This paper characterizes the routing problems arising in distribution of rice-for-the-poor in a district and presents a generic mathematical formulation of vehicle routing problems (VRP) for solving the problems. The proposed generic model, framed as a mixed integer linear programming, is formulated in such a way to encompass three distinct features; namely multiple depots (MD) establishment, multiple trips (MT) transportation, and split delivery (SD) mechanism. This model is implemented for a real-world problem of rice-for-the-poor distribution in the Ponorogo district of Indonesia, involved for deliveries among 3 depots—8, 17, and 23 villages depended on the distribution period—using a fleet of 5 vehicles of homogeneous capacity. Three types of distribution model are identified as MD-MT-VRP, MD-VRP-SD and MD-MT-VRP-SD.

Data envelopment analysis models play an important role in decision making. In this paper, one-stage and two-stage nonlinear programming problems are investigated in order to evaluate the efficiency of two types of network data envelopment analysis model. The first type of network data envelopment analysis model has a series structure with three stages and a feedback between the last step and the middle step, the second model has a three-stage series structure with two feedback between the final step and the first step and the middle step. By examining the overall efficiency of the models based on the one-stage programming problem, a two-stage programming problem is also applied in order to evaluate the efficiency of each step. In order to solve one-stage nonlinear programming problems and two-stage linear and nonlinear programming problems derived from modeling, a linearization method based on coordinate transformation, and constant assumption and gradual growth of some variables is presented. In the last section, the proposed methods have been discussed using some numerical examples.

This paper presents a new penalty function called logarithmic penalty function (LPF) and examines the convergence of the proposed LPF method. Furthermore, the LaGrange multiplier for equality constrained optimization is derived based on the first-order necessary condition. The proposed LPF belongs to both categories: a classical penalty function and an exact penalty function, depending on the choice of penalty parameter. Moreover, the proposed LPF is capable of dealing with some of the problems with irregular features from Hock-Schittkowski collections of test problems.

This paper proposes a framework for selecting energy storage technology (EST) for small-scale renewable energy systems. The present study was able to achieve this using the results that were generated from a Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and a fuzzy quality function deployment (FQFD) model. Using the FQFD model, experts' opinions were incorporated into the framework during the evaluation of the techno-economic requirements importance for the selection process. The FQFD model considered system safety, availability, flexibility, reliability and effectiveness as customers’ requirements, while nominal voltage, nominal discharge current, peak measured voltage, operating cost and cost of energy were among the techno-economic requirements. During the framework application, a questionnaire was used to collect information from experts on the techno-economic requirements importance, while information on the techno-economic (eleven) requirements for four ESTs were obtained from literature. The FQFD model results were incorporated into a PROMETHEE and TOPSIS models. The PROMETHEE and the TOPSIS models identified Lithium-Ion EST as the best EST for small-scale renewable energy systems.

This paper introduces a novel practical variant, namely an open close multiple travelling salesmen problem with single depot (OCMTSP) that concerns the generalization of classical travelling salesman problem (TSP). In OCMTSP, the overall salesmen can be categorized into internal/permanent and external/outsourcing ones, where all the salesmen are positioned at the depot city. The primary objective of this problem is to design the optimal route such that all salesmen start from the depot/base city, and then visit a given set of cities. Each city is to be visited precisely once by exactly one salesman, and only the internal salesmen have to return to the depot city whereas the external ones need not return. To find optimal solutions, an exact pattern recognition technique based Lexi-search algorithm (LSA) is developed which has been subjected in Matlab. Comparative computational results of the LSA have been made with the existing methods for general multiple travelling salesman problem (MTSP). Further, to test the performance of LSA, computational experiments have been carried out on some benchmark as well as randomly generated test instances for OCMTSP, and results are reported. The overall computational results demonstrate that the proposed LSA is efficient in providing optimal and sub-optimal solutions within the considerable CPU times.

