Kunpeng Li

Synchronized scheduling of assembly and multi-destination air-transportation in a consumer electronics supply chain

The paper studies the problem of synchronizing air transportation and assembly manufacturing to achieve accurate delivery with minimized cost in a consumer electronics supply chain. The motivation for this study came from a major computer manufacturer in a consumer electronics industry that faces challenges in the final stages of delivery of a supply chain. The synchronization problem is decomposed and formulated as two sub-problems, i.e. a multi-destination air transportation allocation problem and an assembly-scheduling problem. The first sub-problem is formulated and solved as an integer linear programming problem. For the assembly-scheduling problem, two solution methodologies using the concepts of forward and backward scheduling are presented. Computational results indicate that the proposed methodologies can achieve considerable cost reduction compared with the existing methodology applied in industry.

Scheduling of single stage assembly with air transportation in a consumer electronic supply chain

Consumer electronics supply chain being a booming industry with intensive competition, multinational organizations are rapidly moving towards global manufacturing networks to minimize the costs and improve delivery efficiency. The studies on modeling and scheduling of global supply chains in consumer electronics industry have lead to growing interests in recent years. In this paper, the problem of synchronized scheduling of assembly with air transportation to minimize delivery costs is addressed. The overall problem is decomposed into sub-problems viz., the air transportation allocation problem and assembly scheduling problem. The air transportation allocation problem is shown to have the structure of the regular transportation problem, while the assembly scheduling problem is shown to be NP-hard. We investigate the case of process delays in assembly for reasons such as machine breakdowns, shortages of materials, etc., and present a schedule repair heuristic. We evaluate the performance of the proposed heuristic with industry practice methods by testing the heuristics on randomly generated problems of size varying from 20 to 100 jobs.

An inventory–routing problem with the objective of travel time minimization

In this paper, we consider an inventory–routing problem (IRP) in a large petroleum and petrochemical enterprise group. Compared to many other IRPs, the problem in this paper includes some special aspects due to the operational constraints, such as hours-of-service regulations of the company and the industry. Also, in some cases, it is more important to avoid stock out for any station, rather than purely focusing on transportation cost minimization. The objective is to minimize the maximum of the route travel time, which is not addressed in the literature so far. We present a tabu search algorithm to tackle the problem, which builds in an efficient and effective procedure to improve the search quality in each iteration. Moreover, lower bounds of reasonable sized problems, which are intractable in the formulated mathematical model by existing optimization software, are obtained via Lagrangian relaxation technique. Computational results indicate that the lower bounds are tight and the tabu search is capable of providing near optimal, close-to-lower-bound solutions in a computational time effective manner.

Complexities and algorithms for synchronized scheduling of parallel machine assembly and air transportation in consumer electronics supply chain

In this paper, we study the problem of synchronized scheduling of assembly and air transportation to achieve accurate delivery with minimized cost in consumer electronics supply chain. This problem was motivated by a major PC manufacturer in consumer electronics industry. The overall problem is decomposed into two sub-problems, which consist of an air transportation allocation problem and an assembly scheduling problem. The air transportation allocation problem is formulated as an integer linear programming problem with the objective of minimizing transportation cost and delivery earliness tardiness penalties. The assembly scheduling problem seeks to determine a schedule ensuring that the orders are completed on time and catch the flights such that the waiting penalties between assembly and transportation is minimized. The problem is formulated as a parallel machine scheduling problem with earliness penalties. The computational complexities of the two sub-problems are investigated. The air transportation allocation problem with split delivery is shown to be solvable. The parallel machine assembly scheduling problem is shown to be NP-complete. Simulated annealing based heuristic algorithms are presented to solve the parallel machine problem.

Optimisation of flow-shop scheduling with batch processor and limited buffer

This paper deals with a flow-shop scheduling problem with limited intermediate buffer. Jobs are grouped in incompatible job families. Each job has to be processed by a batch processor followed by a discrete processor in the same order. The batch processor can process several jobs simultaneously so that all jobs of the same batch start and complete together. We assume that the capacity of batch processor is bounded. The batch processing time is identical for batches of the same family. A batch which has completed processing on the batch processor may block the processor until there is a free unit in the buffer. The objective is to determine a batching and scheduling for all jobs so as to minimise mean completion time. A lower bound and two heuristics algorithm are developed. Moreover, a two-stage method embedded with a Differential Evolution (DE) algorithm is also developed. DE is one of the latest evolutionary computation algorithms, which implements mutation, crossover, and selection operators to improve the candidate solutions iteratively. Three variants of DE are first compared with a continuous Genetic Algorithm employing the random key representation. Then, one variant of the DE with the best convergence speed is selected. Numerical experiments are conducted to evaluate the performances of the selected two-stage meta-heuristic and two heuristics.

