Multi-choice Goal Programming Approach Research Articles

Pavement Condition Management Coupled with Skid Management at the Network Level: A Multi-Choice Goal Programming Approach

Highways promote economic growth and social development. Traditionally, pavement management considers pavement conditions for maintenance and rehabilitation (M&R) decision-making along with system constraints. One of the major tasks of highway agencies is to promote safety. Highway safety involves a complicated interaction of multiple factors. However, it is well established that there is a link between pavement surface friction and safety. Pavement surface characteristics affect the safety and comfort of passengers. Since the pavement management process considers only pavement physical conditions in M&R decision-making, there is a need to include safety indicators in the process to promote both riding comfort and safety. This study aims to integrate the pavement surface friction, that is, the skid resistance, into traditional pavement management. An optimization model has been proposed based on multi-choice goal programming by systematically integrating skid resistance into pavement M&R decision-making along with pavement conditions. Multi-choice goal programming allows setting interval aspirations rather than a fixed target. A case study has been conducted to demonstrate the usefulness of the proposed model in managing pavement conditions and pavement skids together. The case study considered five goals related to pavement conditions, skid states, and M&R costs. Interaction among the goals is discussed in different scenarios. The case study also uses friction treatment with traditional pavement management treatments. The model can capture the interaction between pavement conditions and pavement skids and determine the program that best achieves the goals. The proposed method provides a simple approach to selecting a network-level M&R program and obtaining insights into the management of conditions and skid together.

An Integrated Quality Function Deployment and Multichoice Goal Programming Approach for Sustainable Transportation: The Case of Eskişehir

Limiting individual transportation and increasing the share of public transportation has an important place in planning a sustainable transportation system in the urban environments of today. The most important goals for city governments include the ability to attract automobile users to public transportation. At this stage, there is a need for a public transportation system that aims to optimize the criteria of cost, safety, organization, and comfort. It is highly important to measure, assess, and improve the satisfaction of those who use public transportation for sustainable transportation. This study used an integrated quality function deployment (QFD) and multichoice goal programming approach to assess the satisfaction of people who use public transportation and to improve the transportation systems quality. The proposed approach was implemented for the tram system that is currently operational in Eskişehir in Turkey. According to the results that were obtained, with a low cost, it was seen that customer priorities could be met to a great extent as 93.1%. The results of the study are helpful for the decision makers to develop incentive policies regarding public transportation systems.

A fuzzy programming model for optimizing the inventory management problem considering financial issues: A case study of the dairy industry

In today's competitive environment as well as the dynamic nature of the business environment, managing inventory and financial flows has become more challenging. As inventory and financial flows are interdependent, setting an optimal plan for managing them simultaneously can have a dramatic impact on the company's efficiency The literature indicates that previous studies have rarely considered financial considerations in the inventory management problem. Thus, this study proposes a multi-objective programming model to optimize the inventory management problem for dairy products. In the model, material substitution is considered in order to assist in managing the inventory value. A fuzzy model is developed to deal with uncertain parameters such as inventory costs, raw milk volume, and the cost of purchasing raw materials. Then, a real-world case study is conducted in the Iranian dairy industry. Additionally, the proposed model is solved using a multi-choice goal programming approach. Further, sensitivity analyses are conducted in order to investigate the impact of critical parameters on the research question. Finally, managerial insights and theoretical implications are discussed. Based on the results, raw milk volume decreases inventory costs, and material substitution decreases inventory value as obsolete materials are used in products.

Designing a sustainable closed-loop supply chain network considering lateral resupply and backup suppliers using fuzzy inference system.

Sustainability is key factor for transforming traditional supply chain networks into modern ones. This study, for the first time, considers the impacts of the backup suppliers and lateral transshipment/resupply simultaneously on designing a Sustainable Closed-Loop Supply Chain Network (SCLSCN) to decrease the shortage that may occur during the transmission of produced goods in the network. In this manner, the fuzzy multi-objective mixed-integer linear programming model is proposed to design an efficient SCLSCN resiliently. Moreover, the concept of circular economy has been studied in this paper to reduce environmental effects. This study aims to optimize total and environmental costs, including energy consumption and pollution emissions, while increasing job opportunities. A demand uncertainty component is considered to represent reality more closely. Due to the importance of demand, this parameter is estimated using the Fuzzy Inference System (FIS) as an input into the proposed mathematical model. Then, the fuzzy robust optimization approach is applied in a fuzzy set’s environment. The model is tackled by a Multi-Choice Goal Programming Approach with Utility Function (MCGP-UF) to be solved in a timely manner, and the equivalent auxiliary crisp model is employed to convert the multi-objective function to a single objective. The proposed model is tested on the case study of the tire industry in terms of costs, environmental impacts, and social effects. The result confirmed that considering the concept of lateral resupply and backup supplier could considerably decrease the total costs and reduce shortages on the designed SCLSCN. Finally, sensitivity analysis on some crucial parameters is conducted, and future research directions are discussed.

