct approaches, namely extended TOPSIS, weighted goal programming (WGP) and fuzzy programming, are used to derive Pareto-optimal solution from the suggested model. A comparison is drawn among the optimal solutions which are derived from different approaches. It is observed from the extracted results that TOPSIS provides a better optimal solution than WGP and fuzzy programming. TOPSIS also overcomes some difficulties which arise in WGP. Finally, a real-world (industrial) problem is incorporated to show the applicability and feasibility of the proposed problem.
53 citations
TL;DR: A parallel automated resilience-based restoration methodology is presented in the power system to minimize impact due to emergency power outages and uses the pre-emptive method of goal programming to deal with multiple conflicting objectives in the model.
Abstract: A parallel automated resilience-based restoration methodology is presented in the power system to minimize impact due to emergency power outages. In this power restoration process, a black start unit is assigned to a small region (i.e., a section) on an as-needed basis. A mixed integer nonlinear programming model is developed in order to optimally sectionalize the region of interest all the while maximizing the resiliency in terms of load shedding, restoration time, and network connectivity. For solving this large scale optimization model, a bi-level programming approach is proposed. This approach consists of two optimization levels. The sectionalization problem (upper level) is a mixed integer programming model and finds the optimal section set. The restoration problem (lower level) is a linear model and determines the dc optimal power flow and restoration time for the optimal section set identified in the upper level. We use the pre-emptive method of goal programming to deal with multiple conflicting objectives in the model. Our proposed solution approach outperformed mathematical programming with equilibrium constraints and found near optimal solutions. Numerical results and sensitivity analysis from two case studies (6- and 118-bus IEEE test systems) are further discussed to demonstrate the efficiency of the solution approach.
52 citations
TL;DR: Developing a novel robust Bi-Objective Mixed-Integer Linear Programming (BOMILP) model to support the framework, where the maintenance tasks duration is uncertain, makes the contribution of the paper novel and unique.
43 citations
TL;DR: A hybrid approach based on fuzzy TOPSIS, trapezoidal type-2 fuzzy AHP and goal programming is developed that simultaneously considers both qualitative and quantitative criteria, takes case-specific requirements such as capacity constraints, lot size constraints and quantity discounts into account and makes the order allocation.
42 citations
TL;DR: This paper can help decision makers and managers to opt for the best suppliers and also allocate the right numbers of parts to those supplier(s) based on a real situation of each firm.
Abstract: The main purpose of this paper is the allocation of orders to suppliers in an agile and flexible manner suitable to the automobile industry. In this paper, parts supplied by a single source were eliminated from the set of parts. Using mathematical modeling and through the interval-valued fuzzy-rough numbers best worst method (IVFRN-BWM), we try to achieve the results that can meet the proposed model's needs and provide the ideal results by introducing new modes. This paper addressed some new aspects of the subject and achieved robust results by considering five objective functions. These five functions are as follows: minimization of production line disruptions due to the performance of suppliers, minimization of the complaints of production line about supplied parts, minimization of defective parts received from suppliers (PPM), maximization of on-time delivery services, and minimization of overall costs of supplied parts. Reviewing the literature, the originality of this study are as follows: 1) identifying the structure of a supply chain (SC) in general and particularly in an automobile industry SC; 2) investigating the modeling techniques of the existing SC models for coordinating all the members of a product SC; 3) building a hybrid model of IVFRN-BWM and a robust goal programming agile and flexible supply chain in an uncertain situation; and 4) identifying the suitable scenarios/cases for testing the proposed models to validate the models. This paper can help decision makers and managers to opt for the best suppliers and also allocate the right numbers of parts to those supplier(s) based on a real situation of each firm.
40 citations
TL;DR: This study provides an effective method that allows decision makers to incorporate their preference in target setting of a merger for saving specific input(s) or producing certain output(s), as much as possible, through an illustrative application in banking industry.
