The present paper proposes a due-date estimation method for customer orders with unfixed specifications. Such customer orders are defined as those whose products are partially composed of undecided components. The process of clarifying the undecided components is modeled without the loss of generality. Then, a modified version of the due-date estimation method, which has been investigated by the author and others in the last decade, is shown to be able to estimate due dates and fulfill them for all customer orders, including those with unfixed specifications. It is also shown that the desirable specification clarifying dates are obtained from the updated production schedule generated by the due-date estimation, which information is valuable not only for the manufacturer but also the customers holding unfixed specifications to execute efficient production activities.

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Previous researches provided a reactive scheduling method using a genetic algorithm to improve disturbed production schedules, due to unexpected disruptions, such as delays of manufacturing operations. This paper deals with an extension of the reactive scheduling method to modify not only the loading sequences but also machining sequences of jobs by using a co-evolutionary genetic algorithm. Two different kinds of individuals for the loading sequences and the machining sequences are generated and improved in co-evolutionary processes. A prototype of new reactive scheduling system was developed, and the effectiveness of the proposed method was verified through computational experiments.

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We consider a real-time system that requires the time stimuli to the system are processed through sequences of tasks to be within specified upper bounds, where the set of tasks can be the mixture of preemptive and non-preemptive tasks, and we propose an algorithm to design a static priority scheduling for the system. In the algorithm, local search is used to determine priorities of tasks, and whenever the priorities are fixed, the periods of tasks are determined. This subproblem can be described as a mathematical programming formulation and is solved via linear programming techniques. Finally, we report computational results for sample instances from a company.

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Centralized control of production systems is difficult because systems have become increasingly expansive and complex. Autonomous distributed scheduling was proposed to address this problem. Combinatorial-auction based scheduling is one potential method of autonomously distributed scheduling. For such scheduling, it is necessary to decide batch formation and scheduling simultaneously to optimize a production system using batch processing. Therefore, as described in this paper, we propose a simultaneous optimization method for batch formation and scheduling with a combinatorial auction mechanism. In addition, using computational experiments, we aim to evaluate the efficiency and validity of the proposed scheduling methodology with batch formation.

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High-skill tasks such as aircraft maintenance procedures generally require considerable on-the-job training (OJT). Because OJT requires a considerable amount of time, long-term scheduling of OJT by considering both working efficiency and skill development is required. The authors have proposed education coefficient (EC) as a parameter that adjusts the frequency of OJT and have proposed a scheduling support system for long-term scheduling of OJT considering working efficiency and skill education. Users of the proposed scheduling support system can easily adjust the balance between working efficiency and skill education by changing EC. However, it is difficult for them to find effective values of EC that realize a good balance between working efficiency and skill education to maintain a certain number of skilled workers. In this paper, we propose a simulation-based scheduling optimization system using a simple genetic algorithm (GA). The purpose of this study is to adjust the value of EC automatically by considering the balance between working efficiency and skill education and to find effective rules for adjusting the value of EC. The experimental results show that the proposed system computes solutions that are better than those computed by using a constant EC.

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In this paper, we discuss lexicographic bi-criteria combinatorial optimization problems arising in two types of double-layered food packing systems, which we call upright and diagonal types. Such food packing systems are known as so-called automatic combination weighers. The first and second layers in a double-layered food packing system consist of n weighing hoppers and n booster hoppers, respectively. Some amount of food such as a green pepper and a handful of potato chips is thrown into each hopper, and it is called an item. The food packing system performs an operation of choosing a subset I' from the set I of the current 2n items to produce a package of the food. Then, the resulting empty hoppers are supplied with next items, and the set I is updated. By repeating the packing operation, a large number of packages are produced one by one. The boosters are just hoppers without weighing function, but the weights of items in the boosters can be known since each type of double-layered food packing systems has its own constructional feature such that they receive next items from the weighing hoppers. The primary objective of lexicographic bi-criteria food packing problems is to minimize the the total weight of chosen items for each package, making the total weight no less than a specified target weight T. The second objective is to maximize the total priority of chosen items for each package so that items with longer durations in hoppers are preferably chosen. The priority of an item is given as its duration in hopper. In this paper, we prove that the lexicographic bi-criteria food packing problems can be solved in O(nT) time by dynamic programming if all input data are integral. We also show the executive efficiency of the pseudo-polynomial dynamic programming algorithms by conducting numerical experiments.

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Recently, we have concerned strategic optimization on logistics network design and developed an efficient two-level solution method. To cope with extremely large-scale problems, in this paper, we propose an extended algorithm of the two-level algorithms that utilizes the analogy between the algorithm and master-worker configuration of PC cluster for parallel computing. To enhance efficiency both in speed and accuracy, we adopted a population-based algorithm like particle swarm optimization (PSO) and developed a new binary algorithm of PSO to deal with the binary decision variables standing for open or close of DC sites. Then, we have developed a parallel procedure that can reduce the inefficiency coming from idle time and overhead and finally bring about a high performance for the parallel computing suitable for the present goal and circumstance. Through numerical experiments, we confirmed effectiveness of the proposed method through the certain aspects to evaluate effectiveness of the parallelization.

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In this paper, we study a buyback contract model of the competitive newsvendor problem between a single supplier and multiple retailers under simultaneous price and safety stock competition. A price completion factor and a spill rate translate the price and safety stock competition, respectively. Our model is an extension of the problem analyzed in Zhao and Atkins ⁽²⁾ by the adoption of a buyback rate in the chain, which gives new Nash equilibrium conditions. Then, we compute numerically the Nash equilibrium prices, optimal wholesale prices for the supplier’s and the retailers’profits and supply chain optimal retailers’prices. The effect of competition and distribution parameters will be discussed and compared to that of the non-competitive model and chain optimization.