Seaweed had proven to become a fisheries commodity to provide a significant multiplier economic effect for coastal community in Southeast Maluku District during 2005-2013. However, the declining on the productivity of seaweed has given a direct impact on welfare of farmers and coastal communities in this region during the recent years, due to asymmetric information on seaweed farming associated with prices, latest technology and all pre and post production activities. Thus, forming a dedicated information centre for seaweed farming in the region has become a necessity. As small island regions, in Southeast Maluku district, farmers and all stakeholder have to deal with cliché problems such as insufficient infrastructure, lack of transportation, farmers locations that spread across the islands. This paper focused on the selection of suitable location for the information centre for seaweed farming in Southeast Maluku district, Indonesia. Analytical hierarchy process (AHP), in classical and fuzzy forms, was an approach used on this study for suitability analysis. Moreover, GIS for spatial analysis in addition to AHP was used to compare the best results for the location of information centre for seaweed farmers. The results show that fuzzy AHP could provide more suitability location compared with conventional AHP. Most of suitability locations were located in three sub-districts regions and also most of them were located in Kei Kecil Island and Kei Besar Islands due to the geographical characteristics of the region. The sensitivity analysis was conducted to measure the sensitivity of the results when the weight of each criterion was changed. The outcome of this study was to serve its purpose as an input and comparison study for a recent local government of Southeast Maluku' policy on developing seaweed farming in this region.

The purpose of this paper is to examine the extent to which the Intelligent Enterprise Resource Planning (I-ERP) System can be used in company operations. Machine learning is embedded in a decision tree algorithm to demonstrate the viability of intelligent technology in an ERP system and to enhance the quality of operations through an I-ERP system. The study consists of two steps. In the first step, the algorithm uses the decision tree algorithm to demonstrate the application of intelligent technology in an ERP system. In the second step, the proposed model analyzes four quality criteria related to company operations through I-ERP system in order to determine whether or not I-ERP has significant improvement on managers’ decisions. As a result, the use of I-EPR may improve the quality of operations, agile respond to market demand, increase the efficiency and the competitiveness in organizations. An illustration example is provided to demonstrate the application of I-ERP.

Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed.

This paper proposes a novel form of supplier selection involving the supply chain dyad as the buyer and the suppliers as sellers. The main proposed contribution is a multi-attribute decision hybrid protocol for supplier selection based on collaboration and negotiation, adapted to dyadic collaboration in a supply chain context. Suppliers and the purchasing dyad can reach an agreement on the details of the products simultaneously and exploit the preferences of the customer dyadic partner to enlarge the criteria choices of the products. For this, the proposed protocol combines a one-to-one bilateral dyadic collaboration protocol inside the purchasing dyad along with a one-to-many multi-bilateral bargaining protocol between the purchasing dyad and suppliers. Illustrative multi-agent simulation experiments were carried out to prove the effectiveness of the proposed protocol. The protocol implementation shows better negotiation results than the classic supplier selection process, along with expected higher customer partner satisfaction and a more embedded dyadic relationship.

Determining 2n values of fuzzy measure prior to applying Choquet integral normally turns into a complex undertaking, especially when the decision problem entails a large number of evaluation attributes, n. Many patterns of fuzzy measure have thus been suggested to deal with this complexity. λ0-measure is one such pattern. However, the original λ0-measure identification method was found to be unsuccessful in providing clear-cut indications on the relationships held by the attributes. A revised version of the method was then introduced to tackle this issue, but unfortunately it requires a large amount of initial data from the respondents compared to the original method. This paper therefore proposes an alternate version of λ0-measure identification method that synchronously compensates the shortcomings associated with each existing method. The proposed method uses interpretive structural modelling (ISM) to uncover the actual relationships held by the attributes. The outputs of ISM (i.e. digraph, driving power and dependence power) are then utilised to determine the inputs required to identify the complete set of λ0-measure values. A supplier selection problem was used to demonstrate the feasibility of the method. Also, the usability of the method was compared over the existing ones.