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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.

Most of the sustainable scheduling research deals with some environmental issues such as electricity consumption or carbon emissions although there are many other environmental and social indicators. Whereas, more and more manufactures are executing many solutions not only via implementation of environmentally-oriented but also socially responsible economically sound and management program. This proposed research has an objective to get a comprehensive view of the state and progress of sustainable scheduling by considering more sustainable indicators to assist many manufacturers to apply environmentally sound technological solutions. To achieve this objective, we, first, create a pool of sustainable manufacturing indicators that refers to some previous research that focus on applicable-proven indicators. Then, we employ an iterative process that is adapted from the general guidelines for literature review. We conceptualize, search, evaluate, analyze, and synthesize all 50 relevant papers resulting a completely new research framework to characterize the research. We use mathematical model components, i.e. manufacturing model, the system of objectives, objective function, constraints, model type, and optimization method, as the dimensions to classify the papers that were included in our analysis. We also apply the triple bottom line pillars – economic, environmental, and social to specify attributes and categories for the objective function and constraints. In literature classification step, the framework seems very sensitive since it can identify 49 different sustainable scheduling configurations from 50 reviewed papers. It means the framework can easily grasp the contributions of each paper. Then, in the analyses phase, we use ‘sustainable link’ to indicate if one research can be classified as sustainable scheduling research. The results show that energy cost and greenhouse gas indicators become the most frequently used indicators in sustainable scheduling. On the other hand, the mapping shows that some links have been rarely or never included, indicating potential areas for further research. We also list four main directions for future research which are: implementing other optimization methods; adding sustainability indicators; extending the model to a larger scale of manufacturing system; and loosening some assumptions.

A dual resource constrained (DRC) with fewer operators who could control parallel semi-automatic simultaneously potentially faces the unbalance workload problem that is related to social topics in Global Reporting Initiative Sustainability Reporting Standards (GRI Standards). Balancing the operator workload, on the other hand, could change the schedule structure resulting in some additional delay. This proposed study develops a multi-objective mixed integer non-linear programming (MINLP) with total tardiness and workload smoothness index (WSI) as the objective functions to measure the delay and workload balance respectively. To solve the model, a well-known non-dominated sorting genetic algorithm II (NSGA-II) is used to yield the non-dominated solutions showing the alternative schedule options. The results show that the effort in balancing the workload could raise the lateness. The important finding is that WSI can be improved significantly in the small proportion on the most left side of the total tardiness range.

This present study compares various non-dominated sorting genetic algorithm II (NSGA-II) variants to deal with multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling to minimize the makespan and workload unbalance among operators. MTSSDRC is a complex problem that needs to integrate two types of scheduling, i.e., job sequencing on the machines and task sequencing, which includes setup, unloading, and moving, by the operators. Since there are two resources, one may be busier than the other. Task sequencing should be prioritized if the operator is very busy, otherwise, we should accentuate the job sequencing. The existing NSGA-II variant prioritizes the machine assignment over the operator. Therefore, its performance is not so well when the operator-to-machine ratio is small. Thus, this proposed research develops two new NSGA-II variants identified by their modified decoding schemes. They posit the operator more important or at least equal to the machine. To the best of our knowledge, this research is the first to analyze MTSDDRC based on the operator-to-machine ratio. The essential finding insists that each variant fits the specific ratio. The new ones are promising for cases with a ratio ⩽ 0.5 . Otherwise, the existing variant is still recommended. Besides, one of the new decoding schemes performs well for all ratio, but not the best.

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.

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.

To realise efficient delivery, the location routing problem (LRP), which determines the locations of the facility and the vehicle routes, needs to be solved. Previous studies have attempted to improve the delivery efficiency of a route delivery, which is an efficient delivery mode if customers are densely located. However, if customers are sparsely located, route delivery is not efficient because the fixed costs of the vehicle become too expensive. In this case, courier delivery, i.e., delivery by a third party, is more efficient than route delivery because the courier delivery costs are proportional to the weight of the delivery amount and delivery distance. In this paper, we extended the LRP model to add the courier delivery model. Our model can select a suitable delivery mode for each customer by evaluating the trade-off between the courier delivery costs and the route delivery costs. The results of numerical experiments are shown.

In recent decades, there are intensive researches on deteriorating inventory. However, only a few researchers focus on the inventory routing problem for deteriorating item. There are many items such as foods, electronic products that deteriorate with time, and many other products in the market also have perishable characteristic. The items not only decay during the stockpiling period but they also deteriorate throughout transportation time. Since deteriorated rate and time is necessary, in this paper, an inventory routing problem with time windows for deteriorating items is developed. Particle Swarm Optimization (PSO) is used to solve the problem since PSO can solve problems in a reasonable period with near optimal solutions. We use two examples to illustrate the model. In a sensitivity analysis, way parameters that impact costs are demonstrated. Our results show that the deteriorating rate in inventory has bigger effects than deteriorating rate in the vehicle, so this research has a significant contribution and managers can give more effort to reduce deteriorating in inventory than the deteriorating rate in vehicles.

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

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.

In the present paper, a multi-item inventory problem with the capacity to replenish carriers is considered. In the present study, we propose a model that considers truck capacity, truck cost, and receiving inspection cost with inventories and shortages in inventory management simultaneously. A number of studies have examined the minimum total cost while considering the ordering cost as fixed and not considering the carrier capacity or receiving inspection cost. However, we consider the stepwise ordering cost, which changes in a stepwise manner according to the ordered quantity and the capacity of the carrier. In addition, we consider a receiving inspection cost that is defined not only by ordered quantity but also by ordered item variety. The problem considered herein was formulated as a mathematical programming problem in a finite planning period, and valid inequalities were considered. We could consider individual objective functions for ordering, holding, lost sales, and receiving inspection cost together. We can observe that the receiving inspection cost could be reduced by aggregating the order quantity.