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This paper studies a multi-objective analysis of periodic review inventory problem in two-echelon supply chain system. Thereat, three strategies of replenishment are proposed to manage inventory in efficient way while trying to improve the system performance by simultaneously minimizing total cost and loss rate of the supply chain. Associated with the problem, it is apparent that preventing loss rate will increase total cost and vice versa. Then between these conflicting goals, we can examine the profitable situation in which all members in the chain can achieve proportional satisfaction. To concern with the finally formulated problem, we applied multi-objective differential evolution (MODE) after giving the original method a suitable procedure for the resulting problem. Through numerical experiments for a case study, we show that the intimate coordination strategy between manufacturer and retailer become more effective as increase in demand uncertainty.

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In this paper, an approach to complement legacy rules for the elevator operation is proposed. The approach is derived from the analysis that the elevator operation in the real world often obeys a heuristic rule, and such a rule can be divided into a legacy rule and ad-hoc rules. In the approach, ad-hoc rules are represented as polysemous rules, and a Genetics-Based Machine Learning (GBML) method is applied to acquire such rules. Here, a polysemous rule encodes, not a set of environments’ states as the well-known if-then rule does, but a relative attribute vector of an arbitrary elevator. The elevator selection rule based on polysemous rules is simple: if there is a polysemous rule which matches one of attribute vectors of the elevators, select the elevator which corresponds to the matching vector; otherwise select an elevator according to a legacy rule. In computer illustrations, the GBML method is applied to 3 traffic patterns formed by the system’s users. It is shown that the resultant polysemous rules seem to complement an existing (legacy) operational rule. Furthermore, polysemous rules, which are selected among those acquired by the GBML method, are successfully applied to harder problems with more elevators than those used in learning.

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In this paper, we show that the transshipment problem with a permutable transit vector remains NP-hard even when each entry of the given transit vector takes either zero or two. We prove the hardness by a reduction from an NP-complete problem by the name of 3DM. We also show that the transshipment problem can be solved in polynomial time if each entry of the transit vector takes either zero or one. We obtain the time complexity by applying the successive shortest path algorithm for the minimum cost flow problem.

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In this paper, we propose a technique for the communication between agents in the dynamic generation of a production plan in an autonomous production system using product agents, parts agents and assembly machine agents. A parts agent leads the promotion of processes in this type of assembly system. First, product agents create the assembly process model that contains the assembly sequence to complete the product. Next, product agents generate parts agents that have the assembly process model. Parts agents will be assembled by machines, with some communications between other agents. The proposed system is defined as an event driven architecture. The increase in the costs of communication between agents is polynomial in the number of the agents. Moreover, this system adjusts the production schedule dynamically using only local negotiation when conditions are changed. Finally, we show experimental results with simply examples.

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This paper proposes an application of the option valuation approach to evaluate a project investment in three stages: research (R), development (D) and acquisition (A). To reflect different correlation and effect of each type of uncertainty on option values and, consequently, investment decisions, the proposed valuation and decision model incorporates both technical and market uncertainties into the first two technical stages (R&D) of the project, and the market uncertainty only into the last stage (A). Changes in project values are accordingly captured in each stage by the combined geometric Brownian motion and Poisson jump downward processes. The model incorporates the patent sale alternatives in the development and acquisition stages. A dynamic programming, decision tree model is solved to determine the option values and optimal decisions subject to decision rules, critical values, and certain boundary conditions. We subsequently evaluate the model effectiveness by comparing its decisions with those of an existing valuation model and the net present value method. The Monte Carlo simulation results show that under the option valuation by which the loss is limited to the initial costs of investment, a positive profit in a wide range can be obtained with more than 50% chance, in spite of the small average profit. The results of simulation also verify the significance of the chance to sell the patent as a safer decision under the high market uncertainty. In addition, a shorter term of patent agreement significantly improved advantage of the patent sale over the immediate investment in an optimistic situation.

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In this paper, we propose an application of Petri Net decomposition approach for the simultaneous optimization of dispatching and conflict-free routing for automated guided vehicles in the dynamic situation where transport requests are given in real time. The objective is to maximize the total throughput of the AGV transport system during the time horizon. In order to solve the dynamic problem, static problems are periodically solved when the transport requests are given to the AGV system. The dispatching and conflict-free routing are simultaneously optimized by the Petri Net decomposition approach. In the proposed method, the Petri Net is decomposed into several subnets for task subproblems and AGV subproblems that can be solved by the shortest path algorithm on the reachability graph. The local solutions for the subnets are coordinated by a penalty function algorithm. To ensure the generation of conflict-free routing, a new deadlock avoidance strategy is incorporated in the optimization algorithm. The effects of simultaneous optimization of dispatching and conflict-free routing are investigated for routing problems in dynamic environments.

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Associated with a strategic optimization of logistics network design to improve supply chain efficiency, we proposed a method termed hybrid tabu search, and applied it to various real-life problems through imposing additionally practical conditions on the basic model according to situations. Since multi-commodity delivery is popular in practice, we can make a more reliable and operational decision by simultaneously taking into accounts dynamic circumstances. In this study, therefore, we have extended the previous method to cope with this aspect that has scarcely considered so far, i.e., dynamic integrated capacitated multi-commodity. Numerical experiments revealed the role of inventory management over planning horizon and the validity of the proposed method through comparison with the results from commercial software.

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