Zhanguo Zhu

Single-machine group scheduling with resource allocation and learning effect

This paper addresses single-machine scheduling problems under the consideration of learning effect and resource allocation in a group technology environment. In the proposed model of this paper the actual processing times of jobs depend on the job position, the group position, and the amount of resource allocated to them concurrently. Learning effect and two resource allocation functions are examined for minimizing the weighted sum of makespan and total resource cost, and the weighted sum of total completion time and total resource cost. We show that the problems for minimizing the weighted sum of makespan and total resource cost remain polynomially solvable. We also prove that the problems for minimizing the weighted sum of total completion time and total resource cost have polynomial solutions under certain conditions.

Lower and upper bounds for a two-stage capacitated facility location problem with handling costs

We study in this paper multi-product facility location problem in a two-stage supply chain in which plants have production limitation, potential depots have limited storage capacity and customer demands must be satisfied by plants via depots. In the paper, handling cost for batch process in depots is considered in a realistic way by a set of capacitated handling modules. Each module can be regards as alliance of equipment and manpower. The problem is to locate depots, choose appropriate handling modules and to determine the product flows from the plants, opened depots to customers with the objective to minimize total location, handling and transportation costs. For the problem, we developed a hybrid method. The initial lower and upper bounds are provided by applying a Lagrangean based on local search heuristic. Then a weighted Dantzig–Wolfe decomposition and path-relinking combined method are proposed to improve obtained bounds. Numerical experiments on 350 randomly generated instances demonstrate our method can provide high quality solution with gaps below 2%.

Recent advances and opportunities in sustainable food supply chain: a model-oriented review

Faced with the challenges associated with sustainably feeding the world’s growing population, the food industry is increasingly relying on operations research (OR) techniques to achieve economic, environmental and social sustainability. It is therefore important to understand the context-specific model-oriented applications of OR techniques in the sustainable food supply chain (SFSC) domain. While existing food supply chain reviews provide an excellent basis for this process, the explicit consideration of sustainability from a model-oriented perspective along with a structured outline of relevant SFSC research techniques are missing in extant literature. We attempt to fill this gap by reviewing 83 related scientific journal publications that utilise mathematical modelling techniques to address issues in SFSC. To this end, we first identify the salient dimensions that include economic, environmental and social issues in SFSC. We then review the models and methods that use these dimensions to solve issues that arise in SFSC. We identify some of the main challenges in analytical modelling of SFSC as well as future research directions.

Multitasking scheduling problems with a rate-modifying activity

Motivated by the behavioral phenomena that occur while human operators are carrying out tasks, we study multitasking scheduling problems with a rate-modifying activity. In the problems, the processing of a selected task suffers from interruptions by other tasks that are available but unfinished, and the human operators regularly engage rest breaks during work shifts allowing them to recover or mitigate some of the negative effects of fatigue. The objectives are to respectively minimize: makespan, total completion time, maximum lateness, and due-date assignment related cost by determining when to schedule the rate modifying activity and the optimal task sequence in the presence of multitasking. Scheduling models and algorithms are proposed to solve the problems. The numerical examples are presented to illustrate the theorems and algorithms.

Due-Window Assignment and Scheduling with Multiple Rate-Modifying Activities under the Effects of Deterioration and Learning

This paper discusses due-window assignment and scheduling with multiple rate-modifying activities. Multiple types of rate-modifying activities are allowed to perform on a single machine. The learning effect and job deterioration are also integrated concurrently into the problem which makes the problem more realistic. The objective is to find jointly the optimal location to perform multiple rate-modifying activities, the optimal job sequence, and the optimal location and size of the due window to minimize the total earliness, tardiness, and due-window-related costs. We propose polynomial time algorithms for all the cases of the problem under study.

