This paper proposes a mathematical optimization model for designing safe walking routes for children from school to homes. We focus on the existing bi-objective model, the safe walking route design problem, which minimizes both the total walking risk and the total route distances for all children from school to home. The original problem assumes that the combined routes for all children forms a directed out-tree rooted at the school. However, making this assumption excludes solutions such as those involving cycles. This paper explores how to relax the assumption that the graph is an out-tree by using a layered network constructed from the original network. Using the layered network formulation, we obtain some solutions that have better objective values than those obtained by the original formulation when we strongly emphasize reducing the risk objective. The results also show that the new formulation produces the same out-tree solution when minimization of the risk objective and the distance objective are suitably balanced.

Due to the frequency increase of natural disasters such as the earthquake and tsunami, the humanitarian supply chain management (SCM) became an important topic for scientific research. This paper proposes a two-stage stochastic model to optimize the cost of the whole relief process under uncertainty about demand, pre-positioned stocks and the condition of the transportation network. Many factors are considered such as fleet size, route availability and the extra stocking amount. The model also requires that in each scenario the satisfied proportion should be at least meet the lowest satisfaction value, which is decided by the properties of each area and the event. A case study application is used to illustrate the model and how it supports an emergency relief strategy.

In the dial-a-ride problem, DARP for short, given a set of requests on pick up and delivery, we aim at finding a minimum-cost set of vehicle routes that meets all the requests. In this study, we focus on users’ inconvenience costs, and propose a problem of minimizing them in order to achieve fairness. We call the resulting problem DARP-F. We demonstrate that DARP-F has considerable advantages over the conventional one using time-window constraints, DARP-T for short. In particular, DARP-T is infeasible in the presence of requests that conflict each other, while DARP-F is always feasible and offers a best compromise solution taking the inconvenience costs into account. We discuss how the inconvenience costs should be defined and be treated in practice through numerical experiments. In addition, we show that DARP-F is NP-hard, and give an integer programming model, which can solve middle-sized instances in reasonable computation time.

We consider the design of distribution network using power flow controller. In power delivery systems, the use of dispersed generation and security control to improve network utilization requires the optimal use of system control devices. The installation of loop controller allows the distribution system to operate in a loop configuration, achieving effective management of voltage and power flow. In the investment planning process, it is important to identify the optimal location and installed capacity of the equipment such that all operational constraints are satisfied. This paper presents a method for identifying the optimal location and capacity with the minimum installation cost. Our novel approach uses an economic model that considers the fixed costs. A slope scaling procedure is presented, and its efficiency is demonstrated using numerical experiments.

Cluster tools are widely used for semiconductor manufacturing. A multi-cluster tool consists of several single cluster tools which are interconnected with each other. The multi-cluster tool causes a deadlock due to non-existence of intermediate buffer. Deadlock is a serious problem leading decrease of throughput and deterioration of wafers. In this paper, the optimal supervisor proposed by Chen et al. (2011) is applied to a Petri net model of the multi-cluster tools. From the comparison of performance with the conventional deadlock prevention policies, we find that the maximally permissive supervisor does not always produce a better scheduling performance. We propose a design method of a Petri net supervisor to improve the scheduling performance. Our main idea is to eliminate legal markings that may eventually cause the increase of makespan. The proposed method is applied to the two-connected multi-cluster tool. The results indicate that the scheduling performance can be improved by the proposed method without lowering maximal permissivity.

We address a game theoretical analysis of the leadership structure of two-echelon supply chains with a single supplier and a single retailer involving a risky supplier under demand uncertainty. The risky supplier has a risk of unavailability of delivering the product to the retailer. The equilibrium solutions for different leadership structures are analytically derived. The influence of the probability of risky supplier availability on the optimal leadership structure is investigated. The model is extended to consider a two-echelon supply chain with two risky suppliers. The total profits for each leadership structure are evaluated from the perspective of the entire supply chain. The analytical results show that the leadership structure with the risky supplier as a leader can maximize the total profit. Numerical examples are provided to show the validity of the proposed analysis.

This paper considers optimal production scheduling of an agricultural product to find the best matching between farmers and retailers with contract. It is important to construct the agricultural production system to hold the win-win relationship among all stakeholders in terms of sustainability. Therefore, in this paper, a multiobjective programming problem for the sustainable agricultural supply chain management is formulated to both find the optimal matching between farmers and retailers. It is generally difficult to obtain the best solution for all stakeholders directly. Therefore, Bellman-Zadeh approach is introduced, and the efficient algorithm to obtain these optimal solutions is also developed.

