Irohara Takashi

Fuel-cell vehicles that use hydrogen as the fuel are one of the next-generation vehicles in the world. However, an inefficient hydrogen supply chain network (HSCN) hinders the spread of fuel-cell vehicles, and the main cause of inefficiency is the danger of transporting super-cold liquefied hydrogen or highly compressed hydrogen gas. In order to solve this problem, organic hydride is currently being developed. This technology transports hydrogen as methylcyclohexane, and this enables the use of the same infrastructure as petrol. Due to the novelty of organic hydride, there’s no research regarding a HSCN involving organic hydride. In this research, a HSCN with three ways of obtaining hydrogen and three ways of transporting is modeled as Mixed Integer Linear Programming. Hydrogen is obtained through domestic production, importation, or production at onsite hydrogen fueling stations (HFSs), and transported as liquid, gas, or organic hydride. In order to test the effectiveness of organic hydride, three scenarios are compared for 25 different sized instances, and a sensitivity analysis is conducted to determine the changes in operational cost affected by a budget crunch. From the results, it is possible to say that organic hydride makes a HSCN more effective when ports, dehydrogenation plants, and HFSs are sufficiently close. Additionally, the more choices involved in the HSCN, the more effective the HSCN can be designed, regardless of the size of the problem. Furthermore, the actual decisions of what to use in a HSCN are decided based on the relative positions between each infrastructure, capacity, level of demand, and budget. In addition, when the amount of budget decreases, operational cost contrarily increases due to the impossibility of constructing an onsite HFS, and the longer traveling distance required due to the reduction of the number of possible construction locations.

This paper addresses the storage assignment problem in a mixed-shelves warehouse. In a mixed-shelves warehouse, a stock-keeping unit（SKU）is assigned to several storage locations, and many kinds of SKUs are assigned in neighboring areas. Such an arrangement can reduce the length of the picking tour when many different orders, each involving small amounts for each SKU, are to be filled. However, locating SKUs in several storage areas makes it difficult to decide the best position for each SKU. Moreover, when the pickers pick an SKU for an order, they must decide where to start, and which storage locations should be visited in order to ensure the shortest picking tour. In this paper, we first propose a mathematical model for the mixed-shelves problem. We then propose two methods to obtain approximate solutions to the problem. The first method uses a two-step approach in which we first decide the best SKU pairs to assign to the same storage area and then determine the best position for these SKU pairs. The other method consists of three steps. The first step decides the SKU pairs, the second decides the storage location assignment, and the third decides the routing. To produce the best solution, a simulated annealing-based algorithm for the assignment problem is proposed. A numerical experiment is conducted to verify the effectiveness of the two proposed methods relative to the exact method for solving the proposed mathematical formulation, especially for large size datasets.

Hazardous Waste Management is defined as the safe and efficient handling of hazardous waste to decrease its toxicity to humans and the environment by means of proper transportation, processing, and disposal. Hazardous waste is generated by different sectors, including the medical sector, industrial sector, domestic sector, among others. Industrial hazardous waste consists of chemicals and compounds of a complex structure, which poses a great risk for public health and the environment alike. This potential risk heavily influences decision-makers when choosing a suitable location for establishing a waste processing facility. Therefore, the process of designing a hazardous waste transportation network comes with many challenges. This paper presents a profit-oriented mixed-integer linear programming model for the hazardous waste location-routing problem, with the main objective of maximizing the overall profit in the network and conditions focusing on minimizing the associated risk in terms of population exposure. The transportation network includes waste generators which are, in this case, factories, along with three different types of hazardous waste processing centers: treatment, recycling, and disposal centers. The formulated problem is coded in Python and optimally solved by Gurobi Optimizer. Furthermore, to deal with the NP-hard nature of the problem for large numerical instances, a metaheuristics algorithm based on non-dominated sorting genetic algorithm II (NSGA-II) is applied, and the model is solved with both NSGA-II and Gurobi to investigate the improvements done by utilizing the genetic algorithm. A case study is conducted for the textile industry in Jordan, as to put the proposed model into practice.

The objective of this research is to establish a model that characterizes integrated humanitarian operations management in response to flood disasters, during which an optimal framework that describes the interactions between different elements in the relief supply chain is crucial. In principle, it is necessary to develop a model that controls the total flow of supply distribution and evacuation planning, as well as the location routing of facilities that handle relief supplies from the sources all the way down the relief chain to the hands of the recipients. The location-routing model was proposed in response to the temporary depot problem that is particular to such disasters. To formulate this problem, the use of a multi-period approach to describe the problem was presented, and multimodal transportation was considered in order to more closely mimic the realistic behavior of a flooding disaster.

In order to minimize the damage caused by large-scale disasters, efficient operation immediately following the incident is very important. However, normal logistics networks may not be available due to traffic congestion or road damage. In recent years, the use of drones for disaster relief has attracted attention. Drones have many characteristics that are beneficial for this purpose, such as autonomous control and being able to take off and land in small spaces. Research 011 commercial drone logistics is being actively conducted, but few studies have focused 011 their application to disaster relief In this study, we propose a model to make effective use of drones during disasters. In our model, drones have two roles, namely, transporting relief supplies and collecting information, and can perform these two tasks simultaneously in one flight. Since drones have a shorter range than other transportation methods, drones generally need to make stops at charging stations. This complicates the proposed mathematical model, and it becomes difficult to obtain an optimal solution when the problem scale is large; accordingly, we construct an algorithm that assumes practical use. The proposed algorithm solves the mathematical model by dividing it into subproblems, allowing for a practical solution to be obtained in a reasonable time. Numerical experiments verify the validity of the model under various conditions. I11 addition, we formulate the facility location problem of depots and charging stations as a p-median and p-center problem, and verify the significance of its optimization.

