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This paper aims to identify the factors that hinder the successful empowerment of waqf land in Indonesia empirically by applying the analytic networking process (ANP).

In this study, after the waqf land problem criteria were introduced, an ANP model was developed and applied to identify the main problem, solution and strategy as a framework to guide stakeholders to produce policy recommendations appropriately and optimally.

The results showed that the main priority of the problem is the nadzir aspect. Furthermore, each criterion’s problems are a low public trust, unprofessional nadzir (waqf manager), a huge number of idle waqf lands, the absence of waqf certificates and non-economic waqf lands utilisation. Meanwhile, the main priorities for each aspect of the solution are socialisation and education to the community, nadzir certification, utilisation of information technology for the administration of waqf land governance, waqf land certification and innovation of waqf land development projects. Then, the long-term strategic priority is optimising the community’s role in the empowerment of waqf land.

The development of the ANP model would give the institutions a modest, flexible and convenient approach to evaluate the barriers present in waqf land. However, ANP requires greater effort because it is a highly complex methodology that compels a study to use more numerical calculations in assessing composite priorities.

Not many studies on waqf land issues in Indonesia have been discussed empirically. Most of these studies are in the form of descriptive studies. This study introduces the ANP approach to illustrate the main problems of waqf land empowerment in Indonesia. ANP is a new approach to the decision-making process through a process that produces a general framework for treating decisions without making assumptions regarding independence between elements.

This study aims to formulate a strategy for optimizing zakat governance in zakat institutions in East Java Province by identifying priority problems, creating solutions and developing strategies.

This qualitative research uses the analytical network process method. Ten respondents representing practitioners, academics, associations and regulators were selected for their expertise in zakat governance. The data were processed using Super Decision software program and Excel.

Priority issues in optimizing zakat governance found in this study are lack of information on Mustahik’s needs and development to Muzakki and the low motivation and ability of Mustahik to develop. Improving the quality and capacity of Amil’s, especially in the technological aspect, is a priority solution. The priority strategy considers intensification (by developing Amil’s ability to use technology) and extensification (by increasing Amil’s numbers who master technology).

The results highlight the urgency of increasing Amil’s capacity and capability in technology-based zakat management. Zakat institutions need to prepare for management’s transformation toward zakat technology as one of the priorities in optimizing zakat governance.

Problems, solutions and strategies for optimizing zakat governance are collected by connecting it to the Zakat Core Principles, namely, the ninth principle of collection management and the tenth principle of distribution management. Further, for identifying problems, solutions and strategies, four aspects must be considered of Amil, Muzakki, Mustahiq and other supporting elements to present better policies to optimize zakat governance.

The purpose of this paper is to develop the method of generating assembly sequences, which can be used in the shipbuilding industry. The method must take into account the assumptions specific for assembly processes of large-size steel ship hulls, among others, a large number of connections, multi-stage and parallel assembly, set priority relations between connections.

The assembly sequence is presented as a directed acyclic graph, whose vertices are mutually uniquely assigned to connections on a hull structure. The minimization of the number of unmet priority precedence of performing connections has been proposed as a criterion of optimization. The genetic algorithm has been proposed as a method to solve problems.

The proposed method allows to model the acyclic assembly process of welded structures and find solutions minimizing the objective function even for very complex problems. Because of this, the method has a chance to be used in shipbuilding.

Mathematical formulation of priority assumptions is quite laborious. The possibility of partial automation of this process should be considered. Due to the complexity of the problem, a relatively simplified objective function has been proposed. In assembling a hull, additional criteria should be taken into account. It is the direction of further research.

The method can be successfully used in shipbuilding and in planning the production of other steel welded structures, among others, tanks, components of bridges, offshore structures. Examples of calculations were performed on an actual structure of a hull fragment.

A new way of coding the acyclic serial-parallel sequence was designed. The proposed method allows to analyse the sequence using the graph theory. Original, two-part crossover and mutation operators for assembling sequence were proposed.

The implementation of lean production in small manufacturing enterprises (SMEs) harbors a great deal of barriers on practical, theoretical, financial and organizational sides. In view of this, the purpose of this paper is to explore and prioritize the solutions to overcome these barriers and pitfalls in order to identify a list of the most feasible and efficient solutions on which SMEs must focus to success their lean journey.

First, a review of the published articles along with the interview of some lean experts have allowed to identify 20 barriers grouped into five categories along with 17 solutions proposed to overcome these barriers. Afterwards, an integrated fuzzy Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) was proposed to evaluate and rank the solutions of lean implementation in SMEs and overcome its barriers. The AHP is used to determine weights of the barriers as criteria, and the fuzzy TOPSIS method is used to determine the final ranking of the solutions of lean adoption in SMEs.

