Maritime Supply Chain Optimization by Using Fuzzy Goal Programming

• Propose novel behavioral membership functions within the fuzzy goal programming approach. • Model decision-makers’ behavioral aspects and risk perceptions. • Develop a behavior-based fuzzy goal programming approach. • Enhance utility functions within the fuzzy goal programming methodology. • Demonstrate applicability and efficacy through a venture capital investment problem. A relevant extension of traditional goal programming (GP), fuzzy goal programming (FGP) can handle uncertainty and imprecision in multi-objective optimization problems. Based on fuzzy set theory, the notion of membership functions has been introduced to consider the fuzziness related to objectives and constraints. These membership functions are mainly intended for fuzziness in the GP rather than modeling the decision-maker's (DM's) preferences and his/her attitude toward risk in the decision-making process. In the satisfying philosophy of FGP, little attention has been given to how preferences evolve in terms of the behavior of the decision-maker (DM) and how these preferences may affect decisions in risky scenarios. To address this issue, we suggest novel behavioral-type utility functions for the FGP approach by introducing the concept of behavioral membership functions. This concept offers an innovative procedure for simulating the DM's behavioral preferences in the FGP approach. First, two main categories of objectives in relation to the DM's behavioral preferences are distinguished in this work. A risk aversion parameter is integrated into membership functions according to the nature of each objective type, obtaining the so-called behavioral membership functions. A behavioral FGP approach is subsequently formulated. Finally, an illustrative example of venture capital investments, a sensitivity analysis, and comparisons with other FGP approaches are provided to demonstrate the validity and practicality of our proposed approach.

In this paper, we propose an alternate modified solution procedure based on fuzzy goal programming (FGP) approach for solving bi-level multiobjective linear fractional programming problems (BL-MOLFPPs) with some major modifications in the algorithm suggested by Abo-Sinna and Baky (2010). The proposed modified technique is simpler and requires less computational works than that of earlier technique. In the formulation of FGP models, each objective function (at both the levels - leader's level and follower's level) as well as control variables (of leader's level) are transformed into fuzzy goals with modified membership functions. Then FGP approach is used for achieving the highest degree of each of membership goals. To avoid decision deadlock situations and difficulties associated with bi-level programming problems, solution preferences by decision makers (at leader's and follower's levels) are not taken into account in proposed algorithm. A comparative analysis is also carried out with numerical example in order to show efficiency of proposed algorithm over earlier technique.

This chapter describes a Genetic Algorithm (GA) based Fuzzy Goal Programming (FGP) model to solve a Multiobjective Bilevel Programming Problem (MOBLPP) with a set of chance constraints within a structure of decentralized decision problems. To formulate the model, the chance constraints are converted first to their crisp equivalents to employ FGP methodology. Then, the tolerance membership functions associated with fuzzily described goals of the objective functions are defined to measure the degree of satisfaction of Decision Makers (DMs) with achievement of objective function values and also to obtain the degree of optimality of vector of decision variables controlled by upper-level DM in the decision system. In decision-making process, a GA scheme is adopted to solve the problem and thereby to obtain a proper solution for balancing execution powers of DMs in uncertain environment. A numerical example is provided to illustrate the method.

Supplier selection is a strategic complex multi-criteria problem of selecting the appropriate supplier. The main objective of this paper is to propose a hybrid model including intuitionistic fuzzy AHP using triangular intuitionistic fuzzy numbers and goal programming for the supplier selection problem. Intuitionistic fuzzy AHP is a useful tool for handling the complex problems with hierarchical form under intuitionistic fuzzy environment. And the goal programming is a linear programming based method and deals with multiple conflicting objective measures effectively. In this study, we evaluated three suppliers according to five criteria by using intuitionistic fuzzy AHP which utilizes triangular intuitionistic fuzzy scale. By applying intuitionistic fuzzy AHP, the priority weights of the criteria and suppliers are calculated. Then, the weights are used in the goal programming model to find the best alternatives. The results point out that the supplier selection problem can be tackled with using the proposed hybrid method effectively.

