Takashi Irohara

Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response

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.

From Preparedness to Recovery: A Tri-Level Programming Model for Disaster Relief Planning

This paper proposes a tri-level programming model for disaster preparedness planning. The top level addresses facility location and inventory pre-positioning decisions; the second level represents damage caused by the disaster, while the third level determines response and recovery decisions. We use an interdiction framework instead of a stochastic or chance-constrained model. This allows the extent of damage to be treated as a parameter to facilitate scenario exploration for decision-support. We develop an iterative dual-ascent solution approach. Computational results show that our approach is efficient, and we can also draw some insights on disaster relief planning.

Robust flow shop scheduling with random processing times for reduction of peak power consumption

Abstract Recently, power shortages have become a major problem all over Japan, due to the Great East Japan Earthquake, which resulted in the shutdown of a nuclear power plant. As a consequence, production scheduling has become a problem for factories, due to considerations of the availability of electric power. For factories, the contract with the electric power company sets the maximum power demand for a unit period, and in order to minimize this, it is necessary to consider the peak power when scheduling production. There are conventional studies on flowshop scheduling with consideration of peak power. However, these studies did not consider fluctuations in the processing time. Because the actual processing time is not constant, there is an increase in the probability of simultaneous operations with multiple machines. If the probability of simultaneous operations is high, the probability of increasing the peak power is high. Thus, we consider inserting idle time (delay in inputting parts) into the schedule in order to reduce the likelihood of simultaneous operations. We consider a robust schedule that limits the peak power, in spite of an unexpected fluctuation in the processing time. However, when we insert idle time, the makespan gets longer, and the production efficiency decreases. Therefore, we performed simulations to investigate the optimal amount of idle time and the best point for inserting it. We propose a more robust production scheduling model that considers random processing times and the peak power consumption. The results of experiments show that the effectiveness of the schedule produced by the proposed method is superior to the initial schedule and to a schedule produced by another method. Thus, the use of random processing times can limit the peak power.

Integrated Relief Supply Distribution and Evacuation: A Stochastic Approach

This chapter presents a stochastic optimization model for disaster management planning. In particular, the focus is on the integrated decisions about the distribution of relief supplies and evacuation operations. The proposed decision-making approach recommends the best relief distribution centers to use as storage locations and determines their optimal inventory levels. The model also incorporates the priorities for the evacuation of particular communities, as well as specific disaster scenarios with estimates of the transportation needs and demand for aid. A case study is presented to determine the distribution of aid for a flood emergency in Thailand that uses a flood hazard map.

Bi-objective-optimisation of an international transportation problem for CO 2 -efficient schedules

The transportation sector accounts for a considerable portion of global CO2 emissions and the demand for transport, especially for international transport, is increasing. Current approaches to reduce CO2 emission on an operational level ignore time-related objectives and focus on simultaneous optimisation of economic and ecological objectives. In general, considering the minimisation of CO2 emissions in transportation scheduling introduces a trade-off between economic and ecological concerns. The concept of eco-efficiency is important in order to evaluate the economical value and ecological quality of business decisions. Nevertheless, this concept has primarily been applied to strategic decisions. In the present paper we demonstrate the applicability of this concept to operative decisions and introduce a bi-objective evolutionary optimisation approach for the approximation of CO2-efficient schedules in an international transportation problem.

Dual Resource Constrained Scheduling Considering Operator Working Modes and Moving in Identical Parallel Machines Using a Permutation-Based Genetic Algorithm

This paper proposes a novel dual resource constrained (DRC) scheduling problem under identical parallel machine environment that consider operator working modes and moving activity between machines with regards to the makespan minimization objective. We define the working modes as all operator activities when the operators interact with the machines such as loading, setup, controlling, and unloading. Firstly, we provide the mathematical model of the problem using Mixed Integer Linear Programming (MILP). We add unloading activity beside setup to be included in the model. Also, we consider the moving activity that is usually neglected in DRC scheduling problem. Moreover, we propose a permutation-based genetic algorithm (PGA) to tackle the computational burden of the bigger size problem. Then, we run a full factorial experiment with replication to compare the solution quality and computational time of our PGA to the solver and random search method. The results show that our proposed PGA could solve the problem in a reasonable time that is faster than the solver with a good quality solution that is better than random search.

Supply Chain Risk Management: A Comprehensive Review

The purpose of this chapter is to investigate recent research developments in supply chain risk management (SCRM) and to provide a comprehensive outline for researchers and practitioners who are trying to identify the existing state of research in the SCRM area. The importance of a literature review is to enhance the understanding of researchers by cataloging previous research in an area and clarifying the strengths and weaknesses of existing studies and what they might mean. Since the number of studies on SCRM has increased dramatically, several review papers of existing papers have been published. By finding significant number of review papers in the area, we convinced about the necessity of including summary of review papers. Therefore, we first summarize previous SCRM review papers. Second, we review recent papers that have not been mentioned in these review papers. Third, we develop a framework by which to categorize these papers. We conclude by presenting the observed pattern of SCRM research.

Semiconductor supply planning by considering transit options to take advantage of pre-productions and order cancellations

Abstract One of the objectives of supply planning is to find when and how many productions have to be started to minimize total cost. We aim to find the optimum. Base data like the length of transit time are important parameters to plan for the optimum start of production. In this research, we considered two kinds of transit options: normal transit and emergency transit, and we distinguished between planned and executed transit. The decision regarding which transit option to choose for the execution is trivial because emergency is only used when it is needed since normal transit is more cost efficient. However, for planning purpose, it is more difficult to decide which transit option should be used since the uncertainty in demand and supply between plan and execution can allow to plan an emergency transit but to execute the delivery with normal transit, which is a huge benefit in the competitive capital intensive semiconductor industry. If we planned an emergency, we can save inventory and production cost as we can delay the start of production. In contrast, we need pay additional transit cost in case that emergency transit is actually executed. Many characteristics of the semiconductor industry, which might be the front runner for many other industries, are considered in this model such as demand uncertainty, supply uncertainty and economic volatility. In our numerical experiments, we could gain the optimum, depending on each economic situation. Furthermore, we conducted sensitivity analysis of the effect of demand and supply uncertainties on total cost.

A Basic Study on the Installation of Distributed Autonomous Production Scheduling System in Ubiquitous Environment

This paper considers an auction-based scheduling system in a job shop equipped with ubiquitous network environment to cope with dynamically changing market demands. Under such environment all machines and jobs are assumed to have computing and communication devices and can serve as intelligent agents. The functions for an auctioneer and for a participant are investigated to install the scheduling system as a distributed multi-agent system. The systems sending and receiving messages for the auction form a distributed system on a network, enabling an autonomous scheduling.

A Layout Technique of Three Dimensions for Unequal Area and Shape Departments

The facility layout problem can be formulated as a combinatorial optimization problem to minimize the total cost owing to material flow. Most of the paper is focusing on such a layout problem in two-dimensions. However it would be better to treat the layout problem in three dimension (3 D) in order to minimize the distance between equipment and the land area. For binpacking problem, there are some papers in 3 D, but in those model flow between bin is not considered. Thats why this paper proposed a 3 D-layout technique for unequal shape and area departments. Computational experiments are suggesting that proposed algorithm will produce better solution than plain layout which is the result of previous algorithm.