The overall planning process undertaken by a bus company is traditionally composed of five sub-processes: timetabling, vehicle scheduling, maintenance scheduling, crew scheduling, and crew rostering. Solving the full optimization problem is believed to be computationally intractable, and therefore in practice the five sub-processes are usually optimized in sequence. In this paper, we present a model that integrates the problems of vehicle scheduling and maintenance scheduling. The objective is to minimize the differences in mileage between buses, the total distance traveled, and the daily differences in the number of maintenance tasks. We propose a heuristic algorithm using the framework of greedy randomized adaptive search procedure (GRASP), and we improve the neighborhood search procedure by using an ordered list of possible trips. We compare the neighborhood search procedure with and without this mechanism and show that the ordered list reduces the number of neighbors to be checked by more than 95%, and it reduces the time to obtain solutions of the same or better quality by an average of 70%. Through computational experiments on instances generated from real-world data, we show that the proposed algorithm finds solutions that are as good as or better than those obtained by a commercial solver in less than 5% of the time required by the latter.

Overcrowding in hospital emergency departments has been a longstanding global issue. Due to the steadily increasing patient demand, the higher complexity of cases and the limited resources available in hospitals, this overcrowding problem has become more challenging. The overcrowding problem is urgently important as the provision of the necessary medical services to patients may be delayed, leading to various adverse consequences. In order to alleviate overcrowding, hospital administrators have been studying and implementing different solutions for the enhancement of system efficiency. In this paper, we present our work that applies system simulation to model the operations and patient flows at a hospital emergency department. We examine the implications of a fast service track on the efficiency of service provision. This fast-track system, without a doubt, can benefit some groups of patients who can be provided with more responsive medical services but, at the same time, may increase the waiting time of some other groups of patients. With the simulation model, we are able to assess these possible tradeoffs between the waiting times of different types of patients when the fast track is adopted. Our computational experiments suggest that the adoption of a fast-track system can be more beneficial, in terms of the reduction in overall patient waiting time, to emergency departments which have more urgent patients. Our simulation model is not restricted to modeling this fast-track system only, but can also be adopted to examine different policy implications and “what-if” scenarios when managing hospital emergency services.

The rectilinear block packing problem is a problem of packing a set of rectilinear blocks into a larger rectangular container with fixed width and unrestricted height. A rectilinear block is a polygonal block whose interior angles are either 90◦ or 270◦. The objective is to pack all the blocks into the container so as to minimize the height of the container. This problem is among classical combinatorial optimization problems and is known to be NP-hard. In this paper, we propose two exact algorithms for the rectilinear block packing problem: one is based on two IP problems and the other is based on a new solution representation. The basic idea of our algorithms is that we iteratively compute lower and upper bounds on the optimal value until the lower bound on the value of an optimal solution for the current search space becomes larger than or equal to the best upper bound found during the search by then, or the search space becomes empty, which means that an optimal solution of this problem has been found. The computational results show that both algorithms obtain five exact and one heuristic solutions for six instances. The algorithm based on a new solution representation improves the running time of the algorithm based on two IP problems.

A new configuration on supply chain strategies was proposed with focus on product variety management. The supply chain strategies were clustered into two types, i.e., a target strategy with a stable product variety, a growth strategy with a changing product variety. Decisions on these two choices may be impacted by managers’ behavior. The matching relationships between these two types of supply chain strategies and supply chain systems were empirically examined and assessed with supply chain performance. Two types of supply chain systems were adopted, i.e., a modularity system, an integration system. Four dimensions of supply chain performance were adopted, i.e., quality, cost, delivery, and flexibility. There were 110 samples in the empirical study. Factor analysis and Structural Equation Modeling (SEM) were used for data analysis. The results suggested that the new proposal on supply chain strategies could be supported. There are significant matching relationships between the growth supply chain strategy and the integration supply chain system, and between the target supply chain strategy and the modularity supply chain system. At last, some suggestions were given on product variety management from the strategic perspective.

The quality of service at restaurants depends not only on the quality of dishes served but also on the waiting time and the timing of when the dishes are provided. For high quality service, scheduling the cooking processes of the ordered dishes is needed. This scheduling problem can be regarded as a kind of job shop scheduling. When many orders pour in at about the same time, and when the number of dishes requiring serving increases, arranging the cooking processes so as to keep the quality of food and service becomes difficult. This paper clarifies a cooking process scheduling model and establishes a basic algorithm. This cooking process scheduling needs to be online because it decides which cooking process is executed at the time when a cook becomes idle or new orders are received. Thus, we use dispatching rules that evaluate the priorities of all jobs not yet executed and that select one job to start next. We first compare dispatching rules for four pieces of order information taken from actual orders by customers. The result showed that the SLACK rule performed superiorly for our order information. We next propose three scheduling methods, taking account of slightly prospective situations in order to make a preferred schedule in which multiple dishes are served at the same time for the same table. The proposed methods worked well for the service of providing dishes at the same time for our data, although the effectiveness of the proposed methods was greatly influenced by the characteristics of the order information.