Abstract: This paper suggests a novel method to deal with target setting in mergers using goal programming (GP) and inverse data envelopment analysis (InvDEA). A conventional DEA model obtains the relative efficiency of decision making units (DMUs) given multiple inputs and multiple outputs for each DMU. However, the InvDEA aims to identify the quantities of inputs and outputs when efficiency score is given as a target. This study provides an effective method that allows decision makers to incorporate their preference in target setting of a merger for saving specific input(s) or producing certain output(s) as much as possible. The proposed method is validated through an illustrative application in banking industry.
40 citations
TL;DR: The methodology proposed involves an optimization model to obtain a relative measure of CSR at the firm level that can guide performance improvements, and a goal programming model to identify a common set of weights for the key performance indicators, enabling the evaluation of all firms on common grounds.
TL;DR: A constraint based nonlinear goal programming (multi-objective) model for weapon assignment problem to minimize survival probability was developed and not only gave optimum assignment but also resulted in engagement times and defense success for multi-defense sites.
TL;DR: A consistency index is introduced to evaluate the consistency degree for intuitionistic multiplicative preference relations (IMPRs), and a consistency optimization approach is presented to jointly improve the consistency degrees of several IMPRs that do not satisfy the predefined consistency threshold.
Abstract: Interval-valued intuitionistic multiplicative preference relations (IVIMPRs) form a suitable conceptual framework to represent and process simultaneously uncertain preferred and nonpreferred judgments of decision makers (DMs). The focus of this paper is on group decision-making (GDM) problems realized with IVIMPRs. First, a consistency index is introduced to evaluate the consistency degree for intuitionistic multiplicative preference relations (IMPRs), and a consistency optimization approach is presented to jointly improve the consistency degrees of several IMPRs that do not satisfy the predefined consistency threshold. Then, a consistency definition and an acceptable consistency definition for IVIMPRs are established by splitting an IVIMPR into two IMPRs. For several IVIMPRs with unacceptable consistency, a goal program-based approach is proposed to simultaneously improve their consistency. Subsequently, by minimizing the degree to which the opinions of individual DMs deviate from those of the group, a maximum consensus-based goal program is established to determine the DMs’ weights. Furthermore, an aggregation approach is applied to integrate individual IVIMPRs into a collective one. A linear program is then built to determine the interval-valued intuitionistic multiplicative priority weights of alternatives coming from the collective IVIMPR. A consistency-based GDM algorithm is proposed. Finally, a practical example is offered to show the application of the new algorithm, and a comparative analysis is presented to highlight the advantages of the new method.
TL;DR: This paper proposes a non-egoistic principle which states that each DMU should propose its allocation proposal in such a way that the maximal cost would be allocated to itself, and integrates a goal programming method with data envelopment analysis methodology to propose a new model under a set of common weights.
Abstract: In many real applications, there exist situations where some independent and decentralized entities will construct a common platform for production processes. A natural and essential problem for the common platform is to allocate the fixed cost or common revenue across these entities in an equitable way. Since there is no powerful central decision maker, each decision-making unit (DMU) might propose an allocation scheme that will favor itself, giving itself a minimal cost and/or a maximal revenue. It is clear that such allocations are egoistic and unacceptable to all DMUs except for the distributing DMU. In this paper, we will address the fixed cost allocation problem in this decentralized environment. For this purpose, we suggest a non-egoistic principle which states that each DMU should propose its allocation proposal in such a way that the maximal cost would be allocated to itself. Further, a preferred allocation scheme should assign each DMU at most its non-egoistic allocation and lead to efficiency scores at least as high as the efficiency scores based on non-egoistic allocations. To this end, we integrate a goal programming method with data envelopment analysis methodology to propose a new model under a set of common weights. The final allocation scheme is determined in such a way that the efficiency scores are maximized for all DMUs through minimizing the total deviation to goal efficiencies. Finally, both a numerical example from prior literature and an empirical study of nine truck fleets are provided to demonstrate the proposed approach.
