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Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of an optimal policy for maintenance can be a good solution for industrial units. In fact, by managing constraints such as costs, working hours and human workforce causing sudden equipment failure, production and performance can increase.

Therefore, in this research a model was presented to select the best maintenance strategy at Kaghaz Kar Kasra Co of Iran. In this study, it was tried to integrate the two techniques of goal programming and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize maintenance strategies. First, all factors affecting maintenance were identified, and based on the Best Worst Method (BWM) the degree of their importance was determined.

After the evaluation, only 14 criteria in the 4 dimensions of cost, added value, safety and feasibility were selected. The highest points were given to the criteria of equipment cost and depreciation, equipment and personnel performance, equipment installation time and technical feasibility, respectively. In the next stage, using the TOPSIS method the item of maintenance strategy was ranked, and the 3 strategies of preventive maintenance (PM), predictive maintenance (PDM) and corrective maintenance (CM) were chosen. Modeling was performed utilizing a goal programming approach to select the optimal maintenance strategy for 13 devices. All the technical specifications, cost limits and the device time were extracted. After the model was finished and solved the best item for each device was specified.

1. Developing a goal programming model and decision-making dashboard. 2. Identifying the criteria and factors affecting the selection of the maintenance strategy for paper production Industry

This paper seeks to develop a model for the assignment of employees to workplaces. Assignment methods are of high relevance in practice because employees should be assigned to workplaces according to their competences and preferences to ensure that motivated employees carry out tasks effectively and efficiently.

Two goal programming models are introduced with inputs and valuations using the analytic hierarchy process.

The two goal programming models for the assignment of employees to workplaces, which take into account both employee competences and preferences as well as workplace competence requirements and attributes, seem to be effective in helping to arrive at an optimal assignment decision.

In practice, one major problem is that the input data for the goal programming models are not updated regularly. Thus, the documentation of the competence profiles and the preferences of the employees might be out of date or incomplete.

The development of the two goal programming models which could be applied immediately in practical competence management is what makes the work valuable and addresses a gap in the modelling of personnel assignment methodologies.

Due to the ever increasing concern toward sustainability, suppliers nowadays are evaluated on the basis of environmental performances. The data on supplier’s performance are not always available in quantitative form and evaluating supplier on the basis of qualitative data is a challenging task. The purpose of this paper is to develop a framework for the selection of suppliers by evaluating them on the basis of both quantitative and qualitative data.

Literature on sustainability, green supply chain and lean practices related to supplier selection is critically reviewed. Based on this, a two phase fuzzy goal programming approach integrating hyperbolic membership function is proposed to solve the complex supplier selection problem.

Results obtained through the proposed approach are compared to the traditional models (Jadidi et al., 2014; Ozkok and Tiryaki, 2011; Zimmermann, 1978) of supplier selection and were found to be optimal as it achieves higher aspiration level.

The proposed model is adaptive to solve real world problems of supplier selection as all criteria do not possess the same weights, so the managers can change the criteria and their weights according to their requirement.

This paper provides the decision makers a robust framework to evaluate and select sustainable supplier based on both quantitative and qualitative data. The results obtained through the proposed model achieve greater satisfaction level as compared to those achieved by traditional methods.

In order to accomplish the goals of maintaining an effective supply chain, the firm needs to establish and maintain long-term relationships with its suppliers. Most supply chain problems inherently involve multiple conflicting criteria. Risks in the supply chain are dynamic in nature. While some risks can be either reduced or eliminated, new ones will appear.

This chapter will focus on the development of a mufti-criteria model using meta-goal programming to develop a supplier selection process. The goals of the model include (1) the minimization of the net cost for all items, (2) the minimization of the defective items, and (3) the minimization of late-delivered items.

Goal programming is an extension of linear programming. Suggests that, although linear programming is used widely in decision‐making processes, it has a major limitation which restricts the users of the technique to narrowing their problems to a single objective function. Goal programming was developed to handle problems with multiple objectives. Proposes that used properly, this technique can be excellent in strategic planning. Shows how the improper structure of a goal programming model induces misleading results.

Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system.

In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL).

A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN.

The principal contribution of the proposed model is the improved modelling of transportation and inventory, which are affected by different characteristics of SCN. To demonstrate computational information of the suggested methods and proposed model, a case illustration of SCN is provided. Also, environmentalism is increasingly becoming a significant global concern. Hence, the concept proposed could be extended to include environmental aspects as an objective function or constraint.

Efficient integration of logistical cost components, such as transportation costs, inventory costs, with mathematical programming models is an important open issue in logistics optimization. This study expands conventional facility location models to incorporate a range of logistic system elements such as transportation cost and different types of inventory cost, in a multi-product, multi-site network. The research is original and is focused on case studies of real life.

The purpose of this paper is to survey and express the advantages and disadvantages of the existing approaches for solving grey linear programming in decision-making problems.

After presenting the concepts of grey systems and grey numbers, this paper surveys existing approaches for solving grey linear programming problems and applications. Also, methods and approaches for solving grey linear programming are classified, and its advantages and disadvantages are expressed.

The progress of grey programming has been expressed from past to present. The main methods for solving the grey linear programming problem can be categorized as Best-Worst model, Confidence degree, Whitening parameters, Prediction model, Positioned solution, Genetic algorithm, Covered solution, Multi-objective, Simplex and dual theory methods. This survey investigates the developments of various solving grey programming methods and its applications.

Different methods for solving grey linear programming problems are presented, where each of them has disadvantages and advantages in providing results of grey linear programming problems. This study attempted to review papers published during 35 years (1985–2020) about grey linear programming solving and applications. The review also helps clarify the important advantages, disadvantages and distinctions between different approaches and algorithms such as weakness of solving linear programming with grey numbers in constraints, inappropriate results with the lower bound is greater than upper bound, out of feasible region solutions and so on.

There are several factors which account for the inefficiencies observed in the educational system of a less developed country. Some of these factors, with particular reference to the educational system of Greece, are briefly mentioned in this paper. However, the main objective of the paper is to show how the application of goal programming could be of assistance to the educational administrators in the country with respect to determining: (1) how to allocate the available resources among the different subsystems of the educational system and among the different components within each subsystem; (2) the quality level of education that the country can afford; and (3) the financial sacrifices necessary for different levels of improvement.

Multi-criteria optimization by meta-goal programming of a portfolio of asset allocation mutual funds is the focus of this chapter. Asset allocation is generally defined as the allocation of an investor's portfolio across a number of different asset classes. The standard classical portfolio model uses the nonlinear model of quadratic programming to minimize risk and maximize return by mean absolute deviation. Instead of the variance measure of the risk of the rate of return, the mean absolute deviation is used as a measure of risk. In this chapter, three types of meta-goals are Type 1: a meta-goal relating to other percentage sum of unwanted deviations, Type 2: a meta-goal relating to the maximum percentage deviation, and Type 3: a meta-goal relating to the percentage of L∞ goals.

With the use of meta-goal programming, a portfolio model, based on Morningstar Stock Sector and Morningstar Bond Sectors, is developed. These sectors are part of an indexed mutual fund for stock and for bonds. The asset allocation is based upon a set of four meta-goals: (1) forecasted earnings growth, (2) forecasted revenue growth, (3) unwanted deviation of absolute deviation for the risk for stock investments, and (4) unwanted deviation of absolute deviation for the risk for bond investments.