A new model for lane reservation problem with time-dependent travel times

This paper studied an optimal lane reservation problem with time-dependent link travel times. It aims to design time-guaranteed paths by converting some existing general-purpose lanes to reserved lanes with the objective of minimizing the total traffic impact of reserved lanes on general-purpose lanes. The traffic impact is caused by reserved lanes because they can be used by some special road users only and the adjacent general-purpose lanes may be more congested. The considered problem is to NP-hard. We propose a new mixed integer nonlinear programming model and transform it to an equivalent tractable linear model. We propose a cut-and-solve based algorithm in which new strategies are developed for generating piercing cuts. Computational results show that it is much more efficient to solve the proposed new model in the paper than the model in the literature and the overall performance of the proposed algorithm outperforms a direct use of an optimization solver of CPLEX on randomly generated instances.

Scheduling with resource allocation and past-sequence-dependent setup times including maintenance

In this work, scheduling problems with resource allocation and past-sequence-dependent (p-s-d) setup times are considered. Under these settings, the actual job processing times are dependent on the amount of resource allocated and the setup times are proportionate to the length of the already processed jobs. The objective function is a combination of the total completion time and the total resource consumption. In addition, the machine may need maintenance to improve its production efficiency, so the maintenance is also integrated. The optimal job sequence, the optimal amount of resource allocated to each job, and the optimal maintenance position are determined jointly. It is shown that the problem under study is polynomial time solvable.

Integrated production inventory routing planning with time windows for perishable food

This paper investigates an integrated production inventory routing problem with time windows where a central depot is responsible for supplying single type of perishable food to multiple retailers within the planned time horizon. A mixed integer linear programming (MILP) model aiming at maximizing the total profit is formulated with explicitly tracing the food quality. To strengthen the formulation, a series of valid inequalities are introduced. Randomly generated instances with up to 40 retailers and 3 time periods are used to verify the effectiveness and the complexity of the proposed model, which is solved by the linear programming solver CPLEX. The computational results show that the proposed model is able to provide integrated plan for the decision makers, and instances with 20 retailers and 3 time periods are optimally solved with 102.97s on average. The results also indicate that the introduced valid inequalities are useful in helping CPLEX generate better upper bounds (maximization problem) for 20 out of 23 instances that are not optimally solved within the time limit.

An efficient three-level heuristic for the large-scaled multi-product production routing problem with outsourcing

Abstract A classic production routing problem (PRP), in which a plant produces and distributes a single product to a set of customers over a finite time horizon, consists of planning simultaneously the production, inventory and routing activities to minimize the total cost. The last few decades have witnessed the increasing efforts made to solve such a complex problem. In this paper, we investigate a generalized PRP by considering multiple products and outsourcing (MPRP-OS). The newly studied problem is first formulated into a mixed integer linear program. Then a three-level mathematical-programming-based heuristic called TLH is developed to solve it. TLH combines a two-phase iterative method, a repairing strategy and a fix-and-optimize procedure to find near-optimal solutions. In addition, it is adaptable to solve the classic PRP. Computational experiments on 225 newly generated MPRP-OS instances with up to 200 customers, 20 vehicles, 6 periods and 12 products show the effectiveness and efficiency of the proposed heuristic. The performance of TLH is further demonstrated by testing 1530 classic PRP benchmark instances with up to 200 customers, 13 vehicles and 20 periods. Experimental results indicate that TLH is able to solve large-sized MPRP-OS instances within short computation times. In addition, TLH provides new best solutions for 283 out of 1530 benchmark instances.

Scheduling on a two-machine permutation flow shop under time-of-use electricity tariffs

We consider a two-machine permutation flow shop scheduling problem to minimise the total electricity cost of processing jobs under time-of-use electricity tariffs. We formulate the problem as a mixed integer linear programming, then we design two heuristic algorithms based on Johnson’s rule and dynamic programming method, respectively. In particular, we show how to find an optimal schedule using dynamic programming when the processing sequence is fixed. In addition, we propose an iterated local search algorithm to solve the problem with problem-tailored procedures and move operators, and test the computational performance of these methods on randomly generated instances.