Haoxun Chen

Deadlock avoidance policy for Petri-net modeling of flexible manufacturing systems with shared resources

Multiple products through a flexible manufacturing system (FMS) with limited resources can lead to deadlock. In this paper, the authors study the problem of deadlock avoidance by using the Petri net (PN) model for FMSs and introducing the concept of deadlock structure. The necessary and sufficient conditions to prevent deadlock are characterized. The authors use a state feedback restriction policy which prevents some enabled transitions from firing for avoiding deadlock in the system. In particular, when the number of any key kind of resources is greater than one, this policy is minimally restrictive and allows the maximal use of resources in the system. The authors present the PN realization of these restriction policies when the closed-loop system can be modeled by a live PN. The restriction policies can be easily implemented. An example is provided for illustration.

Cyclic scheduling of a hoist with time window constraints

This paper proposes a model and a related algorithm for generating optimal cyclic schedules of hoist moves with time window constraints in a printed circuit board (PCB) electroplating facility. The algorithm is based on the branch and bound approach and requires the solution of a specific class of linear programming problems (LPP). These LPP are equivalent to the problems of the cycle time evaluation in bi-valued graphs. Computational experience is presented to compare the results obtained using this new algorithm with the ones proposed in the literature.

A genetic algorithm for flexible job-shop scheduling

Genetic algorithms have been applied to the scheduling of job shops-a class of very complicated combinatorial optimization problems. Among these algorithms for job shops, a common assumption is that the routes that jobs visit machines are fixed, this is not true for flexible job shops such as flexible manufacturing systems, where jobs have machine route flexibility. The paper presents a new genetic algorithm to solve the flexible job-shop scheduling problem with makespan criterion. The representation of solutions for the problem by chromosomes consists of two parts. The first part defines the routing policy and the second part the sequence of the operations on each machine. Genetic operators are introduced and used in the reproduction process of the algorithm. Numerical experiments show that our algorithm can find out high-quality schedules.

A new model and hybrid approach for large scale inventory routing problems

This paper studies an inventory routing problem (IRP) with split delivery and vehicle fleet size constraint. Due to the complexity of the IRP, it is very difficult to develop an exact algorithm that can solve large scale problems in a reasonable computation time. As an alternative, an approximate approach that can quickly and near-optimally solve the problem is developed based on an approximate model of the problem and Lagrangian relaxation. In the approach, the model is solved by using a Lagrangian relaxation method in which the relaxed problem is decomposed into an inventory problem and a routing problem that are solved by a linear programming algorithm and a minimum cost flow algorithm, respectively, and the dual problem is solved by using the surrogate subgradient method. The solution of the model obtained by the Lagrangian relaxation method is used to construct a near-optimal solution of the IRP by solving a series of assignment problems. Numerical experiments show that the proposed hybrid approach can find a high quality near-optimal solution for the IRP with up to 200 customers in a reasonable computation time.

Ant colony optimization for solving an industrial layout problem

This paper presents ACO_GLS, a hybrid ant colony optimization approach coupled with a guided local search, applied to a layout problem. ACO_GLS is applied to an industrial case, in a train maintenance facility of the French railway system (SNCF). Results show that an improvement of near 20% is achieved with respect to the actual layout. Since the problem is modeled as a quadratic assignment problem (QAP), we compared our approach with some of the best heuristics available for this problem. Experimental results show that ACO_GLS performs better for small instances, while its performance is still satisfactory for large instances. 2006 Elsevier B.V. All rights reserved.

A fast heuristic for solving a large-scale static dial-a-ride problem under complex constraints

This paper presents a heuristic, which concentrates on solving a large-scale static dial-a-ride problem bearing complex constraints. In this heuristic, a properly organized local search strategy and a diversification strategy are used to improve initial solutions. Then the improved solutions can be refined further by an intensification strategy. The performance of this heuristic was evaluated by intensive computational tests on some randomly generated instances. Small gaps to the lower bounds from the column generation method were obtained in very short time for instances with no more than 200 requests. Although the result is not sensitive to the initial solution, the computational time can be greatly reduced if some effort is spent to construct a good initial solution. With this good initial solution, larger instances up to 2000 requests were solved in less than 10 hours on a popular personal computer.