TL;DR: A multiobjective optimization framework is presented for an overall water management system that includes the allocation of freshwater for hydraulic fracturing and optimal management of the resulting wastewater with different techniques, and the neutrosophic goal programming approach is suggested.
Abstract: Shale gas energy is the most prominent and dominating source of power across the globe. The processes for the extraction of shale gas from shale rocks are very complex. In this study, a multiobjective optimization framework is presented for an overall water management system that includes the allocation of freshwater for hydraulic fracturing and optimal management of the resulting wastewater with different techniques. The generated wastewater from the shale fracking process contains highly toxic chemicals. The optimal control of a massive amount of contaminated water is quite a challenging task. Therefore, an on-site treatment plant, underground disposal facility, and treatment plant with expansion capacity were designed to overcome environmental issues. A multiobjective trade-off between socio-economic and environmental concerns was established under a set of conflicting constraints. A solution method—the neutrosophic goal programming approach—is suggested, inspired by independent, neutral/indeterminacy thoughts of the decision-maker(s). A theoretical computational study is presented to show the validity and applicability of the proposed multiobjective shale gas water management optimization model and solution procedure. The obtained results and conclusions, along with the significant contributions, are discussed in the context of shale gas supply chain planning policies over different time horizons.
TL;DR: An additive-consistency- and consensus-based method for group decision making (GDM) with LPRs is developed and a consistency index is proposed for gauging the agreement degree among individual L PRs.
Abstract: Linguistic preference relations (LPRs) can indicate the decision makers (DMs)’ qualitative pairwise judgments regarding a set of alternatives in uncertain multicriteria decision-making problems. This paper examines several goal programming models for managing the additive consistency and consensus of LPRs and then develops an additive-consistency- and consensus-based method for group decision making (GDM) with LPRs. First, this paper offers a consistency index to quantify the consistency level for LPRs and define acceptable consistent LPRs. For an LPR that is unacceptably additive consistent, several additive-consistency-based programming models are developed to address the inconsistency and to establish an acceptably consistent LPR. Then, an additive-consistency-based procedure to generate the priority weight vector from the LPR is offered. An additive-consistency-based algorithm for decision making with an LPR is presented. Subsequently, considering the consensus in GDM, a consensus index is proposed for gauging the agreement degree among individual LPRs. Regarding individual LPRs that do not exhibit acceptably additive consistency or acceptable consensus, several goal programming models to derive new LPRs with acceptable consistency and consensus are provided. Afterward, the DMs’ weights are determined objectively, and individual LPRs are integrated into a collective LPR. An additive-consistency- and consensus-based GDM method with a group of LPRs is developed. Finally, two practical numerical examples are offered, and a comparative analysis is presented.
TL;DR: In this article, the problem of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail-Road PI-Hub cross-docking terminal was formulated as a Multi-Objective Mixed-Integer Programming model and solved with CPLEX solver using Lexicographic Goal Programming.
Abstract: In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.
TL;DR: This study demonstrates the aggregation of environmental and economic supplier performance criteria into a single (eco-efficiency) index using Data Envelopment Analysis (DEA) for interval data and can be used for supplier evaluation, selection and monitoring.
19 May 2019
TL;DR: This paper investigates a novel fuzzy multi-objective multi-period Aggregate Production Planning (APP) problem under seasonal demand and demonstrates that uncertain conditions and considering real-world assumptions can yield different results in developing a practical aggregate production plan.
Abstract: This paper investigates a novel fuzzy multi-objective multi-period Aggregate Production Planning (APP) problem under seasonal demand. As two of the main real-world assumptions, the options of workforce overtime and outsourcing are studied in the proposed Mixed-Integer Linear Programming (MILP) model. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/unemployment costs, and maximize the customers’ satisfaction level. To deal with demand uncertainty, triangular fuzzy numbers are considered for demand parameters. Then the proposed model is validated by solving an illustrative example using a Weighted Goal Programming (WGP) method and CPLEX solver. Finally, it is demonstrated that uncertain conditions and considering real-world assumptions can yield different results in developing a practical aggregate production plan. Moreover, a sensitivity analysis is then performed to provide qualitative managerial insights and decision aids.