Fuzzy goal programming for health-care organization

Application of fuzzy goal programming and genetic algorithm is considered in this paper. We extend a multi-objective model for integrated inventory-production-distribution planning in supply chain (SC). We consider a supply chain network which consists of a manufacture, with multiple plants, multiple distribution centers (DCs), multiple retailers and multiple customers. The manufacturer produces several items. Decision maker’s imprecise aspiration levels of goals are incorporated into model using fuzzy goal programming approach. Due to the complexity of problem in large size and in order to get a satisfactory near optimal solution with great speed, a new genetic algorithm is proposed to solve constrained problems. To show the efficiency of the used model and fuzzy goal programming approach and genetic algorithm for the collaborative inventory-production-distribution problem, computational experiments are performed on a hypothetically constructed case problem.

A fuzzy goal programming approach for vendor selection problem in a supply chain

A Fuzzy Goal Programming Model For Production Planning in Furniture Company

Global supplier selection is a multi-goal multi-criteria problem which needs to consider both qualitative and quantitative factors. Which suppliers are the best and how much should be purchased from the selected suppliers is an important purchasing issue for manufacturers. Traditionally, decision makers can determine the best supplier from evaluating few suppliers with qualitative supplier selection criteria by using fuzzy analytic hierarchy process (FAHP), but evaluate dozens of global suppliers simultaneously or determine the order quantity from them will be complex and difficult. Meanwhile, decision makers can determine the order quantity form the suitable suppliers by using fuzzy goal programming (FGP); however, it is not easy to decide weights for each goal of global supplier selection with different supply chain strategies. This study integrated the FAHP and FGP (FAHP-FGP) method to be a new approach for global supplier selection in considering the manufacturer’s supply chain strategies. With FAHP-FGP method, the manufacturer can consistently integrate multi-manager’ opinions in determining weights of each goal and obtain the order quantities for suitable suppliers based on manufacturer’s strategies. To demonstrate the usefulness of the proposed method, a real-world case of a digital consumer products manufacturer is presented.

This study proposes a Fuzzy Goal Programming approach to optimize the problem of aggregate planning of production processes in companies that produce charcoal husks. The application proposed to the company describes the process of Rice Husk Charcoal Production taking into account the uncertain factors involved in the aggregate planning process of Rice Husk charcoal production. Decision-making related to the level of material needs in each type of rice husk charcoal product is considered based on planning for the next 12 months by including weighting value in membership function, determination of membership function of each function objectives with equivalent Crip of fuzzy goal programming. Fuzzification is based on three main objectives with three membership levels for each purpose of Goal Programming. This research provides the results of the proposed adaptive model applied to companies that produce charcoal husks.

This chapter presents two methodologies for solving quadratic programming problems with multiple objectives under fuzzy stochastic environments. The right side parameters of the chance constraints of both the models are chosen as fuzzy random variables (FRVs) following different probability distributions. Like the previous chapters, chance constrained programming (CCP) methodology is employed to the fuzzy chance constraints to develop fuzzy programming model. In the first model, cut of fuzzy sets and fuzzy partial order relations are incorporated to the fuzzy programming model to develop an equivalent deterministic model. For the second model, defuzzification method of fuzzy numbers (FNs), which are presented in Chapter 2, are taken into consideration to generate equivalent quadratic programming model in a crisp environment. As the objective functions are quadratic in nature, it is easy to understand that the membership functions obtained through methodological development process are also quadratic in nature. To linearize the quadratic membership functions, linearization techniques are employed in this chapter. Finally, for achieving the maximum degree of each of the membership goals of the objectives, a fuzzy goal programming (FGP) approach is developed for the linearized membership goals and solved by minimizing under-deviational variables and satisfying modified system constraints in fuzzy stochastic decision-making environments. To illustrate the acceptability of the developed methodology presented in this chapter, some numerical examples are included.

This paper presents a fuzzy goal programming (FGP) approach for modeling and solving multiobjective fractional programming problems (MOFPPs) with fuzzy numbers parameter sets. In the proposed approach, first the notion of α-cut in fuzzy sets (FSs) is used to transform a problem into conventional MOFPP by using the tolerance membership functions in FSs. In model formulation, membership functions are converted into fuzzy goals for measuring the degree of satisfaction of decision maker (DM) with the solution for achievement of fuzzily described objectives of the problem. In the solution process, an iterative parametric method is addressed within the framework of minsum FGP model to reach the highest membership value (unity) to the extent possible in the decision making environment. The efficiency of the proposed approach is illustrated by a numerical example. The model solution is also compared with the solution obtained by using other approaches studied previously.