Production-Distribution Problem Optimization in a Green Closed-Loop Supply Chain

Integrated production-distribution planning in traditional forward supply chain has attracted a lot of attention in recent years and its economic advantages are particularly noticeable. In this paper, the issue of integrated production-distribution planning of a multi-level green closed loop, including recycling centers, manufacturing / remanufacturing factories and decentralized distribution centers, is investigated. A three-level bi-objective model with the aim of increasing profit and reducing the adverse environmental effects is presented. A revised multi-choice goal programming approach and a hierarchical iteration algorithm is used to solve it.

Robust Bi-Objective Operating Rooms Scheduling Problem Regarding the Shared Resources

In recent years, many efforts have been made to provide different strategies for enhancing the scheduling and planning of the operating rooms. The efficient planning and scheduling of ORs is a complex task since it has to account for the availability of human resources, medical equipment, and medication required for each surgery but that are often shared between different ORs. This paper proposes a mathematical approach to enhance the management of OR resources. It presents a bi-objective robust optimization approach for scheduling the surgeries in the ORs and recovery room, regarding the uncertainty of the surgery time, uncertainty of hospitalization time in the recovery room, and shared resources. The first objective function aims to minimize the maximum completion time of the surgeries and the second one minimizes the sum of the earliness-tardiness of the surgical operations. The suggested approach utilizes the multi-choice goal programming approach with utility function to solve the proposed model. The proposed approach is applied to a real case in the Shahid Beheshti hospital, Babol, Iran. The obtained results show that the suggested bi-objective robust optimization approach can enhance OR scheduling and should be designed into a decision support system for OR management.

Selecting Suitable, Green Port Crane Equipment for International Commercial Ports

Responding to the increasing global need for environmental protection, a green port balances economic vibrancy with environmental protection. However, because exhaust emissions (e.g., CO2 or sulfide) are difficult to monitor around ports, data on such emissions are often incomplete, which hinders research on this topic. The present study aimed to fill this gap in this topic. To remedy this problem, this study formulated a new data envelopment analysis (DEA) method for collecting CO2 emissions data at their source. This method was applied to collect real-world operating data from a large container-handling company in Taiwan. Specifically, we provide a real example using a novel green energy index to account for undesirable outputs. Our main objective was to formulate two methods that combine: (1) data envelopment analysis based on a modified slack-based measure, and (2) a multi-choice goal programming approach. The contributions of this paper included the finding that rubber-tired gantry cranes are the greenest and should be used in ports. Finally, our findings aid port managers in selecting port equipment that provides the best balance between environmental protection and profitability.

A multi-objective stochastic programming model to configure a sustainable humanitarian logistics considering deprivation cost and patient severity

As the occurrence of disasters has increased frequently and has resulted in growing concern about their adverse effects on the environment, Sustainable Humanitarian Logistics (SHL) has received great attention recently. SHL aims to reduce disaster damages in an environmentally-friendly manner in the shortest possible time. The terms including ‘environmentally-friendly’ and ‘shortest possible time’ refer to the environmental and social aspects of sustainability. This research proposes a stochastic multi-objective mixed-integer programming model to configure an SHL network during the response phase. Having compared to the research literature, this is the first study that considers economic, social, and environmental aspects of sustainability by incorporating relief cost, deprivation cost, and carbon emissions, respectively. Then, the improved multi-choice goal programming approach is applied to solve the proposed multi-objective model. To indicate the validity of the proposed model, an earthquake that occurred in a region of Kermanshah, Iran, in 2017 is investigated as a real case study. Finally, sensitivity analysis is performed and several managerial and theoretical insights are provided. The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect. Furthermore, a minor increase in the budget of the preparedness phase drastically decreases the response costs.