TL;DR: Two intuitionistic fuzzy portfolio selection models for optimistic and pessimistic scenarios, respectively are proposed, which provide avenues for the inclusion and minimization of the hesitation degree into the decision making, resulting in a significantly better portfolio.
TL;DR: A systematic and semi-quantitative decision support framework for risk management of hazmats road transportation based on the combination of quality function deployment, fuzzy analytic hierarchy process, fuzzy failure mode and effect analysis, and nonlinear goal programming is proposed.
Abstract: Risk management of hazardous materials (hazmats) road transportation has long been a concern because of the potential hazards that poses to society and the environment. In this work, a systematic and semi-quantitative decision support framework for risk management of hazmats road transportation based on the combination of quality function deployment (QFD), fuzzy analytic hierarchy process (F-AHP), fuzzy failure mode and effect analysis (F-FMEA), and nonlinear goal programming is proposed. The QFD is used innovatively to construct the overall framework, which contains three main components of general risk management: risk identification, risk assessment, and risk control. The F-AHP is used to build a hierarchical risk assessment system and determine the importance rating of each risk factor. The F-FMEA is used to evaluate the potential risks of risk control measures and determine the risk adjustment coefficient of each risk measure, which is used subsequently to modify the fulfillment level of risk measure in the nonlinear goal programming model. To address the inherent vagueness and uncertainty contained in the risk management process, the fuzzy set theory is introduced as an effective tool. An empirical case on risk management of a hazmats transportation company is presented to demonstrate the effectiveness and feasibility of the proposed methodology. Some managerial implications on risk management of hazmats road transportation are provided based on the obtained findings.
TL;DR: The weight variable is introduced to the above two methods to provide decision-makers with a mechanism to evaluate the discrepancy between the maximum aggregate achievement and the most balanced solution, enabling decision-maker to reach the preferable decision for their situation.
TL;DR: The results of implementation of proposed framework based on the extended MCGP-U model on an active company in paint and coating industry show that delivery time criterion has the most effect and priority on suppliers’ evaluations.
Abstract: Due to the effects of supplier evaluation and selection problem on the quality of products and companies’ business activities, supplier selection is considered as a strategic issue in organizations’ development plans. The purpose of this study is to provide an integrated framework for supplier selection problem regarding to the loss of criteria deviation from specification limits, causal relationships between criteria and the preferences of decision makers (DMs) in the supplier selection problem. Thus, in the first step, the loss of each criterion is calculated using Taguchi loss function (TLF), then fuzzy cognitive map (FCM) and hybrid learning algorithm are applied to determine criteria weights. Finally, considering outputs of TLF and FCM methods, multi-choice goal programming with utility function (MCGP-U) is used to select an optimal supplier and to increase the DMs’ expected utility values, simultaneously. The results of implementation of proposed framework based on the extended MCGP-U model on an active company in paint and coating industry show that delivery time criterion has the most effect and priority on suppliers’ evaluations. Also among six qualified suppliers, a supplier with the least total loss value and the most utility values is selected as the optimal supplier for the under consideration company.
TL;DR: In this paper, a multi-objective interval transportation problem (MOITP) with consideration of minimizing pollution is studied, where a case study is conducted to justify the methodology by utilizing the environmental impact.