PurposeExisting emission and toxicity characterization models in life cycle assessment are currently not suitable for assessing pesticide-related impacts of crop cultivation in tropical regions. This study aims to parameterize the scientific consensus model USEtox for Thai environments to derive toxicity characterization factors of pesticide emissions from agricultural systems in Thailand. Potential human toxicity and ecotoxicity impacts and related damage costs of pesticides used on nine crops cultivated in Thailand are quantified.MethodsConsidering country-specific conditions, USEtox was adapted by applying the landscape and consumption parameters specific to Thailand. Related Thai-specific characterization factors of identified pesticides used in Thai agriculture were quantified. Four emission inventory models were applied to determine pesticide emission fractions in different environmental compartments. The consistent combination of pesticide emission mass and associated characterization factors yielded potential toxicity impact scores. Pesticide impact-related damage costs (external costs) on human health and ecosystem quality were quantified using valuation factors for Thailand. The crops with the highest total damage costs were selected and compared with the annual net incomes of the respective crop production systems.Results and discussionPesticide toxicity impacts assessed by using Thai-specific factors were different from the use of global average factors ranging from 1 to 169% (human toxicity) and from 0.1 to 3587% (ecotoxicity). Our results indicated the variability in impact scores influenced by emission modeling choices. Following PestLCI consensus emission estimation model, mango cultivation showed the highest human toxicity impacts of 0.07 DALY/ha, resulting in high human health damage costs mainly caused by Propineb (93%). Rice cultivation with a dry direct-seeded system exhibited the highest ecotoxicity impacts (3934 PDF m3 day/ha) and associated damage costs mainly caused by Oxadiazon (92%). Pesticides used in cultivation of nine crops resulted in total damage costs of 7188 and 3.01 million THB/crop-year for human health and ecotoxicity, respectively. Mango and rice production accounted for 70% and 17% of the total damage costs, which were 36% and 20% of the respective crops’ annual net income.ConclusionsOur study illustrates the ranking of pesticides applied throughout the crop calendar causing toxicological impact and related damage costs on human health and ecosystem quality. This helps identify crops and the main contributors to pesticide-related toxicity impacts in Thailand. Our study highlights the need for proper emission quantification and for the use of characterization factors locally parameterized to increase accuracy. Our results will be useful for future improvement toward more sustainable pesticide use.Graphical abstract

Because of the uncertainty and complexity of geological disasters, fuzzy comprehensive analysis can be illuminating when applied to earthquake disaster supply chain performance evaluations. To address the uncertainties in disaster emergency supply chains, this paper establishes a novel supply chain performance evaluation method, the FAHP(Fuzzy analytic hierarchy process) based on triangular fuzzy numbers. First, a supply chain performance evaluation system is built that considers supply chain reliability, emergency preparedness and operational effects. Then, to overcome the defects in traditional analytic hierarchy processes (AHP), triangular fuzzy numbers are used to develop a new performance evaluation method -the TFAHP(Triangular fuzzy analytic hierarchy process). This model not only overcomes the shortcomings of traditional weight calculations, but also introduces group decision-making, making the TFAHP of more practical significance for emergency supply chain performance evaluations.

Preventive health care is unlike health care for acute ailments, as people are less alert to their unknown medical problems. In order to motivate public and to attain desired participation levels for preventive programs, the attractiveness of the health care facility is a major concern. Health economics literature indicates that attractiveness of a facility is significantly influenced by proximity of the clients to it. Hence attractiveness is generally modelled as a function of distance. However, abundant empirical evidence suggests that other qualitative factors such as perceived quality, attractions nearby, amenities, etc. also influence attractiveness. Therefore, a realistic measure should incorporate the vagueness in the concept of attractiveness to the model. The public policy makers should also maintain the equity among various neighborhoods, which should be considered as a second objective. Finally, even though the general tendency in the literature is to focus on health benefits, the cost effectiveness is still a factor that should be considered. In this paper, a fuzzy bi-objective model with budget constraints is developed. Later, by modelling the attractiveness by means of fuzzy triangular numbers and treating the budget constraint as a soft constraint, a modified (and more realistic) version of the model is introduced. Two solution methodologies, namely fuzzy goal programming and fuzzy chance constrained optimization are proposed as solutions. Both the original and the modified models are solved within the framework of a case study in Istanbul, Turkey. In the case study, the Microsoft Bing Map is utilized in order to determine more accurate distance measures among the nodes.