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.

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 fficiency when the variance between suppliers' total cost is lower.

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.

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.

This study aimed to propose a new formulation for the university course timetabling problem (UCTTP) when a day and time pattern is introduced. The day and time pattern would be set by the university and all assigned courses should follow this pattern. The different points of the proposed formulation would structure the model using starting and ending timeslots instead of a single timeslot to deal with courses with consecutiveness, periodic repeat, and multi-period sessions. To present the effectiveness of the proposed model, three test cases were solved and the results showed that the number of variables and constraints reduced with less computational time. Then, the case study was solved with multiple objectives based on goal programming (GP). The results of the case study show that the number of assignments during the undesirable time was minimised and that the total satisfaction score of all the lecturers was improved.

Cross docking is a logistic management strategy for reducing storage level, inventory management cost, and customer delivery lead-time. In the large distribution network in which products are transferred through one or more cross docks, the decision of whether to synchronise inbound and outbound trucks for the whole network is crucial since it significantly impacts on supply chain performance. This study presents the novel mathematical model of truck scheduling problem in the cross docking network consisting of several cross docks and addresses the importance of synchronising the truck schedule in a network as a whole. The model is formulated as mixed integer programming (MIP) with an objective to find the optimal truck schedule and product transshipment to minimise the makespan. The results demonstrate that simultaneously optimising truck schedule throughout the network improves the solution quality compared to those obtained from models, that optimise the truck schedule in each cross dock sequentially.

It is difficult to find an appropriate ordering policy for a many types of items. One of the reasons for this difficulty is that each item has a different demand trend. We will classify items by shipment trend and then decide the ordering policy for each item category. In this study, we indicate that categorizing items from their statistical characteristics leads to an ordering policy suitable for that category. We analyze the ordering policy and shipment trend and propose a new method for selecting the ordering policy which is based on finding the strongest relation between the classification of the items and the ordering policy. In our numerical experiment, from actual shipment data of about 5,000 items over the past year, we calculated many statistics that represent the trend of each item. Next, we applied the canonical correlation analysis between the evaluations of ordering policies and the various statistics. Furthermore, we applied the cluster analysis on the statistics concerning the performance of ordering policies. Finally, we separate items into several categories and show that the appropriate ordering policies are different for each category.

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.

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.

In this paper, we address the problem Of cellular manufacturing system, which, in essence, consists of three main problems, cell formation and machine duplication and part subcontract. While a number of research have been conducted on the problem, most of them considered machine duplication and part subcontract separately. One paper considering cell formation, machine duplication and part subcontract is published, but the paper proposes two step method, so has a problem for possibility of local search. On the contrary this paper propose an integrated approach using Genetic Algorithm (GA) to solve the problems simultaneously on one step method. Our numerical experiments and results show that our method performs better compared to a conventional method which using two step method.

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 address the issue of a cellular munufacturing system layout design, which, in essence, consists of two main problems, cell formation and cell layout, While a number of studies have been conducted on this issue, the two problems were usually considered sequentially, and the impact of cell layout was ignored when solving cell formation. Moreover, it was often assumed that all cells are equal in area and shape, which is not practical for industry-scaled problems. We propose an integrated approach using the Genetic Algorithm (GA) to solve the problems simultaneously, with an assumption that not all cell areas and shapes are equal. Our numerical experiments and results show that our method performs better compared to a conventional method in which cell formation and cell layout are considered sequentially.

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.

Recently, due to consumer demands have been varied, it is essential that a facility layout is able to adapt to the demands dynamically. In this paper, we present a technique to solve a dynamic layout problem. We deal with the problem by simulataneously considering two kinds of flexibility. One is involved with the rearrangement and the changing size of departments. The other is for the fixed area and shape of the building. The Bottom-Left (BL) algorithm is integrated in the technique in order to treat rectangular departments. The two steps Geretic Algorithm (GA) to improve the solutions, which involved an operation called GENITOR algorithm, is proposed. Numerical experiments are carried out to demonstrate the effectiveness of the proposed technique.

A Space Filling Curve (SFC) has been proposed, which allows for exchange among departments and offers increased flexibility to the optimization process, as a facility layout technique which can treat any departments of unequal area. However little attention has been paid to the department shape in spite of the fact that SFC determines the department shape. The Hilbert Curve (HC) is used as the SFC, but it gives poor estimation of department shape and is not applicable for use in some rectangular buildings. A new SFC is thus developed, which offers better estimation of department shape than SFC based on HC, and applicability for any rectangular building. Numerical results are given to demonstrate the effectiveness of the presented SFC.

This paper presents a technique to solve the facility layout problem with rectangular departments of unequal areas. This problem can be formulated as a combinatorial optimization problem to minimize the total cost owing to material flow between departments. It is, however, computationally timeconsuming to find optimal solutions for large problems, because they are highly combinatorial in nature. Furthermore, these problems exhibit many local minim. These properties make them suitable candidates for the application of simulated annealing, which is a stochastic optimization procedure. A suboptimal method is then developed, which is a new improvement-type technique based on simulated annealing. In this technique, a new algorithm for updating placement of departments is proposed. This algorithm enables the application of simulated annealing to solve the problems considered here. This technique also offers increased flexibility to the optimization process, making the solution much less dependent upon the initial layout. Numerical results are given to demonstrate the effectiveness of the presented technique.

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.