This paper develops a two-phase LP-based heuristic for the Capacitated Vehicle Routing Problem (CVRP). It considers three objectives: (1) minimizing the total costs of fuel consumption and overtime, (2) maximizing the total personal relationships between customers and drivers, and (3) balancing the delivery weights of vehicles. The two-phase LP-based heuristic (cluster-first route-second) is proposed. First, in the clustering stage, three LP-based clustering models (denoted by C1, C2, and C3) are developed. Customers are grouped into clusters based on real distances between the customers for C1, personal relationships between the customers and drivers for C2, and the delivery weights of vehicles for C3. Second, in the routing stage, an LP-based traveling salesman problem model is used to form a route for each cluster, to minimize the total costs of fuel consumption and overtime labor. The experimental results from a case study of Thai SMEs show that when the C2 clustering model is applied, the performances are the best. Significant contributions of this paper include: (1) it is an original paper that proposes the C2 clustering model, and it has the best performances based on the experimental results, and (2) the proposed two-phase LP-based heuristic methods are suitable for practical use by SMEs since the required computational time is short, and it has multiple models with different objectives that can be selected to match a user's requirements.

Make-and-pack production is characterized by two stages of production namely, "make-stage" and "packstage" where each stage consists of parallel processing units. In make-stage, raw materials are converted into final products by batch processing. Then, the final products are packed into containers in pack-stage. This paper develops finite capacity scheduling (FCS) system of make-and-pack production with multi-objectives and options to adjust processing time (OAPT). Multi-objectives including minimizations of total tardiness, total earliness, total flow time, and total processing costs are conflicting and a compromised solution is needed. Moreover, the processing time can be adjusted by adding some special chemicals. This paper proposes mixed-integer linear programming models to determine the compromised solution by using weighted average of satisfaction levels (WASL) of all objectives as performance measure. The proposed compromised solution method consists of three steps, (1) determining the best and worst values of each objective, (2) determining the initial compromised solution of all objectives when OAPT is not included in the model, and (3) determining the compromised solution with OAPT. The effect of chemical costs to the OAPT is evaluated. The results showed that the proposed FCS system offered a compromised solution between conflicting objectives. The compromised solution is relatively good but not the best for all objectives. The OAPT can improve the performance of the system and it is significantly affected by the chemical cost per unit. When the chemical cost per unit is reduced, the special chemical is used more to reduce processing time per batch and then the performance measure is more improved.

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Make-and-pack production is characterized by two stages of production namely, "make-stage" and "packstage" where each stage consists of parallel processing units. In make-stage, raw materials are converted into final products by batch processing. Then, the final products are packed into containers in pack-stage. This paper develops finite capacity scheduling (FCS) system of make-and-pack production with multi-objectives and options to adjust processing time (OAPT). Multi-objectives including minimizations of total tardiness, total earliness, total flow time, and total processing costs are conflicting and a compromised solution is needed. Moreover, the processing time can be adjusted by adding some special chemicals. This paper proposes mixed-integer linear programming models to determine the compromised solution by using weighted average of satisfaction levels (WASL) of all objectives as performance measure. The proposed compromised solution method consists of three steps, (1) determining the best and worst values of each objective, (2) determining the initial compromised solution of all objectives when OAPT is not included in the model, and (3) determining the compromised solution with OAPT. The effect of chemical costs to the OAPT is evaluated. The results showed that the proposed FCS system offered a compromised solution between conflicting objectives. The compromised solution is relatively good but not the best for all objectives. The OAPT can improve the performance of the system and it is significantly affected by the chemical cost per unit. When the chemical cost per unit is reduced, the special chemical is used more to reduce processing time per batch and then the performance measure is more improved.