This is the first to comprehensively review the issues such as contract parameters and research models/methods of Quantity Flexibility Contract (QFC) in a supply chain system. We are experiencing a low-frequency-high-impact (LFHI) event such as coronavirus (COVID-19), and rapid technological changes, bringing about a rapid change in customer demand now. Matching supply with demand is one of the biggest challenges in Supply Chain Management (SCM). That is because a sudden rise or drop in customer demand makes it difficult to coordinate objectives between a manufacturer and a retailer. Meanwhile, a QFC has been highlighted as an intensive method in response to this rapid change by gaining time for better observing the market demand before placing its final orders. The intention of this paper is twofold. First, we classify QFC’s articles into contract parameters, comparison and composition of QFC with other strategies and contract types, research models, and methods up to the present point. Second, we guide the future research directions and developments of QFC so that many researchers start easily approaching and studying it. We hope that our discovery from the review provides opportunities to spot gaps in the literature that could be accomplished and values for further future research directions.

This paper investigates the 224 active fuel stations to find thee optimal distribution system. This research aims to design a model based on simulation to optimize the queuing system and fuel station sales. This study combines simulations and experimental design techniques that lead to a predictable and experimental model for optimizing the system and performance of a fuel station by considering two perspectives of queue length and sales rate. Initially, the fuel station was simulated using Arena software. After simulating the fuel station system, we tried to optimize the station

PT. Nusahadi Citraharmonis is a leading manufacturing company in Indonesia that produces spare parts for various automotive industries. The company is an active supplier for several well-known car manufacturer in Indonesia, which are: NTC, HMMI, TMMIN, and ASKA. However, the company is currently having difficulty in fulfilling all customer demand on time. Thus, creating backlogged order in the system and decreasing the daily total complete shipment. The objective of this research is to redesign new inventory level to increase the daily total complete shipment. To solve the problem, Define-Measure-Analyze-Improve-Control (DMAIC) is used as the procedure, cause effect analysis is used as a tool to identify the key factors affecting daily total complete shipment, and system dynamics simulation is used as a tool to redesign inventory level. From the result of system dynamics simulation, the newly redesigned inventory level is able to decrease the backlogged order in the system and increase the daily total complete shipment. Besides the system dynamics simulation, standard operating procedure for operating the system is also made and given to the company for future use.

Although the Rare event has a very wide application range, it has limitations in that it requires mathematical knowledge and the calculation process is complicated for the Bayesian approach, which was mainly used in previous studies. Therefore, rare events need to be easily analyzed. In this study, the critical failures of the ROK Naval ship were analyzed. Bayesian inference was not applied. The long-term trend (B-spline) and short-term probability (phase-type distribution) were fitted and combined. Phase-type distribution is a useful technique that can estimate the probability distribution of a desired shape with one parameter. This study is meaningful in that only an easy methodology was used, Phase-type distribution was applied to the analysis of rare events, and only a very small number of data were analyzed.

This study discusses the integration model of production and distribution planning in the shrimp agroindustry supply chain, consisting of four echelons: shrimp suppliers, shrimp agroindustry, logistics provider companies, and buyers. The shrimp agroindustry supply chain is an essential part of the supply chain of processed product food, which transforms raw shrimp into various processed shrimp frozen products. One form of collaboration between supply chain actors is the integration of production and distribution planning activities. A model is developed to determine the flow of goods from each echelon, the number of processed shrimp products in the agroindustry, and the supplies of processed shrimp products. The bi-objective mixed-integer linear programming is proposed to describe the characteristics of the problem to minimize the total supply chain costs and maximize service level. Non-dominated sorting genetic algorithm II (NSGA-II) is designed to solve the shrimp agroindustry supply chain problem. The sample problem from the shrimp agroindustry in East Java, Indonesia, is applied to exhibit an algorithm’s efficiency. The result shows the best solution for the total supply chain is 1.75 trillion, and the service level is 1,502,264.5.