The top five barriers of lean implementation in SMEs were found to be: lack of management involvement, lack of adapted methodology of lean implementation, short-term vision, fear and resistance to change and lack of understanding of lean. Moreover, the top five solutions to overcome the barriers of lean adoption in SMEs are as follows: commitment and participation of management, adoption of simple measurement and KPIs, development of organizational learning culture, early deployment of lean culture through training and allocation of sufficient time and resources for change.

The findings of this study can provide a basis for researchers and practitioners in order to understand the real reasons of lean failure in SMEs and develop improvement plans to increase its success rate. Moreover, it proposes a more reliable, robust and systematic decision support tool for stepwise implementation of the solutions of lean adoption in SMEs since they lack sufficient resources to address all the solutions simultaneously.

This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type.

In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem.

As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms.

The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability.

The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.

Aims to demonstrate how conceptual frameworks can guide managerial training in education. Such purpose should apply to various countries because the concepts, by their nature, have universal characteristics. Uses three managerial functions: the rationale for their selection; the methods of teaching them; and the results of teaching as benchmarks of the use of the guidelines. Also provides an argument that other managerial functions can be taught in the same way. This work, which focuses on communication, style and problem solving, is considered experimental in nature. More research is needed to understand better the relations between conceptual frameworks and administration training.

This study aims to propose priority solutions for mitigating the impact of the Covid-19 pandemic through the Social Safety Net (SSN) based on the Islamic objectives.

The analytic network process method is used in this study. Focus group discussions and in-depth interviews with relevant stakeholders were used to collect data and supplemented by a literature review to explore comprehensive information.

The findings indicate different opinions among experts, including regulators, practitioners, associations and academics regarding the most important priority solutions to the impact of Covid-19. However, experts agree that the highest priority solution is the SSN program in the lineage sector, specifically the distribution of the Family Hope Program. A program in the field of protecting the mind is the second priority solution. The program in the field of soul and wealth is the third priority solution.

This study contributes to the development of government policy to deal with the socioeconomic impact of Covid-19 based on the Islamic objectives by mapping the SSN through the five elements of protection, namely, faith, soul, mind, lineage and wealth protection based on priority solution.

This study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of m_k identical machines in stage k and the production flow is immaterial.

Computational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances.

The proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality.

The authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem (JSP), a class of NP‐hard optimization problems. The approach is to be built on a PSO with multiple independent swarms. PSO was inspired by bird flocking and animal social behaviors. The particles operate collectively like a swarm that flies through the hyperdimensional space to search for possible optimal solutions. The behavior of the particles is influenced by their tendency to learn from their personal past experience and from the success of their peers to adjust their flying speed and direction. Research in fusing the multiple‐swarm concept into PSO is well‐established in solving single objective optimization problems and multimodal problems.

This study examines the optimization of the JSP via a search space division scheme and use of the meta‐heuristic method of PSO by assigning each machine in a JSP an independent swarm of particles. The use of multiple swarms in PSO is motivated by the idea of “divide and conquer” to reduce the computational complexity incurred through solving a NP‐hard combinatorial optimization problem. The resulted design, JSP/PSO algorithm, fully exploits the computing power presented by the multiple‐swarm PSO.

Simulation experiments show that the proposed JSP/PSO algorithm can effectively solve the JSP problems from small to median size. If certain mechanism of information sharing between swarms can be incorporated, it is believed that the new design could offer even more computing power to tackle the large‐sized problems.

The proposed JSP/PSO algorithm is effective in solving JSPs. The proposed algorithm shows considerable promise when searching the space of non‐delay schedules. It demands relatively lower number of function evaluations compared to other state‐of‐the‐art. The drawback to the JSP/PSO is that the GT scheduling adopted is too computationally expensive. Future works will address this concern.

Processes, in practice, may involve more than one quality characteristic that are of interest. It is quite possible for such quality characteristics to not be independent of each other since the magnitude of one of the characteristics may influence the magnitude of the other characteristics. Under this setting, it is of interest to determine the optimal settings of the process parameters (usually the process mean and the process standard deviation of each quality characteristic) under various objectives. Some of the objectives may be conflicting to each other. In general, it may be possible for the decision-maker to prioritize the objectives. Using such a prioritized scheme, it is of interest to determine the optimal settings of the process mean and standard deviation for each quality characteristic that is being monitored. Such solutions could be labeled as “satisficing” solutions. Sensitivity analyses of the decision variables to the chosen objectives and parameter values are also investigated.