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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.

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.

Shortages and delays in a humanitarian logistics system can contribute to the pain and suffering of survivors or other affected people. Humanitarian logistics budgets should be sufficient to prevent such shortages or delays. Unlike commercial supply chain systems, the budgets for relief supply chain systems should be able to satisfy demand. This study describes a comprehensive model in an effort to satisfy the total relief demand by minimizing logistics operations costs. We herein propose a strategic model which determines the locations of distribution centers and the total inventory to be stocked for each distribution center where a flood or other catastrophe may occur. The proposed model is formulated and solved as a mixed-integer programming problem that integrates facility location and inventory decisions by considering capacity constraints and time restrictions in order to minimize the total cost of relief operations. The proposed model is then applied to a real flood case involving 47 disaster areas and 13 distribution centers in Thailand. Finally, we discuss the sensitivity analysis of the model and the managerial implications of this research.

With a steep increase of the global disaster relief efforts around the world, the relief supply chain and humanitarian logistics play an important role to address this issue. A broad overview of operations research ranges from a principle or conceptual framework to analytical methodology and case study applied in this field. In this paper, we provide an overview of this challenging research area with emphasis on the corresponding optimization problems. The scope of this study begins with classification by the stage of the disaster lifecycle system. The characteristics of each optimization problem for the disaster supply chain are considered in detail as well as the logistics features. We found that the papers related to disaster relief can be grouped in three aspects in terms of logistics attributes: facility location, distribution model, and inventory model. Furthermore, the literature also analyzes objectives and solution algorithms proposed in each optimization model in order to discover insights, research gaps and findings. Finally, we offer future research directions based on our findings from the investigation of literature review.

Environmental problems have received a great deal of attention in recent years. In particular, CO2 emissions worsen global warming and other environmental problems. The transport sector accounts for 20% of the total CO2 emissions. Therefore, the CO2 emission reduction of the transport sector is of great importance. In order to reduce emissions effectively, it is necessary to change the distribution and transportation processes. The purpose of this study is to minimize both the transportation costs and CO2 emissions during transportation. Our model considers a transportation scheduling problem in which loads are transported from an overseas production base to three domestic demand centers. The need for time-space networks arises naturally to improve the model. It is possible to know the distance carriers are moving, and also consider the timetables of carriers during transportation. Carrier choice, less-than carrier load, and domestic transportation among demand centers are considered as the three target areas to reduce CO2 emissions during the distribution process. The research model was formulated as a mixed integer programming (MIP) problem. It achieves cost reduction, and will contribute to improvement of the natural environment.

At present, it is important problem to reduce transportation cost for most of corporation. In recently, most of customer have specifying time to be served by transportation companies, and it is important for the companies to keep it. Because both reducing cost and improving quality of services are hoped by many transportation companies. Thus, one of the best ways to keep the time window is better scheduling of trucks. And useless waiting time and running time can be reduced. But these are made difficult by following two things. 1 : Time window of customers. 2 : Limited number of berths. Particularly in case of multi route service, 2 is very important. And it is very difficult to solve VRP under limited number of berths. So how to make schedules including both 1 and 2 is needed very much. But there is no research until now. Moreover by making these, it can be easy to work at depot. For these reasons, in this research I develop how to solve Vehicle Routing Scheduling Problem under Limited Number of Berths.

This paper presents an algorithm to get a solution of the facility layout problem for multiple-floors. In a number of previous research, these problems are formulated as a combinatorial optimization problem to minimize only the cost owing to material flow between departments ignoring the utilization of the elevators, which is a typical transportation equipment for vertical transportation. In our proposed algorithm, the capacity of elevators is taken into consideration. Then the proposed algorithm optimizes the number and location of elevator with the consideration of the assignment of each carriage to the elevators. Numeric results are given to demonstrate the effectiveness of the proposed algorithm.

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. That's 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.

In this paper, a new type of Vehicle Routing Problem in which distribution centers exist between factories and customers, is addressed. Each distribution center has no fixed category, which means all customers can be served from any distribution center. When demands occur, products are transported from factories to distribution centers by large fleets, and distributed to customers by small fleets. This results in an inter-dependent relationship between transportation and distribution. However, to take into consideration both transportation from factories to distribution centers and distribution from distribution centers to customers, the solution space becomes extremely large. Thus, it is utterly impracticable to solve the problem by using optimization. This paper, therefore, proposes an integrated heuristic using Tabu Search and Simulated Annealing with two-phase approaches to solve the problem. Numerical experiments has been carried out to demonstrate an effectiveness of the proposed technique.

In this paper, we proposed a hybrid genetic algorithm (Hybrid GA) approach to a multi-objective vehicle routing problem (MVRP). The objective functions considered in this MVRP are (1). to minimize the number of vehicles used, (2). to minimize the total traveling distance for vehicles, (3). to minimize the total waiting time for vehicles, and (4). to maximize the grade of customer satisfaction with due-time. With respect to customer satisfaction with due-time, we used the concept of fuzzy due-time because it can describe customers' preference with service time better than crisped expression of satisfaction with 0 and 1. To handle such multi-objectivity, a set of Pareto optimal solutions are searched by Hybrid GA. Among Pareto optimal solutions, we furthermore targeted at compromise solutions whose objective functions take almost intermediate values each, in order to produce realistic routing plans for vehicles. In the proposed algorithm, a local search procedure is applied to each solution at each generation for efficient search of solutions. The computational results show that the proposed algorithm is efficient for solving MVRP.

In this paper, a layout technique for unequal area and shape departments with a main aisle is proposed. In this technique, layout is represented by the flexible aisle structure (FAS), which is an improvement on the flexible bay structure (FBS), by considering the main aisle inside the building. FBS and FAS treat unequal area and unfixed rectangular shape departments. By using FAS, assignments of departments and aisle structure are optimized simultaneously during the search. In the objective function, which is based on the material handling cost between departments, we use the distance between departments along aisles, in order to take into account the existence of the main aisle. Also, a new measurement for evaluating department shapes, which have unequal area, unfixed rectangles, is proposed. Experimental results indicate the effectiveness of controlling department shapes by this new measurement and of using distance along aisles in the objective function.

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.