A multi-objective distributionally robust model for sustainable last mile relief network design problem

Natural disasters not only inflict massive life and economic losses but also result in psychological damage to survivors, at times even causing social unrest. It is necessary to design a sustainable last mile relief network for distributing relief supplies regarding social factors, disaster relief efficiency as well as the economic cost of three perspectives in terms of sustainability. We establish a multi-objective distributionally robust optimization model for a sustainable last mile relief network problem that maximizes the equitable distribution of relief supplies and simultaneously minimizes the transportation time and operation cost. Under the partial probability information of uncertainties, such as the disaster situation, transportation time, freight, road capacity, and demand, we characterize the uncertain variables in an ambiguity set incorporating the bounds, means and the mean absolute deviations. Then, the bounds on the objective values and the safe approximations of the chance constraints are deduced under the ambiguity sets. Based on a revised multi-choice goal programming approach, we obtain a computationally tractable framework of the multi-objective model. To verify the effectiveness of the model and methods, a case study of the Banten tsunami is illustrated. The results demonstrate our proposed model can obtain a trade-off between the equitability, timeliness and economics for relief distribution in a relief network.

Multi-choice goal programming approach to solve multi-objective probabilistic programming problem

Stochastic Programming is an art of modeling optimization problems in an environment, where randomness occurs. In this manuscript, we present a multi-objective probabilistic programming problem, where the random parameter follow logistic distribution. We transform the probabilistic programming model to an equivalent deterministic mathematical model by using chance constrained technique. Multiple number of aspiration levels are allocated to the objective function by Decision maker, the main aim is to obtain such a decision. After allocating several aspiration levels to the objective function, which will provide minimum deviation from objective function and aspiration level. Such minimization of deviation is possible by using multi-choice goal programming technique. Multi-choice parameters are handled by three different techniques viz; binary variable approach, Vandermonde’s interpolating polynomial approach and linear least square approximation approach. To illustrate the methodology, a numerical example is presented.

Solving fuzzy transportation problem using multi-choice goal programming

This paper explores the study of fuzzy transportation problem (FTP) using multi-choice goal-programming approach. Generally, the decision variable in transportation problem (TP) is considered as real variable, but here the decision variable in each node is chosen from a set of multi-choice fuzzy numbers. Here, we formulate a mathematical model of FTP considering fuzzy goal to the objective function. Thereafter, the solution procedure of the proposed model is developed through multi-choice goal programming approach. The proposed approach is not only improved the applicability of goal programming in real world situations but also provided useful insight about the solution of a new class of TP. A real-life numerical experiment is incorporated to analyze the feasibility and usefulness of this paper. The conclusions about our proposed work including future studies are discussed last.

Multiobjective Transportation Problem Using Fuzzy Decision Variable Through Multi-Choice Programming

This paper analyzes the study of Multiobjective Transportation Problem (MOTP) under the consideration of fuzzy decision variable. Usually, the decision variable in a Transportation Problem is taken as real variable. But, in this paper, the decision variable in each node is selected from a set of multi-choice fuzzy numbers. Inclusion of multiple objectives into transportation problem with fuzzy decision variable makes it a Multiobjective Fuzzy Transportation Problem (MOFTP). In this paper, a new formulation of mathematical model of MOFTP with fuzzy goal of each objective function is enlisted. Thereafter the solution technique of the formulated model is described through multi-choice goal programming approach. Finally, a numerical example is presented to show the feasibility and usefulness of this article.

Sustainable supplier selection and order allocation through quantity discounts

Within activities related to the global supply chain, proper supplier selection regarding sustainability issues, especially economic, environmental and social, plays an important role in sustainable development. In this paper, a new multi-objective mathematical programming model is designed to consider sustainable supplier selection and order allocation simultaneously in a multi-period, multi-item and multi-supplier supply chain. In addition, quantity discounts are considered under two assumptions: all-unit and incremental. The proposed model is solved following a revised multi-choice goal programming approach in order to find a compromise solution. Furthermore, a real-life case is studied to justify the applicability of the proposed model.

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: A case study

In this paper, a bi-objective optimization model is proposed for a dynamic virtual cellular manufacturing system and supply chain design. The presented model considers important manufacturing features thoroughly such as multi-plants and facility locations, multi-markets allocations, multi-period production planning under uncertain demand of products and capacities of resources. The proposed model is converted into an equivalent auxiliary crisp model. Then, to solve the model, a revised multi-choice goal programming approach is applied for finding a favorite solution. The aims of the proposed model are to minimize the total cost of supply chain design which includes holding cost, outsourcing cost, maintenance and overhead cost of machines, fixed cost, external transportation cost and minimizing the total number of exceptional elements and movements of the labors between active plants. The Computational results are explained in terms of a real case study as well as the validity of the proposed model solution method.