Evaluating order throughput time with variable time window batching

Given increased pressure to provide short delivery times, minimising customer order throughput time is a very important objective in warehousing operations. There are many factors that may affect the performance of an order picking system, such as layout of the warehouse, the storage strategy, the routing policy, the zoning method and the batching policy. In this study, we propose a simple travel time model and analyse the effect of order batching on the expected customer order throughput time with variable time window batching. In addition, we discussed the impact of some parameters on the batch size and the expected customer order throughput time. In the practice of Wuhan Dong Hon Logistics co. Ltd, the storage rack in the warehouse has two levels: the low storage level and the high storage level. These two levels are visited by pickers and forklifts, respectively. The classification improves the performance of order picking. In this study, we build the model based on this practice.

A two-dimensional bin-packing problem with conflict penalties

In this paper, we address the two-dimensional bin-packing (2BP) problem with variable conflict penalties, which incur if conflicting items are loaded into the same bin. Such a problem is observed in applications such as supermarket chains and automobile components transportation. The problem not only focuses on minimisation of number of bins used, but also deals with the conflict penalties at the same time. We propose a heuristic method based on the IMA algorithm and adapt it to solve this problem. A local search procedure is also designed to further improve the solutions. For instances derived from benchmark test data, the computational results indicate that the adapted IMA heuristic algorithm with local search effectively balances the number of bins used and the conflict penalties. The algorithm outperforms several adapted approaches that are well known for the 2BP problems.

A coordinated algorithm for integrated production scheduling and vehicle routing problem

In this paper, the integrated production scheduling and vehicle routing problem is considered for a Make-to-Order manufacturer, who has a single machine for production and limited vehicles with capacity constraints for transportation. The objective is to determine production scheduling and vehicle routing, which are two interacted decisions, to minimise the maximum order delivery time. A property on optimal production sequence is proposed first, based on which backward and forward batching methods are developed and are embedded into a proposed genetic algorithm. The proposed genetic algorithm is capable of providing high-quality solutions by determining the two decisions simultaneously. For comparison purpose, a two-stage algorithm is developed, which decomposes the overall problem into two successively solved sub-problems. The experiments show that the proposed genetic algorithm can provide higher quality solutions than the proposed two-stage algorithm and two published algorithms studying related problems.

Synchronized Scheduling of Manufacturing and 3PL Transportation

Many companies are enhancing their competitiveness by offering Just-in-Time (JIT) delivery. Costs or penalties are incurred by delivering an order either earlier or later than the customer’s due-dates. Besides, maintaining short response time from order acceptance to final delivery is one of the key competitive advantages. Thus, many companies deliver products to customers directly after production without holding finished product inventory. This is particularly true for the industries with short product life cycle, such as consumer electronics manufacturing, ready-mix concrete supplying and food catering industry (Garcia et al. 2004; Chen and Vairaktarakis, 2005; Li et al., 2005). In order to improve customer service and reduce production and transportation costs, scheduling of assembly manufacturing and transportation should be synchronized. Nowadays, due to the professional services provided by third party logistics (3PL) provider, it is more efficient to outsource the transportation or distribution to 3PL. There are two types of operations. If 3PL only serves one customer, the schedule of 3PL’s vehicle is determined by the order completion time in manufacturing. This type of operation is particularly true for 3PLs that providing road transportation services. On the hand, if 3PL provides services to more than one manufacturer, the departure and arrival time of the vehicle is determined by 3PL rather than by the manufacturer. In addition, the unit transportation cost of each vehicle varies. The manufacturer can book capacity on the available vehicles accordingly. Then, decision on allocation of orders to the vehicles is made to utilize the booked capacity efficiently. The typical case is the air-cargo transportation service provided by cargo airlines. Motivated by above application, this chapter studies the problem of synchronized scheduling of assembly manufacturing and transportation in the make-to-order (MTO) consumer electronics supply chain (CESC). In this context, materials or components are kept in inventory before assembly. Upon reception of customer orders, the materials are transferred to manufacturing job shop. Through several processes such as assembly, testing, packing, the assembly manufacturing is completed. Then, the order is transported to customers directly by the vehicle of 3PL. Chen and Vairaktarakis (2005) addressed the