The study of a dynamic dial-a-ride problem under time-dependent and stochastic environments

This paper studies a dynamic dial-a-ride problem bearing complex constraints on a time-dependent network. A flexible scheduling scheme is proposed to dynamically cope with different stochastic events, such as the travelling time fluctuation, new requests, absences of customers, vehicle breakdowns, cancellations of requests, traffic jams and so on. A fast heuristic is proposed to re-optimize the schedule when a new event occurs. This heuristic consists of a properly organized local search strategy and uses a secondary objective function to drive the search out of local optima. Intensive computational simulations were carried out to evaluate the performance of this scheduling scheme and the influence of different stochastic factors. The simulation results of different scenarios with different percentage of dynamic requests reveal that this scheduling scheme can generate high quality schedules and is capable of coping with various stochastic events.

Modeling and performance evaluation of supply chains using batch deterministic and stochastic Petri nets

Batch deterministic and stochastic Petri nets are introduced as a tool for modeling and performance evaluation of supply chains. The new model is developed by enhancing deterministic and stochastic Petri nets (DSPNs) with batch places and batch tokens. By incorporating stochastic Petri nets (SPNs) with the batch features, inhibitor arcs, and marking-dependent weights, operational policies of supply chains such as inventory policies can be easily described in the model. Methods for structural and performance analysis of the model are developed by extending existing ones for DSPNs. As applications, an inventory system and an industrial supply chain are modeled and their performances are evaluated analytically and by simulation, respectively, using this BSPN model. The applications demonstrate that our model and associated methods can solve some important supply chain modeling and analysis issues. Note to Practitioners-This paper was motivated by the problem of performance analysis and optimization of supply chains but it also applies to other discrete event systems where materials are processed in finite discrete quantities (batches) and operations are performed in a batch way because of batch inputs and/or in order to take advantages of the economies of scale. Existing Petri net modeling and analysis tools for such systems ignore their batch features, making their modeling complicated. This paper suggests a new model called batch deterministic and stochastic Petri nets (BDSPNs) by enhancing deterministic and stochastic Petri nets with batch places and batch tokens. Methods for structural and performance analysis of the model are developed. We then show how an inventory system and a real-life supply chain can be modeled and their performances can be evaluated analytically and by simulation respectively based on the model. The model and associated analysis methods therefore provide a promising tool for modeling and performance evaluation of supply chains.

Profit allocation mechanisms for carrier collaboration in pickup and delivery service

In collaborative logistics, multiple carriers may form an alliance by sharing their transportation requests and vehicle capacities in order to increase vehicle utilization rates and reduce empty back hauls. In this paper, a carrier collaboration problem in pickup and delivery service (CCPPD) is studied. CCPPD has two important issues which are the optimal reallocation of transportation requests among the carriers to maximize a total profit of the alliance and a fair allocation of the profit among the carriers so as to maintain the stability of the alliance. In this study, the profit allocation among carriers is addressed under a centralized collaboration framework. We propose three profit allocation mechanisms which are based on Shapley value, the proportional allocation concept, and the contribution of each carrier in offering and serving requests, respectively. All the mechanisms ensure that the allocation is in the core if the latter is not empty. They are evaluated and compared with numerical experiments on randomly generated instances.

A multi-agent and auction-based framework and approach for carrier collaboration

Carrier collaboration in transportation means multiple carriers form an alliance to optimize their transportation operations through sharing transportation requests and vehicle capacities. In this paper, we propose a multi-agent and auction-based framework and approach for carrier collaboration in less than truckload transportation. In this framework, the carriers outsource/acquire requests through multiple auctions, one for outsourcing each request; a carrier acts as an auctioneer when it wants to outsource a request to other carriers, whereas the carrier acts as a bidder when it wants to acquire a request from other carriers; for each carrier, which requests it should outsource and acquire are determined by solving its outsourcing requests selection problem and requests bidding problem, respectively. These two decision problems are formulated as mixed integer programming problems. The auction of each request is multiround; in each round, the auctioneer determines the outsourcing price of the request and each bidder determines whether it acquires the request at the given price; the auctioneer lowers the outsourcing price if multiple carriers bid for the request or raises the price if no carrier bids for it. The auction process continues until only one carrier bids for the request or a given number of rounds are achieved. In the second case, if no agent bids for the request, then it is returned to the outsourcing agent; if multiple bidding agents compete for the request, a conflict resolution procedure is used to determine which carrier wins it. The approach is decentralized, asynchronous, and dynamic, where multiple auctions may occur simultaneously and interact with each other. The performance of the approach is evaluated by randomly generated instances and compared with an individual planning approach and a centralized planning approach.