An Integrated Maximizing Consistency and Multi-Choice Goal Programming Approach for Hybrid Multiple Criteria Group Decision Making Based on Interval-Valued Intuitionistic Fuzzy Number

Interval-valued intuitionistic fuzzy numbers (IVIFNs) characterized by a membership function and a non-membership function with values that are intervals, have strong ability to handle imprecise and ambiguous information in real-world applications. This paper proposes an integrated maximizing consistency and multi-choice goal programming (MCGP) approach to handle hybrid multi-criteria group decision making problems based on IVIFNs. Firstly, the hybrid decision infor- mation (including crisp numbers, intervals, intuitionistic fuzzy numbers and linguistic variables) are normalized into the IVIFNs. Then, an ordinal consistency index and a cardinal consistency in- dex are proposed to measure the consistency between the individual opinion and the group opinion, respectively. And an optimal model based on maximizing consistency is constructed to derive the weights of experts. Afterwards, the comprehensive ratings and the ranking values of alternatives are obtained by the hybrid weighted aggregation operator and the proposed ratio function of IVIFNs, respectively. Furthermore, a MCGP model based on the ranking values is constructed to identify the optimal alternatives and their optimum quantities. At length, an illustrative case is provided to verify the proposed approach.

An improved multi-choice goal programming approach for supplier selection problems

In this study, a supplier selection problem is first modeled as a multi-objective optimization problem with three minimization objectives: price, rejects and lead-time. In reality, the objectives may have different relative weights. In addition, due to uncertainty/imprecision, it may be easier for decision makers to determine an interval goal or aspiration level for every objective, instead of a single one. Also, the decision makers may have other preferences such as the purchasing cost not significantly exceeding the budget. For this purpose, a new multi-choice goal programming (MCGP) approach is proposed. One of the main advantages of the proposed model is that it provides the decision makers with more control over their preferences. Finally, an illustrative example demonstrates the effectiveness of our proposed model.

Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters

Cell formation, as one of the most important decision problems in designing a cellular manufacturing system, includes grouping machines in cells and the parts as part families. In a dynamic environment, the part demand/mix change is considered over a planning horizon divided to several periods. So, the cell formation for one period may no longer be effective for future periods and hence reconfiguration of cells is essential. Due to the variation of demand and necessity of cells reconfiguration, virtual cell formation concept is introduced by researchers to take the advantage of cell formation without reconfiguration charges. On the other hand, Simultaneous consideration of supply chain and cell formation results in lower distribution and procurement costs and faster response to customers. In this paper, a new bi-objective possibilistic optimisation mathematical model is developed for integrating procurement, production and distribution planning considering various conflicting objectives simultaneously as well as the imprecise nature of some critical parameters such as customer demands and machine capacities. Then, a revised multi-choice goal programming approach is applied to solve the proposed mathematical model and to find a preferred compromise solution. Moreover, a real-world industrial case is provided to validate how the proposed model works.

Evaluation Model for Applying an E-Learning System in a Course: An Analytic Hierarchy Process—Multi-Choice Goal Programming Approach

With the widespread use of the Internet, adopting e-learning systems in courses has gradually become more and more important in universities in Taiwan. However, because of limitations of teachers' time, selecting suitable online IT tools has become very important. This study proposes an analytic hierarchy process (AHP)–multi-choice goal programming (MCGP) evaluation model as a decision aid to help decision makers obtain appropriate online IT tools considering the time limitations of teachers and students. To demonstrate the usefulness of the proposed method, this study implements the evaluation model in computer courses at a university in central Taiwan. For different purposes, teachers can set different time limitations and aspiration levels for each goal to rank the range of possible IT tools. With the AHP–MCGP evaluation model, teachers can find the most suitable time for the allocation of possible IT tools in the e-learning system to improve students' learning practice.

A Revised Multi-choice Goal Programming Framework for Economic-environmental Power Generation and Dispatch Problem

This paper presents the potential use of revised multi-choice goal programming approach for modelling and solving economic- environmental power generation and dispatch problems in a thermal power plant operation and planning horizon. In the proposed approach, the objective functions and certain constraints having the characteristics of nonlinear programming are first transformed into their equivalent linear forms to employ linear goal programming methodology in the process of solving the problem. Then, in the model formulation, the objectives with multiplicity of aspiration levels are converted into the standard form of goals in goal programming approach to arrive at a most satisfactory decision in the multiobjective decision making environment. In the solution process, minsum goal programming approach is addressed for achievement of objective goals within their tolerance ranges specified in the decision making context. To illustrate the potential use of the approach, the proposed model is tested on IEEE 6-Generator 30-Bus system. In addition, the model solution is compared with the solutions obtained by using other approaches studied previously.