Abstract: Sustainable development is treated as the achievement of continued economic development without detriment to environmental and natural resources. Now-a-days, in a competitive market scenario, most of us are willing to pay less and to gain more in quickly without considering negative externalities for the environment and quality of life for future generations. Recalling this fact, this paper explores the study of time variant multi-objective transportation problem (MOTP) with consideration of minimizing pollution. Time of transportation is of utmost importance in reality; based on this consideration, we formulate a MOTP, where we optimize transportation time as well as the cost function. The parameters of MOTP are interval-valued, so this form of MOTP is termed as a multi-objective interval transportation problem (MOITP). A procedure is taken into consideration for converting MOITP into deterministic form and then for solving it. Goal programming is applied to solve the converted transportation problem. A case study is conducted to justify the methodology by utilizing the environmental impact. At last, conclusions and future research directions are included regarding our study.
TL;DR: This research has introduced a mathematical model for humanitarian logistic applying the fact of redistribution of resources to minimize the total cost of whole operation and the total time for redistribution phase so as to response the emergency situation quickly.
01 Jan 2019
TL;DR: A new additive consistency concept for ILFPRs is introduced, which satisfies all properties of the additive consistent concept for fuzzy preference relations and a new method for group decision making with ILF PRs is developed, which is based on the additive consistency and consensus analysis.
Abstract: Interval linguistic fuzzy preference relations (ILFPRs) are powerful tools to denote the decision makers’ uncertain qualitative preferences. To avoid the inconsistent ranking results, consistency analysis is very critical. This paper introduces a new additive consistency concept for ILFPRs, which satisfies all properties of the additive consistency concept for fuzzy preference relations. Then, a model to judging the additive consistency of ILFPRs is constructed. When ILFPRs are inconsistent, an approach to deriving additive consistent ILFPRs is presented. Considering the incomplete case, goal programming models to determining the missing values are established. Subsequently, a distance measure-based group consensus index is given to measuring the consensus of individual ILFPRs. Furthermore, a new method for group decision making with ILFPRs is developed, which is based on the additive consistency and consensus analysis. Finally, two numerical examples are offered to show the application of the developed procedure, and a comparison analysis is performed.
TL;DR: This work overcomes shortcomings in developing a SMS towards the minimization of total cost, energy consumption and environmental impact, in particular, of the CO2 emissions.
TL;DR: In this paper, the authors developed an analytical model for manufacturing firm for selection of suppliers based on customers' expectations which are reflected as retailers' expectation level and also for bringing in financial and socio-environmental stability to the whole SC.
18 Feb 2019
TL;DR: This study presents an applicable shift schedule of workers in a large-scale natural gas combined cycle power plant (NGCCPP), which realize 35.17% of the total electricity generation in Turkey alone, as at of the end of 2018.
Abstract: Shift scheduling problems (SSPs) are advanced NP-hard problems which are generally evaluated with integer programming. This study presents an applicable shift schedule of workers in a large-scale natural gas combined cycle power plant (NGCCPP), which realize 35.17% of the total electricity generation in Turkey alone, as at of the end of 2018. This study included 80 workers who worked three shifts in the selected NGCCPP for 30 days. The proposed scheduling model was solved according to the skills of the workers, and there were nine criteria by which the workers were evaluated for their abilities. Analytic network process (ANP) is a method used for obtaining the weights of workers’ abilities in a particular skill. These weights are used in the proposed scheduling model as concepts in goal programming (GP). The SSP–ANP–GP model sees employees’ everyday preferences as their main feature, bringing high-performance to the highest level, and bringing an objective functionality, and lowering the lowest success of daily choice. At the same time, the model introduced large-scale and soft constraints that reflect the nature of the shift requirements of this program by specifying the most appropriate program. The required data were obtained from the selected NGCCPP and the model solutions were approved by the plant experts. The SSP–ANP–GP model was resolved at a reasonable time. Monthly acquisition time was significantly reduced, and the satisfaction of the employees was significantly increased by using the obtained program. When past studies were examined, it was determined that a shift scheduling problem of this size in the energy sector had not previously been studied.
TL;DR: This paper presents a formulation for the optimal planning of the daily production of sawmills using a Goal Programming approach that allows different criteria to be appropriately weighted.