In this paper, we conduct a case study of a company that collects and processes used home appliances and sells them as new recycled resources based 011 the Home Appliance Recycling Law. The target company earns sales by selling multiple resources to multiple sales destinations with different selling prices. There are various restrictions 011 such sales, which creates complexity. Due to this complexity, the sales amount cannot be maximized using only the intuition and experience of employees. Therefore, we propose a model that maximizes the sales amount obtained from selling each resource. As a result, using the proposed model, different sales amounts and sales destinations of each resource were obtained, achieving better results. Specifically, compared to the current total sales amount, 3.6% better results are expected. Furthermore, as a sensitivity analysis, experiments were conducted in which the credit constraints of each sales destination were varied. It was found that risk management can be performed without affecting the objective function value by setting credit appropriately.

We proposed a traffic flow simulation model using the cellular automata method to investigate reducing the environmental load along the roads in the coastal area of Kawasaki City. Specifically, if multiple travel routes are available from a given starting point to a destination, we search for a route that is economically advantageous, i.e., one that reduces the load on the atmospheric environment while also shortening the travel time required. The intention is to improve the roadside environment by suppressing the traffic volume and encouraging vehicles that produce larger quantities of exhaust fumes, such as large diesel vehicles, to choose routes that would balance their distribution within the area. Via numerical experiments, we verified to what extent NOx emissions are affected by changes in the mixing ratio of large vehicles to regular vehicles and the number of switched routes .

This comprehensive study develops advantageous optimization methods to solve a nascent problem, namely multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling. MTSSDRC is a complex problem that deals with machine assignment, job sequencing, operator allocation, and task sequencing. Setup and unloading must be scheduled to operators, and they are allowed to leave machines while processing jobs. Earlier research on MTSSDRC developed a permutation-based genetic algorithm (PGA) with a specific decoding scheme, namely DSE, to solve the problem. Many previous studies succeed in solving scheduling problems by modifying well-known metaheuristic techniques. Therefore, we are inspired by this to explore further modifications to particular metaheuristics. The first contribution of the present study lies in the development of new decoding schemes that can perform better than the existing option. Five new decoding schemes are considered. Two of those schemes, namely DS2 and DS4, perform significantly better than DSE, reaching 6% relative deviation. DS4 is superior in terms of solution quality, but DS2 can run eight times faster. Another contribution is the development of six modified metaheuristics that are implemented for the MTSSDRC problem: tabu search, simulated annealing, particle swarm optimization, bees algorithm (BA), artificial bee colony, and grey wolf optimization. The performance of these metaheuristics is compared with that of the PGA. The results show that the PGA and BA are consistently superior for medium- and large-sized problems. The BA is more promising in terms of solution quality, but the PGA is faster.

In this study we propose a proactive optimization approach to manage the supply chain risk. The purpose of presented model in this paper is supplier selection with the objective of minimizing the total cost. We apply the multi sourcing strategy to avoid the supply disruption while restricting the selection by suppliers' corporate groups and the second-tier suppliers. We examine the model by different datasets. The findings suggest that this approach has more efficiency when the variance between suppliers' total cost is lower.

This paper proposes a stochastic linear mixed-integer programming model for integrated decisions in the preparedness and response stages in pre- and post-disaster operations, respectively. We develop a model for integrated decisions that considers three key areas of emergency logistics: facility and stock prepositioning, evacuation planning and relief vehicle planning. To develop a framework for effective relief operations, we consider not only a cost-based but also an equity-based solution approach in our multiple objectives model. Then a normalised weighted sum method is used to parameterise our multiple objective programming model. This paper suggests a compromise between the cost, and the equity of relief victims. The experiments also demonstrate how time restrictions and the availability of relief vehicles impact the two objective functions.

<p>In the shipping area of a cross-docking center, it is important to decide the chutes to be assigned each store and the docks each truck should be assigned to in order to reduce the transportation time of items between chutes and docks. However, there are cases that consume more time than anticipated because of congestion. Most of the existing literature does not consider the relaxation of congestion. Some researchers have been studying it, but there is still insufficient knowledge about it under specific situations. In this study, we propose a mathematical model for a chute and dock assignment problem based on a traditional model that not only reduces transportation time of items, but also relaxes congestion. To verify the characteristics of the proposed model, we compare this model with the traditional model in terms of objective function and results of assignment. To solve a large-scale problem that cannot be solved by an exact method, we propose a hybrid metaheuristic based on a standard genetic algorithm and local search algorithm. To evaluate the performance of the proposed metaheuristic, we compare it with the optimal solution and a standard genetic algorithm in a small-scale problem, as well as with a random search algorithm and multi-point local search algorithm in a large-scale problem.</p>