We consider a capacity expansion problem for a railway network under uncertainty. In our approach, integer and stochastic programming provide a basic framework. We develop a multistage stochastic programming model in which some of the variables are restricted to integer values. Given the distribution of the number of customers in a scenario, the problem of minimizing the expected value of the total investment cost is considered. The problem is reformulated as a problem with first stage integer variables and continuous second stage variables. An L-shaped algorithm is proposed to solve this problem.

The diversification of customer needs and the shortening of product life cycle have increased the significance of small batch production. The growth of interest in environmental issues has generated a focus on methods such as recycling and reuse. Therefore, companies must pay attention to the environmental impact of their production cycle, discharge their social responsibility, and maintain their competitive advantage. In this research, we addressed the mixed-model assembly line, which is used to assemble or remanufacture multiple items simultaneously. Despite their versatility, these assembly lines carry a high risk of line stoppages, especially in dynamic environments. We describe the task assignment problem and item process sequencing problem of a mixed-model assembly line with multiple stations. A heuristic method for solving each of these problems was investigated.

This paper describes the idea of adopting CNC wire bending technology into dentistry wire bending. Till recently, the dentistry wire bending is traditionally performed in the hand-made operation. Innovations in the field of archwire bending in orthodontics have been reported which use robots to execute the desired wire bending operation. In contrast, CNC wire bending technology has not been successfully explored. In this regard, the manufacturing workflow which integrates CAD/CAM into the system is proposed. In addition, several interfaces to execute the bend points planning for different cases are introduced. The B-code generation program, which automatically converts the XYZ Cartesian coordinates for each bend point into the desired theoretical wire bending parameters (L, 𝛽, θ) has been developed with no consideration of the material properties. The feasibility of this methodology is demonstrated through an example, starting from the bend points planning, follows by the XYZ coordinates extraction and ends with the B-code generation. Moreover, the graphical simulation of the wire bending operation by the designed mechanism is also presented.

To address issues such as global warming and material starvation, the manufacturing should be required to pay much attention to these environmental issues. Recycling is one of the effective methods to overcome both global warming and material starvation as it reduces CO₂ emissions, one of the causes of global warming, at material production stage by means of material circulation. End-of-life (EOL) products require a disassembly phase for recycling in order to separate the parts into material types, which determine the choice of operations needed for recycling. Then, manual disassembly operations are carried out because of various complex disassembly tasks, thereby leading to higher recycling costs. In order to make recycling environmentally friendly and economical, selection of disassembly parts is conducted to minimize the recycling cost and to maximize the CO₂ saving rate, which is defined as the rate at which CO₂ volumes can be saved by material recycling instead of producing virgin materials. Thus, disassembly parts selection is considered as a multi-criteria decision making problem. Goal programming is well known as one of the effective methods to overcome the multi-criteria decision making problem. This requires setting of tolerable and sufficient targets for each goal. The material type of the selected parts is important for recycling process. Therefore, this study proposes a bi-objective environmentally friendly and economical disassembly parts selection for minimizing the recycling cost and maximizing the CO₂ saving/recycling rates by goal programming, and analyzes the results in terms of weights and cost for each material type. First, a design procedure is explored and formulated using goal programming. Next, disassembly parts selection is carried out for maximizing CO₂ saving/recycling rates and minimizing recycling cost. Finally, the results are analyzed to pursue the cost effectiveness for the recycled materials for each part in order to identify bottlenecks and propose improvement plans.

The management of the automobile industry has changed recently because of the influence of the financial crisis and the industrial boom in developing countries such as Brazil, Russia, India, and China (BRICs). In this paper, we consider a global automobile-production optimization problem (GAPOP), which we model as a mixed integer programming (MIP) problem. The GAPOP determines global production bases and transportation plans to minimize the total cost of production, transportation, and facilities. It is a unified model that contains the facility location, production planning and transportation problems. We analyze the model for instances generated from real-world data with up to 20 production bases and 133 importing countries for the 18 years from 1997 to 2014. The computational results show that near-optimal solutions to our model are close to the present real-world situation. We also analyze our model with various parameter settings and observe from the results that, at each production base, changes in the number of production lines are affected mainly by labor and material costs. In addition, the proportion of each automobile type when allocating production is influenced by the material costs of all automobile types and the demands of nearby importing countries. Our model is expected to be of use to the automobile industry for making forecasts.