The problem studied in this paper is the p hub center and the network structure is hierarchical and in three levels; where level one is for demand nodes, level two is for hub nodes, and level three is for central hubs. Central hubs have a complete network and hubs in the network have the capacity constraint. Given that the issue under consideration is for the purpose of transporting perishable goods, Such problems are often used in transportation systems in which customer response time is of great importance and sensitivity; Therefore, the objectives of the proposed model are to find the best location for hubs in the network as well as the best allocation of nodes to hubs so that network transportation costs are reduced and the maximum travel time between each pair of origin destination nodes is minimized. To evaluate the model, a numerical example with CAB dataset is introduced and to review and analyze the results, GAMS software with CPLEX solver is used. The results show that the discount coefficient of central hubs compared to the discount coefficient of second level hubs has the greatest impact on the cost of transportation and travel time.

This paper integrates decisions about the three key components of the supply chain including pricing, routing and inventory control, and multi-product multi-cycle in a two-tier supply chain; includes a manufacturer and a collection of dedicated retailers. In addition to pricing, inventory management and distribution planning, production planning is also considered in the study. The goal is to maximize profits in the supply chain, where products are delivered to retailers by a fleet of limited-capacity identical vehicles under a multiple-delivery strategy. Also, limited production and storage capacity and unauthorized shortages are assumed. The proposed model will then be solved using CPLEX software package in GAMS environment and genetic algorithm in Matlab software to determine the optimal supply chain policy.

This research was conducted to study the issue of relief facility location hierarchically by consideration of possible road closure during the crisis conditions, road safety, and arrival time of relief facilities under disaster circumstances. High costs are allocated for facilities deployment in a suitable location to meet the demands of injured people. Therefore, location-allocation of emergency facility should be considered in a way to use them for long-term periods. To this end, the extant research designed a multi-objective optimization model to minimize the pre-disaster costs including costs of facilities deployment and road use, and to minimize the post-disaster costs such as cost transportation in-network roads. Moreover, the innovative part of the studied model in this research examined the road safety and reduction in time taken to have critical facilities in affected areas. To investigate the functional accuracy of the mathematical model, a numerical example with small dimensions was solved using CPLEX Solver, and required sensitivity analysis was described. As the facility location-allocation is an NP-hard issue, two meta-heuristic algorithms were used to solve numerical representations in real dimensions to examine numerical analyses effectively. Results showed that the dragonfly algorithm had the highest efficiency compared to other developed algorithms. The obtained results can be considered as an efficient managerial tool in management organizations involved in the crisis.

초록·키워드 목차 오류제보하기 In recent years, supply chain management with the aim of making the supply chain more efficient and reducing over-all costs, is one of the key issues addressed in every industry. In today"s energy supply chain, determining the level of sales for manufactured goods in specific time periods and to specific customer groups is very important. This becomes even more important when we have to do this for sensitive marine products that have different customer categories. This article presents a model for determining the optimal level of sales of thermal energy in two levels and according to customer demand in different time periods to determine the optimal level of sales using accurate methods and some meta-innovative methods. For this purpose, two meta-heuristic methods of particle swarm optimization (PSO) and the evolutionary algorithm CPSO have been used. #Hybrid Optimization #Two Echelon Supply Chain #PSO Algorithm #Vendor Inventory Management #Thermal industry ABSTRACT1. INTRODUCTION2. INTRODUCTION OF THERMAL ENERGY SUPPLY CHAIN3. LITERATURE REVIEW4. METHOD5. RESULTS6. CONCLUSIONREFERENCES

In knowledge-based approaches, intellectual capital is a central issue for achieving competitive advantage. Because competitive advantage is an important issue in decision making. In a knowledge-based economy, intellectual capital is used to create and increase the value of companies. The main objective of this paper is to propose an optimized model for industrial management of the projects. For this purpose, the value added Intellectual Capital is considered as a main factor and also the statistical analysis is performed to evaluate the role of intellectual capacity. To conduct the analysis, we used the system generalized method of moments for a broad sample of industrial projects. A mathematical model and descriptive analytical approach were employed. With the increase of companies