伊呂原 隆

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.

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>クロスドッキングセンター内の出庫エリアにおいて，シュートからドックへの商品の運搬時間を削減するためどの店舗宛の商品をどのシュートに落とすか，どの店舗行きのトラックをどのドックに到着させるかを考えることは重要である．しかし，運搬時間のみを考慮し需要量の大きい店舗が特定のシュート・ドックに偏ると混雑が発生し，本来想定しているよりも長い作業時間を費やしてしまうことがある．本研究では，商品運搬時間の削減と混雑緩和のトレードオフに着目したシュート・ドックへの店舗割り当てモデルを提案する．また，厳密解法の適用が困難な大規模問題を解くため，遺伝的アルゴリズムと局所探索法を組み合わせた近似解法を提案する．</p>